Operation monitoring management and control method and system for chemical raw material conveying robot

Abstract

The invention discloses an operation monitoring management and control method and system for a robot for conveying chemical raw materials, and relates to the technical field of intelligent monitoring, and the method comprises the following steps: fusing multi-source operation state parameters, and obtaining a robot operation state reference; state change analysis is conducted according to the robot operation state reference, and a state change sequence is obtained; performing operation health assessment according to the state change sequence to obtain a health characterization value; performing risk grading judgment according to the health characterization value to obtain an operation risk grade; adjusting operation parameters according to the operation risk level to obtain operation control parameters; and performing robot operation constraint according to the operation control parameters to obtain an operation result. Through the time window and serialization processing, the time change characteristics of the operation state are described, the state change sequence is constructed, the stability and reliability of the operation monitoring result are improved, and the applicability of the monitoring management and control method is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology, specifically to a method and system for monitoring and controlling the operation of a robot used for conveying chemical raw materials. Background Technology

[0002] As chemical production processes evolve towards continuous and automated operations, the transportation of chemical raw materials is gradually shifting from manual operation and fixed conveying equipment to flexible transportation methods undertaken by industrial robots. Robots used for chemical raw material transportation typically need to complete tasks such as pipeline docking, container transfer, and quantitative delivery under complex working conditions. Their operational status is not only affected by their own mechanical structure and drive system but also closely related to the physical properties of the transported medium, process cycle time, and environmental conditions. In actual operation, chemical raw material transportation robots are constantly subjected to frequent load changes, continuous operating rhythms, and harsh working environments, and their operational status often exhibits a gradual change over time. Once the operational status evolves abnormally, it can easily lead to decreased conveying accuracy, accelerated fatigue of mechanical components, and even safety risks such as media leakage. Therefore, continuous monitoring of the robot's operational status and effective control based on this monitoring are important technical requirements for ensuring the safety of chemical production.

[0003] Existing technologies for robot operation status analysis have shortcomings: existing robot operation monitoring technologies typically only compare the current operation status with a fixed reference value instantaneously, which makes it difficult to truly reflect the continuous evolution characteristics of the robot's operation status and affects the reliability of the state evolution judgment. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for monitoring and controlling the operation of a robot used for conveying chemical raw materials, thereby solving the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides a method and system for monitoring and controlling the operation of a robot used for conveying chemical raw materials, comprising the following steps: S1. Multi-source operating state parameters are fused to obtain the robot's operating state benchmark; S2. Analyze the state changes based on the robot's operating state baseline to obtain the state change sequence; S3. Perform an operational health assessment based on the state change sequence to obtain health characterization values; S4. Determine the operational risk level by classifying the risk based on the health indicator values; S5. Adjust the operating parameters according to the operating risk level to obtain the operating control parameters; S6. Perform robot operation constraints based on the operation control parameters to obtain the operation results.

[0006] To further optimize this technical solution, the parameter fusion in step S1 includes: By determining the composition range and rated reference value of the operating state parameters, the multi-source operating state parameters are processed and fused in a unified scale to obtain a robot operating state reference with a unified scale and clear physical meaning.

[0007] To further optimize this technical solution, the state change analysis in step S2 includes: Based on the obtained robot operating state benchmark, a state change sequence describing the characteristics of robot operating state changes over time is constructed through time windowing and serialization processing.

[0008] To further optimize this technical solution, the time window and serialization processing include: Based on the robot's operational state benchmark, a fixed-length time window is constructed, and the state change is calculated for each time window. The state changes are compared and analyzed between multiple consecutive time windows to identify the characteristics of operational state changes.

[0009] To further optimize this technical solution, the operational health assessment in step S3 includes: Based on the obtained state change sequence, the health characterization value of the operating state is obtained by extracting the change amplitude feature, analyzing the duration feature, identifying the directional consistency feature, and integrating multiple features to calculate the health characterization value.

[0010] To further optimize this technical solution, the calculation of the health characterization value includes:

[0011] in: Health indicator values; : The characteristic quantity of the change range; : Duration of change characteristic quantity; : Consistency characteristic of change direction; Weighting coefficients for the magnitude of change; : Weighting coefficient for duration; Weighting coefficient for directional consistency; The health characterization value is obtained by weighted comprehensive calculation based on the characteristics of change amplitude, duration, and directional consistency.

