A positive pressure type explosion-proof industrial robot intelligent control method, device and medium

Abstract

This invention discloses an intelligent control method, equipment, and medium for a positive-pressure explosion-proof industrial robot, relating to the field of positive-pressure explosion-proof control technology for industrial robots. The method includes: collecting internal and external pressures and combustible gas concentrations inside the positive-pressure housing and outside the working area; calculating the positive-pressure safety index for each grid point according to the grid division of the working area and classifying and marking it to obtain a positive-pressure safety grid; converting the positive-pressure safety grid into a positive-pressure safety guidance graph with grid points as nodes and determining edge costs based on the positive-pressure safety index between adjacent grid points; obtaining a path grid sequence on the positive-pressure safety guidance graph with minimum cost and avoiding dangerous grid points through path search; determining ventilation and speed levels based on the positive-pressure safety index classification of each grid point in the path grid sequence; converting the ventilation and speed levels into continuously executed robot motion commands and positive-pressure ventilation control commands, and generating an operation record. This improves the explosion-proof safety of the positive-pressure explosion-proof industrial robot.

Description

Technical Field

[0001] This invention relates to the field of positive pressure explosion-proof control technology for industrial robots, and in particular to an intelligent control method, equipment and medium for positive pressure explosion-proof industrial robots. Background Technology

[0002] In hazardous environments such as petrochemicals, oil and gas storage and transportation, fine chemicals, and combustible dust operations, positive-pressure explosion-proof industrial robots are gradually replacing manual labor in high-risk processes such as welding, maintenance, sampling, and valve operation. These devices typically use a positive-pressure shell to isolate potential ignition sources. By continuously supplying clean gas into the shell and monitoring the pressure difference and combustible gas concentration inside and outside the shell, along with measures such as interlocked shutdown, speed-limited operation, and zonal ventilation, intrinsic safety protection is achieved for the mechanical structure, electrical components, and actuators. In current engineering practices, explosion-proof control mostly relies on PLCs or robot controllers to perform threshold comparisons and logical judgments on a small number of signals such as pressure, concentration, and temperature. Under the premise of meeting explosion-proof standards, they complete start-stop control, simple path planning, process cycle control, and fixed-level ventilation. Some systems have introduced functions such as obstacle avoidance and process optimization, but overall, they are still mainly rule-driven and experience-based. The coupling between positive-pressure ventilation, motion control, and environmental perception is limited, making it difficult to fully reflect the intelligent safety decision-making capabilities in complex scenarios.

[0003] Under conventional thinking, positive pressure explosion-proof control often sets a uniform safety margin and ventilation strategy around the work area or the whole machine. It is difficult to identify local high-risk areas along specific operation paths and adjust the robot's movement rhythm and ventilation intensity accordingly. At the same time, safety thresholds and control rules are mostly configured once during the commissioning stage. During operation, the utilization of historical records of internal pressure and combustible gas concentration is limited, making it difficult to detect long-term high-risk spaces in a timely manner and feed them back to subsequent path selection and positive pressure control strategy optimization. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an intelligent control method for positive pressure explosion-proof industrial robots to solve the problems of difficulty in identifying local high-risk areas along the work path and finely linking ventilation and motion control, and difficulty in timely utilizing operation records to adaptively optimize positive pressure safety rules.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides an intelligent control method for a positive-pressure explosion-proof industrial robot, comprising,

[0008] Internal and external pressures and combustible gas concentrations are collected inside the positive pressure shell and outside the working area. The positive pressure safety index of each grid point is calculated according to the grid points of the working area and graded and marked to obtain the positive pressure safety grid.

[0009] The positive pressure safety grid is converted into a positive pressure safety guidance graph with grid points as nodes and the edge cost is determined according to the positive pressure safety index between adjacent grid points. The path grid point sequence with minimum cost and avoiding dangerous grid point nodes is obtained on the positive pressure safety guidance graph through path search.

[0010] Based on the positive pressure safety index classification of each grid point in the path grid sequence, the ventilation level and speed level are determined, and the ventilation level and speed level are converted into continuously executed robot motion commands and positive pressure ventilation control commands, and an operation record is generated.

[0011] High-risk grid points are identified by running records. Based on the distribution of high-risk grid points in the positive pressure safety grid, the positive pressure safety index is adjusted and the edge costs in the positive pressure safety guidance graph are corrected.

[0012] As a preferred embodiment of the intelligent control method for the positive-pressure explosion-proof industrial robot of the present invention, the specific steps for collecting internal and external pressures and combustible gas concentrations inside the positive-pressure shell and outside the working area are as follows:

[0013] Inside the positive pressure shell of the positive pressure explosion-proof industrial robot, select representative internal measuring points, and outside the working area, select external environmental measuring points. Install pressure sensors and combustible gas concentration sensors at each internal representative measuring point and external environmental measuring point.

