A smart handling control system and method for hazardous chemical products

Abstract

The application provides a chemical dangerous goods intelligent carrying control system and method. The safety constraint speed of each path inflection point in a carrying task is determined through the path confidence constraint of the carrying speed of the chemical dangerous goods according to the dangerous attribute of the chemical dangerous goods, the path curvature of each path inflection point in the carrying task and the path area type. When the current carrying speed of the carrying robot is greater than the safety constraint speed of the front path inflection point, the environmental risk features and material risk features are extracted from the real-time environmental state of the carrying robot and the real-time state of the dangerous goods in the carrying task, respectively, the collaborative risk coefficient of the dangerous goods in the carrying task is determined through the environmental risk features and the material risk features, and the moving speed of the carrying robot at the next path inflection point is cooperatively adjusted based on the collaborative risk coefficient and each safety constraint speed. The above scheme can realize the cooperative constraint of the moving speed of the carrying robot in the chemical dangerous goods carrying process.

Description

Technical Field

[0001] This application relates to the field of material handling control technology, and more specifically, to an intelligent material handling control system and method for hazardous chemicals. Background Technology

[0002] Hazardous chemicals refer to chemicals that possess dangerous characteristics such as flammability, explosiveness, toxicity, toxicity, and corrosivity during chemical production, transportation, use, and disposal. Their physicochemical properties are unstable, and under specific conditions, they may cause major safety accidents such as leaks, fires, explosions, or poisoning, posing a serious threat to personnel, equipment, and the environment. Therefore, strict special protection and management measures must be taken.

[0003] Current chemical hazardous materials handling robots employ static speed control strategies based on pre-set maps. These strategies only provide a rough speed limit based on the geometric features of a pre-defined path or fixed area types, lacking real-time perception and response capabilities to dynamic operating environments. When sudden risks arise during transport, such as unauthorized personnel gatherings or abnormal equipment overheating, the robots cannot acquire and process information promptly. Furthermore, existing static control systems lack effective monitoring methods for the condition of the containers carrying hazardous materials, such as abnormal wall temperatures due to frictional heating, pressure fluctuations caused by internal material reactions, and increased mechanical vibration. They also lack a fusion mechanism to integrate this information into the decision-making loop. This lack of perception directly leads to a delayed response from the control system to dynamic risks, preventing proactive speed adjustments at the initial stages of a risk and creating potential hazards such as leaks and explosions. Therefore, achieving coordinated speed constraints for handling robots during the transport of chemical hazardous materials has become a significant challenge for the industry. Summary of the Invention

[0004] This application provides an intelligent handling control system and method for hazardous chemicals, which can realize the coordinated constraint of the movement speed of handling robots during the handling of hazardous chemicals.

[0005] Firstly, this application provides an intelligent handling control method for hazardous chemical products, comprising:

[0006] Monitor the handling tasks of hazardous chemicals, obtain the hazardous properties of the hazardous chemicals in the handling tasks, and initialize the handling path information of the handling robots in the handling tasks;

[0007] The path curvature and path region type of each path inflection point in the transportation task are extracted from the transportation path information, and the path confidence constraint of the hazard attribute on the transportation speed of chemical hazardous materials is determined. Then, the basic speed in the transportation task is constrained by the path confidence constraint, each path curvature and each path region type to obtain the safety constraint speed of each path inflection point in the transportation task.

[0008] When the current transport speed of the transport robot is greater than the safety constraint speed at the inflection point of the path ahead, risk mapping is performed on the real-time environmental state of the transport robot and the real-time state of the hazardous chemicals in the transport task, and environmental risk characteristics and material risk characteristics are output. The transport risk of the hazardous chemicals is collaboratively evaluated through the environmental risk characteristics and the material risk characteristics to obtain the collaborative risk coefficient of the hazardous chemicals in the transport task.

[0009] Based on the aforementioned collaborative risk coefficient and various safety constraint speeds, the moving speed of the transport robot at the next path inflection point is collaboratively adjusted.

[0010] In some embodiments, extracting the path curvature and path region type of each path inflection point in the transportation task from the transportation path information specifically includes:

[0011] The transport path information is discretized to obtain a path point sequence for the transport task;

[0012] The path curvature of each path point is determined by the coordinates of its predecessor and successor points in the path point sequence.

[0013] Based on the curvature of each path, all path inflection points in the transportation task are selected from all path points, and then the path curvature of each path inflection point is determined.

[0014] The coordinates of each path inflection point are matched with the digital map of the chemical hazardous materials factory to obtain the path area type of each path inflection point.

[0015] In some embodiments, determining the path confidence constraint of the hazardous attribute on the handling speed of hazardous chemicals specifically includes:

[0016] By quantifying the risk of each parameter in the aforementioned hazardous attributes at different handling speeds, the risk factors of hazardous chemicals at different handling speeds are obtained.