[0012] To further optimize this technical solution, the risk classification determination in step S4 includes: Based on the obtained health characterization values, a mapping relationship between health characterization values ​​and operational risk levels is established by setting risk judgment intervals, and the current operational status of the robot is judged to obtain the operational risk level.

[0013] To further optimize this technical solution, the adjustment of operating parameters in step S5 includes: Determine the operating control parameters, set the rated operating baseline, establish the corresponding relationship of parameter adjustment range based on the obtained operating risk level, calculate the operating control parameters, and obtain the operating control parameters.

[0014] To further optimize this technical solution, the robot operation constraints in step S6 include: Based on the obtained operating control parameters, the operating control parameters are written into the robot control system to realize real-time operating status monitoring and constraint application, and obtain the robot's operating results.

[0015] This technical solution has been further optimized, including the following functional modules: The module includes a benchmark construction module, a state evolution analysis module, an operational health assessment module, a risk classification and determination module, an operational parameter adjustment module, and an operational constraint module.

[0016] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of the method and system for monitoring and controlling the operation of a chemical raw material conveying robot as described in the first aspect of the present invention.

[0017] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of the method and system for monitoring and controlling the operation of a chemical raw material conveying robot as described in the first aspect of the present invention.

[0018] Compared with the prior art, the present invention provides a method and system for monitoring and controlling the operation of a robot used for conveying chemical raw materials, which has the following beneficial effects: This method and system for monitoring and controlling the operation of a robot used for transporting chemical raw materials, through time windows and serialization processing, characterizes the temporal changes in the operating status, constructs a sequence of state changes, improves the stability and reliability of the operation monitoring results, and enhances the applicability of the monitoring and control method. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating a method for monitoring and controlling the operation of a robot used for conveying chemical raw materials, as proposed in this invention. Figure 2 This is a schematic diagram of a module for monitoring and controlling the operation of a robot used for conveying chemical raw materials, as proposed in this invention. Detailed Implementation

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0022] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0024] Example 1: Reference Figure 1 This is the first embodiment of the present invention, which provides a method for monitoring and controlling the operation of a robot used for conveying chemical raw materials, comprising the following steps: S1. Multi-source operating state parameters are fused to obtain the robot's operating state benchmark.

[0025] In this embodiment, the parameter fusion includes: Robots used for transporting chemical raw materials simultaneously bear the combined effects of mechanical motion loads, electric drive loads, and chemical process loads during operation. Their operational state is not determined by a single factor, but rather by a comprehensive result of the coupling of multiple state parameters. Relying solely on a single operational parameter, such as joint current or transport pressure, cannot accurately reflect the robot's overall operational level under real-world conditions, and can easily lead to misjudgments. Furthermore, chemical raw material transport scenarios are typically characterized by long continuous operating times, slow changes in operating conditions, but significant cumulative effects. Different types of parameters exhibit significant differences in dimensions, magnitude of change, and sensitivity. Without unified processing and integration of these parameters, it is not only difficult to conduct comparative analysis over time, but also impossible to provide a unified basis for subsequent state evolution and health assessment.

[0026] By determining the composition range and rated reference value of the operating state parameters, the multi-source operating state parameters are processed and fused in a unified scale to obtain a robot operating state reference with a unified scale and clear physical meaning, thereby describing the robot's comprehensive operating level at a certain moment or within a certain time period, which serves as the basis for subsequent steps.

