Real-time and deterministic communication monitoring system and method for big and small brain of robot

Abstract

The invention discloses a real-time and deterministic communication monitoring system and method for large and small brains of a robot, and relates to the technical field of communication monitoring of the large and small brains, and the method comprises the steps: collecting a communication time sequence and a walking phase state sequence of an EtherCAT master station for brain planning and cerebellum servo driving in the walking control process of the robot; constructing the phase tissue density of the initial monitoring time window, and performing multi-scale time unwrapping and phase resampling on the phase tissue density to generate a plurality of candidate tissue densities; based on phase-time entropy preserving mapping, the organization density is converted into an executable monitoring scheme which is respectively applied to master station periodic communication monitoring, communication feature extraction and time sequence return evaluation, and finally the optimal organization density is screened out, so that key phase high-precision real-time communication monitoring is realized. The problem that existing communication monitoring is insufficient in sensitivity to key phases or excessively sensitive to transient jitter is solved.

Description

Technical Field

[0001] This invention relates to the field of cerebellar-cerebrum communication monitoring technology, and more specifically, to a real-time and deterministic communication monitoring system and method for the cerebellum and cerebrum of a robot. Background Technology

[0002] With the development of complex motion robots such as humanoid robots and legged robots, robot control systems are gradually showing a trend towards a hierarchical "brain-cerebellum" architecture. The brain side primarily undertakes tasks such as gait planning, state estimation, and global decision-making, while the cerebellum side mainly handles joint-level servo control and rapid closed-loop adjustment. In this architecture, the brain and cerebellum typically communicate periodically via real-time industrial Ethernet such as EtherCAT to meet the stringent requirements of real-time performance and determinism for millisecond-level control cycles. Existing monitoring methods for this type of communication mainly focus on master station periodic communication timestamp statistics, communication delay and jitter analysis, and frame loss detection, primarily used to assess the overall stability of the communication link in the time domain.

[0003] However, in strongly coupled dynamic scenarios such as robot walking control, the real-time performance and determinism of communication are not only related to the communication link itself, but also closely related to the robot's motion state. During walking, the dynamic characteristics, control sensitivity, and fault tolerance corresponding to different phase states vary significantly, especially in critical stages such as phase switching and foot contact, where the tolerance for communication delays and jitter is significantly reduced. Therefore, the organization of the monitoring time window within the walking phase domain during communication monitoring, i.e., the density of the monitoring time window in the phase, directly affects the monitoring results' ability to perceive and effectively identify communication anomalies in critical phases.

[0004] The density of the monitoring time window in the phase domain reflects the allocation strategy of communication monitoring resources across different motion phases, and its level essentially reflects the degree of focus of the monitoring process on key phases. When the monitoring time is more compactly organized near the key phase, the system can more sensitively capture transient communication anomalies; while when the monitoring time is more evenly distributed across the entire phase domain, the monitoring results exhibit stronger smoothness and stability. Therefore, the density of the monitoring time window in the phase domain is not necessarily better the higher or lower it is, but rather an important moderating factor that has a directional impact on the real-time and deterministic monitoring results of the robot's cerebellar-cerebral communication.

[0005] When the organization density of the phase is low within the monitoring time window, communication monitoring typically uses a uniform, continuous, or approximately uniform time window to evaluate the master station's periodic communication. This approach has good stability in an overall statistical sense and can suppress the impact of short-term communication fluctuations on the monitoring results. However, its drawback is the lack of targeted attention to critical phases of walking. Since communication anomalies within critical phases often have transient and phase-specific characteristics, low-organization-density monitoring methods can easily drown them out by the global average effect, making it difficult to identify deterministic degradation between the robot's cerebrospinal and cerebrospinal phases in a timely manner.

[0006] Conversely, when the monitoring time window has a high phase density, the monitoring time is highly concentrated in a few key phase regions, making communication monitoring more sensitive to critical events such as phase switching transients and foot contact. However, this approach also has significant drawbacks: excessive phase focus amplifies transient jitter and occasional disturbances in the communication link, making the monitoring results highly sensitive to noise, thereby increasing the risk of misjudgment and potentially leading to a decrease in the overall stability of the monitoring system, making it difficult to form reliable deterministic assessment conclusions.

[0007] In summary, existing technologies generally lack methods for systematically modeling, adjusting, and optimizing the phase density of the monitoring time window. Communication monitoring strategies often rely on empirical choices between high and low focus, making it difficult to adaptively balance critical phase sensitivity and overall monitoring stability based on the robot's walking state. This lack of targeted adjustment in monitoring methods results in real-time and deterministic communication issues between the robot's brain and brain being either dominated by noise or masked in average statistics, making it difficult to meet the dual requirements of communication monitoring accuracy and reliability in complex walking control scenarios.

[0008] To address the above problems, this invention proposes a solution. Summary of the Invention

[0009] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a real-time and deterministic communication monitoring system and method for the cerebellum and cerebrum of a robot. By adjusting the tissue density of the EtherCAT master station's periodic communication monitoring time window in the walking phase, the system addresses the problems of insufficient sensitivity to key phases or excessive sensitivity to transient jitter in existing communication monitoring.

[0010] To achieve the above objectives, the present invention provides the following technical solution: A method for real-time and deterministic communication monitoring of the cerebellum and cerebrum of a robot includes the following steps: During the robot's walking control process, the EtherCAT master station cycle communication time sequence driven by the brain planning cycle and the cerebellum servo cycle, as well as the corresponding walking phase state sequence, are collected. Based on the phase locking distribution of communication events in the walking phase domain, the initial monitoring time window is constructed in the phase organization density, i.e., the first window organization density. Multi-scale time unfolding and phase resampling are performed on the tissue density of the first window to generate several second window tissue densities with different phase coverage continuity and phase concentration. Based on the entropy-preserving mapping between the phase domain and the time domain, the distribution transfer relationship of organizational density is constructed, and the window organizational density is transformed into a practical control target that can be executed for communication monitoring. The organization density of each second window was converted and applied to the EtherCAT master station periodic communication timing monitoring. Feature extraction was performed on monitoring under different tissue densities, and corresponding tissue density scores were generated based on the value assessment of accumulated time-series returns. The optimal second-window tissue density was selected based on the tissue density score and multi-phase coverage consistency judgment, and then applied to the EtherCAT master station periodic communication timing monitoring.

[0011] A system for real-time and deterministic communication monitoring of a robot's cerebellum and cerebellum includes a phase-correlated communication acquisition and window initialization module, a multi-scale reconstruction module for monitoring time windows, a phase-time entropy preservation mapping and transformation module, an EtherCAT master station periodic communication phase perception monitoring module, a tissue density monitoring feature extraction and reward accumulation evaluation module, and a multi-phase coverage consistency discrimination and optimal tissue density selection module. The phase-correlated communication acquisition and window initialization module is used to acquire the EtherCAT master station periodic communication time sequence driven by the cerebellum planning cycle and the cerebellum servo cycle during robot walking control, as well as the corresponding walking phase state sequence. Based on the phase-locked distribution of communication events in the walking phase domain, it constructs the initial monitoring time window's tissue density in the phase, i.e., the first window tissue density. The multi-scale reconstruction module for monitoring time windows is used to perform multi-scale time unfolding and phase resampling on the first window tissue density, generating... The system comprises several second-window organization densities with varying phase coverage continuity and phase concentration; a phase-time entropy preservation mapping conversion module, used to construct the distribution transfer relationship of organization density based on the entropy preservation mapping between the phase domain and the time domain, transforming the window organization density into an executable operational target for communication monitoring; an EtherCAT master station periodic communication phase perception monitoring module, used to convert each second-window organization density and apply it to EtherCAT master station periodic communication time-series monitoring; an organization density monitoring feature extraction and reward accumulation evaluation module, used to extract features from monitoring under different organization densities and generate corresponding organization density scores based on the value evaluation of time-series reward accumulation; and a multi-phase coverage consistency discrimination and optimal organization density selection module, used to select the optimal second-window organization density based on the organization density score and multi-phase coverage consistency discrimination, and apply it to EtherCAT master station periodic communication time-series monitoring.

[0012] An electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a real-time and deterministic communication monitoring method for the cerebellum and cerebrum of a robot.

[0013] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements a method for real-time and deterministic communication monitoring of the cerebellum and cerebrum of a robot.

