Intelligent Multi-compartment Washing Machine Washing Scheduling Algorithm Based on Multi-level Allocation

Abstract

The present invention relates to the technical field of cleaning scheduling of medical cleaning machines, and specifically relates to a cleaning scheduling algorithm for an intelligent multi-chamber cleaning machine based on multi-level allocation. It includes constructing an AGV scheduling system, and through the AGV scheduling system, scheduling idle robots to carry cleaning objects to idle cleaning machines. The present invention greatly saves labor costs by using the AGV scheduling system and robots. The working mode of the multi-chamber cleaning machine assembly line saves cleaning time, solves the waiting time required when using a single-chamber cleaning machine, and can effectively ensure the cleaning effect and drying effect. Through the intelligent allocation algorithm, the robot at the shortest distance is scheduled to carry the cleaning object to the nearest cleaning machine, further saving transportation time and improving cleaning efficiency. Through the image classification and recognition algorithm, corresponding cleaning steps are formulated for different cleaning objects, and the cleaning is carried out according to appropriate steps according to the type of cleaning object. At the same time, through the metering and weighing algorithm, corresponding cleaning liquids are allocated to cleaning objects of different weights, reasonably utilizing the cleaning liquid and reducing unnecessary resource waste.

Description

Technical Field

[0001] The present invention relates to the technical field of cleaning scheduling of medical cleaning machines, and specifically, to a cleaning scheduling algorithm for an intelligent multi-cabin cleaning machine based on multi-level allocation. Background Art

[0002] Medical cleaning machines are used to clean medical devices. Medical devices refer to instruments, equipment, utensils, and other similar or related items that directly or indirectly act on the human body. In the prior art, reusable medical devices usually need to be thoroughly cleaned, dried, disinfected, and sterilized before they can be used again. The cleaning process generally includes multiple steps, such as primary cleaning, enzyme cleaning, rinsing, drying, and disinfection, and these steps are carried out sequentially.

[0003] The applicant previously applied for a Chinese invention patent with a publication number of CN112845303A and a title of "A Multi-Cabin Medical Cleaning Machine". This medical cleaning machine integrates a cleaning cabin, an ultrasonic cabin, a disinfection cabin, and a drying cabin on the same body, so as to complete the cleaning, disinfection, sterilization, and drying operations of medical devices in one cleaning machine, thereby improving the cleaning and disinfection efficiency of medical devices.

[0004] During the use of the cleaning machine, most of the existing scheduling methods use manual monitoring of the cleaning machine status. After the cleaning machine finishes cleaning, the medical devices to be cleaned and disinfected are transported to the idle cleaning machine for cleaning. When multiple groups of cleaning machines work simultaneously, the cleaning time of each device is different. Relying solely on manual scheduling, it is easy for the idle cleaning machine to be in an idling state, resulting in a significant reduction in the working efficiency of the cleaning machine. Even if the existing scheduling methods attempt to schedule through a system, the scheduling function is limited and cannot meet the cleaning work of medical devices of different varieties or weights. Only a unified standard can be used for the rinsing step and the corresponding cleaning liquid can be allocated, which easily wastes cleaning resources. Summary of the Invention

[0005] The purpose of the present invention is to provide a cleaning scheduling algorithm for an intelligent multi-cabin cleaning machine based on multi-level allocation to solve the problems raised in the above background art.

[0006] To achieve the above purpose, a cleaning scheduling algorithm for an intelligent multi-cabin cleaning machine based on multi-level allocation is provided, including the following steps:

[0007] S1. Construct an AGV scheduling system, and use the AGV scheduling system to schedule idle robots to carry cleaning objects to idle cleaning machines;

[0008] S2. Determine idle cleaning machines and idle robots through background monitoring;

[0009] S3. Adopt an intelligent allocation algorithm to schedule and allocate the idle robot at the shortest distance to carry the cleaning object to the idle cleaning machine at the shortest distance.

[0010] S4. Build a cleaning machine cleaning system, formulate corresponding cleaning steps through the cleaning system, and control the cleaning machine to perform step-by-step cleaning on the cleaning object.

[0011] S5. Formulate corresponding cleaning steps for different cleaning objects through an image classification and recognition algorithm.