[0027] The steps involved in parameter fusion include: Determining the range of operational status parameters: Based on the structural and technological characteristics of the chemical raw material conveying robot, determine the types of operational status parameters that need to be included in the integration. This parameter range should at least include parameters that reflect mechanical motion status, drive load status, thermal status, conveying process status, and structural stability. All types of parameters should be defined with a single physical meaning to avoid the same parameter reflecting different states repeatedly. Rated reference determination: For each type of operating state parameter, its corresponding rated reference value or allowable upper limit value is determined. This reference value is used to characterize the reasonable range of the parameter under safe and stable operating conditions. It is determined based on equipment design capabilities, process allowable range, and long-term operating experience, and serves as a reference for subsequent unified processing. The rated reference remains unchanged throughout the entire operating cycle to ensure the stability of the state reference. Unified scale processing: Different operating state parameters have natural differences in dimensions and numerical ranges. For example, angle, temperature, pressure, flow rate and vibration amplitude cannot be directly compared. While keeping the physical meaning of each parameter unchanged, each operating state parameter is proportionalized to its corresponding rated benchmark. This maps parameters with different dimensions and value ranges to a unified dimensionless scale to represent their deviation from the safe operating benchmark, ensuring that each parameter is comparable during the fusion process. Weighting determination: Based on the degree of influence of each parameter on the safety, stability and equipment reliability of chemical raw material transportation, a fixed weight is pre-assigned to each parameter. The more direct the influence and the higher the risk, the greater the weight, so as to truly reflect the operational risk structure and keep it constant to avoid introducing uncertainty. Multi-source operational state parameter weighted fusion: After completing the unified scale processing and weight setting, the various operational state parameters are weighted and fused to obtain a single comprehensive operational state value, which is the robot's operational state benchmark at the current moment, used to reflect the overall operational level under the combined effect of multiple operational factors. Operating status baseline output: The obtained operating status baseline is provided as a standardized output result for subsequent steps.

[0028] S2. Analyze the state changes based on the robot's operating state baseline to obtain the state change sequence.

[0029] In this embodiment, the state change analysis includes: In chemical raw material transportation scenarios, robot operation anomalies often have a gradual characteristic, such as a gradual increase in load, continuous accumulation of heat, and slow amplification of structural vibration. In the initial stage, such anomalies usually do not cause the operating status benchmark to deviate significantly from the normal range. Therefore, judging based solely on the instantaneous state is prone to missing potential risks. If the time evolution process of the operating status is not analyzed, a response can only be made when the anomaly has become explicit, which does not match the need for early risk identification in chemical scenarios.

[0030] Based on the obtained robot operating state benchmark, a state change sequence describing the characteristics of robot operating state changes over time is constructed through time windowing and serialization processing. This makes the operating state not only numerically meaningful, but also has the characteristics of change direction, change rate and persistence. This provides a basis for distinguishing between short-term disturbances and continuous degradation, provides input basis for subsequent health assessment, improves the stability and reliability of operation monitoring, and enhances the applicability of monitoring and control methods.

[0031] Furthermore, the time window and serialization process include: Based on the robot's operational state baseline, a fixed-length time window is constructed. State changes are calculated for each time window, and the state changes are compared and analyzed across multiple consecutive time windows to identify operational state change characteristics. Specific implementation steps include: Continuous observation over time: Using the operating state benchmark output in step S1 as the observation object, the benchmark is continuously recorded at a preset time interval during the continuous operation of the robot. This time interval is kept fixed to ensure that the state changes between different time periods are comparable. Continuous time window construction: Based on continuous recording, a fixed-length time window is introduced to describe the change characteristics of the operating status within a local time period. Each time window contains multiple continuous operating status benchmark values ​​to reflect the overall change of the status within that time period, rather than a single instantaneous fluctuation, thus preventing normal process fluctuations from being misjudged as abnormal status and improving the reliability of status evolution judgment. State change calculation: Within each time window, the changes in the operating state baseline between the start and end times of the window are described to obtain the state change corresponding to that time window, so as to reflect whether the operating state tends to be stable, good, or deteriorating. State change identification: By comparing the amount of state change across multiple consecutive time windows, the consistency of the direction and the continuity of time of the state change are determined, i.e., whether it presents a consistent direction and whether there are characteristics of continuous amplification or continuous weakening, thereby distinguishing isolated fluctuations from trend changes. State change sequence formation: The state change descriptions corresponding to each time window are arranged in chronological order to form a state change sequence. This sequence fully records the evolution of the operating state baseline in the time dimension, providing continuous and structured input for subsequent steps.

[0032] Furthermore, the state change calculation includes:

[0033]

[0034] in: : Rate of change of state, calculated based on the amount of state change within a time window, is used to describe the direction and magnitude of the change of the operating state reference within a unit of time, so as to facilitate comparison between time windows of different lengths. When it is greater than 0, it indicates that the operating state reference is on an upward trend within the time window; when it is less than 0, it indicates that the operating state reference is on a downward trend within the time window; when it is equal to or close to 0, it indicates that the overall operating state remains stable. : Change in state; At the start of the time window The robot's operating state baseline at the time of the end is used to calculate the state change within the time window by combining the difference between the operating state baseline at the end time and the start time. At the end of the time window Reference point for robot operating status at that time; : Length of time window; Based on the robot's operational state baseline at the start and end of the time window, the state change amount is calculated, and the state change rate is obtained by combining the time window length.