[0014] The technical effects and advantages of the real-time and deterministic communication monitoring system and method for the cerebellum and cerebrum of a robot according to the present invention are as follows: 1. This invention performs phase-locking analysis on the periodic EtherCAT master station communication during the walking control process of a robot's cerebellum and cerebellum, and constructs an initial monitoring time window organization density based on the distribution of communication events in the walking phase domain, achieving fine-grained allocation of communication monitoring time in the phase domain. Multiple candidate organization densities are generated through multi-scale time unfolding and phase resampling, and then transformed into executable communication monitoring and control sequences. This allows the monitoring system to concentrate sampling near critical phases while maintaining smooth monitoring in non-critical phases, thereby significantly improving the ability to capture communication delays, jitter, and transient anomalies during critical motion phases, and enhancing the real-time and deterministic monitoring accuracy of communication.

[0015] 2. This invention extracts features and accumulates time-series rewards from communication monitoring results under different tissue densities, calculates a tissue density score, and selects the optimal second-window tissue density based on a multi-phase coverage consistency index. This achieves a balance between the benefits of communication monitoring and minimizes fluctuations across different walking phase intervals. This method ensures timely identification of critical phase anomalies while avoiding misjudgments or monitoring noise interference caused by oversensitivity. Therefore, it balances monitoring accuracy and system stability in real-time and deterministic communication monitoring of the robot's cerebellum, improving the overall reliability and feasibility of monitoring. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a real-time and deterministic communication monitoring method for the cerebellum and cerebrum of a robot according to the present invention.

[0017] Figure 2 This is a schematic diagram of the structure of a real-time and deterministic communication monitoring system for the cerebellum and cerebrum of a robot according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1, Figure 1 This invention provides a real-time and deterministic communication monitoring method for the cerebellum and cerebrum of a robot, comprising the following steps: S1, during the robot's walking control process, the EtherCAT master station cycle communication time sequence driven by the brain planning cycle and the cerebellum servo cycle, as well as the corresponding walking phase state sequence, are collected. Based on the phase locking distribution of communication events in the walking phase domain, the initial monitoring time window is constructed in the phase organization density, i.e. the first window organization density. In this embodiment, during the robot's walking control process, the EtherCAT master station cycle communication time sequence driven by the brain planning cycle and the cerebellum servo cycle, and the corresponding walking phase state sequence, are used to construct the initial monitoring time window's phase organization density, i.e., the first window organization density, based on the phase-locked distribution of communication events in the walking phase domain. Specifically: During the robot's walking control process, the time sequence of control commands sent by the brain planning module to the cerebellum control module, the time sequence of feedback returned by the cerebellum control module to the actuator, and the walking phase state sequence collected synchronously with the communication process are collected and time-aligned according to the periodic communication beat of the EtherCAT master station to form a communication event time sequence under a unified time reference. The walking phase state sequence is subjected to phase expansion processing, and the discrete walking phase identifier in each control cycle is mapped to the phase value in the interval [0, 2π). The phase value is accumulated and expanded according to the control cycle sequence to obtain a continuous phase sequence that evolves monotonically with time. Based on the continuous phase sequence, the time series of communication events under a unified time base is mapped to each event phase, and the occurrence time of each communication event is mapped to the corresponding phase value to construct a communication event-phase value sequence. The communication event-phase numerical sequence is divided into several phase intervals according to the phase division rules. The number of communication events is counted in each phase interval to obtain the communication event count sequence corresponding to each phase interval. Based on the communication event counting sequence, the proportion of the number of communication events in each phase interval to the total number of communication events in all phase intervals is calculated to form a phase interval communication proportion sequence. Based on the communication ratio sequence of the phase interval, the length of the monitoring time window is allocated proportionally within the complete walking phase domain, so that the length of the monitoring time window corresponding to each phase interval corresponds one-to-one with its communication ratio, and the arrangement sequence of the monitoring time window in the walking phase domain is constructed. The phase interval index and the corresponding monitoring time window length form a mapping set. The mapping set is used to characterize the organization of communication monitoring time in the walking phase domain, and serves as the initial monitoring time window organization density in the phase, i.e., the first window organization density.

[0020] It should be noted that the following are computational examples of feasible time alignment modeling: The EtherCAT master station cycle is: ; The unified time axis is discrete time: , ; Time sequence of brain planning instruction transmission: ; Cerebellar servo feedback time series: ; Time alignment mapping: ; Construct a unified communication event time index set: ; In the formula, This refers to the time point corresponding to the nth EtherCAT master communication cycle. Main station communication cycle index. This represents the total number of communication cycles at the main station during the data collection period. A time-series set of control commands sent to the brain's planning module. To control the timing of command transmission, For control command sequence number, To control the total number of instructions, This is a time-series set of feedback returned by the cerebellar control module to the actuator. For the time of feedback, For the feedback event sequence number, To provide the total number of events, This refers to the master station periodic index corresponding to the control command. For the main site periodic index corresponding to the feedback event, For floor operations, A set of periodic indexes for communication events under a unified time base. For the set of periodic indices corresponding to control instructions, This is the set of periodic indices corresponding to feedback events.

[0021] It should be noted that the following is a feasible calculation example for the walking phase unfolding: The discrete travel phase identifier within each control cycle is: ; To eliminate phase jumps, phase expansion is performed: ; in: ; The final result is: ; In the formula, This serves as a discrete walking phase identifier within the control cycle. To control the periodic index, For the range of phase values, The phase values ​​for continuous walking after unfolding. This refers to the cumulative number of phase cycles. For phase sequence index, This is an indicator function that takes the value 1 if the condition is true, and 0 otherwise. It is a continuous walking phase sequence.

[0022] It should be noted that the following are feasible calculation examples for phase interval discretization: Let the number of phase intervals be M, then ; The phase interval is defined as: Communication event count: ; Obtain the communication event count vector: ; In the formula, The phase width of a single phase interval. For the phase interval, This represents the number of communication events within the phase interval. The phase value corresponds to the communication event, and mod is the modulo operation. This is a vector consisting of the number of communication events in each phase interval.

[0023] It should be noted that the following is a calculation example for the feasible phase interval communication ratio: Total number of communication events: Phase interval communication ratio: Form a proportion vector: ; In the formula The total number of communication events. This represents the proportion of communication events within the phase interval. This is the communication proportion vector for each phase interval.

[0024] It should be noted that the following is a feasible calculation example for allocating monitoring time windows by proportion: Let the total available monitoring time within a complete walking cycle be: ; The monitoring time window length for the phase interval is: ; Form a window length vector: ; In the formula, This refers to the total monitoring time. The monitoring time window length for the phase interval. This represents the total monitoring time.

[0025] It should be noted that the following is a calculation example of a feasible formal expression of tissue density: Define the tissue density of the first window as a set of phase-window length mappings: ; "Organization density" is not a scalar, but a structured mapping that describes how monitoring time is distributed in the phase domain.

[0026] In the formula, The density of the first window organization.

[0027] In this embodiment, the brain planning cycle refers to the planning control beat generated by the robot's high-level motion planning module based on the target trajectory, gait pattern, or environmental constraints. This beat is used to describe the frequency of changes in the robot's walking intention at the macro level. Its update cycle is usually slower than the low-level servo control cycle and is used to determine global walking strategies such as gait phase switching, support and swing allocation.

[0028] In this embodiment, the cerebellum servo cycle refers to the control beat of the robot's underlying motion control module for real-time closed-loop control of the actuator. This beat is directly related to the actuator's drive frequency and is used to finely adjust the joint position, speed, or torque under the constraints of the brain planning cycle. Its update frequency is higher than that of the brain planning cycle and is communicated periodically through the EtherCAT master station.

[0029] In this embodiment, the EtherCAT master station periodic communication time series refers to the time series formed by the data exchange process initiated periodically by the master station according to a fixed communication rhythm under the EtherCAT industrial Ethernet architecture. This time series takes the master station cycle as a unified time reference and covers the entire process of control command issuance and execution feedback transmission.

[0030] In this embodiment, the communication event time series refers to the discrete time event sequence formed by uniformly mapping and organizing the moments when the brain planning module sends control commands to the cerebellum control module and the moments when the cerebellum control module returns feedback information to the actuator under the unified EtherCAT master station periodic time reference. This sequence is used to characterize the distribution characteristics of communication behavior on the time axis.

[0031] In this embodiment, the walking phase state sequence refers to the gait stage identifier sequence collected synchronously with the robot's walking control process. It is used to reflect the walking stage state of the robot in a continuous control cycle. This state can correspond to the support phase, swing phase or its sub-phases, and evolves with the control cycle.