[0012] S6. Adopt a metering and weighing algorithm to allocate corresponding cleaning fluids to different heavy cleaning objects.

[0013] S7. Through the background monitoring in S2, the AGV scheduling system calls an idle robot to transport the cleaned cleaning object out.

[0014] As a further improvement of this technical solution, the construction method of the AGV scheduling system in S1 includes the following steps:

[0015] S1.1. Configure the identification data of each robot and formulate corresponding assignment data.

[0016] S1.2. After determining the idle robot, the system identifies the identification data of the idle robot and sends the corresponding assignment data to the idle robot.

[0017] S1.3. Plan the driving route of the idle robot according to the orientation of the assigned idle robot and the selected cleaning machine.

[0018] S1.4. The assigned idle robot carries the cleaning object to the selected cleaning machine according to the planned driving route.

[0019] As a further improvement of this technical solution, the method for determining an idle robot in S2 includes the following steps:

[0020] S2.1. Select the robot with corresponding identification data according to the completed task function and mark it as a robot to be authenticated.

[0021] S2.2. Conduct image monitoring on each robot to be authenticated, judge whether the robot to be authenticated is carrying a cleaning object, eliminate the robot to be authenticated carrying a cleaning object, and mark the remaining robots to be authenticated as pre-authenticated robots.

[0022] S2.3. Judge the moving state of the pre-authenticated robot, eliminate the pre-authenticated robot that is moving, and mark the remaining pre-authenticated robots stationary in place as idle robots.

[0023] As a further improvement of this technical solution, the formula of the intelligent allocation algorithm in S3 is as follows:

[0024]

[0025] In formula (1), S slope is the direct or straight-line distance from the monitoring point to the idle robot and the idle cleaning machine. V0 is the speed of laser propagation in air, which is a fixed value. T0 is the total time taken for the laser to be projected onto the idle robot or the idle cleaning machine and for the laser to be reflected back;

[0026] S horizonta = S slope sinα; (2)

[0027] In formula (2), S horizont is the horizontal distance from the monitoring point to the idle robot or the idle cleaning machine, and α is the angle formed between the direct projection position and the horizontal position of the monitoring point;

[0028]

[0029] In formula (3), is the horizontal distance from the idle robot to the nearest idle cleaning machine, is the horizontal distance from the monitoring point to the idle cleaning machine, is the horizontal distance from the monitoring point to the idle robot;

[0030]

[0031] In formula (4), L slope is the actual distance from the idle robot to the nearest idle cleaning machine, and β is the inclination angle formed by the idle robot and the nearest idle cleaning machine.

[0032] As a further improvement of this technical solution, in formula (4), the comparison of the actual distances from each idle robot to the nearest idle cleaning machine adopts the bubble sort algorithm, and its algorithm formula is as follows:

[0033]

[0034] In formula (5), L is the set of actual distances from each idle robot to the nearest idle cleaning machine, to are the actual distances from different idle robots to the nearest idle cleaning machine. The comparison process is as follows:

[0035] Step 1: Take the first number as the comparison value and compare the adjacent for size;

[0036] Step 2: Judge the size comparison result of the adjacent If Greater than Then Swap with Reverse the order, otherwise keep the original sorting;

[0037] Step 3: Continue to compare With The next adjacent number, and perform replacement or maintain the original sorting according to the judgment criteria in Step 2;

[0038] Step 4: Repeat Step 1, Step 2, and Step 3 for n - 1 times. The order of the entire set is adjusted, and the maximum value in the set is obtained, which is at the last position of the set;

[0039] Step 5: Repeat Step 1, Step 2, Step 3, and Step 4 until the order of the values in the set is adjusted. The obtained set is sorted in ascending order, and the first number in the set is the target number.

[0040] As a further improvement of this technical solution, the cleaning machine cleaning system constructed in S4 includes a loading / unloading module, a cleaning module, a disinfection module, and a drying module. The loading / unloading module is used to control the cleaning rack in the cleaning machine to load and unload the cleaning objects; the cleaning module is used to control the cleaning instruments in the cleaning machine to clean the loaded cleaning objects; the disinfection module is used to control the disinfection instruments in the cleaning machine to disinfect and clean the loaded cleaning objects; the drying module is used to control the drying instruments in the cleaning machine to dry the cleaned cleaning objects.