[0035] S3. Perform an operational health assessment based on the state change sequence to obtain health characterization values.

[0036] In this embodiment, the operational health assessment includes: During the operation of chemical raw material conveying robots, operational anomalies are often not sudden but rather the result of the accumulation of small, long-term changes. Judging solely based on the sequence of state changes requires analyzing multiple factors simultaneously, which is detrimental to subsequent unified decision-making. Furthermore, while the specific forms of state change sequences may differ across different operating stages and conditions, their impact on operational health may be similar. Without unified quantitative indicators of health, it is difficult to conduct horizontal comparisons of operational status across different time periods, equipment, or operating conditions.

[0037] Based on the obtained state change sequence, by extracting the change amplitude features, analyzing the duration features, identifying the directional consistency features, and integrating multiple features to calculate the health characterization value, the health characterization value of the operating state is obtained. This reduces the complexity of subsequent risk assessment and operation control, improves the applicability of the method, provides a unified scale for long-term operating state comparison, and provides a basis for subsequent steps.

[0038] Operating health assessment methods include: Input object determination: The state change sequence output in step S2 is used as the basic input for health assessment. It includes information such as the direction of change, the magnitude of change, and the continuity of time, ensuring that the health assessment results are completely based on the verified state evolution information, thereby maintaining the overall consistency and traceability of the method. Variation amplitude feature extraction: Variation amplitude is the most intuitive basis for judging whether the operating state deviates from the stable range. In the state change sequence, the magnitude of the state change in each time period is identified to reflect the extent of the operating state deviating from the stable benchmark. Variation amplitude is used to measure whether the state has obvious fluctuations. In the process of chemical raw material transportation, slight changes usually belong to process disturbances or normal load adjustments, while large-amplitude continuous changes often mean that the mechanical load, thermal state or process conditions are developing in an unfavorable direction. Duration characteristic analysis: In actual operation, chemical raw material conveying robots often experience short-term load fluctuations or process adjustments. Even if the magnitude of such changes is large, if the duration is short, the impact on operational health is still limited. Based on the analysis of the magnitude of change, it is further determined whether the state change is continuous. The duration is used to distinguish between short-term disturbances and long-term deviations, so as to avoid misjudging short-term fluctuations in operating conditions as a decline in operational health. The longer the duration, the more obvious the trend of the operating state deviating from the stable zone, and the greater its impact on operational health. Directional consistency feature identification: Compare the direction of change in each time period in the state change sequence to determine whether the change shows a consistent trend. If the direction of change remains consistent for a long time, it usually indicates that the operating state is undergoing a systematic evolution, and its health impact is higher than the change with frequent reversal of direction. If the direction of change is frequently reversed, it is more likely to be a fluctuation in operating conditions rather than a real degradation. Health characterization value formation: The magnitude, duration and consistency of change are comprehensively evaluated and mapped to a pre-defined health range to obtain the health characterization value of the operating status, which is used to represent the health level of the current operating status.

[0039] Furthermore, the calculation of the health characterization value includes:

[0040] in: Health indicator value, ranging from 0 to 1, with a higher value indicating healthier operation; : The change range characteristic, ranging from 0 to 1. The larger the value, the smaller the change range and the healthier the state. : Duration of change feature, used to quantify the impact of persistence on health, ranging from 0 to 1, obtained by statistically analyzing the proportion of continuous time windows in which the change in state exceeds a preset threshold. The smaller the value, the less the duration of the deviation and the better the health condition. The consistency of change direction characteristic is used to determine whether the change direction of the state in a continuous window is consistent. It is obtained by calculating the average value of the number of times the direction of the time window changes compared to the previous time window. The larger the value, the more frequent the reversal of the change direction, which is more likely to be a fluctuation in the working condition rather than a true degradation. Therefore, the larger the health characterization value, the better the health condition. : Weighting coefficient of the change range, used to control the impact of the change range on the health characterization value, ranging from 0 to 1, with the sum of all weights being 1, which can be set according to the importance of each factor in the chemical transportation scenario; The weighting coefficient for duration is used to control the impact of varying duration on health characterization values. The weighting coefficient for directional consistency is used to control the impact of directional consistency on health characterization values. The health characterization value is obtained by weighted comprehensive calculation based on the characteristics of change amplitude, duration, and directional consistency.