[0017] Each risk factor is mapped to a parameter constraint value for the handling speed of hazardous chemicals;

[0018] The path confidence constraints on the handling speed of hazardous chemicals are determined by using all parameter constraint values.

[0019] In some embodiments, the path constraint on the base speed in the transport task is obtained by applying the path confidence constraint, the curvature of each path, and the type of each path region to the safe constraint speed at each path inflection point in the transport task. Specifically, this includes:

[0020] For each path inflection point in the transportation task, obtain the basic speed of the chemical hazardous materials before the path inflection point;

[0021] Determine the curvature effect of path curvature at path inflection points on transport speed;

[0022] Determine the regional impact of the path region type at path inflection points on transport speed;

[0023] The base speed in the transport task is pre-adjusted by the curvature influence, the region influence value, and the path confidence constraint to obtain the safe constraint speed of the path inflection point, and then the safe constraint speed of each path inflection point in the transport task is obtained.

[0024] In some embodiments, the real-time environmental status of the handling robot and the real-time status of the hazardous chemicals in the handling task are risk-mapped, and the output environmental risk characteristics and material risk characteristics specifically include:

[0025] Acquire the real-time environmental status of the handling robot and the real-time status of the hazardous chemicals during the handling task;

[0026] Extract the personnel density and ambient temperature deviation from the real-time environmental state, and then determine the environmental risk characteristics through the personnel density and ambient temperature deviation;

[0027] The real-time status is compared with the standard safety status of the hazardous chemical to obtain the status offset of the hazardous chemical, and then the status offset is mapped to the material risk characteristics.

[0028] In some embodiments, the synergistic assessment of the handling risk of hazardous chemicals using the environmental risk characteristics and the material risk characteristics to obtain the synergistic risk coefficient of hazardous chemicals in handling tasks specifically includes:

[0029] The environmental risk characteristics are mapped to environmental risk sub-coefficients for the handling of hazardous chemicals.

[0030] The material risk characteristics are mapped to material risk sub-coefficients for handling hazardous chemicals in transportation tasks.

[0031] The fusion weights of the environmental risk sub-coefficient and the material risk sub-coefficient are set according to the hazardous properties of the hazardous chemicals.

[0032] The environmental risk sub-coefficient and the material risk sub-coefficient are fused together using various fusion weights to form a collaborative risk coefficient for the handling of hazardous chemicals.

[0033] In some embodiments, the coordinated adjustment of the moving speed of the transport robot at the next path inflection point based on the cooperative risk coefficient and the speeds of each safety constraint specifically includes:

[0034] Obtain the safety constraint speed corresponding to the current position of the handling robot;

[0035] The safety constraint speed is constrained collaboratively using the collaborative risk coefficient.

[0036] The safety constraint speed after collaborative constraints is used as the moving speed of the transport robot at the next path inflection point.

[0037] Secondly, this application provides an intelligent handling control system for hazardous chemical products, including a speed regulation unit, wherein the speed regulation unit includes:

[0038] The acquisition module is used to monitor the handling tasks of chemical hazardous materials, acquire the hazardous properties of chemical hazardous materials in the handling tasks, and initialize the handling path information of the handling robot in the handling tasks.

[0039] The processing module is used to extract the path curvature and path region type of each path inflection point in the transportation task from the transportation path information, and determine the path confidence constraint of the hazard attribute on the transportation speed of chemical hazardous materials. Then, the basic speed in the transportation task is constrained by the path confidence constraint, each path curvature and each path region type to obtain the safety constraint speed of each path inflection point in the transportation task.

[0040] The processing module is also used to perform risk mapping on the real-time environmental status of the handling robot and the real-time status of the hazardous chemicals in the handling task when the current handling speed of the handling robot is greater than the safety constraint speed of the inflection point of the path ahead, output environmental risk characteristics and material risk characteristics, and perform collaborative assessment of the handling risk of the hazardous chemicals through the environmental risk characteristics and the material risk characteristics to obtain the collaborative risk coefficient of the hazardous chemicals in the handling task.

[0041] The execution module is used to coordinately adjust the moving speed of the handling robot at the next path inflection point based on the cooperative risk coefficient and the speed of each safety constraint.

[0042] Thirdly, this application provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-described intelligent handling control method for hazardous chemicals.

[0043] Fourthly, this application provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the aforementioned intelligent handling control method for hazardous chemical products.