[0014] Internal and external pressure values, combustible gas concentration values, and joint status are collected synchronously in each sampling cycle. The end position and end linear velocity are obtained through forward kinematics, and the data collected in continuous sampling cycles are averaged to form a filtered data record.

[0015] As a preferred embodiment of the intelligent control method for a positive-pressure explosion-proof industrial robot according to the present invention, the specific steps of calculating the positive-pressure safety index of each grid point according to the grid division of the working area and classifying and marking it to obtain a positive-pressure safety grid are as follows:

[0016] The three-dimensional space accessible to the positive pressure explosion-proof industrial robot is divided into grid points. Each grid point is associated with internal representative measurement points and external environmental measurement points. Based on the internal and external pressure, internal concentration and exhaust distance of each grid point, the positive pressure safety index is calculated, which is composed of the minimum value of three normalized safety margins.

[0017] Based on the positive pressure safety index, the grid points are divided into the first safety level, the second safety level, and the third safety level, forming a positive pressure safety grid with positive pressure safety index and safety level markings on all grid points in the working area.

[0018] As a preferred embodiment of the intelligent control method for a positive-pressure explosion-proof industrial robot described in this invention, the specific steps of converting the positive-pressure safety grid into a positive-pressure safety guidance graph with grid points as nodes and edge costs determined based on the positive-pressure safety index between adjacent grid points are as follows:

[0019] Each grid point in the positive pressure safety grid is taken as a node. A unique identifier is assigned to each node in the controller memory. For each node, the adjacent grid points that are one grid point apart from the node in the coordinate axis direction and are still within the working area are found on the three-dimensional coordinates. The node identifiers of each adjacent grid point are combined with the current node identifier to generate connecting edges.

[0020] When determining the edge cost of each connecting edge, the positive pressure safety index of the nodes at both ends of the connecting edge is read, and the smaller positive pressure safety index of the two nodes is selected as the safety benchmark value of the connecting edge. The difference between the upper limit of the positive pressure safety index and the safety benchmark value is used as the edge cost, and the positive pressure safety grid is converted into a positive pressure safety guidance graph composed of nodes and connecting edges.

[0021] As a preferred embodiment of the intelligent control method for a positive-pressure explosion-proof industrial robot according to the present invention, the specific steps of obtaining a path grid sequence with minimum cost and avoiding dangerous grid nodes on the positive-pressure safety guidance graph through path search are as follows:

[0022] In the positive pressure safety grid, nodes at the locations of workpieces, fixtures, and impassable areas are marked as nodes containing obstacles. The relevant connecting edges of nodes containing obstacles are deleted in the positive pressure safety guidance diagram. Nodes marked as third safety level are considered dangerous grid nodes and their relevant connecting edges are deleted.

[0023] Select the node closest to the starting point of the task as the starting node, and select the node closest to the ending point of the task as the ending node. Within the range of adjacent nodes that are not marked as containing obstacles and are not considered dangerous grid points, perform a minimum cost path search based on edge cost accumulation. The obtained minimum cost path from the starting node to the ending node is restored to the path grid point sequence from the starting grid point to the ending grid point in the node order.

[0024] As a preferred embodiment of the intelligent control method for the positive pressure explosion-proof industrial robot of the present invention, the specific steps for determining the ventilation level and speed level based on the positive pressure safety index classification of each grid point in the path grid sequence are as follows:

[0025] Three ventilation levels are preset for positive pressure ventilation control. The first ventilation level corresponds to the lowest air intake and normal exhaust mode. The second ventilation level corresponds to the medium air intake and enhanced exhaust mode. The third ventilation level corresponds to the highest air intake and enhanced exhaust combined with intermittent exhaust mode. At the same time, three speed levels are preset for the end effector movement. The first speed level corresponds to the standard process speed. The second speed level corresponds to half of the standard process speed. The third speed level corresponds to one-quarter of the standard process speed.

[0026] Establish a mapping relationship between safety level and ventilation level and speed level in the controller. The first safety level corresponds to the first ventilation level and the first speed level, the second safety level corresponds to the second ventilation level and the second speed level, and the third safety level corresponds to the third ventilation level and the third speed level.

[0027] As a preferred embodiment of the intelligent control method for a positive pressure explosion-proof industrial robot according to the present invention, the specific steps for converting ventilation levels and speed levels into continuously executed robot motion commands and positive pressure ventilation control commands are as follows:

[0028] Under representative operating conditions, the ventilation was operated at three different ventilation levels. The time required for the internal combustible gas concentration to drop to the upper limit of the allowable concentration was measured and used as the gas renewal time of the ventilation level. During task execution, the path grid sequence was traversed, and the ventilation level and speed level were determined according to the grid safety level. The expected dwell time of the grid neighborhood was calculated by combining the grid spacing and the running speed of the end effector under the speed level.

