An intelligent scheduling method and system for multi-robot collaborative operation

Abstract

The present invention relates to the technical field of intelligent scheduling of multi-robot collaborative operations, and specifically discloses an intelligent scheduling method and system for multi-robot collaborative operations, the method comprising: handling robot confirmation, delivery information extraction, delivery robot confirmation and delivery saturation analysis; the present invention confirms the handling robot corresponding to each cargo by combining the load-bearing adaptability between the cargo and the handling robot, confirms the delivery robot corresponding to each cargo to be delivered by combining the power adaptability between the delivery robot and the delivered cargo, and analyzes the delivery saturation corresponding to each delivery sub-area by combining the total number of delivered cargo and the delivery fullness of the delivery robot, thereby improving the safety and stability of cargo handling, avoiding affecting the timely delivery of cargo, thereby avoiding customer loss or increased complaints, improving the accuracy and effectiveness of decision-making, and making reasonable adjustments and optimizations according to actual conditions.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent scheduling of multi-robot collaborative operations, and relates to an intelligent scheduling method and system for multi-robot collaborative operations. Background Art

[0002] At present, most of the goods delivery in the domestic logistics industry is done by manual delivery. With the rise of e-commerce, the number of logistics companies is increasing, the number of logistics delivery personnel is increasing, the labor cost is greatly increased, the per capita delivery pressure is high, and delays and wrong delivery often occur. Therefore, for the frequent transportation and delivery of goods between warehouses, the use of robot delivery has an incomparable advantage.

[0003] The existing scheduling and analysis methods for logistics handling and distribution of multi-robot collaborative operations still have the following problems: 1. During the cargo handling process, the handling robots corresponding to each cargo are not confirmed based on the compatibility between the volume and weight of the cargo and the rated volume and rated weight of the handling robot. It is impossible to ensure that the selected handling robots can meet the cargo handling needs, which reduces the safety and stability of cargo handling. At the same time, it is impossible to avoid the waste of handling resources, thereby reducing the handling efficiency.

[0004] 2. During the goods delivery process, the delivery robot’s power consumption was not analyzed to determine whether it could meet the power requirements for the delivery of the corresponding goods. At the same time, the abnormal power loss of the delivery robot was not analyzed in combination with its historical delivery information. This reduced the accuracy of the delivery robot’s current available power analysis, affected the timely delivery of goods, and may also increase additional delivery costs. It cannot meet customers’ requirements for the reliability and timeliness of delivery services, thus leading to customer churn or increased complaints.

[0005] 3. The delivery saturation of the delivery sub-area is not analyzed and fed back in combination with the total number of delivered goods and the daily delivery situation of the delivery robot, which means that the decision lacks data support, which may lead to inaccurate and ineffective decisions and inability to make reasonable adjustments and optimizations based on actual conditions. Summary of the invention

[0006] In view of this, in order to solve the problems raised in the above background technology, an intelligent scheduling method and system for multi-robot collaborative operation is proposed.

[0007] The objective of the present invention can be achieved through the following technical solutions: The first aspect of the present invention provides an intelligent scheduling method for collaborative operation of multiple robots, including: S1, transport robot confirmation: extracting the placement position, volume, weight and distribution sub-area position corresponding to each cargo in the first-level storage warehouse in the target area, and extracting the rated volume, rated weight and current working status of each transport robot in the first-level storage warehouse, and confirming the transport robot corresponding to each cargo in the first-level storage warehouse.

[0008] S2. Extraction of delivery information: Extract the sub-warehouse locations corresponding to each delivery sub-area in the secondary distribution point, move each cargo to the corresponding sub-warehouse location, obtain each cargo to be delivered in each delivery sub-area, extract the rated power, current remaining power, current delivery status and historical delivery information of each delivery robot in each delivery sub-area, and extract the delivery distance between each delivery sub-area and the secondary distribution point.

[0009] S3. Delivery robot confirmation: confirm the delivery robot assigned to each to-be-delivered cargo in each delivery sub-area.