[0041] As a further improvement of this technical solution, the image classification and recognition algorithm in S5 includes the following steps:

[0042] S5.1: The monitoring point takes pictures of the cleaning objects to be cleaned to obtain the picture information of the cleaning objects;

[0043] S5.2: Determine the types of various cleaning objects according to the picture information of the cleaning objects;

[0044] S5.3: Supplement the types of various cleaning objects in advance, and formulate corresponding cleaning steps accordingly to generate a cleaning database;

[0045] S5.4: Determine the type according to the cleaning object to be cleaned, compare with the cleaning database, and select the corresponding cleaning steps.

[0046] As a further improvement of this technical solution, the metering and weighing algorithm in S6 is as follows:

[0047] G: [g1, g2,..., g m ; (6)

[0048] In formula (6), G is the set of weights of the cleaning objects to be cleaned, g1 to g m are the weights of the cleaning objects to be cleaned, and f(g) is the cleaning object distribution function;

[0049]

[0050] In formula (7), k1, k2, and k3 are the amounts of cleaning liquid spent on cleaning the cleaning objects at different weights, g is the weight of the cleaning object to be judged currently, g0 is the standard weight. When the weight g of the cleaning object to be judged currently is less than the standard weight g0, the cleaning object distribution function f(g) outputs k1, indicating that the amount of cleaning liquid spent on the cleaning object weight g in this range is k1. When the weight g of the cleaning object to be judged currently is not less than the standard weight g0 and does not exceed 2 times the standard weight g0, the cleaning object distribution function f(g) outputs k2, indicating that the amount of cleaning liquid spent on the cleaning object weight g in this range is k2. When the weight g of the cleaning object to be judged currently is greater than 2 times the standard weight g0 and less than 4 times the standard weight g0, the cleaning object distribution function f(g) outputs k3, indicating that the amount of cleaning liquid spent on the cleaning object weight g in this range is k3.

[0051] As a further improvement of this technical solution, the method for taking out the cleaning object in S7 includes the following steps:

[0052] S7.1. Identify the cleaning machine that has completed cleaning and mark it as the cleaning machine to be processed;

[0053] S7.2. Identify the identification data corresponding to the robot and mark the identified robot as the robot to be taken out;

[0054] S7.3. Conduct image monitoring on each robot to be taken out and select the idle robot among them;

[0055] S7.4. Calculate the distances between each idle robot and the cleaning machine to be processed, and select the idle robot with the shortest distance and mark it as the target robot.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] In the cleaning scheduling algorithm of the multi-stage distribution intelligent multi-cabin cleaning machine, the use of the AGV scheduling system and robots greatly saves labor costs. The working mode of the multi-cabin cleaning machine assembly line saves cleaning time, solves the waiting time required when using a single-cabin cleaning machine, and can effectively ensure the cleaning effect and drying effect. Through the intelligent allocation algorithm, the nearest robot is scheduled to carry the cleaning object to the nearest cleaning machine, further saving transportation time and improving cleaning efficiency. Through the image classification and recognition algorithm, corresponding cleaning steps are formulated for different cleaning objects, and the cleaning is carried out according to appropriate steps according to the type of cleaning object. At the same time, through the metering and weighing algorithm, corresponding cleaning fluids are allocated to different heavy cleaning objects, rationally using the cleaning fluid and reducing unnecessary resource waste. Brief Description of the Drawings

[0058] Figure 1 It is the overall process flow chart of the present invention;

[0059] Figure 2 It is the process flow chart of the construction method of the AGV scheduling system of the present invention;

[0060] Figure 3 It is the process flow chart of the method for determining idle robots of the present invention;

[0061] Figure 4 It is the process flow chart of the image classification and recognition algorithm of the present invention;

[0062] Figure 5 It is the process flow chart of the method for taking out cleaning objects of the present invention. Detailed Embodiments

[0063] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0064] Please refer to Figures 1 - 5 As shown, a cleaning scheduling algorithm for a multi-stage distribution intelligent multi-cabin cleaning machine is provided, including the following steps:

[0065] S1. Construct an AGV scheduling system, and schedule idle robots to carry cleaning objects to idle cleaning machines through the AGV scheduling system;

[0066] S2. Determine idle cleaning machines and idle robots through background monitoring;

[0067] S3. Adopt an intelligent allocation algorithm to schedule and allocate the nearest idle robot to carry the cleaning object and move it to the nearest idle cleaning machine;

[0068] S4. Build a cleaning system for the cleaning machine, formulate corresponding cleaning steps through the cleaning system, and control the cleaning machine to clean the objects to be cleaned step by step;

[0069] S5. Formulate corresponding cleaning steps for different objects to be cleaned through an image classification and recognition algorithm;

[0070] S6. Adopt a metering and weighing algorithm to allocate corresponding cleaning solutions to different heavy objects to be cleaned;

[0071] S7. Through the background monitoring in step S2, the AGV scheduling system calls an idle robot to transport the cleaned objects to be cleaned out.

[0072] In specific use, first, build an AGV scheduling system. Through the AGV scheduling system, dispatch an idle robot to carry the object to be cleaned to an idle cleaning machine, plan the driving route of the idle robot, and the idle robot carries the object to be cleaned to the idle cleaning machine according to the planned driving route. Subsequently, through background monitoring, determine the current idle cleaning machine and the idle robot, adopt an intelligent allocation algorithm to determine the distance between each idle robot and the idle cleaning machine closest to it, generate an idle distance, compare each idle distance, and select the smallest idle distance from them. At this time, identify the idle robot corresponding to the smallest idle distance, and the AGV scheduling system sends an instruction to this idle robot. Subsequently, this idle robot carries the object to be cleaned to the nearest idle cleaning machine. Build a cleaning system for the cleaning machine, including primary cleaning, enzyme cleaning, rinsing, air drying, and disinfection of the objects to be cleaned in the cleaning machine. Formulate corresponding cleaning steps for different objects to be cleaned through an image classification and recognition algorithm, monitor the images of the objects to be cleaned stored, judge the type of the objects to be cleaned, formulate corresponding cleaning steps according to the type of the objects to be cleaned, and then calculate the weight of the objects to be cleaned stored through a metering and weighing algorithm, pre-store the cleaning solution comparison list corresponding to the weight of the objects to be cleaned, select the amount of cleaning enzyme solution corresponding to the measured weight of the objects to be cleaned according to the comparison list, and then add it to the cleaning machine to clean the objects to be cleaned. After the objects to be cleaned are cleaned, determine the idle robot through background monitoring, and call a cleaning robot through the AGV scheduling system to transport the cleaned objects to be cleaned out.

[0073] The present invention greatly saves labor costs by using an AGV scheduling system and robots. The working mode of the multi-chamber cleaning machine assembly line saves cleaning time, solves the waiting time required for using a single-chamber cleaning machine, and can effectively ensure the cleaning effect and drying effect. Through an intelligent allocation algorithm, the robot at the nearest distance is scheduled to carry the cleaning object to the nearest cleaning machine, further saving transportation time and improving cleaning efficiency. Through an image classification and recognition algorithm, corresponding cleaning steps are formulated for different cleaning objects, and the cleaning is carried out according to the appropriate steps according to the type of cleaning object. While ensuring the cleaning effect, it can prevent the impact on the cleaning object caused by unnecessary cleaning steps. At the same time, through a metering and weighing algorithm, corresponding cleaning liquids are allocated to different heavy cleaning objects, reasonably using the cleaning liquid and reducing unnecessary resource waste.

[0074] In addition, the construction method of the AGV scheduling system in S1 includes the following steps:

[0075] S1.1. Configure the recognition data of each robot and formulate corresponding assignment data;

[0076] S1.2. After determining the idle robot, the system recognizes the recognition data of the idle robot and sends the corresponding assignment data to the idle robot;

[0077] S1.3. Plan the driving route of the idle robot according to the orientation of the assigned idle robot and the selected cleaning machine;

[0078] S1.4. The assigned idle robot carries the cleaning object to the selected cleaning machine according to the planned driving route.