[0029] The estimated dwell time is compared with the gas renewal time. If the estimated dwell time is insufficient, the speed level is reduced first. If it is still insufficient, the ventilation level is increased until the estimated dwell time is not less than the gas renewal time. Robot motion commands are generated in chronological order based on the path grid sequence and speed level. Positive pressure ventilation control commands are also generated in chronological order based on the path grid sequence and ventilation level. During task execution, robot motion commands and positive pressure ventilation control commands are issued synchronously according to the sampling period.

[0030] As a preferred embodiment of the intelligent control method for positive pressure explosion-proof industrial robots described in this invention, the specific steps of adjusting the positive pressure safety index classification and correcting the edge costs in the positive pressure safety guidance graph according to the distribution of high-risk grid points in the positive pressure safety grid are as follows:

[0031] Based on the preset safety difference values ​​for internal combustible gas concentration and internal and external pressure difference in the controller, when either the internal combustible gas concentration is found to be close to the upper limit of allowable pressure or the internal and external pressure difference is found to be close to the depressurization trend within a continuous sampling period, the grid point is marked as a high-risk grid point and a high-risk grid point set is formed.

[0032] In the positive pressure safety grid, the proportion of high-risk grid points within each safety level is statistically analyzed. When the number of high-risk grid points within any safety level accounts for a fixed proportion or higher than the total number of grid points in that safety level, the positive pressure safety index boundary value between the safety level and the adjacent safety level is automatically tightened towards the safe side, the edge grid points are downgraded to a lower safety level, and the edge cost of the related connecting edges of high-risk grid points and adjacent grid points is increased by a preset edge cost correction amount in the positive pressure safety guidance diagram. The updated safety level boundary value and edge cost correction amount are persisted, the safety level is re-marked, and the positive pressure safety guidance diagram is reconstructed.

[0033] 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 is executed by the processor, it implements any step of the intelligent control method for a positive pressure explosion-proof industrial robot as described in the first aspect of the present invention.

[0034] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the intelligent control method for a positive pressure explosion-proof industrial robot as described in the first aspect of the present invention.

[0035] The beneficial effects of this invention are as follows: by forming a positive pressure safety grid with positive pressure safety index and safety level markings, the overall positive pressure safety status of the path grid points in the work space is quantified, local high-risk areas are identified along the work path and movement and positive pressure ventilation are coordinated, high-risk grid points are identified through operation records and the positive pressure safety index classification is adjusted and the edge cost in the positive pressure safety guidance graph is corrected, so as to realize the adaptive update of the positive pressure safety rules to the historical high-risk distribution, thereby improving the explosion-proof safety of the positive pressure explosion-proof industrial robot. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the 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.

[0037] Figure 1 This is a flowchart of an intelligent control method for a positive-pressure explosion-proof industrial robot.

[0038] Figure 2 This is a flowchart for calculating the positive pressure safety index.

[0039] Figure 3 The flowchart shows the edge cost calculation process.

[0040] Figure 4 This is a flowchart mapping ventilation and speed settings. Detailed Implementation

[0041] 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.

[0042] 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.

[0043] Secondly, the term "one 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 in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0044] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides an intelligent control method for a positive pressure explosion-proof industrial robot, comprising the following steps:

[0045] S1. Collect internal and external pressure and combustible gas concentration inside the positive pressure shell and outside the working area. Calculate the positive pressure safety index of each grid point according to the grid division of the working area and classify and mark it to obtain the positive pressure safety grid.

[0046] Four representative measurement points were selected inside the positive pressure shell of the positive pressure explosion-proof industrial robot: the base cavity, the shoulder cavity, the wrist cavity, and the cavity near the end. Pressure sensors and combustible gas concentration sensors were installed at each representative measurement point. The positions of the representative measurement points were calibrated once in the robot's calibration coordinate system. External environmental measurement points were arranged around the robot's reachable space outside the working area. Pressure sensors and combustible gas concentration sensors were installed at each external environmental measurement point. The correspondence between the robot's calibration coordinate system and the coordinates of the external environmental measurement points was established in the controller.

[0047] The controller refers to the computing and control equipment installed inside the body of a positive pressure explosion-proof industrial robot.