[0041] Furthermore, the variation amplitude characteristic includes:

[0042] in: The total number of time windows in the state change sequence; : No. The state change amount for each time window is obtained through step S2; : The maximum permissible offset value of the operating status baseline, used to standardize the magnitude of change; The magnitude of change is calculated by taking the arithmetic mean of the absolute values ​​of the standardized state changes.

[0043] S4. Based on the health indicator values, risk classification is determined to obtain the operational risk level.

[0044] In this embodiment, the risk classification determination includes: In the transportation of chemical raw materials, the objects of operational risk management are usually not continuously changing values, but rather clearly categorized risk states. For example, different risk levels often correspond to different operational restrictions, interlocking constraints, or response mechanisms. If only the continuous indicator of health status is retained, subsequent management steps need to interpret its meaning, which can easily lead to inconsistencies in understanding or unclear judgment boundaries. By using risk grading, health information can be explicitly linked to safety constraints.

[0045] Based on the obtained health characterization values, a mapping relationship between health characterization values ​​and operational risk levels is established by setting risk judgment intervals. The current operational status of the robot is then assessed for risk level, resulting in an operational risk level with clear engineering semantics. This serves as the basis for subsequent steps, reflecting the current state and the relative distance from the unacceptable risk boundary, and unifying the interpretation of the health status.

[0046] Risk classification and determination methods include: Risk assessment interval setting: Based on the structural characteristics, process requirements and safety constraints of the chemical raw material conveying robot, the range of health characterization values ​​is pre-divided into multiple continuous intervals, such as three-level intervals of low risk, medium risk and high risk, or five-level intervals, corresponding to different operating states such as "sufficient safety margin", "safety margin convergence" and "insufficient safety margin". Each interval corresponds to a clear meaning of operating risk, and the interval boundary remains fixed during operation. Mapping relationship establishment: After the interval division is completed, a unique operational risk level identifier is assigned to each health interval. This mapping relationship is a one-to-one correspondence, that is, any health indicator value can only fall into a certain risk level interval, avoiding ambiguity in risk judgment. Risk range determination: The current health characterization value obtained in step S3 is compared with the set risk determination range to determine its position in the range, reflecting its real operational risk at the current moment, thereby ensuring the real-time nature and accuracy of risk determination. Operational risk level output: Based on the range to which the health characterization value belongs, the corresponding operational risk level is output as the risk assessment result of the current robot operation status, representing the risk level of the current robot operation status at the safety level.

[0047] S5. Adjust the operating parameters according to the operating risk level to obtain the operating control parameters.

[0048] In this embodiment, the adjustment of operating parameters includes: In chemical raw material transportation scenarios, robot operational safety is not determined by a single control parameter, but rather by the combined effect of multiple operating parameters. If the operational risk level corresponds to only a single control parameter adjustment, it can easily create new instability factors in the system, or even trigger secondary risks. For example, simply reducing joint speed without limiting the medium transport pressure may lead to pressure concentration within the pipeline; limiting pressure without constraining motion acceleration may cause mechanical shock and structural fatigue. Therefore, it is necessary to establish a multi-parameter linkage between risk level and operational behavior, ensuring that the robot's kinematic and technological behaviors remain coordinated and consistent under any risk level.

[0049] Determine the operating control parameters, set the rated operating benchmark, establish the corresponding relationship of parameter adjustment range based on the obtained operating risk level, calculate the operating control parameters, and obtain the operating control parameters. This provides clear upper limit conditions for robot motion control, ensuring that the robot's operation is always within the allowable range of the risk level and providing reliable parameter support.