[0044] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0045] This application provides an intelligent handling control system and method for hazardous chemicals. The system monitors the handling task of hazardous chemicals, acquires the hazardous attributes of the chemicals in the handling task, and initializes the handling path information of the handling robot in the handling task. From the handling path information, it extracts the path curvature and path region type of each path inflection point in the handling task, and determines the path confidence constraint of the hazardous attributes on the handling speed of the hazardous chemicals. Then, it applies path constraints to the basic speed in the handling task using the path confidence constraints, each path curvature, and each path region type, obtaining the safety constraint speed at each path inflection point in the handling task. When the current handling speed of the handling robot is greater than the safety constraint speed at the next path inflection point, it performs risk mapping on the real-time environmental state of the handling robot and the real-time state of the hazardous chemicals in the handling task, outputting environmental risk characteristics and material risk characteristics. It then uses these environmental risk characteristics and material risk characteristics to collaboratively assess the handling risk of the hazardous chemicals, obtaining a collaborative risk coefficient for the hazardous chemicals in the handling task. Based on the collaborative risk coefficient and each safety constraint speed, it collaboratively adjusts the moving speed of the handling robot at the next path inflection point.

[0046] Therefore, in this application, the moving speed of the handling robot at the next path inflection point is coordinated and adjusted based on the aforementioned collaborative risk coefficient and various safety constraint speeds. First, the process of determining the safety constraint speed involves analyzing the path curvature, path region type, and path confidence constraints of hazardous attributes on the handling speed to obtain the safety constraint speed at each path inflection point. This ensures that the handling robot has clear speed limits under different path conditions, thereby ensuring the safety of the handling process at the physical path level, avoiding risks such as collisions and spills caused by excessive speed, effectively reducing the probability of accidents during the handling of hazardous chemicals, and ensuring the smooth progress of the handling task. Then, the process of determining the collaborative risk coefficient involves risk mapping between the real-time environmental state of the handling robot and the real-time state of the hazardous chemicals, outputting environmental risk characteristics and material risk characteristics, and based on this, collaboratively evaluating to obtain the collaborative risk coefficient of the hazardous chemicals in the handling task. This helps to comprehensively and dynamically grasp the risk situation in the handling process, enabling the handling robot to adjust its speed in a timely manner according to the risk coefficient, achieving precise risk control, further improving the safety and reliability of the handling process, and ensuring the efficient and safe completion of chemical hazardous materials handling tasks; in summary, based on the above solution, the collaborative constraint of the movement speed of the handling robot in the process of handling chemical hazardous materials can be realized. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is an exemplary flowchart of an intelligent handling control method for hazardous chemical products according to some embodiments of this application;

[0049] Figure 2 This is a flowchart illustrating the implementation of coordinated regulation according to some embodiments of this application;

[0050] Figure 3 This is a schematic diagram of the structure of a speed regulation unit according to some embodiments of this application;

[0051] Figure 4 This is a schematic diagram of the structure of a computer device for implementing an intelligent handling control method for hazardous chemical products, according to some embodiments of this application. Detailed Implementation

[0052] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] refer to Figure 1 The figure is an exemplary flowchart of an intelligent handling control method for hazardous chemical products according to some embodiments of this application. The intelligent handling control method for hazardous chemical products mainly includes the following steps:

[0054] In step 101, the handling task of the chemical hazardous materials is monitored, the hazardous properties of the chemical hazardous materials in the handling task are obtained, and the handling path information of the handling robot in the handling task is initialized.

[0055] It should be noted that in this application, the transport path information is a sequence of spatial coordinate data of a continuous trajectory from the starting position to the target position; the transport task is an operation instruction that requires the transport robot to transport the chemical hazardous materials from the starting position to the target position; the hazard attribute is a set of parameters used to describe the inherent hazardous characteristics of the chemical hazardous materials, and the parameters in the hazard attribute include sensitivity level, flash point, boiling point and corrosivity.

[0056] In practice, firstly, the system receives a handling task instruction from the Manufacturing Execution System (MES) via an application programming interface (API). Based on the material code carried in the handling task instruction, it queries the safety data sheet of the material in a pre-set hazardous chemical information database and extracts the hazard attribute parameters that are strongly related to handling safety from the safety data sheet. Finally, the set of all hazard attribute parameters is taken as the hazard attribute of the chemical in the handling task. Then, after receiving the handling task, the path planning module initializes a globally optimal path based on the starting and target positions specified in the task, combined with a pre-stored factory environment map model, using a path search algorithm. This path search algorithm can employ Dijkstra's algorithm, A* algorithm, or fast probing random tree algorithm. The globally optimal path includes the coordinates of the path point sequence, the path direction, and the path topology. This globally optimal path is used as the handling path information for the handling robot in the handling task.

[0057] In step 102, the path curvature and path region type of each path inflection point in the transportation task are extracted from the transportation path information, and the path confidence constraint of the hazard attribute on the transportation speed of chemical hazardous materials is determined. Then, the basic speed in the transportation task is constrained by the path confidence constraint, each path curvature and each path region type to obtain the safety constraint speed of each path inflection point in the transportation task.

[0058] In some embodiments, extracting the path curvature and path region type of each path inflection point in a transportation task from the transportation path information can be achieved using the following steps:

[0059] The transport path information is discretized to obtain a path point sequence for the transport task;

[0060] The path curvature of each path point is determined by the coordinates of its predecessor and successor points in the path point sequence.