[0048] At the beginning of each sampling period, the controller reads the internal pressure and internal combustible gas concentration values ​​from the internal representative measuring points, and the external pressure and external combustible gas concentration values ​​from the external environmental measuring points in the working area. At the same time, it reads the current joint angles and angular velocities from the joint encoders and drivers, and calculates the end effector's end position and end linear velocity in the robot's calibration coordinate system through forward kinematics. The internal pressure, internal combustible gas concentration, external pressure, external combustible gas concentration, end position, and end linear velocity are concatenated to form the raw data record for the current sampling period. The controller performs a moving average filter on the readings of the same sensor in consecutive sampling periods to obtain the smoothed values ​​of the internal pressure, internal combustible gas concentration, external pressure, and external combustible gas concentration at the current moment, and calculates the corresponding smoothed values ​​of the end position and end linear velocity, forming a filtered data record.

[0049] An end effector is the device at the very end of a positive pressure explosion-proof industrial robot that directly contacts the outside world to complete specific tasks.

[0050] The three-dimensional space accessible to the positive-pressure explosion-proof industrial robot is divided into grid points, each corresponding to an external location of the working area. The controller stores the spatial coordinates of each grid point and assigns an internal representative measuring point to each grid point based on its geometric proximity to the internal representative measuring point. This internal representative measuring point reflects the pressure and combustible gas concentration of the corresponding internal area. Based on the spatial distance between the grid point and the external environment measuring points, one or more external environment measuring points are assigned to each grid point. The smoothed values ​​of external pressure and external combustible gas concentration are mapped to the external pressure and external combustible gas concentration values ​​of the grid point. In each sampling cycle, the controller traverses all grid points in the working area, retrieves the corresponding smoothed values ​​of internal pressure and internal combustible gas concentration for each grid point, and calculates the positive-pressure safety index of the grid point using the mapped external pressure value. The expression is:

[0051] ;

[0052] in, The positive pressure safety index represents the grid points. This represents the smoothed internal pressure value at the measurement point corresponding to the grid point. This represents the smoothed value of the external pressure at the grid point location obtained by mapping external environment measurement points. This indicates the positive pressure reference pressure difference selected based on the design of the positive pressure type explosion-proof enclosure. This indicates the upper limit of the permissible concentration as determined by explosion-proof standards. This represents the smoothed value of the internal combustible gas concentration at the measurement point corresponding to the grid point. This indicates a reference value for the exhaust distance determined based on the casing structure and ventilation path. This represents the spatial distance from the grid point to the nearest exhaust location under positive pressure in the housing.

[0053] Will The grid points are marked as the first security level, and The grid points are marked as the second security level. The grid markings indicate the third safety level. The positive pressure safety index is composed of the minimum of the following: the normalized safety margin of the difference between internal and external pressure relative to the positive pressure reference pressure difference; the normalized safety margin of the difference between the upper limit of allowable concentration and the internal combustible gas concentration relative to the upper limit of allowable concentration; and the normalized safety margin of the difference between the exhaust distance reference value and the current exhaust distance relative to the exhaust distance reference value. When the positive pressure safety index is greater than 0, it indicates that each physical quantity has a positive safety margin. When the positive pressure safety index is equal to 0, it indicates that at least one physical quantity is safe. When the full margin is exhausted, and the positive pressure safety index is less than 0, it means that at least one physical quantity has exceeded the safety boundary. Statistically, under typical working conditions, the positive pressure safety index for slight over-limit is mostly distributed between -1 and 0. However, under working conditions where combustible gas is significantly over-limit or the positive pressure protection capability is lost, the positive pressure safety index is usually less than -1. Therefore, positive pressure safety index equal to 0 and -1 is selected as the dividing value. The controller records the safety level mark on the corresponding grid point, forming a positive pressure safety grid with positive pressure safety index and safety level mark on all grid points in the working area.

[0054] S2. Convert the positive pressure safety grid into a positive pressure safety guidance graph with grid points as nodes and determine the edge cost based on the positive pressure safety index between adjacent grid points. Then, obtain the path grid point sequence with the minimum cost and avoiding dangerous grid point nodes on the positive pressure safety guidance graph through path search.

[0055] In the controller, each grid point in the positive pressure safety grid is treated as a node. A unique identifier is assigned to each node in the controller's memory, and the spatial coordinates, positive pressure safety index, and safety level marker corresponding to the node are written into the node attribute table. For each node in the node attribute table, the controller searches for adjacent grid points in the three-dimensional coordinate system that are one grid point apart from the node in the coordinate axis direction and are still within the working area. The node identifiers corresponding to each adjacent grid point are combined with the current node identifier to generate a connection edge record. The controller also records the identifiers of the two nodes associated with each connection edge, the positive pressure safety index of the two end nodes, and the safety level marker. All levels of labels are written into the connection edge attribute table. When determining the edge cost of each connection edge, the controller reads the positive pressure safety index of the nodes at both ends of the connection edge, selects the smaller positive pressure safety index of the two nodes as the safety reference value of the connection edge, and takes the difference between the upper limit of the positive pressure safety index (the theoretical maximum value of the positive pressure safety index under the safest working condition, which is a dimensionless fixed constant of 1 and pre-stored in the controller) and the safety reference value as the edge cost. The node attribute table and the connection edge attribute table are stored in the controller according to the adjacency relationship, and the positive pressure safety grid is converted into a positive pressure safety guidance diagram composed of nodes and connection edges.