[0032] In this embodiment, the phase unrolling process refers to converting a discrete phase identifier that originally changes cyclically within a single walking cycle into a continuous phase representation that evolves monotonically over time by continuously accumulating across cycles. This eliminates the discontinuity caused by phase wrap-around and allows the phase to be used as an ordered variable in the time evolution process.

[0033] In this embodiment, the continuous phase sequence refers to the phase value sequence obtained after phase expansion processing. This sequence can reflect the stage evolution relationship of the robot's walking process over a long time scale and is used to map communication events into a unified and continuous walking phase domain.

[0034] In this embodiment, the communication event-phase value sequence refers to the one-to-one correspondence between communication events and phase values, which is formed by mapping the time point of each communication event to the walking phase position at the time of its occurrence based on a continuous phase sequence. This sequence is used to analyze the distribution of communication behavior in the walking phase domain.

[0035] The phase division rule in this embodiment refers to the rule of dividing the complete walking phase domain according to a preset phase resolution. This rule is used to divide the continuous phase domain into multiple phase intervals so as to statistically compare the distribution of communication events in different phase intervals.

[0036] In this embodiment, the phase interval refers to several continuous phase sub-intervals formed within the complete walking phase domain according to the phase division rules. Each phase interval is used to represent a local stage range in the walking process and is the basic unit for subsequent statistical communication event distribution and organizational monitoring time window.

[0037] In this embodiment, the communication event counting sequence refers to the counting result sequence formed by counting the number of communication events falling into each phase interval. This sequence is used to characterize the density difference of communication events in the walking phase domain.

[0038] In this embodiment, the phase interval communication ratio sequence refers to the ratio sequence obtained by normalizing the number of communication events in each phase interval to the total number of communication events in all phase intervals. This sequence reflects the relative concentration of communication behavior in different phase intervals.

[0039] In this embodiment, the monitoring time window length refers to the length of time resources allocated to a certain phase interval for collecting and analyzing communication behavior during the communication monitoring process. This time window is used to limit the time range in which communication monitoring is actually performed within the corresponding phase interval.

[0040] In this embodiment, the arrangement sequence of monitoring time windows in the walking phase domain refers to the time window arrangement result formed by allocating the total monitoring time resources according to the phase interval order based on the phase interval communication ratio sequence, which is used to describe the distribution structure of monitoring time in the complete walking phase domain.

[0041] In this embodiment, the organization density of the monitoring time window in the phase refers to a quantitative description used to characterize the compactness and allocation structure of the communication monitoring time distribution within the walking phase domain. The motivation for this name is that in the communication monitoring process, the evaluation of communication behavior no longer adopts a uniform continuous window, but rather adopts a more compact time organization method near the critical phase and a smoother organization method in the non-critical phase, so that the allocation of monitoring resources matches the communication sensitivity during the walking process.

[0042] In this embodiment, the first window organization density refers to the initial organization result directly constructed based on the phase-locked distribution of communication events in the walking phase domain. This organization density serves as the basic input for subsequent multi-scale time unfolding and phase resampling processing, and is used to provide the original communication-phase correlation structure.

[0043] It should be noted that this embodiment does not limit the specific physical meaning of the walking phase or the type of gait model. The walking phase state can be derived from the logical state output of the gait planner, or from the joint movement cycle, foot contact state, or a combination thereof, as long as it can be synchronized with the control cycle and reflect the evolution relationship of the walking stage.

[0044] It should be noted that the allocation of the monitoring time window in this embodiment does not change the communication cycle of the EtherCAT master station itself, but rather affects the time organization method in the communication monitoring and analysis process, so that the collection and evaluation of communication behavior presents a non-uniform distribution characteristic related to the walking phase on the time axis, thereby providing structured input for subsequent monitoring optimization based on organization density.

[0045] S2, perform multi-scale time expansion and phase resampling on the tissue density of the first window to generate several second window tissue densities with different phase coverage continuity and phase concentration. In this embodiment, the process of performing multi-scale time unfolding and phase resampling on the first window tissue density to generate several second window tissue densities with different phase coverage continuity and phase concentration is specifically as follows: Based on the mapping set of phase interval index and corresponding monitoring time window length in the first window tissue density, the monitoring time window length sequence corresponding to each phase interval in the complete walking phase domain is extracted as the initial time window allocation sequence. The initial time window allocation sequence is subjected to multi-scale time expansion. According to different time expansion scales, the length of the monitoring time window corresponding to each phase interval is proportionally scaled to generate several time expansion sequences with different window length allocation granularities while keeping the total monitoring time unchanged. For each time-expanded sequence, the walking phase domain is re-divided according to the preset phase resampling rules, the original phase intervals are merged or subdivided into a new set of phase intervals, and the monitoring time window length in the time-expanded sequence is remapped to the new phase interval to form the corresponding phase resampling sequence. For each phase resampling sequence, the proportion of the monitoring time window length corresponding to each new phase interval to the monitoring time of the complete walking phase domain is recalculated, and a new phase interval-monitoring time window length mapping set is constructed. The multiple phase interval-monitoring time window length mapping sets obtained under different time expansion scales and different phase resampling results are used as the phase organization density of the second monitoring time window with different phase coverage continuity and phase concentration distribution.

[0046] In this embodiment, the first window organization density refers to the initial organization result of the monitoring time window constructed in step S1 based on the distribution relationship of communication events in the walking phase domain within the phase domain. This organization density uses the phase interval as the basic unit and clearly gives the length of the monitoring time window allocated to each phase interval, which is used to describe the initial distribution state of communication monitoring resources in the complete walking phase domain.

[0047] In this embodiment, the phase interval index refers to the sequential number assigned to distinguish different phase intervals after the complete walking phase domain is divided. This index is used to maintain the sequential relationship between phase intervals in subsequent processing, so that the time window allocation result can be consistent with the evolution order of the walking phase.

[0048] In this embodiment, the monitoring time window length refers to the continuous time length allocated to the corresponding phase interval during the communication monitoring process. This time length limits the scale of time resources for communication acquisition and analysis within the phase interval and is a direct reflection of the monitoring time organization method.

[0049] In this embodiment, the mapping set refers to the data set consisting of the phase interval index and its corresponding monitoring time window length. This set is used to fully describe the distribution structure of monitoring time in the walking phase domain and is the basic input for subsequent time expansion and phase resampling processing.

[0050] In this embodiment, the initial time window allocation sequence refers to the time length sequence formed by sequentially arranging the monitoring time window lengths corresponding to each phase interval in the first window tissue density according to the order of the phase intervals in the walking phase domain. This sequence maintains the original phase order relationship and is used as the starting sequence for multi-scale time unfolding processing.

[0051] In this embodiment, multi-scale time unfolding refers to adjusting the overall proportion of the monitoring time window length corresponding to each phase interval in the initial time window allocation sequence by setting different time unfolding scales while keeping the total monitoring time unchanged, thereby generating multiple time window allocation schemes with different time allocation granularities.

[0052] In this embodiment, the time expansion scale refers to a preset scale parameter used to control the scaling of the monitoring time window length. This parameter determines whether the time window allocation in the phase domain tends to be concentrated or uniform, thereby artificially introducing adjustable control over the compactness of the monitoring time organization.

[0053] In this embodiment, the time-expanded sequence refers to a new time window length arrangement sequence formed by scaling the initial time window allocation sequence under a specific time-expanded scale. This sequence presents different time allocation structures while maintaining a consistent total monitoring time.

[0054] In this embodiment, the phase resampling rule refers to a preset rule used to redivide the walking phase domain. This rule specifies whether the original phase intervals need to be merged or subdivided after the time unfolding sequence is determined, and how to form a new set of phase intervals to ensure that the phase division matches the new time allocation granularity.

[0055] In this embodiment, phase resampling refers to the process of reorganizing the phase intervals in the original walking phase domain according to the phase resampling rules. This process merges or subdivides the phase intervals to make the phase interval structure consistent with the monitoring time window allocation structure after time expansion.

[0056] In this embodiment, the new phase interval set refers to a set of phase intervals obtained after phase resampling. This set of phase intervals may differ from the original phase intervals in terms of the number of intervals, the width of the intervals, or the position of the interval boundaries. It is used to carry the redistributed monitoring time window.