[0061] Based on the curvature of each path, all path inflection points in the transportation task are selected from all path points, and then the path curvature of each path inflection point is determined.

[0062] The coordinates of each path inflection point are matched with the digital map of the chemical hazardous materials factory to obtain the path area type of each path inflection point.

[0063] It should be noted that in this application, path curvature is a quantitative measure used to describe the degree of path bending at path points; path region type is a semantic classification label of different functional regions in each path inflection point; and path point sequence is a set of ordered coordinate points obtained by sampling at fixed distance intervals.

[0064] In practice, firstly, starting from the beginning of the trajectory line in the transport path information, points on the trajectory line are sequentially sampled at a preset fixed sampling interval (e.g., 0.5 meters) along the trajectory line, and their coordinates are recorded as path points. This process continues until the end of the trajectory line. The final set of all sequentially generated path points is the path point sequence for the transport task. Secondly, for each path point in the path point sequence except for the first and last points, the coordinates of the path point, the coordinates of the previous path point (i.e., the coordinates of the preceding point), and the coordinates of the next path point (i.e., the coordinates of the following point) are obtained. The reciprocal of the radius of the circle defined by these three consecutive path points at the path point is calculated as the path curvature of that path point. Through the above method, each path can be obtained. The path curvature of each path point is calculated. Then, the path curvature of each path point is compared with a preset curvature threshold. All path points with curvature values ​​greater than the threshold are selected as path inflection points, thus obtaining all path inflection points in the transportation task and the path curvature of each path inflection point. Finally, for each path inflection point, a preset factory digital map is read. This factory digital map has been labeled with different types of areas. The coordinates of the path inflection point are compared with the factory digital map to determine which polygonal area the coordinates of the path inflection point are located in. The semantic label corresponding to the polygonal area is obtained as the path area type of the path inflection point. The path area type of each path inflection point can be obtained in the above way.

[0065] In some embodiments, determining the path confidence constraint of the hazardous attribute on the handling speed of hazardous chemicals can be achieved by the following steps:

[0066] By quantifying the risk of each parameter in the aforementioned hazardous attributes at different handling speeds, the risk factors of hazardous chemicals at different handling speeds are obtained.

[0067] Each risk factor is mapped to a parameter constraint value for the handling speed of hazardous chemicals;

[0068] The path confidence constraints on the handling speed of hazardous chemicals are determined by using all parameter constraint values.

[0069] It should be noted that, in this application, the path confidence constraint is a constraint condition used to safely adjust the base speed on the transport path; the risk factor is a quantitative value used to characterize the overall risk level caused by the combined effect of various hazardous attributes of chemical hazardous materials at different transport speeds; and the parameter constraint value is a quantitative value used to directly limit the transport speed.

[0070] In practice, the first step is to obtain the maximum handling speed of the hazardous chemical in historical handling data. Then, the speed range from 0 to the maximum handling speed is divided into intervals of 1 m / s, resulting in multiple handling speed intervals. For each handling speed interval, the risk contribution of each parameter in the hazard attribute within the handling speed interval is selected from a pre-set risk-speed relationship mapping table. The risk contribution of all parameters within the handling speed interval is then combined into a comprehensive value as the risk factor for the hazardous chemical within the handling speed interval. The merging can be done using weighted summation or multiplicative fusion methods; no specific method is specified here. This process yields the risk factor for the hazardous chemical in each handling speed interval. By identifying the risk factors within the speed range, the risk factors of hazardous chemicals at different handling speeds can be obtained. Then, a monotonically decreasing mapping function from the risk factors to parameter constraint values ​​is obtained from the central control console via the application programming interface. That is, for each risk factor, the monotonically decreasing mapping function is queried to calculate and output a corresponding coefficient. This coefficient decreases as the risk factor increases. The calculated coefficient is used as the parameter constraint value for the handling speed range corresponding to the risk factor. Through the above, the parameter constraint values ​​for the handling speed range corresponding to each risk factor can be obtained. Finally, the set of all parameter constraint values ​​is used as the path confidence constraint of the hazard attribute on the handling speed of hazardous chemicals.

[0071] In some embodiments, the path constraint on the base speed in the transport task using the path confidence constraint, the curvature of each path, and the type of each path region, to obtain the safe constraint speed at each path inflection point in the transport task, can be achieved by the following steps:

[0072] For each path inflection point in the transportation task, obtain the basic speed of the chemical hazardous materials before the path inflection point;

[0073] Determine the curvature effect of path curvature at path inflection points on transport speed;

[0074] Determine the regional impact of the path region type at path inflection points on transport speed;

[0075] The base speed in the transport task is pre-adjusted by the curvature influence, the region influence value, and the path confidence constraint to obtain the safe constraint speed of the path inflection point, and then the safe constraint speed of each path inflection point in the transport task is obtained.