[0056] Based on the spatial location of workpieces, fixtures, and impassable areas in the actual working environment, the controller marks the nodes corresponding to the grid points in the positive pressure safety grid that fall into the spatial location of workpieces, fixtures, and impassable areas as nodes containing obstacles, and deletes all connecting edges related to nodes containing obstacles in the positive pressure safety guidance diagram. The controller reads the node attribute table, regards the nodes marked as the third safety level as dangerous grid nodes, and deletes all connecting edges related to dangerous grid nodes.

[0057] The distance between the target endpoint of the task start point and the spatial coordinates of each node is calculated. The node closest to the target endpoint of the task start point is selected as the start node. The distance between the target endpoint of the task end point and the spatial coordinates of each node is calculated. The node closest to the target endpoint of the task end point is selected as the end node. The start node and end node are marked as the start and end points of the path search in the node attribute table. The controller starts from the start node and performs a path search process based on edge cost accumulation on the positive pressure safety guidance graph. The edge cost accumulation value from the start node to each node to be expanded is calculated. The edge cost accumulation value is used as the cost sum. For each node to be expanded, the next expansion node is selected only from the adjacent nodes that are not marked as containing obstacles and not marked as dangerous grid nodes, until the end node is found.

[0058] The target end effector position refers to the preset spatial coordinates of the end effector in the robot's calibration coordinate system.

[0059] The nodes to be expanded refer to the nodes that are currently pending processing.

[0060] When the path search process finds a path from the starting node to the ending node with the minimum cost in the positive pressure safety guidance graph, the controller restores the nodes on the path to grid points in the positive pressure safety grid in the order of the nodes in the search results, forming a path grid point sequence from the starting grid point to the ending grid point, and outputs the path grid point sequence along with the positive pressure safety index and safety level label corresponding to each grid point.

[0061] S3. Determine the ventilation level and speed level based on the positive pressure safety index classification of each grid point in the path grid sequence, convert the ventilation level and speed level into continuously executed robot motion commands and positive pressure ventilation control commands, and form an operation record.

[0062] The controller has three preset ventilation levels for positive pressure ventilation control. The first ventilation level corresponds to the lowest air intake and normal exhaust mode, the second ventilation level corresponds to the medium air intake and enhanced exhaust mode, and the third ventilation level corresponds to the highest air intake and enhanced exhaust combined with intermittent exhaust mode. At the same time, it has three preset speed levels for the end effector movement. The first speed level corresponds to the standard process speed, the second speed level corresponds to half of the standard process speed, and the third speed level corresponds to one-quarter of the standard process speed. The controller establishes a mapping relationship between safety level and ventilation level and speed level, mapping the first safety level to the first ventilation level and the first speed level, the second safety level to the second ventilation level and the second speed level, and the third safety level to the third ventilation level and the third speed level. This ensures that each grid point in the path grid sequence can uniquely determine the ventilation level and speed level according to its safety level.

[0063] The three speed settings are based on the standard process speed, half of the standard process speed, and one-quarter of the standard process speed, forming an integer multiple relationship. This was determined during the trial operation phase after considering the process cycle time requirements and the positive pressure safety dilution time requirements. This ensures that each speed reduction can multiply the residence time in the grid neighborhood, providing graded speed control from conventional to the most conservative for different safety levels while ensuring process acceptability.

[0064] The controller is calibrated during trial operation and operates under representative working conditions at three ventilation levels. The time required for the current combustible gas concentration to decrease to the upper limit of the allowable concentration is recorded at the internal representative measuring points. The required time is used as the gas renewal time for the corresponding ventilation level, so that each ventilation level corresponds to a gas renewal time. During task execution, the controller traverses the path grid sequence, determines the ventilation level and speed level for each grid according to the safety level, and reads the gas renewal time corresponding to the ventilation level. At the same time, based on the grid spacing between grid points and the running speed of the end effector at the corresponding speed level, the controller calculates the expected dwell time of the end effector when passing through the grid neighborhood.