[0057] In this embodiment, the phase resampling sequence refers to the time window allocation sequence formed after remapping the monitoring time window length in the time unfolding sequence to each new phase interval under a new set of phase intervals. This sequence reflects the monitoring time organization method under the combined effect of the current time unfolding scale and the phase resampling rule.

[0058] In this embodiment, the proportion of the monitoring time window length refers to the ratio of the monitoring time window length corresponding to each phase interval in the new set of phase intervals to the total monitoring time in the complete walking phase domain. This proportion is used to characterize the relative concentration of monitoring time in the phase domain.

[0059] In this embodiment, the phase interval-monitoring time window length mapping set refers to the data set formed by associating each new phase interval with its corresponding monitoring time window length ratio. This set is used to fully describe the allocation structure of monitoring time in the phase domain under the current conditions.

[0060] In this embodiment, the second monitoring time window's phase organization density refers to multiple monitoring time organization schemes obtained under different time expansion scales and different phase resampling results. Each organization density corresponds to a monitoring time allocation structure with different phase coverage continuity and phase concentration, which is used to provide candidate organization methods for subsequent communication monitoring strategy selection.

[0061] It should be noted that this embodiment uses a combination of multi-scale time unfolding and phase resampling to make the allocation of the monitoring time window in the walking phase domain no longer unique and fixed, but to form a set of tissue density candidates with different focusing characteristics, thereby providing a structured basis for subsequent trade-offs between communication monitoring accuracy and stability.

[0062] It should be noted that the phase coverage continuity and phase concentration described in this embodiment are not abstract evaluation indicators, but observable results directly reflected in the distribution structure of the number of phase intervals, the width of the phase intervals, and the length of the corresponding monitoring time window. Their changes will directly affect the degree of attention paid to key phases and the smoothing effect on non-key phases during communication monitoring.

[0063] S3, based on the entropy-preserving mapping between the phase domain and the time domain, constructs the distribution transfer relationship of tissue density, and transforms the window tissue density into an actual control target that can be executed by communication monitoring; In this embodiment, the construction of the distribution transfer relationship of tissue density based on the entropy-preserving mapping between the phase domain and the time domain transforms the window tissue density into a practical control target that can be executed for communication monitoring. Specifically: Based on the proportion of the monitoring time window length corresponding to each phase interval in the phase organization density of each second monitoring time window, a probability distribution sequence of the phase interval is constructed, and the information entropy value of each phase interval in the complete walking phase domain is calculated according to the probability distribution sequence to form a phase domain information entropy distribution sequence. Based on the frequency of each communication event on the time axis in the EtherCAT master station periodic communication time series, a probability distribution of communication events for equal-length time intervals on the communication time axis is constructed, and the information entropy value of the corresponding time interval is calculated to form a time domain information entropy distribution sequence. Under the constraint of maintaining the overall entropy value of the phase domain information entropy distribution sequence and the time domain information entropy distribution sequence consistent, a one-to-one mapping relationship between phase intervals and communication time periods is established so that the information entropy value corresponding to each phase interval is equal to the information entropy value of the communication time period it maps to, and a phase-time entropy preservation mapping table is generated. Based on the phase-time entropy preservation mapping table, the proportion of the monitoring time window length corresponding to each phase interval is converted into the specific time period length on the communication time axis, thus obtaining the communication time period allocation sequence within the phase. Based on the order relationship of adjacent phase intervals in the phase domain, the mapped communication time intervals are merged into adjacent intervals to generate a cross-phase communication time interval set covering the phase switching boundary; Based on the phase-intra-phase communication time period allocation sequence and the cross-phase communication time period set, the start and end times of data acquisition for different time periods during the EtherCAT master station's periodic communication monitoring process are determined, forming a time window control sequence that can be directly used for communication monitoring and scheduling.

[0064] It should be noted that in the process of transforming window organization density into an executable target for communication monitoring, this embodiment does not directly configure communication parameters empirically. Instead, it introduces statistical consistency constraints between the phase domain and the time domain as an intermediate bridge. By maintaining the information distribution pattern, the dynamic change characteristics implicit in the walking phase can be stably and repeatedly mapped to the periodic communication monitoring and scheduling of the EtherCAT master station. This avoids the loss of phase-sensitive information or waste of monitoring resources caused by fixed communication rhythms or manual experience settings. This technology extension enables organization density not only to exist as an analytical indicator, but also to directly participate in the formation process of communication scheduling and control.

[0065] In the second monitoring time window of this embodiment, the organization density of the phase refers to the distribution of monitoring time windows corresponding to different phase intervals within the overall monitoring time in the complete walking phase domain. Its intrinsic meaning is not simply the length of time, but reflects the relative importance and attention intensity of each phase interval in the allocation of monitoring resources. This organization density provides a basic description for subsequent probability distribution and information entropy modeling.

[0066] In this embodiment, the proportion of the monitoring time window length corresponding to the phase interval is based on the ratio of the monitoring time window length allocated within a single phase interval to the total monitoring time of the complete walking phase domain. Its inherent function is to transform time windows of different absolute lengths into comparable relative quantities, thereby eliminating the influence of changes in total monitoring time on phase distribution analysis and enabling different tissue density schemes to be aligned and mapped on the same scale.

[0067] In the probability distribution sequence of the phase interval in this embodiment, the probability distribution sequence is an ordered sequence formed by taking the proportion of the monitoring time window length of each phase interval as the probability weight of the phase occurrence. It does not represent the probability of random occurrence, but is used to characterize the discrete distribution structure of the monitoring attention level in the walking phase domain, providing a unified statistical expression for the calculation of information entropy.

[0068] In the phase domain information entropy distribution sequence of this embodiment, the information entropy distribution sequence is a measurement result obtained based on the phase interval probability distribution sequence, used to characterize the degree of dispersion of monitoring time allocation in different phase intervals. Its inherent meaning is to reflect the uniformity or concentration of monitoring resource distribution within the walking phase domain. A high entropy state indicates that the monitoring time is relatively evenly distributed among multiple phases, while a low entropy state indicates that the monitoring time is highly concentrated in a few phase intervals.

[0069] In the EtherCAT master station periodic communication time series of this embodiment, the periodic communication time series refers to the time arrangement of communication events formed by the EtherCAT master station according to a fixed control rhythm during periodic operation. Its internal structure reflects the time basis for the communication system to be scheduled and segmented, and is the time carrier for time domain modeling and communication monitoring window generation.

[0070] In this embodiment, the frequency of occurrence of communication events on the time axis refers to the relative density of communication events occurring within an equal time period. It is not a simple count, but rather a description of the distribution of communication load on the time axis, providing a basis for constructing the probability distribution of communication time periods.

[0071] In this embodiment, the equal-length time intervals on the communication time axis are time units formed by uniformly dividing the EtherCAT periodic communication time series. The purpose of setting these intervals is to first establish a discrete structure in the pure time domain without introducing phase information, so that the communication behavior can be statistically described and participate in entropy distribution calculation.

[0072] In the time-domain information entropy distribution sequence of this embodiment, the time-domain information entropy distribution sequence is an information entropy arrangement formed by the probability distribution of communication events in equal time periods on the communication time axis. Its inherent meaning is to reflect the degree of dispersion of communication activities on the time axis, so as to characterize the balance of communication resource usage in different time periods.

[0073] Under the constraint of consistent overall entropy value in this embodiment, consistent overall entropy value means that when establishing a mapping between the phase domain and the time domain, the overall level of information entropy of the two domains remains unchanged. Its inherent purpose is not to force complete local consistency, but to ensure that the information complexity in the phase domain can be completely transferred to the time domain, and to avoid information compression or diffusion distortion during the mapping process.

[0074] In the one-to-one mapping relationship between phase intervals and communication time periods in this embodiment, the one-to-one mapping relationship means that each phase interval is uniquely mapped to a set of communication time periods, and there is no situation where multiple phase intervals compete for the same communication time period. This mapping method ensures the traceability of the phase structure on the time axis and the determinism of scheduling execution.

[0075] In the phase-time entropy preservation mapping table of this embodiment, the mapping table is a data structure used to record the correspondence between phase intervals and communication time periods. Its inherent function is to solidify the mapping results between the phase domain and the time domain, so that subsequent communication monitoring and scheduling can be directly executed based on the table without repeating the entropy distribution matching calculation.