[0050] The steps for adjusting operating parameters include: Determining the composition of operation control parameters: Based on the main controlled objects during the operation of the chemical raw material conveying robot, determine the set of operation control parameters that need to be calculated for risk linkage. The set includes parameters reflecting the intensity of mechanical motion and parameters reflecting the intensity of the conveying process, ensuring that the controlled objects cover the key sources of risk in robot operation; Rated operating baseline determination: Determine the rated operating baseline value for each operating control parameter. This baseline value represents the maximum operating level that the robot can achieve under low-risk and stable working conditions at the lowest risk level. It is the operating state that can be maintained for a long time under the premise of meeting the safety requirements for chemical raw material transportation. The rated operating baseline value is kept fixed and serves as the upper limit of the reference for subsequent parameter calculations to ensure that parameter changes under different risk levels only reflect the impact of risk factors and do not include differences in other working conditions. Correspondence establishment: Based on the operational risk level obtained in step S4, set corresponding parameter adjustment ranges for different risk levels. The higher the risk level, the closer the operational state is to the safety boundary, the larger the parameter adjustment range, and the smaller the corresponding parameter allowable range. Based on the minimum operational capability requirement, determine the minimum adjustment coefficient corresponding to the highest risk level, that is, the adjustment coefficient corresponding to when the robot can still maintain basic safe operation in a high-risk state without completely losing its function. Between the lowest and highest risk levels, set discrete step-like adjustment coefficients for each intermediate risk level. For example, each level corresponds to a fixed proportion of operational capability convergence, so that the coefficient difference between adjacent risk levels remains interpretable. Calculation of operating control parameters: After obtaining the current operating risk level, the rated benchmarks of each operating control parameter are adjusted according to its corresponding adjustment range, and the allowable values ​​of the parameters under the current risk level are calculated. Operation control parameter output: Combine the obtained operation control parameters to form a set of operation control parameters for the current operation state, which is then used as the output result for subsequent steps.

[0051] Furthermore, the calculation of the operation control parameters includes:

[0052] in: : No. The upper limit of the allowed value of each operation control parameter represents the maximum value that the operation control parameter is allowed to reach under the current operation risk level, and is used to limit the operation boundary of the robot under the current risk level. The parameter adjustment coefficient represents the percentage level that the operating control parameter can reach relative to its rated operating baseline under the current operating risk level. The range is (0,1]. When the value is 1, it means that the parameter is not constrained by risk and can reach the rated operating baseline. The higher the risk level, the smaller the value of the parameter adjustment coefficient and the smaller the allowable range of the parameter. : No. The rated operating baseline value of each operating control parameter serves as the upper limit of the reference in the calculation of the operating control parameter. It is used to define the maximum level of robot operation under low-risk and stable conditions and is set according to equipment design parameters, process safety boundaries and historical operating data. Based on the rated operating baseline values ​​of the operating control parameters and the parameter adjustment coefficients corresponding to the robot's operating risk level, the upper limit of the allowable values ​​for the operating control parameters is calculated.

[0053] S6. Perform robot operation constraints based on the operation control parameters to obtain the operation results.

[0054] In this embodiment, the robot operation constraints include: In the transportation of chemical raw materials, even with clear health characteristics and risk levels, robots may still move beyond safe limits if control parameters are not actually executed, potentially causing risks such as mechanical impact, media leakage, or pipeline damage. Therefore, control parameters need to be applied to robot movement in real time to ensure its operation is strictly controlled.

[0055] Based on the obtained operating control parameters, the operating control parameters are written into the robot control system to realize real-time operating status monitoring and constraint application, obtain the robot's operating results, thereby achieving controlled delivery and ensuring safe and stable operation.

[0056] The robot's operational constraints include: Write the operation control parameters: Write the operation control parameters output in step S5 into the motion and process constraint module of the robot controller through the industrial robot control interface. These parameters include joint speed, joint acceleration, load limit, conveying pressure and flow rate, etc., so that they become the constraint basis that cannot be bypassed in subsequent operation. Controlled motion mode activation: Enable controlled motion mode in the controller so that when the robot is performing chemical raw material transportation tasks, every action of the robot is controlled by the written operating parameters. This mode is based on industrial robot motion control technology, which is mature and reliable. It can monitor and limit the range of motion in real time to ensure that any action to be performed does not exceed the boundary specified by the control parameters. Operational status monitoring: During operation, the actual state of the robot is affected by various factors such as load changes, process fluctuations and external interference. If there is a lack of real-time monitoring and constraints, it may deviate from the safe range in a short period of time. Therefore, the controller collects joint angles, speeds, accelerations and process transport status in real time and compares these real-time statuses with the allowable values ​​in step S5. When the actual operation approaches the constraint boundary, the action is immediately restricted to bring the operating status back to the allowable range. Results obtained: Through the real-time constraints and control of the above steps, the robot always remains within the operating boundary allowed by the risk level, resulting in safe and stable operation. This ensures both the continuity of the chemical raw material transportation task and meets the safety requirements of health assessment and risk classification.