[0076] It should be noted that, in this application, the safety constraint speed is the highest speed value that is ultimately allowed to pass through each path inflection point; the base speed refers to the initial reference speed value of each path inflection point during the handling of hazardous chemicals; the curvature influence is a quantitative value used to represent the degree of reduction of the safe handling speed due to the curvature at the path inflection point; and the regional influence value is a quantitative value used to represent the degree of reduction of the safe handling speed due to the safety level of the area where the path inflection point is located.

[0077] In practice, firstly, for each path inflection point in the transportation task, the basic speed of the chemical hazardous materials before the path inflection point is obtained from the central control console via the application programming interface. Secondly, a curvature-influence lookup table is obtained from a pre-set path planning parameter database. This lookup table is set so that the larger the curvature value, the smaller the output coefficient. The path curvature value of the path inflection point is input into this lookup table, and the influence quantity output by the lookup table is used as the curvature influence quantity of the path curvature on the transportation speed. Then, a mapping dictionary of region type and speed influence coefficient is obtained from the pre-set path planning parameter database. The path region type of the path inflection point is input into this mapping dictionary, and the speed influence coefficient output by the mapping dictionary is used as the region influence value of the path region type on the transportation speed. Finally, the parameter constraint value of the transportation speed interval where the basic speed is located is obtained from the path confidence constraints. The product of the curvature influence quantity, the region influence value, and the parameter constraint value is used as the adjustment coefficient of the transportation speed in the transportation task. The product of this adjustment coefficient and the basic speed is used as the safety constraint speed of the path inflection point. The safety constraint speed of each path inflection point in the transportation task can be obtained in the above way.

[0078] In step 103, when the current transport speed of the transport robot is greater than the safety constraint speed at the inflection point of the path ahead, risk mapping is performed on the real-time environmental state of the transport robot and the real-time state of the hazardous chemicals in the transport task, and environmental risk characteristics and material risk characteristics are output. The transport risk of the hazardous chemicals is collaboratively evaluated through the environmental risk characteristics and the material risk characteristics to obtain the collaborative risk coefficient of the hazardous chemicals in the transport task.

[0079] It should be noted that in this application, when the current handling speed of the robot exceeds the safety constraint speed at the inflection point of the path ahead, this safety constraint speed is the highest safety limit calculated after comprehensively considering the inherent risks of hazardous chemicals, the curvature of the path, and the type of area. Exceeding this speed will significantly increase multiple risks: First, the centrifugal force when turning can easily cause containers to shift, tip over, or even break and leak; second, the response time to emergencies in high-risk areas is shortened, for example, it will be difficult to stop the robot in an emergency if personnel approach or equipment malfunctions; third, for vibration-sensitive hazardous chemicals, the mechanical impact generated by passing through the inflection point at high speed may cause material decomposition or reaction. Therefore, it is necessary to immediately activate the dynamic risk assessment and speed adjustment mechanism, and make collaborative decisions by sensing the environment and material status in real time to forcibly constrain the speed to a safe range, thereby avoiding a chain of safety accidents caused by speed loss.

[0080] In some embodiments, risk mapping is performed on the real-time environmental state of the handling robot and the real-time state of the hazardous chemicals in the handling task, and the environmental risk characteristics and material risk characteristics are output. This can be achieved by the following steps:

[0081] Acquire the real-time environmental status of the handling robot and the real-time status of the hazardous chemicals during the handling task;

[0082] Extract the personnel density and ambient temperature deviation from the real-time environmental state, and then determine the environmental risk characteristics through the personnel density and ambient temperature deviation;

[0083] The real-time status is compared with the standard safety status of the hazardous chemical to obtain the status offset of the hazardous chemical, and then the status offset is mapped to the material risk characteristics.

[0084] It should be noted that, in this application, environmental risk characteristics are numerical characteristics used to quantitatively assess the degree of threat posed by the external environment to handling safety; material risk characteristics are numerical characteristics used to quantitatively assess the degree of threat posed by abnormal conditions of the hazardous chemical container itself to handling safety; real-time environmental state is the raw data set of the working conditions around the robot; and real-time state is the raw data set of the physical state of the hazardous chemical container itself.

[0085] In practice, firstly, the number of people within a designated area around the robot is calculated in real time using data streams from the LiDAR and camera integrated into the robot body, and the ambient temperature value is read using an infrared thermal imager. The set of the number of people and the ambient temperature value is used as the real-time environmental state of the handling robot. Secondly, data is read in real time using vibration sensors installed on the gripping mechanism, temperature sensors attached to the container wall, and pressure sensors connected to the container valve. The set of all read data is used as the real-time state of the hazardous chemical. Thirdly, the number of people per unit area is calculated from the real-time environmental state as the personnel density, and the absolute value of the difference between the current ambient temperature and the ignition point of the hazardous chemical is calculated as the ambient temperature deviation. The set of personnel density and ambient temperature deviation is used as the environmental risk characteristic. Finally, the container wall temperature, internal pressure, and vibration amplitude readings are extracted from the real-time state of the hazardous chemical. These are subtracted from the preset standard safety threshold for the hazardous chemical to obtain the state offset of the corresponding parameters. The set of all state offsets is used as the material risk characteristic.