[0076] In the phase-interval communication time period allocation sequence of this embodiment, the allocation sequence is an ordered sequence formed by converting the proportion of the monitoring time window length corresponding to each phase interval into the specific time period length on the communication time axis. It reflects the direct projection result of the phase organization density on the communication time axis and is the core carrier of communication scheduling executability.

[0077] In the cross-phase communication time period set of this embodiment, the cross-phase communication time period set is a time period set formed by merging the mapped communication time periods based on the sequential relationship of adjacent phase intervals. Its inherent purpose is to avoid the communication monitoring window being interrupted at the phase switching boundary, thereby ensuring the continuity and integrity of the monitoring data during the phase transition phase.

[0078] In the time window control sequence of this embodiment, the time window control sequence is a communication monitoring start and end time sequence determined by the intra-phase communication time period allocation sequence and the cross-phase communication time period set. It is directly used to guide the scheduling and execution of the EtherCAT master station's periodic communication monitoring, so that the aforementioned organizational density analysis results can be finally implemented into an implementable communication control strategy.

[0079] S4, respectively converts the organization density of each second window and applies it to the EtherCAT master station periodic communication timing monitoring; In this embodiment, the conversion and application of the organization density of each second window to the EtherCAT master station periodic communication timing monitoring specifically involves: Based on the time window control sequence corresponding to the tissue density in each second monitoring time window, the EtherCAT master station periodic communication time axis is divided into several monitoring time periods of unequal length according to the phase-time mapping result. During the EtherCAT master station periodic communication, the current walking phase status is acquired in real time, and the monitoring time period to which the current master station communication cycle belongs is determined according to the phase-time mapping relationship. When the communication cycle of the master station is within a certain monitoring time period, the master station sending timestamp and the slave station returning timestamp are continuously collected according to the monitoring time window length corresponding to that time period to form a periodic communication timestamp sequence within that time period. When the communication cycle of the main station crosses the boundary of adjacent monitoring time periods, based on the cross-phase communication time period set, several main station communication cycles before and after the boundary are jointly collected to form a continuous communication timestamp sequence covering the phase switching process. The periodic communication timestamp sequence is arranged according to the master station communication cycle order and its corresponding phase interval index is marked to form EtherCAT master station periodic communication timing monitoring data with phase identifier; The EtherCAT master station periodic communication timing monitoring data with phase identifiers is used as the master station communication monitoring result executed under the current second window organization density constraint.

[0080] S5 extracts features from monitoring under different tissue densities and generates corresponding tissue density scores based on the value assessment of accumulated time-series returns. In this embodiment, the feature extraction for monitoring under different tissue densities and the generation of corresponding tissue density scores based on the value assessment of accumulated time-series returns are specifically as follows: Based on the EtherCAT master station periodic communication timing monitoring data with phase identifiers obtained under the current second window organization density constraint, the master station sending timestamp and slave station return timestamp are paired according to the monitoring time period, and the communication round-trip delay corresponding to each master station communication cycle is calculated to form a communication delay time series with phase identifiers. According to the walking phase interval index, the communication delay time series is grouped, and the mean and variance of the communication delay are calculated in each phase interval to form the communication delay concentration feature within the phase. The mean is used to characterize the overall communication delay level within the phase interval, and the variance is used to characterize the communication delay fluctuation amplitude. In terms of phase interval order, the mean communication delay of adjacent phase intervals is differentially calculated to calculate the change sequence of the mean communication delay of adjacent phase intervals, forming a phase switching sensitivity feature, which is used to reflect the degree of abrupt change in communication performance at the phase boundary. The delay difference between adjacent cycles is calculated according to the communication cycle sequence of the main station to form a periodic delay disturbance sequence. The mean square value of the periodic delay disturbance sequence is calculated to obtain the communication rhythm dispersion characteristics, which are used to characterize the amplification of transient communication fluctuations by the monitoring time organization method. Within the complete monitoring timeline, the number of communication cycles with communication latency exceeding the preset baseline latency threshold is counted, and the proportion of these cycles to the total number of monitored communication cycles is calculated to form an abnormal latency ratio feature, which is used to characterize the frequency of transient anomalies being captured under the current organizational density. The characteristics of communication delay concentration, phase switching sensitivity, communication rhythm dispersion, and abnormal delay ratio within the phase are normalized according to a unified time order and formed into a communication monitoring feature sequence corresponding to the current second window organization density. Based on the communication monitoring feature sequence, the feature values ​​are accumulated and summed in the order of communication cycle on the monitoring time axis to form a time-series return accumulation sequence that reflects the evolution of communication monitoring results over time. The final cumulative value is calculated for the cumulative time-series report sequence, and this cumulative value is used as the tissue density score corresponding to the tissue density in the second window, which is used to characterize the overall monitoring value of the tissue density in the EtherCAT master station periodic communication time-series monitoring.

[0081] In this embodiment, the monitoring under the influence of the second window organization density refers to the EtherCAT master station periodic communication monitoring process executed under the constraint of the organization method of a specific monitoring time window determined in the preceding steps within the phase domain. This organization density limits the allocation intensity of monitoring time in different phase intervals, thereby directly affecting the distribution pattern of communication data acquisition on the time axis and phase axis.

[0082] In this embodiment, the EtherCAT master station periodic communication timing monitoring data with phase identifiers refers to the time series data set formed by simultaneously recording the master station's transmission timestamp and the slave station's return timestamp for each master station communication cycle during the communication monitoring process, combined with the corresponding travel phase interval index at that time. This data not only contains communication delay information but also clarifies the travel phase background of the communication behavior, providing a foundation for subsequent phase correlation analysis.

[0083] In this embodiment, the communication round-trip delay refers to the time interval between the master station sending a message and receiving a slave station response within the same master station communication cycle. This time interval is obtained by the difference between the master station's sending timestamp and the slave station's returning timestamp, and is used to directly reflect the real-time performance status of EtherCAT cycle communication within that cycle.

[0084] In this embodiment, the communication delay time sequence refers to the sequence formed by arranging the communication round-trip delays calculated in each cycle according to the time order of the master station's communication cycle, and adding the corresponding travel phase interval identifier to each delay data, thereby forming a time-series data structure that simultaneously possesses time continuity and phase distinguishability.

[0085] In this embodiment, the phase interval index refers to the sequential identifier assigned to different phase intervals within the complete walking phase domain during the aforementioned phase division process. This identifier is used to distinguish the specific phase segment in which a communication event or communication cycle is located within the walking cycle. This index serves as the basis for phase grouping and phase sequence analysis.

[0086] In this embodiment, the intra-phase communication delay concentration characteristic refers to the comprehensive characterization result obtained by statistically analyzing the communication delay data belonging to the same phase interval within the same walking phase interval. The mean of the communication delay reflects the overall communication delay level within the phase interval, and the variance of the communication delay reflects the dispersion and fluctuation of the communication delay within the phase interval. This characteristic is used to characterize the stability of communication performance under fixed phase conditions.

[0087] In this embodiment, the phase switching sensitivity feature refers to the comparison of the average communication delay of adjacent phase intervals according to the natural order of the walking phase intervals, and the resulting communication delay change trend characterization. This feature is used to reflect whether there is a significant change in the EtherCAT periodic communication performance when the robot's walking state transitions from one phase interval to the next, thereby reflecting the sensitivity of communication to phase switching.

[0088] In this embodiment, the periodic delay perturbation sequence refers to the sequence formed by comparing the communication delay of two adjacent communication cycles according to the time sequence of the master station's communication cycle, and obtaining the delay change between adjacent cycles. This sequence is used to characterize the fluctuation characteristics of communication delay on a microscopic time scale.

[0089] In this embodiment, the communication rhythm dispersion feature refers to the result obtained after performing overall statistics on the periodic delay disturbance sequence. It is used to reflect the fluctuation intensity of communication delay between consecutive periods under the current monitoring time window organization method. This feature can reflect whether the monitoring time organization method amplifies transient communication jitter.

[0090] In this embodiment, the preset benchmark latency threshold is a reference communication latency upper limit set in advance according to the real-time communication requirements, control cycle constraints, and actuator response requirements of the EtherCAT system. It is used to distinguish between normal communication cycles and potentially abnormal communication cycles. This threshold is not dynamically adjusted with changes in tissue density and is used as a unified comparison benchmark between monitoring results of different tissue densities.

[0091] In this embodiment, the abnormal latency ratio feature refers to the proportion of communication cycles in which the communication latency exceeds the baseline latency threshold within the complete monitoring time range, out of all monitored communication cycles. It is used to reflect the frequency with which the monitoring process captures abnormal communication behavior under the current window organization density.