[0079] In specific use, first, configure the recognition data of each robot to identify the identity information of each robot. The robots are divided into old cleaning object handling robots and new cleaning object handling robots. Old cleaning object handling robots: used to handle uncleaned cleaning objects. New cleaning object handling robots: used to handle cleaned cleaning objects. At the same time, the system formulates the assignment data corresponding to each recognition data. After determining the idle robot (old cleaning object handling robot), the system recognizes the recognition data of the idle robot and sends the corresponding assignment data to the idle robot. The start, stop, driving, and regulation of the cleaning object of the robot are controlled through the assignment data. At the same time, according to the orientation of the assigned idle robot and the selected cleaning machine, the driving route of the idle robot is planned, and the assigned idle robot carries the cleaning object to the selected cleaning machine according to the planned driving route, realizing the functions of regulating, allocating, and route planning of the idle robot.

[0080] Furthermore, the method for determining the idle robot in step S2 includes the following steps:

[0081] S2.1. Select the robot with the corresponding recognition data according to the function of completing the task and mark it as the robot to be authenticated;

[0082] S2.2. Monitor the images of each robot to be authenticated, determine whether the robot to be authenticated is carrying a cleaning item, eliminate the robot to be authenticated carrying the cleaning item, and mark the remaining robots to be authenticated as pre-authenticated robots;

[0083] S2.3. Determine the moving state of the pre-authenticated robots, eliminate the pre-authenticated robots that are moving, and mark the remaining pre-authenticated robots that are stationary in place as idle robots.

[0084] In specific use, first, according to the function of completing the task, select the robots corresponding to the recognition data and mark them as robots to be authenticated. For example, if it is necessary to dispatch idle robots to transport old cleaning items, only the recognition data of the old cleaning item handling robots needs to be recognized, and the remaining idle robots do not need to be judged. Subsequently, monitor the images of each robot to be authenticated, determine whether the robot to be authenticated is carrying a cleaning item, eliminate the robot to be authenticated carrying the cleaning item, and mark the remaining robots to be authenticated as pre-authenticated robots. Finally, determine the moving state of the pre-authenticated robots, eliminate the pre-authenticated robots that are moving, and mark the remaining pre-authenticated robots that are stationary in place as idle robots, thereby improving the dispatch efficiency of idle robots. At the same time, according to the function of completing the task, distinguish the types of robots in advance to prevent the phenomenon of chaotic dispatch of various robots.

[0085] Furthermore, the formula of the intelligent allocation algorithm in step S3 is as follows:

[0086]

[0087] In formula (1), S slope is the direct distance or straight-line distance from the monitoring point to the idle robot and the idle cleaning machine. V0 is the speed of laser propagation in the air, which is a fixed value. T0 is the total time spent for the laser to project onto the idle robot or idle cleaning machine and the laser to return.

[0088] S horizonta = S slope sinα; (2)

[0089] In formula (2), S horizont is the horizontal distance from the monitoring point to the idle robot or idle cleaning machine, and α is the angle formed between the direct projection position and the horizontal position of the monitoring point.

[0090]

[0091] In formula (3), is the horizontal distance from the idle robot to the nearest idle cleaning machine, is the horizontal distance from the monitoring point to the idle cleaning machine, is the horizontal distance from the monitoring point to the idle robot;

[0092]

[0093] In formula (4), L slope is the actual distance from the idle robot to the nearest idle cleaning machine, and β is the inclination angle formed by the idle robot and the nearest idle cleaning machine.

[0094] In specific use, first, the distances from the monitoring point to each idle robot and its nearest idle cleaning machine are measured. Here, the laser ranging method is adopted. The laser is projected from the monitoring point to the projection point of the idle robot or the idle cleaning machine, and then the time T0 taken for the laser projected from the monitoring point to hit the projection point and reflect back to the monitoring point is recorded. In the initial state, the projection points of each monitoring point are vertically irradiating the ground. During the ranging process, the angle α of the monitoring point is adjusted so that the laser projected by the monitoring point can directly hit the projection point of the idle robot or the idle cleaning machine. Then, according to the Pythagorean theorem, the horizontal distance S from the monitoring point to the idle robot or the idle cleaning machine is determined horizonta , and the horizontal distance from the idle robot to the nearest idle cleaning machine is determined Again, using the Pythagorean theorem, the actual distance L from the idle robot to the nearest idle cleaning machine is calculated slope , and then the actual distances L of each are compared slope , and the L with the smallest value is selected slope , and the corresponding idle robot and the nearest idle cleaning machine are the selected targets.