[0086] In some embodiments, the synergistic assessment of the handling risk of hazardous chemicals based on the environmental risk characteristics and the material risk characteristics to obtain the synergistic risk coefficient of hazardous chemicals in handling tasks can be achieved through the following steps:

[0087] The environmental risk characteristics are mapped to environmental risk sub-coefficients for the handling of hazardous chemicals.

[0088] The material risk characteristics are mapped to material risk sub-coefficients for handling hazardous chemicals in transportation tasks.

[0089] The fusion weights of the environmental risk sub-coefficient and the material risk sub-coefficient are set according to the hazardous properties of the hazardous chemicals.

[0090] The environmental risk sub-coefficient and the material risk sub-coefficient are fused together using various fusion weights to form a collaborative risk coefficient for the handling of hazardous chemicals.

[0091] It should be noted that in this application, the collaborative risk coefficient is the overall risk quantification value used for the final speed decision; the environmental risk sub-coefficient is a normalized value that comprehensively characterizes the magnitude of risk caused by external environmental factors; the material risk sub-coefficient is a normalized value that comprehensively characterizes the magnitude of risk caused by the abnormal state of the hazardous chemical itself; and the fusion weight is the proportional coefficient allocated to the environmental risk sub-coefficient and the material risk sub-coefficient.

[0092] In specific implementation, firstly, a mapping dictionary between environmental risk characteristics and environmental risk coefficients is obtained from a pre-set path planning parameter database. The environmental risk coefficients corresponding to the environmental risk characteristics are then selected from this mapping dictionary as environmental risk sub-coefficients for the handling of hazardous chemicals. Secondly, a mapping dictionary between material risk characteristics and material risk coefficients is obtained from the pre-set path planning parameter database. The material risk coefficients corresponding to the material risk characteristics are then selected from this mapping dictionary as material risk sub-coefficients for the handling of hazardous chemicals. Then, the fusion weight of the environmental risk sub-coefficient and the material risk sub-coefficient is 0.5 by default. The hazard attributes of the hazardous chemicals are queried. If the hazardous chemical... If the chemical has a low ignition point or strong oxidizing properties, a higher fusion weight is assigned to the environmental risk sub-coefficient, i.e., the fusion weight of the environmental risk sub-coefficient is increased by 0.1. If the chemical has a high sensitivity level or poor chemical stability, a higher fusion weight is assigned to the material risk sub-coefficient, i.e., the fusion weight of the material risk sub-coefficient is increased by 0.1. The sum of the two fusion weights is then equal to one, thus obtaining the fusion weights of the environmental risk sub-coefficient and the material risk sub-coefficient. Finally, the environmental risk sub-coefficient is exponentially multiplied by its fusion weight, and the material risk sub-coefficient is exponentially multiplied by its fusion weight. The two results of the exponentiation are then multiplied together to obtain the synergistic risk coefficient of the chemical in the handling task.

[0093] In step 104, the moving speed of the transport robot at the next path inflection point is adjusted in a coordinated manner based on the cooperative risk coefficient and the speed of each safety constraint.

[0094] In some embodiments, the moving speed of the transport robot at the next path inflection point is coordinated and adjusted based on the cooperative risk coefficient and the speeds of each safety constraint, with reference to... Figure 2 As shown in the figure, this is a schematic diagram of the process of implementing coordinated regulation in some embodiments of this application. In this embodiment, coordinated regulation can be implemented by the following steps:

[0095] In step 1041, the safety constraint speed corresponding to the current position of the handling robot is obtained;

[0096] In step 1042, the safety constraint speed is subjected to collaborative constraint through the collaborative risk coefficient;

[0097] In step 1043, the safety constraint speed after collaborative constraint is used as the moving speed of the transport robot at the next path inflection point.

[0098] In practice, firstly, the next path inflection point of the transport robot at its current position is obtained, and the safety constraint speed of that path inflection point is used as the safety constraint speed of the transport robot at its current position. Then, the product of the collaborative risk coefficient and the safety constraint speed is used as the safety constraint speed after collaborative constraint. Finally, the safety constraint speed after collaborative constraint is set as the highest command speed that the transport robot follows during its movement from the current position to the next path inflection point, and this highest command speed is sent to the motion controller of the transport robot to obtain the moving speed of the transport robot at the next path inflection point.

[0099] Furthermore, in another aspect of this application, in some embodiments, this application provides an intelligent handling control system for hazardous chemicals, which includes a speed regulation unit, referenced... Figure 3 The figure is a schematic diagram of the structure of a speed adjustment unit according to some embodiments of this application. The speed adjustment unit includes: an acquisition module 201, a processing module 202, and an execution module 203, which are described below:

[0100] The acquisition module 201 in this application is mainly used to monitor the handling task of chemical hazardous materials, acquire the hazardous properties of chemical hazardous materials in the handling task, and initialize the handling path information of the handling robot in the handling task.