[0057] Example 2: Reference Figure 2 This is the second embodiment of the present invention, which provides a monitoring and control system for the operation of a robot used for conveying chemical raw materials, including the following functional modules: Operational Status Benchmark Construction Module: This module receives operational status parameters from different monitoring units during robot operation, integrates and processes them in a unified manner, and constructs an operational status benchmark that reflects the normal operation characteristics of the robot, serving as a reference basis for subsequent state evolution analysis. State Evolution Analysis Module: Based on the operational state benchmark, it describes the process of robot operational state changes over time, forming a continuous state change sequence, providing time-correlated state input for subsequent operational health assessment, and avoiding judgment based solely on the state at a single moment. Operational health assessment module: comprehensively evaluates the state change sequence, extracts feature information that reflects operational stability and deviation, and forms a unified health characterization value to quantitatively describe the health status of the robot's current operating state; Risk classification and determination module: Based on health characterization values, classify the robot's operating status according to preset risk determination ranges and output the corresponding operating risk level; Operating parameter adjustment module: Taking the operating risk level as input, it calculates multiple operating control parameters required for robot operation based on the constraint relationships corresponding to different risk levels, and obtains the set of operating control parameters under the current risk level, which is used to limit the operating boundary of the robot in the current state; Operational constraint module: The operational control parameters are used to control the robot, and the robot's operation behavior is constrained in real time. This ensures that the robot is always within the parameter limits when performing chemical raw material transportation tasks, thereby forming a controlled transportation state and achieving safe and stable operation.

[0058] Example 3: In practical applications, this invention can be used to monitor, evaluate and manage the operation of robots used for conveying chemical raw materials during continuous operation. The following description uses the scenario of conveying chemical raw materials in a chemical production workshop as a typical application scenario.

[0059] In this application scenario, a chemical raw material conveying robot is fixedly positioned in the feeding area, responsible for transporting the chemical raw materials to the inlet. During operation, the robot's joint drive components are subjected to the weight of the raw materials, pipeline resistance, and mechanical load changes caused by frequent start-stop cycles. Simultaneously, the internal pressure and flow rate of the conveying pipeline also vary with the process cycle. By continuously acquiring the motion state parameters of each joint of the robot and relevant parameters of the conveying process, and based on these parameters, a benchmark for the robot's operating state under normal production conditions is constructed to describe the standard operating characteristics of the robot in stable conveying conditions.

[0060] During continuous operation, the actual operating state of the robot is described over time with reference to the operating state baseline, forming a state change sequence that reflects changes in joint motion intensity and fluctuations in the conveying process. When the robot load changes due to changes in pipeline resistance or fluctuations in medium viscosity, the state change sequence can accurately reflect the degree of deviation of this change relative to the normal operating state, without being amplified by short-term process disturbances.

[0061] A health assessment is performed on the robot's current operating status to obtain a health characterization value that reflects its overall operational stability. This health characterization value comprehensively considers the magnitude, duration, and trend of operational status deviations to determine whether the robot is in a stable delivery state. When the health characterization value shows a continuous decline, a risk classification is further performed to clarify the operational risk level corresponding to the current operating status.

[0062] Based on the determined operational risk level, multiple operational control parameters involved in robot operation are calculated in tandem to determine the permissible ranges for joint speed, joint acceleration, and conveying pressure and flow rate under the current risk level. Subsequently, these operational control parameters are written into the robot control system to impose real-time constraints on the robot's behavior, ensuring that the robot's motion intensity and conveying process conditions remain under control while it continues to perform its material conveying task.

[0063] Through the above specific implementation, the robot remains in a controlled conveying state during the chemical raw material transportation process. Even when the physical properties of the raw materials change or the process cycle fluctuates, it can operate stably within a safe range, thereby effectively reducing the risk of equipment malfunction and improving the safety and continuity of chemical raw material transportation operations.