[0095] Specifically, the comparison of the actual distances from each idle robot to the nearest idle cleaning machine in formula (4) uses the bubble sort algorithm, and its algorithm formula is as follows:

[0096]

[0097] In formula (5), L is the set of actual distances from each idle robot to the nearest idle cleaning machine, to are the actual distances from different idle robots to the nearest idle cleaning machine, and the comparison process is as follows:

[0098] Step 1: Take the first number as the comparison value and compare the adjacent sizes;

[0099] Step 2: Judge the adjacent size comparison result. If is greater than then and Swap the order, and vice versa, keep the original sorting;

[0100] Step 3: Continue the comparison and with the next adjacent number, and perform replacement or maintain the original sorting according to the judgment criteria in Step 2;

[0101] Step 4: Repeat Step 1, Step 2, and Step 3 for n - 1 times. The order of the entire set is adjusted, and the maximum value in the set is obtained and is at the last position of the set;

[0102] Step 5: Repeat Step 1, Step 2, Step 3, and Step 4 until the size order of each value in the set is adjusted. The obtained set is sorted in ascending order, and the first number in the set is the target number.

[0103] In addition, the cleaning system of the cleaning machine constructed in Step S4 includes a loading / unloading module, a cleaning module, a disinfection module, and a drying module. The loading / unloading module is used to control the cleaning rack in the cleaning machine to load and take out the cleaning object; the cleaning module is used to control the cleaning instruments in the cleaning machine to clean the loaded cleaning object; the disinfection module is used to control the disinfection instruments in the cleaning machine to disinfect and clean the loaded cleaning object; the drying module is used to control the drying instruments in the cleaning machine to dry the cleaned cleaning object. During specific use, first, through the control of the loading module, the cleaning rack with the old cleaning object is conveyed to the inner end of the cleaning instrument. Through the control of the cleaning module, the cleaning instrument cleans the loaded cleaning object. During this process, the disinfection module controls the disinfection instruments in the cleaning machine to disinfect and clean the loaded cleaning object. After the cleaning process is completed, through the control of the drying module, the drying instruments in the cleaning machine dry the cleaned cleaning object to obtain the cleaned cleaning object, and then the unloading module controls the cleaning rack to take out the cleaned cleaning object.

[0104] Furthermore, the image classification and recognition algorithm in Step S5 includes the following steps:

[0105] S5.1: The monitoring point takes pictures of the cleaning object to be cleaned to obtain the picture information of the cleaning object;

[0106] S5.2: Determine the types of various cleaning objects according to the picture information of the cleaning object;

[0107] S5.3: Supplement the types of various cleaning objects in advance and formulate corresponding cleaning steps accordingly to generate a cleaning database;

[0108] S5.4: Judge the type of the cleaning object to be cleaned, compare with the cleaning database, and select the corresponding cleaning steps.

[0109] During specific use, first, the cleaning object to be cleaned is photographed through the monitoring point to obtain the image information of the cleaning object. Subsequently, the image information of the cleaning object is analyzed to determine various types of cleaning objects. Various types of cleaning objects are pre-recorded, and corresponding cleaning steps are formulated accordingly to generate a cleaning database. Then, based on the judgment result, the type of the cleaning object is obtained and compared with the cleaning database. After the comparison is completed, the corresponding cleaning steps are selected, and the cleaning object to be cleaned is cleaned according to the selected cleaning steps.