[0101] Processing module 202, in this application, is used to extract the path curvature and path region type of each path inflection point in the transportation task from the transportation path information, and determine the path confidence constraint of the hazard attribute on the transportation speed of chemical hazardous materials. Then, the basic speed in the transportation task is constrained by the path confidence constraint, each path curvature and each path region type to obtain the safety constraint speed of each path inflection point in the transportation task.

[0102] It should be noted that the processing module 202 is also used to perform risk mapping on the real-time environmental status of the handling robot and the real-time status of the hazardous chemicals in the handling task when the current handling speed of the handling robot is greater than the safety constraint speed of the inflection point of the path ahead, output environmental risk characteristics and material risk characteristics, and perform collaborative assessment of the handling risk of the hazardous chemicals through the environmental risk characteristics and the material risk characteristics to obtain the collaborative risk coefficient of the hazardous chemicals in the handling task.

[0103] The execution module 203 in this application is mainly used to coordinately adjust the moving speed of the handling robot at the next path inflection point based on the cooperative risk coefficient and the speed of each safety constraint.

[0104] The foregoing has detailed examples of intelligent handling control systems and methods for hazardous chemicals provided in the embodiments of this application. It is understood that the corresponding apparatus, in order to achieve the above functions, includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0105] In some embodiments, this application also provides a computer device, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for calling and running the computer program from the memory, so that the computer device executes the above-described intelligent handling control method for hazardous chemicals.

[0106] In some embodiments, reference Figure 4 The dashed lines in the figure indicate that the unit or module is optional. This figure is a structural schematic diagram of a computer device for implementing an intelligent handling control method for hazardous chemicals according to an embodiment of this application. The intelligent handling control method for hazardous chemicals described in the above embodiments can... Figure 4 The computer device shown is used to implement this, and the computer device includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device may be a terminal device, a server or a chip.

[0107] Processor 301 can be a general-purpose processor or a special-purpose processor. For example, processor 301 can be a central processing unit (CPU), which can be used to control computer devices, execute software programs, and process data from software programs. The computer device may also include a communication unit 305 for inputting (receiving) and outputting (transmitting) signals.

[0108] For example, the computer device may be a chip, and the communication unit 305 may be the input and / or output circuit of the chip, or the communication unit 305 may be the communication interface of the chip, which may be a component of a terminal device, network device or other device.

[0109] For example, the computer device may be a terminal device or a server, and the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.

[0110] The computer device may include one or more memories 302 storing a program 304. The program 304 can be executed by a processor 301 to generate instructions 303, causing the processor 301 to execute the method described in the above method embodiments according to the instructions 303. Optionally, the memory 302 may also store data (such as a target audit model). Optionally, the processor 301 may also read data stored in the memory 302, which may be stored at the same storage address as the program 304, or it may be stored at a different storage address than the program 304.

[0111] The processor 301 and memory 302 can be configured separately or integrated together, for example, integrated on the system on chip (SOC) of the terminal device.

[0112] It should be understood that each step of the above method embodiment can be completed by hardware logic circuits or software instructions in the processor 301. The processor 301 can be a CPU, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, such as discrete gate, transistor logic devices, or discrete hardware components.

[0113] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0114] For example, in some embodiments, this application also provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the above-described intelligent handling control method for hazardous chemicals.

[0115] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0116] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A chemical dangerous goods intelligent handling control method, characterized in that, The method comprises the following steps: monitoring a chemical dangerous goods carrying task, obtaining dangerous attribute of the chemical dangerous goods in the carrying task, and initializing carrying path information of a carrying robot in the carrying task; extracting path curvature and path area type of each path inflection point in the carrying task from the carrying path information, and determining path confidence constraint of the dangerous attribute on carrying speed of the chemical dangerous goods, and then path-constraining a basic speed in the carrying task through the path confidence constraint, each path curvature and each path area type to obtain safe constraint speed of each path inflection point in the carrying task; when current carrying speed of the carrying robot is greater than safe constraint speed of a front path inflection point, performing risk mapping on real-time environment state of the carrying robot and real-time state of the dangerous goods in the carrying task, outputting environment risk features and material risk features, cooperatively evaluating carrying risk of the dangerous goods through the environment risk features and the material risk features to obtain a cooperative risk coefficient of the dangerous goods in the carrying task; based on the cooperative risk coefficient and each safe constraint speed, cooperatively adjusting moving speed of the carrying robot at a next path inflection point.