[0092] In this embodiment, the communication monitoring feature sequence refers to the time-series feature set formed by combining the phase-internal communication delay concentration feature, phase switching sensitivity feature, communication rhythm dispersion feature, and abnormal delay ratio feature according to the time order of the communication cycle and processing them on a unified scale. This feature sequence comprehensively reflects the multidimensional performance of the communication monitoring results under the current second window organization density constraint.

[0093] In this embodiment, the normalization process refers to adjusting the scale of various features to make them comparable in terms of numerical values ​​when the dimensions and numerical ranges of different features are inconsistent. This avoids a single feature from dominating the overall evaluation result due to its excessively large numerical range. This process only changes the numerical expression form and does not change the relative change relationship between features.

[0094] In this embodiment, the time-series report accumulation sequence refers to the sequence formed by accumulating the feature values ​​in the communication monitoring feature sequence periodically according to the communication cycle of the main station on the monitoring time axis. It is used to describe the evolution process of the communication monitoring effect accumulating over time. This accumulation process reflects the continuous contribution of monitoring value in the time dimension.

[0095] In this embodiment, the organization density score refers to the final cumulative result obtained at the end of the monitoring of the time-series return cumulative sequence, which is used as the overall monitoring value evaluation index corresponding to the current second window organization density. This score is used for horizontal comparison between different organization densities to determine which time window organization method has a better overall effect in EtherCAT master station periodic communication monitoring.

[0096] It should be noted that this step introduces the value assessment concept based on the accumulation of time-series returns. Its core is to avoid relying solely on local communication performance indicators at a certain moment or phase. Instead, it comprehensively evaluates the long-term impact of different monitoring time organization methods on anomaly detection capabilities, phase sensitivity, and communication stability by continuously accumulating communication characteristics throughout the monitoring process. This provides a more robust and globally significant decision-making basis for the subsequent optimization of organization density.

[0097] It should be noted that the above feature extraction and scoring process is not limited to specific numerical models or fixed weight forms, but emphasizes the consistent accumulation of features in time sequence and the correlation of phase structure. This makes the method adaptable to different robot walking modes, different EtherCAT communication load conditions and different control cycle settings. Without changing the overall process, the technology can be expanded and the scenario can be transferred by adjusting the feature composition.

[0098] S6, based on the tissue density score and multi-phase coverage consistency discrimination, selects the optimal second window tissue density and applies it to the EtherCAT master station periodic communication timing monitoring.

[0099] In this embodiment, the step of selecting the optimal second-window tissue density based on the tissue density score and multi-phase coverage consistency discrimination, and applying it to the EtherCAT master station periodic communication timing monitoring, specifically involves: Based on the tissue density scores obtained for each second window tissue density, each tissue density score is divided into a sequence of phase segment scores accumulated in each walking phase interval according to the corresponding phase interval index. Each phase segment score represents the cumulative reward generated by the tissue density for communication monitoring in the corresponding phase interval. For the phase segment scoring sequence corresponding to the same second window tissue density, calculate its mean and variance in all walking phase intervals. The mean is used to characterize the average monitoring benefit level of the tissue density in the overall phase domain, and the variance is used to characterize the dispersion of the monitoring benefit of the tissue density in different phase intervals. Based on the mean and variance of the phase segmentation scoring sequence, a phase coverage consistency index is constructed. The phase coverage consistency index is obtained by normalizing the mean and inversely normalizing the variance, and is used to characterize whether the tissue density has both high returns and low volatility in different phase intervals. Compare the phase coverage consistency index corresponding to all second window tissue densities, and select the second window tissue density with the largest phase coverage consistency index under the condition that the tissue density score is not lower than the preset score lower limit, as the optimal second window tissue density; The phase interval-monitoring time window length mapping relationship corresponding to the optimal second window organization density is used as the final monitoring time organization scheme and is fixedly applied to the EtherCAT master station periodic communication timing monitoring process for real-time monitoring and scheduling of the master station periodic communication during the robot walking control phase.

[0100] In this embodiment, the tissue density score refers to the overall evaluation result obtained by accumulating the time-series reports of communication timing monitoring features for each type of second window tissue density in step S5. This score reflects the comprehensive monitoring value of the tissue density in terms of communication anomaly capture capability, phase sensitivity, and communication stability during the complete monitoring process, and is the basic input for the subsequent optimization process.

[0101] In this embodiment, the phase interval index refers to the identification number assigned to different phase intervals within a unified walking phase domain according to their order in the walking cycle. This index is used to distinguish the contribution of different phase intervals in tissue density scoring, so that the scoring results can be decomposed and analyzed according to the phase dimension.

[0102] In this embodiment, the phase segment scoring sequence refers to the set of scoring results accumulated in each walking phase interval after the overall tissue density score corresponding to a certain second window tissue density is split according to the phase interval index. Each phase segment score reflects the stage-by-stage return level of the tissue density on communication monitoring in the corresponding phase interval.

[0103] In this embodiment, the cumulative return of phase segmentation scoring refers to the result obtained by continuously accumulating the return values ​​formed by communication monitoring characteristics within a certain fixed phase interval according to the communication cycle time sequence. This return reflects the stable performance of the long-term monitoring value of the tissue density under the phase condition, rather than the instantaneous performance.

[0104] In this embodiment, the mean of the phase segment scoring sequence refers to the average level obtained by summarizing the phase segment scores corresponding to the tissue density in the same second window across all walking phase intervals. This mean is used to reflect the overall monitoring benefit capability of the tissue density in the complete walking phase domain.

[0105] In this embodiment, the variance of the phase segment scoring sequence refers to the result obtained by statistically analyzing the difference between the phase segment scores obtained for the same second window tissue density in different phase intervals. This variance is used to reflect whether the monitoring effect of the tissue density is balanced in different phase intervals. The larger the variance, the more outstanding the tissue density is in some phases but there is a significant deficiency in other phases.

[0106] In this embodiment, the phase coverage consistency index is an evaluation index constructed by comprehensively considering the overall mean level of the phase segment scoring sequence and the degree of fluctuation between phases. This index emphasizes that the organization density not only needs to have a high overall monitoring benefit, but also needs to maintain relatively consistent monitoring performance in different phase intervals, so as to avoid the over-concentration of monitoring resources in local phases, resulting in insufficient coverage of other key phases.

[0107] In this embodiment, mean normalization refers to adjusting the mean scores of phase segments corresponding to different tissue densities to a uniform scale when constructing the phase coverage consistency index, so that they are comparable within the same numerical range. This process ensures that the average monitoring benefits of different tissue densities can be compared under a consistent standard.

[0108] In this embodiment, the variance inverse normalization process refers to treating the dispersion of phase segment scores as a suppression term when constructing the phase coverage consistency index. This results in the tissue density with large fluctuations between phases receiving a lower weight in the consistency evaluation, thereby guiding the screening results to tend to show a time window organization mode that is stable under multi-phase conditions.

[0109] In this embodiment, the preset lower limit of the score refers to a minimum acceptable tissue density score standard that is set in advance during the system design stage based on the real-time communication requirements of the EtherCAT master station, the safety requirements of robot walking control, and the upper limit of monitoring resource consumption. It is used to exclude tissue density schemes that have insufficient overall monitoring value or perform well in consistency indicators but have weak actual monitoring effects.

[0110] In this embodiment, the optimal second window organization density refers to the organization method of the monitoring time window with the largest phase coverage consistency index among all candidate second window organization densities, provided that the organization density score is not lower than the preset score lower limit. This organization density achieves the optimal trade-off between overall benefits and phase balance.

[0111] In this embodiment, the final monitoring time organization scheme refers to the fixed mapping relationship between the phase interval determined by the optimal second window organization density and the corresponding monitoring time window length. This scheme serves as a long-term constraint strategy for communication monitoring scheduling and will not be dynamically switched during subsequent EtherCAT master station periodic communication monitoring.

[0112] In this embodiment, the fixed application to EtherCAT master station periodic communication timing monitoring refers to the continuous division and scheduling of the master station periodic communication monitoring time period according to the final monitoring time organization scheme during the robot walking control stage, so that the communication monitoring always follows the phase-sensitive allocation method corresponding to the optimal organization density.