[0064] Example 4: This embodiment also provides a computer device applicable to a method and system for monitoring and controlling the operation of a robot used for transporting chemical raw materials, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the method and system for monitoring and controlling the operation of a robot used for transporting chemical raw materials as proposed in the above embodiment.

[0065] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements a method and system for monitoring and controlling the operation of a chemical raw material conveying robot as proposed in the above embodiments.

[0066] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0067] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0068] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0069] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0070] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0071] It should be noted that the above 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for monitoring and controlling the operation of a robot used for conveying chemical raw materials, characterized in that, Includes the following steps: S1. Multi-source operating state parameters are fused to obtain the robot's operating state benchmark; S2. Analyze the state changes based on the robot's operating state baseline to obtain the state change sequence; S3. Perform an operational health assessment based on the state change sequence to obtain health characterization values; S4. Determine the operational risk level by classifying the health indicator values. S5. Adjust the operating parameters according to the operating risk level to obtain the operating control parameters; S6. Perform robot operation constraints based on the operation control parameters to obtain the operation results.

2. The method for monitoring and controlling the operation of a robot for conveying chemical raw materials according to claim 1, characterized in that, The parameter fusion in step S1 includes: By determining the composition range and rated reference value of the operating state parameters, the multi-source operating state parameters are processed and fused in a unified scale to obtain a robot operating state reference with a unified scale and clear physical meaning.

3. The method for monitoring and controlling the operation of a robot for conveying chemical raw materials according to claim 1, characterized in that, The state change analysis in step S2 includes: Based on the obtained robot operating state benchmark, a state change sequence describing the characteristics of robot operating state changes over time is constructed through time windowing and serialization processing.

4. The method for monitoring and controlling the operation of a robot for conveying chemical raw materials according to claim 3, characterized in that, The time window and serialization process include: Based on the robot's operational state benchmark, a fixed-length time window is constructed, and the state change is calculated for each time window. The state changes are compared and analyzed between multiple consecutive time windows to identify the characteristics of operational state changes.

5. The method for monitoring and controlling the operation of a robot for conveying chemical raw materials according to claim 1, characterized in that, The operational health assessment in step S3 includes: Based on the obtained state change sequence, the health characterization value of the operating state is obtained by extracting the change amplitude feature, analyzing the duration feature, identifying the directional consistency feature, and integrating multiple features to calculate the health characterization value.

6. The method for monitoring and controlling the operation of a robot for conveying chemical raw materials according to claim 5, characterized in that, The calculation of the health indicator values ​​includes: in: Health indicator values; : The characteristic quantity of the change range; : Duration of change characteristic quantity; : Consistency characteristic of change direction; Weighting coefficients for the magnitude of change; : Weighting coefficient for duration; Weighting coefficient for directional consistency; The health characterization value is obtained by weighted comprehensive calculation based on the characteristics of change amplitude, duration, and directional consistency.

7. The method for monitoring and controlling the operation of a robot for conveying chemical raw materials according to claim 1, characterized in that, The risk classification determination in step S4 includes: Based on the obtained health characterization values, a mapping relationship between health characterization values ​​and operational risk levels is established by setting risk judgment intervals, and the current operational status of the robot is judged to obtain the operational risk level.

8. The method for monitoring and controlling the operation of a robot for conveying chemical raw materials according to claim 1, characterized in that, The adjustment of operating parameters in step S5 includes: Determine the operating control parameters, set the rated operating baseline, establish the corresponding relationship of parameter adjustment range based on the obtained operating risk level, calculate the operating control parameters, and obtain the operating control parameters.

9. The method for monitoring and controlling the operation of a robot for conveying chemical raw materials according to claim 1, characterized in that, The robot operation constraints in step S6 include: Based on the obtained operating control parameters, the operating control parameters are written into the robot control system to realize real-time operating status monitoring and constraint application, and obtain the robot's operating results.

10. A monitoring and control system for the operation of a robot used for conveying chemical raw materials, constructed based on the monitoring and control method for the operation of a robot used for conveying chemical raw materials as described in any one of claims 1-9, characterized in that, Includes the following functional modules: The module includes a benchmark construction module, a state evolution analysis module, an operational health assessment module, a risk classification and determination module, an operational parameter adjustment module, and an operational constraint module.

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