2. The method of claim 1, wherein, The extracting of the path curvature and the path area type of each path inflection point in the carrying task from the carrying path information specifically comprises: discretizing the carrying path information to obtain a path point sequence of the carrying task; determining the path curvature of each path point through the coordinates of the previous point and the coordinates of the subsequent point of each path point in the path point sequence; based on each path curvature, screening all path inflection points in the carrying task from all path points, and then determining the path curvature of each path inflection point; type-matching the coordinates of each path inflection point with the work number digital map of the chemical dangerous goods to obtain the path area type of each path inflection point.

3. The method of claim 1, wherein, The determining of the path confidence constraint of the dangerous attribute on the carrying speed of the chemical dangerous goods specifically comprises: risk quantization of each parameter in the dangerous attribute at different carrying speeds to obtain risk factors of the chemical dangerous goods at different carrying speeds; mapping each risk factor into a parameter constraint value of the carrying speed of the chemical dangerous goods; determining the path confidence constraint of the dangerous attribute on the carrying speed of the chemical dangerous goods through all parameter constraint values.

4. The method of claim 1, wherein, The path-constraining of the basic speed in the carrying task through the path confidence constraint, each path curvature and each path area type to obtain the safe constraint speed of each path inflection point in the carrying task specifically comprises: for each path inflection point in the carrying task, obtaining a basic speed of the chemical dangerous goods before the path inflection point; determining a curvature influence amount of the path curvature of the path inflection point on the carrying speed; determining an area influence value of the path area type of the path inflection point on the carrying speed; pre-adjusting the basic speed in the carrying task through the curvature influence amount, the area influence value and the path confidence constraint to obtain the safe constraint speed of the path inflection point, and then obtaining the safe constraint speed of each path inflection point in the carrying task.

5. The method of claim 1, wherein, The risk mapping on the real-time environment state of the carrying robot and the real-time state of the dangerous goods in the carrying task to output the environment risk features and the material risk features specifically comprises: Acquire real-time environment state of the carrying robot and real-time state of the hazardous chemical in the carrying task; Extract the personnel density and the environmental temperature deviation in the real-time environment state, and then determine the environmental risk feature through the personnel density and the environmental temperature deviation; Compare the real-time state with the standard safety state of the hazardous chemical to obtain the state deviation of the hazardous chemical, and then map the state deviation to the material risk feature.

6. The method of claim 1, wherein, Cooperatively evaluate the carrying risk of the hazardous chemical through the environmental risk feature and the material risk feature to obtain a cooperative risk coefficient of the hazardous chemical in the carrying task, which specifically includes: Map the environmental risk feature to an environmental risk sub-coefficient of the chemical hazardous chemical in the carrying task; Map the material risk feature to a material risk sub-coefficient of the chemical hazardous chemical in the carrying task; Set fusion weights of the environmental risk sub-coefficient and the material risk sub-coefficient according to the dangerous attributes of the hazardous chemical; Fuse the environmental risk sub-coefficient and the material risk sub-coefficient into a cooperative risk coefficient of the chemical hazardous chemical in the carrying task using the respective fusion weights.

7. The method of claim 1, wherein, Cooperatively adjust the moving speed of the carrying robot at the next path inflection point based on the cooperative risk coefficient and the respective safety constraint speeds, which specifically includes: Acquire the safety constraint speed corresponding to the current position of the carrying robot; Cooperatively constrain the safety constraint speed through the cooperative risk coefficient; Use the cooperatively constrained safety constraint speed as the moving speed of the carrying robot at the next path inflection point.

8. A chemical dangerous goods intelligent carrying control system, the chemical dangerous goods intelligent carrying control system comprising a speed adjusting unit, characterized in that, The speed adjustment unit includes: An acquisition module for monitoring the carrying task of the chemical hazardous chemical, acquiring the dangerous attributes of the chemical hazardous chemical in the carrying task, and initializing the carrying path information of the carrying robot in the carrying task; A processing module for extracting the path curvatures and path region types of each path inflection point in the carrying task from the carrying path information, and determining the path confidence constraint of the dangerous attributes on the carrying speed of the chemical hazardous chemical, and then path-constraining the base speed in the carrying task through the path confidence constraint, the respective path curvatures, and the respective path region types to obtain the safety constraint speed of each path inflection point in the carrying task; The processing module is further configured to, when the current carrying speed of the carrying robot is greater than the safety constraint speed of the front path inflection point, perform risk mapping on the real-time environment state of the carrying robot and the real-time state of the hazardous chemical in the carrying task, output the environmental risk feature and the material risk feature, cooperatively evaluate the carrying risk of the hazardous chemical through the environmental risk feature and the material risk feature, and obtain a cooperative risk coefficient of the hazardous chemical in the carrying task; An execution module for cooperatively adjusting the moving speed of the carrying robot at the next path inflection point based on the cooperative risk coefficient and the respective safety constraint speeds.

9. A computer device, comprising: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the chemical hazardous chemical intelligent carrying control method in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores instructions or codes, when the instructions or codes are run on the computer, so that the computer executes the chemical dangerous goods intelligent carrying control method as claimed in any one of claims 1 to 7.

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