[0113] It should be noted that this step avoids the bias problem that may be caused by using only a single overall score as the selection criterion by introducing a multi-phase coverage consistency discrimination mechanism. This makes the screening results not only focus on the average effect of communication monitoring, but also explicitly constrain the coverage balance under different walking phases, thereby improving the robustness of the monitoring strategy under complex walking conditions.

[0114] It should be noted that the determination of the optimal second window organization density does not limit the subsequent system expansion. When the robot's walking mode, load state, or EtherCAT communication configuration changes, the organization density generation, evaluation, and screening process can be re-executed to obtain the optimal monitoring time organization scheme that adapts to the new working conditions, thereby achieving the portability and scalability of the method in different application scenarios.

[0115] Example 2, Figure 2 This invention presents a real-time and deterministic communication monitoring system for the cerebellum and cerebrum of a robot, comprising a phase-correlated communication acquisition and window initialization module, a multi-scale reconstruction module for monitoring time windows, a phase-time entropy preservation mapping and transformation module, an EtherCAT master station periodic communication phase perception monitoring module, a tissue density monitoring feature extraction and reward accumulation evaluation module, and a multi-phase coverage consistency discrimination and optimal tissue density selection module. The phase-correlated communication acquisition and window initialization module is used to acquire the EtherCAT master station periodic communication time sequence driven by the cerebrum planning cycle and the cerebellum servo cycle during robot walking control, as well as the corresponding walking phase state sequence. Based on the phase-locked distribution of communication events in the walking phase domain, it constructs the initial monitoring time window tissue density in the phase, i.e., the first window tissue density. The multi-scale reconstruction module for monitoring time windows is used to perform multi-scale time unfolding and phase resampling on the first window tissue density. The system generates several second-window organization densities with different phase coverage continuity and phase concentration. A phase-time entropy preservation mapping conversion module is used to construct the distribution transfer relationship of organization density based on the entropy preservation mapping between the phase and time domains, transforming the window organization density into an executable operational target for communication monitoring. An EtherCAT master station periodic communication phase sensing monitoring module is used to convert each second-window organization density and apply it to EtherCAT master station periodic communication time-series monitoring. An organization density monitoring feature extraction and reward accumulation evaluation module is used to extract features from monitoring under different organization densities and generate corresponding organization density scores based on the value evaluation of accumulated time-series rewards. A multi-phase coverage consistency discrimination and optimal organization density selection module is used to select the optimal second-window organization density based on the organization density score and multi-phase coverage consistency discrimination, and apply it to EtherCAT master station periodic communication time-series monitoring.

[0116] The present invention also includes an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a real-time and deterministic communication monitoring method for the cerebellum and cerebrum of a robot.

[0117] The present invention also includes a computer-readable storage medium storing a computer program that, when executed by a processor, implements a real-time and deterministic communication monitoring method for the cerebellum and cerebrum of a robot.

[0118] In the embodiments provided by this invention, it should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0119] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0120] Therefore, the embodiments should be considered 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 equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0121] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0122] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0123] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0124] In the embodiments provided in this disclosure, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0125] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0126] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for real-time and deterministic communication monitoring of the cerebellum and cerebrum of a robot, characterized in that, Includes the following steps: During the robot's walking control process, the EtherCAT master station cycle communication time sequence driven by the brain planning cycle and the cerebellum servo cycle, as well as the corresponding walking phase state sequence, are collected. Based on the phase locking distribution of communication events in the walking phase domain, the initial monitoring time window is constructed in the phase organization density, i.e., the first window organization density. Multi-scale time unfolding and phase resampling are performed on the tissue density of the first window to generate several second window tissue densities with different phase coverage continuity and phase concentration. Based on the entropy-preserving mapping between the phase domain and the time domain, the distribution transfer relationship of organizational density is constructed, and the window organizational density is transformed into a practical control target that can be executed for communication monitoring. The organization density of each second window was converted and applied to the EtherCAT master station periodic communication timing monitoring. Feature extraction was performed on monitoring under different tissue densities, and corresponding tissue density scores were generated based on the value assessment of accumulated time-series returns. The optimal second-window tissue density was selected based on the tissue density score and multi-phase coverage consistency judgment, and then applied to the EtherCAT master station periodic communication timing monitoring.

2. The real-time and deterministic communication monitoring method for the cerebellum and cerebrum of a robot according to claim 1, characterized in that, During the robot's walking control process, the EtherCAT master station's periodic communication time sequence, driven by both the brain planning cycle and the cerebellum servo cycle, and the corresponding walking phase state sequence, are used to construct the initial monitoring time window's organization density in the phase, i.e., the first window organization density, based on the phase-locked distribution of communication events in the walking phase domain. Specifically: During the robot's walking control process, the time sequence of control commands sent by the brain planning module to the cerebellum control module, the time sequence of feedback returned by the cerebellum control module to the actuator, and the walking phase state sequence collected synchronously with the communication process are collected and time-aligned according to the periodic communication beat of the EtherCAT master station to form a communication event time sequence under a unified time reference. The walking phase state sequence is subjected to phase expansion processing, and the discrete walking phase identifier in each control cycle is mapped to the phase value in the interval [0, 2π). The phase value is accumulated and expanded according to the control cycle sequence to obtain a continuous phase sequence that evolves monotonically with time. Based on the continuous phase sequence, the time series of communication events under a unified time base is mapped to each event phase, and the occurrence time of each communication event is mapped to the corresponding phase value to construct a communication event-phase value sequence. The communication event-phase numerical sequence is divided into several phase intervals according to the phase division rules. The number of communication events is counted in each phase interval to obtain the communication event count sequence corresponding to each phase interval. Based on the communication event counting sequence, the proportion of the number of communication events in each phase interval to the total number of communication events in all phase intervals is calculated to form a phase interval communication proportion sequence. Based on the communication ratio sequence of the phase interval, the length of the monitoring time window is allocated proportionally within the complete walking phase domain, so that the length of the monitoring time window corresponding to each phase interval corresponds one-to-one with its communication ratio, and the arrangement sequence of the monitoring time window in the walking phase domain is constructed. The phase interval index and the corresponding monitoring time window length form a mapping set. The mapping set is used to characterize the organization of communication monitoring time in the walking phase domain, and serves as the initial monitoring time window organization density in the phase, i.e., the first window organization density.

3. The real-time and deterministic communication monitoring method for the cerebellum and cerebrum of a robot according to claim 2, characterized in that, The process of performing multi-scale time unfolding and phase resampling on the tissue density of the first window to generate several second window tissue densities with different phase coverage continuity and phase concentration is as follows: Based on the mapping set of phase interval index and corresponding monitoring time window length in the first window tissue density, the monitoring time window length sequence corresponding to each phase interval in the complete walking phase domain is extracted as the initial time window allocation sequence. The initial time window allocation sequence is subjected to multi-scale time expansion. According to different time expansion scales, the length of the monitoring time window corresponding to each phase interval is proportionally scaled to generate several time expansion sequences with different window length allocation granularities while keeping the total monitoring time unchanged. For each time-expanded sequence, the walking phase domain is re-divided according to the preset phase resampling rules, the original phase intervals are merged or subdivided into a new set of phase intervals, and the monitoring time window length in the time-expanded sequence is remapped to the new phase interval to form the corresponding phase resampling sequence. For each phase resampling sequence, the proportion of the monitoring time window length corresponding to each new phase interval to the monitoring time of the complete walking phase domain is recalculated, and a new phase interval-monitoring time window length mapping set is constructed. The multiple phase interval-monitoring time window length mapping sets obtained under different time expansion scales and different phase resampling results are used as the phase organization density of the second monitoring time window with different phase coverage continuity and phase concentration distribution.