[0065] The controller compares the estimated dwell time and gas renewal time for each grid point in the path grid sequence. When the estimated dwell time of any grid point is not less than the gas renewal time of the corresponding ventilation level, the current ventilation level and speed level of the grid point remain unchanged. When the estimated dwell time of any grid point is less than the gas renewal time of the corresponding ventilation level, the controller prioritizes lowering the speed level by one level at the grid point to extend the estimated dwell time. If the estimated dwell time is still less than the gas renewal time after lowering the speed level, the controller raises the ventilation level by one level at the grid point to shorten the gas renewal time. The controller then recalculates the estimated dwell time and gas renewal time under the new combination of ventilation level and speed level until the estimated dwell time is not less than the gas renewal time or the speed level is lowered to the third speed level and the ventilation level is raised to the third ventilation level. This ensures that the combustible gas inside the positive pressure shell has sufficient time to be diluted to a safe range when the positive pressure explosion-proof industrial robot passes through the grid point.

[0066] The controller generates robot motion commands arranged in chronological order based on the path grid sequence and corresponding speed levels. It converts the spatial coordinates of continuous grid points into the target position trajectory of the end effector and interpolates it in the joint space to form joint position and joint speed commands for each sampling period. At the same time, it generates positive pressure ventilation control commands arranged in chronological order based on the path grid sequence and corresponding ventilation levels. It converts the ventilation levels into intake control commands and exhaust control commands. During task execution, the controller synchronously issues robot motion commands and positive pressure ventilation control commands according to the sampling period. While issuing commands, the controller records the grid index, ventilation level, speed level, and the smoothed values ​​of internal pressure and internal combustible gas concentration obtained in each sampling period to form an operation record corresponding to the path grid sequence.

[0067] S4. Identify high-risk grid points through the operation records, adjust the positive pressure safety index classification according to the distribution of high-risk grid points in the positive pressure safety grid, and correct the edge costs in the positive pressure safety guidance graph.

[0068] Based on explosion-proof standards and the design of a positive-pressure explosion-proof enclosure, the controller is pre-set with internal combustible gas concentration safety difference and internal / external pressure difference safety difference. These safety differences are all pre-stored in the controller. During the operation record analysis, the controller checks each grid point in the path grid point sequence one by one. When it is found that any grid point has a difference between the smoothed internal combustible gas concentration value and the upper limit of the allowable concentration for a continuous sampling period that is less than the internal combustible gas concentration safety difference, or a difference between the smoothed internal pressure value and the smoothed external pressure value for a continuous sampling period that is less than the internal / external pressure difference safety difference, the grid point index corresponding to the grid point is marked as a high-risk grid point index, forming a high-risk grid point set.

[0069] The internal combustible gas concentration safety difference refers to the difference between the upper limit of the allowable concentration in the explosion-proof standard and the highest concentration obtained during normal operation, which is determined by statistical analysis of the internal concentration changes of multiple batches of typical tasks during the trial operation phase. This internal combustible gas concentration safety difference is greater than the concentration changes caused by the measurement error of the combustible gas concentration sensor and short-term fluctuations, but less than the concentration margin that would lead to approaching the lower explosive limit.

[0070] The safety difference between internal and external pressure is determined by statistically analyzing the changes in internal and external pressure during the trial operation phase. In multiple typical tasks where the positive pressure explosion-proof enclosure meets the minimum positive pressure difference condition, the normal fluctuation range of the internal and external pressure difference is statistically analyzed. The pressure difference before a significant tendency to approach depressurization is taken as the pressure warning difference. The difference between the minimum positive pressure difference specified in the positive pressure design and the pressure warning difference is taken as the safety difference between internal and external pressure. This ensures that the safety difference between internal and external pressure is greater than the measurement error of the pressure sensor and the pressure change caused by short-term fluctuations, but less than the pressure margin that would lead to the loss of positive pressure protection capability.

[0071] The positive pressure safety index classification rules are adjusted based on the distribution of high-risk grid points in the positive pressure safety grid. The controller counts the total number of grid points in each safety level and the number of grid points marked as high-risk in each safety level. When the number of grid points marked as high-risk within the same safety level accounts for a fixed proportion or more of the total number of grid points in the current safety level, the controller adjusts the positive pressure safety index boundary value between the safety level and the adjacent safety level, moving the boundary value towards the safety side. This causes grid points that were originally on the edge of the safety level and whose positive pressure safety index was close to the boundary value to be reclassified into a lower safety level.

[0072] A fixed percentage is set at 10% after statistical analysis of multiple batches of typical task operation records during the trial operation phase. If the percentage is set too small, the overall rule tightening will be triggered as soon as a few sensors drift or a local anomaly occurs. If the percentage is set too large, it means that most grid points will have high-risk records before adjustments are made. At this point, the safety grid itself is already obviously distorted and the response is too slow.