[0110] Furthermore, the metering and weighing algorithm in S6 is as follows:

[0111] G: [g1, g2,..., g m ; (6)

[0112] In formula (6), G is the set of weights of each cleaning object to be cleaned, g1 to g m are the weights of each cleaning object to be cleaned, and f(g) is the cleaning object allocation function;

[0113]

[0114] In formula (7), k1, k2, and k3 are the amounts of cleaning liquid consumed for cleaning the cleaning objects under different weights. g is the weight of the cleaning object to be judged currently, g0 is the standard weight. When the weight g of the cleaning object to be judged currently is less than the standard weight g0, the cleaning object allocation function f(g) outputs k1, indicating that the amount of cleaning liquid consumed for the cleaning object weight g in this range is k1. When the weight g of the cleaning object to be judged currently is not less than the standard weight g0 and does not exceed 2 times the standard weight g0, the cleaning object allocation function f(g) outputs k2, indicating that the amount of cleaning liquid consumed for the cleaning object weight g in this range is k2. When the weight g of the cleaning object to be judged currently is greater than 2 times the standard weight g0 and less than 4 times the standard weight g0, the cleaning object allocation function f(g) outputs k3, indicating that the amount of cleaning liquid consumed for the cleaning object weight g in this range is k3. It should be noted that when the cleaning object weight g exceeds 4 times the standard weight g0, at this time, the cleaning object weight g reaches the maximum value that the cleaning machine can bear, and the cleaning object needs to be cleaned in batches.

[0115] In addition, the method for taking out the cleaning object in step S7 includes the following steps:

[0116] S7.1. Identify the cleaning machine that has completed cleaning and mark it as the cleaning machine to be processed;

[0117] S7.2. Identify the identification data corresponding to the robot and mark the identified robot as the robot to be taken out;

[0118] S7.3. Conduct image monitoring on each robot to be taken out and select the idle robot among them;

[0119] S7.4. Calculate the distances between each idle robot and the cleaning machine to be processed, and select the idle robot with the shortest distance, which is marked as the target robot.

[0120] In specific use, first, when the cleaning machine finishes the cleaning work, the cleaning system of the cleaning machine identifies the cleaning machine that has completed the cleaning and marks it as the cleaning machine to be processed. At the same time, it identifies the corresponding identification data of the robot and marks the identified robot as the robot to be taken out. Image monitoring is carried out on each robot to be taken out, and the idle robot (new cleaning object handling robot) is selected. Subsequently, the distances between each idle robot and the cleaning machine to be processed are calculated, and the idle robot with the shortest distance is selected and marked as the target robot. The target robot receives the scheduling of the AGV scheduling system, and a delivery route is formulated for it. The target robot transports the cleaned cleaning object according to the delivery route.

[0121] The above shows and describes the basic principle, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. The cleaning scheduling algorithm for a multi - chamber intelligent multi - stage distribution cleaning machine, characterized in that, It includes the following steps: S1. Construct an AGV scheduling system, and through the AGV scheduling system, schedule idle robots to carry cleaning objects to idle cleaning machines; S2. Determine idle cleaning machines and idle robots through background monitoring; S3. Adopt an intelligent allocation algorithm to schedule and allocate the idle robot at the shortest distance to carry the cleaning object and move it to the idle cleaning machine at the shortest distance; S4. Construct a cleaning system for the cleaning machine, formulate corresponding cleaning steps through the cleaning system, and control the idle cleaning machine to perform step-by-step cleaning on the cleaning object; S5. Formulate corresponding cleaning steps for different cleaning objects through an image classification and recognition algorithm; S6. Adopt a metering and weighing algorithm to allocate corresponding cleaning fluids to different heavy cleaning objects; S7. Through the background monitoring in S2, the AGV scheduling system calls an idle robot to carry out the transported-out of the cleaning object after cleaning; Among them, adopt an intelligent allocation algorithm to determine the distances between each idle robot and the idle cleaning machine at the shortest distance to it, generate idle intervals, compare each idle interval, select the smallest idle interval from them. At this time, mark the idle robot corresponding to the smallest idle interval. The AGV scheduling system sends an instruction to this idle robot, and then this idle robot carries the cleaning object to the nearest idle cleaning machine. Construct a cleaning system for the cleaning machine, including primary washing, enzyme washing, rinsing, air drying, and disinfection of the cleaning object of the cleaning machine; Among them, the construction method of constructing the AGV scheduling system in S1 includes the following steps: S1.