4. The real-time and deterministic communication monitoring method for the cerebellum and cerebrum of a robot according to claim 3, characterized in that, The distribution transfer relationship of tissue density is constructed based on the entropy-preserving mapping between the phase domain and the time domain, transforming the window tissue density into a practically operable control target for communication monitoring. Specifically: Based on the proportion of the monitoring time window length corresponding to each phase interval in the phase organization density of each second monitoring time window, a probability distribution sequence of the phase interval is constructed, and the information entropy value of each phase interval in the complete walking phase domain is calculated according to the probability distribution sequence to form a phase domain information entropy distribution sequence. Based on the frequency of each communication event on the time axis in the EtherCAT master station periodic communication time series, a probability distribution of communication events for equal-length time intervals on the communication time axis is constructed, and the information entropy value of the corresponding time interval is calculated to form a time domain information entropy distribution sequence. Under the constraint of maintaining the overall entropy value of the phase domain information entropy distribution sequence and the time domain information entropy distribution sequence consistent, a one-to-one mapping relationship between phase intervals and communication time periods is established so that the information entropy value corresponding to each phase interval is equal to the information entropy value of the communication time period it maps to, and a phase-time entropy preservation mapping table is generated. Based on the phase-time entropy preservation mapping table, the proportion of the monitoring time window length corresponding to each phase interval is converted into the specific time period length on the communication time axis, thus obtaining the communication time period allocation sequence within the phase. Based on the order relationship of adjacent phase intervals in the phase domain, the mapped communication time intervals are merged into adjacent intervals to generate a cross-phase communication time interval set covering the phase switching boundary; Based on the phase-intra-phase communication time period allocation sequence and the cross-phase communication time period set, the start and end times of data acquisition for different time periods during the EtherCAT master station's periodic communication monitoring process are determined, forming a time window control sequence that can be directly used for communication monitoring and scheduling.

5. The real-time and deterministic communication monitoring method for the cerebellum and cerebrum of a robot according to claim 4, characterized in that, The specific steps involve converting the organization density of each second window and applying it to the EtherCAT master station's periodic communication timing monitoring: Based on the time window control sequence corresponding to the tissue density in each second monitoring time window, the EtherCAT master station periodic communication time axis is divided into several monitoring time periods of unequal length according to the phase-time mapping result. During the EtherCAT master station periodic communication, the current walking phase status is acquired in real time, and the monitoring time period to which the current master station communication cycle belongs is determined according to the phase-time mapping relationship. When the communication cycle of the master station is within a certain monitoring time period, the master station sending timestamp and the slave station returning timestamp are continuously collected according to the monitoring time window length corresponding to that time period to form a periodic communication timestamp sequence within that time period. When the communication cycle of the main station crosses the boundary of adjacent monitoring time periods, based on the cross-phase communication time period set, several main station communication cycles before and after the boundary are jointly collected to form a continuous communication timestamp sequence covering the phase switching process. The periodic communication timestamp sequence is arranged according to the master station communication cycle order and its corresponding phase interval index is marked to form EtherCAT master station periodic communication timing monitoring data with phase identifier; The EtherCAT master station periodic communication timing monitoring data with phase identifiers is used as the master station communication monitoring result executed under the current second window organization density constraint.

6. The real-time and deterministic communication monitoring method for the cerebellum and cerebrum of a robot according to claim 5, characterized in that, The process involves feature extraction from monitoring under different tissue densities and generating corresponding tissue density scores based on the value assessment of accumulated time-series returns. Specifically: Based on the EtherCAT master station periodic communication timing monitoring data with phase identifiers obtained under the current second window organization density constraint, the master station sending timestamp and slave station return timestamp are paired according to the monitoring time period, and the communication round-trip delay corresponding to each master station communication cycle is calculated to form a communication delay time series with phase identifiers. According to the walking phase interval index, the communication delay time series is grouped, and the mean and variance of the communication delay are calculated in each phase interval to form the communication delay concentration feature within the phase. The mean is used to characterize the overall communication delay level within the phase interval, and the variance is used to characterize the communication delay fluctuation amplitude. In terms of phase interval order, the mean communication delay of adjacent phase intervals is differentially calculated to calculate the change sequence of the mean communication delay of adjacent phase intervals, forming a phase switching sensitivity feature, which is used to reflect the degree of abrupt change in communication performance at the phase boundary. The delay difference between adjacent cycles is calculated according to the communication cycle sequence of the main station to form a periodic delay disturbance sequence. The mean square value of the periodic delay disturbance sequence is calculated to obtain the communication rhythm dispersion characteristics, which are used to characterize the amplification of transient communication fluctuations by the monitoring time organization method. Within the complete monitoring timeline, the number of communication cycles with communication latency exceeding the preset baseline latency threshold is counted, and the proportion of these cycles to the total number of monitored communication cycles is calculated to form an abnormal latency ratio feature, which is used to characterize the frequency of transient anomalies being captured under the current organizational density. The characteristics of communication delay concentration, phase switching sensitivity, communication rhythm dispersion, and abnormal delay ratio within the phase are normalized according to a unified time order and formed into a communication monitoring feature sequence corresponding to the current second window organization density. Based on the communication monitoring feature sequence, the feature values ​​are accumulated and summed in the order of communication cycle on the monitoring time axis to form a time-series return accumulation sequence that reflects the evolution of communication monitoring results over time. The final cumulative value is calculated for the cumulative time-series report sequence, and this cumulative value is used as the tissue density score corresponding to the tissue density in the second window, which is used to characterize the overall monitoring value of the tissue density in the EtherCAT master station periodic communication time-series monitoring.

7. The real-time and deterministic communication monitoring method for the cerebellum and cerebrum of a robot according to claim 6, characterized in that, The optimal second-window tissue density is selected based on the tissue density score and multi-phase coverage consistency discrimination, and then applied to the EtherCAT master station periodic communication timing monitoring, specifically as follows: Based on the tissue density scores obtained for each second window tissue density, each tissue density score is divided into a sequence of phase segment scores accumulated in each walking phase interval according to the corresponding phase interval index. Each phase segment score represents the cumulative reward generated by the tissue density for communication monitoring in the corresponding phase interval. For the phase segment scoring sequence corresponding to the same second window tissue density, calculate its mean and variance in all walking phase intervals. The mean is used to characterize the average monitoring benefit level of the tissue density in the overall phase domain, and the variance is used to characterize the dispersion of the monitoring benefit of the tissue density in different phase intervals. Based on the mean and variance of the phase segmentation scoring sequence, a phase coverage consistency index is constructed. The phase coverage consistency index is obtained by normalizing the mean and inversely normalizing the variance, and is used to characterize whether the tissue density has both high returns and low volatility in different phase intervals. Compare the phase coverage consistency index corresponding to all second window tissue densities, and select the second window tissue density with the largest phase coverage consistency index under the condition that the tissue density score is not lower than the preset score lower limit, as the optimal second window tissue density; The phase interval-monitoring time window length mapping relationship corresponding to the optimal second window organization density is used as the final monitoring time organization scheme and is fixedly applied to the EtherCAT master station periodic communication timing monitoring process for real-time monitoring and scheduling of the master station periodic communication during the robot walking control phase.

8. A system for monitoring the real-time and deterministic communication of the cerebellum and cerebrum of a robot as described in any one of claims 1-7, characterized in that, It includes a phase-correlated communication acquisition and window initialization module, a monitoring time window multi-scale reconstruction module, a phase-time entropy preservation mapping and transformation module, an EtherCAT master station periodic communication phase sensing monitoring module, a tissue density monitoring feature extraction and reward accumulation evaluation module, and a multi-phase coverage consistency discrimination and optimal tissue density selection module. The phase-related communication acquisition and window initialization module is used to acquire the EtherCAT master station cycle communication time sequence driven by the brain planning cycle and the cerebellum servo cycle during the robot walking control process, as well as the corresponding walking phase state sequence. Based on the phase locking distribution of communication events in the walking phase domain, the initial monitoring time window is constructed in the phase organization density, i.e. the first window organization density. The monitoring time window multi-scale reconstruction module is used to perform multi-scale time expansion and phase resampling on the tissue density of the first window, and generate several second window tissue densities with different phase coverage continuity and phase concentration. The phase-time entropy preservation mapping conversion module is used to construct the distribution transfer relationship of tissue density based on the entropy preservation mapping between the phase domain and the time domain, and to convert the window tissue density into a practical control target that can be executed for communication monitoring. The EtherCAT master station periodic communication phase sensing monitoring module is used to convert the organization density of each second window and apply it to the EtherCAT master station periodic communication timing monitoring. The Tissue Density Monitoring Feature Extraction and Return Accumulation Evaluation Module is used to extract features from monitoring under different tissue densities and generate corresponding tissue density scores based on the value evaluation of time-series return accumulation. The multi-phase coverage consistency discrimination and optimal tissue density selection module is used to select the optimal second window tissue density based on the tissue density score and multi-phase coverage consistency discrimination, and apply it to the EtherCAT master station periodic communication timing monitoring.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the real-time and deterministic communication monitoring method for the cerebellum and cerebrum of a robot as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the real-time and deterministic communication monitoring method for the cerebellum and cerebrum of a robot as described in any one of claims 1 to 7.

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