[0073] The controller searches the positive pressure safety guidance map for all connecting edges with endpoints corresponding to high-risk grid points and connecting edges with endpoints corresponding to adjacent grid points. Connecting edges with endpoints corresponding to high-risk grid points and connecting edges with endpoints corresponding to adjacent grid points are considered high-risk areas. An edge cost correction amount is added to the edge cost of the connecting edges. The edge cost correction amount is a preset dimensionless constant, which increases the edge cost of high-risk areas and adjacent areas as a whole. In the subsequent path search process, under the principle of minimizing cost, the path grid point sequence naturally tends to bypass high-risk areas and prioritize areas that have not shown high risk in the historical operation records.

[0074] The controller persists the new safety level boundary value and edge cost correction amount, and re-marks the safety level of all grid points in the work area with the new boundary value before the next task starts, and reconstructs the positive pressure safety guidance map with the new edge cost.

[0075] This embodiment also provides a computer device applicable to the intelligent control method for a pressurized explosion-proof industrial robot, comprising: 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 intelligent control method for a pressurized explosion-proof industrial robot as proposed in the above embodiment.

[0076] 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.

[0077] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the intelligent control method for a positive-pressure explosion-proof industrial robot as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0078] In summary, this invention improves the explosion-proof safety of positive pressure explosion-proof industrial robots by: forming a positive pressure safety grid with positive pressure safety index and safety level markings to achieve overall quantification of the positive pressure safety status of path grid points within the work space; identifying local high-risk areas along the work path and coordinating movement with positive pressure ventilation; identifying high-risk grid points through operation records and adjusting the positive pressure safety index classification and correcting the edge costs in the positive pressure safety guidance graph; and achieving adaptive updating of positive pressure safety rules to historical high-risk distributions.

[0079] 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 intelligent control of a positive-pressure explosion-proof industrial robot, characterized in that: include, Internal and external pressures and combustible gas concentrations are collected inside the positive pressure shell and outside the working area. The positive pressure safety index of each grid point is calculated according to the grid points of the working area and graded and marked to obtain the positive pressure safety grid. The positive pressure safety grid is converted into a positive pressure safety guidance graph with grid points as nodes and the edge cost is determined according to the positive pressure safety index between adjacent grid points. The path grid point sequence with minimum cost and avoiding dangerous grid point nodes is obtained on the positive pressure safety guidance graph through path search. Based on the positive pressure safety index classification of each grid point in the path grid sequence, the ventilation level and speed level are determined, and the ventilation level and speed level are converted into continuously executed robot motion commands and positive pressure ventilation control commands, and an operation record is generated. High-risk grid points are identified by running records. Based on the distribution of high-risk grid points in the positive pressure safety grid, the positive pressure safety index is adjusted and the edge cost in the positive pressure safety guidance graph is corrected. The positive pressure safety grid is obtained by dividing the work area into grid points, calculating the positive pressure safety index of each grid point, and classifying and marking it. The specific steps are as follows: the three-dimensional space accessible to the positive pressure explosion-proof industrial robot is divided into grid points, each grid point is associated with internal representative measuring points and external environmental measuring points, and the positive pressure safety index composed of the minimum normalized safety margin is calculated based on the internal and external pressure, internal concentration and exhaust distance of each grid point. Based on the positive pressure safety index, the grid points are divided into the first safety level, the second safety level, and the third safety level, forming a positive pressure safety grid with positive pressure safety index and safety level markings on all grid points in the working area.

2. The intelligent control method for a positive pressure explosion-proof industrial robot as described in claim 1, characterized in that: The specific steps for collecting internal and external pressure and combustible gas concentration inside the positive pressure shell and outside the working area are as follows: Inside the positive pressure shell of the positive pressure explosion-proof industrial robot, select representative internal measuring points, and outside the working area, select external environmental measuring points. Install pressure sensors and combustible gas concentration sensors at each internal representative measuring point and external environmental measuring point. Internal and external pressure values, combustible gas concentration values, and joint status are collected synchronously in each sampling cycle. The end position and end linear velocity are obtained through forward kinematics, and the data collected in continuous sampling cycles are averaged to form a filtered data record.

3. The intelligent control method for a positive pressure explosion-proof industrial robot as described in claim 2, characterized in that: The specific steps for converting the barotropic safety grid into a barotropic safety guidance graph with grid points as nodes and edge costs determined based on the barotropic safety index between adjacent grid points are as follows: Each grid point in the positive pressure safety grid is taken as a node. A unique identifier is assigned to each node in the controller memory. For each node, the adjacent grid points that are one grid point apart from the node in the coordinate axis direction and are still within the working area are found on the three-dimensional coordinates. The node identifiers of each adjacent grid point are combined with the current node identifier to generate connecting edges. When determining the edge cost of each connecting edge, the positive pressure safety index of the nodes at both ends of the connecting edge is read, and the smaller positive pressure safety index of the two nodes is selected as the safety benchmark value of the connecting edge. The difference between the upper limit of the positive pressure safety index and the safety benchmark value is used as the edge cost, and the positive pressure safety grid is converted into a positive pressure safety guidance graph composed of nodes and connecting edges.