1. Configure the identification data of each robot and formulate corresponding assignment data; S1.

2. After determining the idle robot, the system identifies the identification data of the idle robot and sends the corresponding assignment data to the idle robot; S1.

3. Plan the driving route of the idle robot according to the assigned idle robot and the selected orientation of the cleaning machine; S1.

4. The assigned idle robot carries the cleaning object to the selected cleaning machine according to the planned driving route; Among them, the robots are divided into old cleaning object handling robots and new cleaning object handling robots. The old cleaning object handling robots are used to handle the unwashed cleaning objects, and the new cleaning object handling robots are used to handle the cleaned cleaning objects; In S2, the method for determining an idle robot includes the following steps: S2.

1. Select the robot with corresponding identification data according to the completed task function and mark it as a robot to be authenticated; S2.

2. Conduct image monitoring on each robot to be authenticated, judge whether the robot to be authenticated is carrying a cleaning object, eliminate the robot to be authenticated carrying a cleaning object, and mark the remaining robots to be authenticated as pre-authenticated robots; S2.

3. Judge the moving state of the pre-authenticated robots, eliminate the pre-authenticated robots that are moving among them, and mark the remaining pre-authenticated robots stationary in place as idle robots; Among them, the method for taking out the cleaning object in S7 includes the following steps: S7.

1. Identify the cleaning machine after cleaning and mark it as a cleaning machine to be processed; S7.

2. Identify the identification data corresponding to the robot and mark the recognized robot as a robot to be taken out; S7.

3. Conduct image monitoring on each robot to be taken out and select the idle robot among them; S7.

4. Calculate the distances between the idle robots and the cleaning machines to be processed, and select the idle robot with the shortest distance, which is marked as the target robot.

2. The cleaning scheduling algorithm for the multi - compartment intelligent cleaning machine based on multi - level distribution according to claim 1, wherein: The cleaning machine cleaning system constructed in S4 includes a loading / unloading module, a cleaning module, a disinfection module, and a drying module. The loading / unloading module is used to control the cleaning rack in the cleaning machine to load and unload the cleaning objects; the cleaning module is used to control the cleaning instruments in the cleaning machine to clean the loaded cleaning objects; the disinfection module is used to control the disinfection instruments in the cleaning machine to disinfect and clean the loaded cleaning objects; The drying module is used to control the drying instruments in the cleaning machine to dry the cleaned cleaning objects.

3. The cleaning scheduling algorithm of the intelligent multi-cabin cleaning machine based on multi-level distribution according to claim 1, wherein: The image classification and recognition algorithm in S5 includes the following steps: S5.

1. The monitoring point takes pictures of the cleaning objects to be cleaned to obtain the picture information of the cleaning objects; S5.

2. Determine the types of various cleaning objects according to the picture information of the cleaning objects; S5.

3. Supplement and record the types of various cleaning objects in advance, and formulate corresponding cleaning steps accordingly to generate a cleaning database; S5.

4. Judge the type of the cleaning object to be cleaned, compare it with the cleaning database, and select the corresponding cleaning steps.

4. The cleaning scheduling algorithm of the intelligent multi-cabin cleaning machine based on multi-level distribution according to claim 1, wherein: The metering and weighing algorithm in S6 is as follows: ；（6） In formula (6), is the set of weights of each cleaning object to be cleaned, to are the weights of each cleaning object to be cleaned, is the cleaning object distribution function; ；（7） In formula (7), , and are the amounts of cleaning liquid spent on cleaning objects of different weights, is the weight of the cleaning object that needs to be judged currently, is the standard weight. When the weight of the cleaning object that needs to be judged currently is less than the standard weight , the cleaning object distribution function outputs , indicating that the amount of cleaning liquid spent on the cleaning object weight in this range is . When the weight of the cleaning object that needs to be judged currently is not less than the standard weight and does not exceed twice the standard weight , the cleaning object distribution function outputs , indicating that the amount of cleaning liquid spent on the cleaning object weight in this range is . When the weight of the cleaning object that needs to be judged currently is greater than twice the standard weight and less than four times the standard weight , the cleaning object distribution function outputs , indicating that the amount of cleaning liquid spent on the cleaning object weight in this range is .

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