4. The intelligent control method for a positive pressure explosion-proof industrial robot as described in claim 3, characterized in that: The specific steps for obtaining the path grid sequence with minimum cost and avoiding dangerous grid nodes on the positive pressure safety guidance map through path search are as follows: In the positive pressure safety grid, nodes at the locations of workpieces, fixtures, and impassable areas are marked as nodes containing obstacles. The relevant connecting edges of nodes containing obstacles are deleted in the positive pressure safety guidance diagram. Nodes marked as third safety level are considered dangerous grid nodes and their relevant connecting edges are deleted. Select the node closest to the starting point of the task as the starting node, and select the node closest to the ending point of the task as the ending node. Within the range of adjacent nodes that are not marked as containing obstacles and are not considered dangerous grid points, perform a minimum cost path search based on edge cost accumulation. The obtained minimum cost path from the starting node to the ending node is restored to the path grid point sequence from the starting grid point to the ending grid point in the node order.

5. The intelligent control method for a positive pressure explosion-proof industrial robot as described in claim 4, characterized in that: The specific steps for determining the ventilation level and speed level based on the positive pressure safety index of each grid point in the path grid sequence are as follows: Three ventilation levels are preset for positive pressure ventilation control. The first ventilation level corresponds to the lowest air intake and normal exhaust mode. The second ventilation level corresponds to the medium air intake and enhanced exhaust mode. The third ventilation level corresponds to the highest air intake and enhanced exhaust combined with intermittent exhaust mode. At the same time, three speed levels are preset for the end effector movement. The first speed level corresponds to the standard process speed. The second speed level corresponds to half of the standard process speed. The third speed level corresponds to one-quarter of the standard process speed. Establish a mapping relationship between safety level and ventilation level and speed level in the controller. The first safety level corresponds to the first ventilation level and the first speed level, the second safety level corresponds to the second ventilation level and the second speed level, and the third safety level corresponds to the third ventilation level and the third speed level.

6. The intelligent control method for a positive pressure explosion-proof industrial robot as described in claim 5, characterized in that: The specific steps for converting ventilation and speed settings into continuously executed robot motion commands and positive pressure ventilation control commands are as follows: Under representative operating conditions, the ventilation was operated at three different ventilation levels. The time required for the internal combustible gas concentration to drop to the upper limit of the allowable concentration was measured and used as the gas renewal time of the ventilation level. During task execution, the path grid sequence was traversed, and the ventilation level and speed level were determined according to the grid safety level. The expected dwell time of the grid neighborhood was calculated by combining the grid spacing and the running speed of the end effector under the speed level. The estimated dwell time is compared with the gas renewal time. If the estimated dwell time is insufficient, the speed level is reduced first. If it is still insufficient, the ventilation level is increased until the estimated dwell time is not less than the gas renewal time. Robot motion commands are generated in chronological order based on the path grid sequence and speed level. Positive pressure ventilation control commands are also generated in chronological order based on the path grid sequence and ventilation level. During task execution, robot motion commands and positive pressure ventilation control commands are issued synchronously according to the sampling period.

7. The intelligent control method for a positive pressure explosion-proof industrial robot as described in claim 6, characterized in that: The specific steps for adjusting the positive pressure safety index classification and correcting the edge costs in the positive pressure safety guidance graph based on the distribution of high-risk grid points in the positive pressure safety grid are as follows: Based on the preset safety difference values ​​for internal combustible gas concentration and internal and external pressure difference in the controller, when either the internal combustible gas concentration is found to be close to the upper limit of allowable pressure or the internal and external pressure difference is found to be close to the depressurization trend within a continuous sampling period, the grid point is marked as a high-risk grid point and a high-risk grid point set is formed. In the positive pressure safety grid, the proportion of high-risk grid points within each safety level is statistically analyzed. When the number of high-risk grid points within any safety level accounts for a fixed proportion or higher than the total number of grid points in that safety level, the positive pressure safety index boundary value between the safety level and the adjacent safety level is automatically tightened towards the safe side, the edge grid points are downgraded to a lower safety level, and the edge cost of the related connecting edges of high-risk grid points and adjacent grid points is increased by a preset edge cost correction amount in the positive pressure safety guidance diagram. The updated safety level boundary value and edge cost correction amount are persisted, the safety level is re-marked, and the positive pressure safety guidance diagram is reconstructed.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the intelligent control method for the positive pressure explosion-proof industrial robot according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the intelligent control method for the positive pressure explosion-proof industrial robot according to any one of claims 1 to 7.

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