[0010] S4. Delivery saturation analysis: extract the number of goods to be delivered in each delivery sub-area in each historical monitoring period, the number of deliveries by each delivery robot, the delivery distance and delivery time corresponding to each delivery, analyze the delivery saturation corresponding to each delivery sub-area, and provide feedback.

[0011] The second aspect of the present invention provides an intelligent scheduling system for multi-robot collaborative work, including: a handling robot confirmation module, used to extract the placement position, volume, weight and distribution sub-area position corresponding to each cargo in the primary storage warehouse in the target area, and extract the rated volume, rated weight and current working status of each handling robot in the primary storage warehouse, and confirm the handling robot assigned to each cargo in the primary storage warehouse.

[0012] The delivery information extraction module is used to extract the distribution warehouse location corresponding to each distribution sub-area in the secondary distribution point, transport each cargo to the corresponding distribution warehouse location, obtain each cargo to be delivered in each distribution sub-area, extract the rated power, current remaining power, current delivery status and historical delivery information of each delivery robot in each distribution sub-area, and extract the delivery distance between each distribution sub-area and the secondary distribution point.

[0013] The delivery robot confirmation module is used to confirm the delivery robot corresponding to each to-be-delivered cargo in each delivery sub-area.

[0014] The database is used to store the power required for unit delivery distance corresponding to unit cargo weight, and the power loss corresponding to the unit power loss abnormality index.

[0015] The delivery saturation analysis module is used to extract the number of goods to be delivered in each delivery sub-area in each historical monitoring period, the number of deliveries by each delivery robot, the delivery distance and delivery time corresponding to each delivery, analyze the delivery saturation corresponding to each delivery sub-area, and provide feedback.

[0016] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention determines the corresponding handling robot for each cargo by combining the compatibility between the volume and weight of the cargo and the rated load volume and rated load weight of the handling robot, thereby ensuring that the selected handling robot can meet the handling requirements of the cargo, improving the safety and stability of cargo handling, and avoiding waste of handling resources, thereby improving handling efficiency.

[0017] (2) The present invention analyzes whether the current available power of the delivery robot can meet the power required for the delivery of the corresponding goods, and analyzes the abnormal power consumption of the delivery robot in combination with the historical delivery information of the delivery robot, and finally confirms the delivery robot corresponding to each cargo to be delivered. This improves the accuracy of the analysis of the current available power of the delivery robot, avoids affecting the timely delivery of the goods, reduces the additional delivery costs, and can meet the customer's requirements for the reliability and timeliness of the delivery service, thereby avoiding customer loss or increased complaints.

[0018] (3) The present invention analyzes the delivery fullness of the delivery robot by combining the total number of delivered goods and the number of deliveries by the delivery robot, the delivery distance and delivery time corresponding to each delivery, and thus analyzes the delivery saturation corresponding to each delivery sub-area, thereby providing effective data support for decision-making, improving the accuracy and effectiveness of decision-making, and at the same time making reasonable adjustments and optimizations based on actual conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1 The figure is a schematic flow chart of the method steps of the present invention.

[0021] Figure 2 It is a schematic diagram of the system structure connection of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] See also Figure 1 As shown, the first aspect of the present invention provides an intelligent scheduling method for multi-robot collaborative work, including: S1, transport robot confirmation: extracting the placement position, volume, weight and distribution sub-area position corresponding to each cargo in the primary storage warehouse in the target area, and extracting the rated volume, rated weight and current working status of each transport robot in the primary storage warehouse, and confirming the transport robot assigned to each cargo in the primary storage warehouse.

[0024] It should be noted that the placement position, volume, weight and distribution sub-area position corresponding to each cargo in the first-level storage warehouse in the target area are extracted from the cargo management system of the first-level storage warehouse, and the rated load volume, rated load weight and current working status of each handling robot in the first-level storage warehouse are extracted from the handling robot management system of the first-level storage warehouse.

[0025] In a specific embodiment of the present invention, the specific method of confirming the corresponding assigned handling robot for each cargo in the primary storage warehouse is: A1. Compare the volume and weight corresponding to each cargo in the primary storage warehouse with the rated load volume and rated load weight of each handling robot. If the volume and weight corresponding to a certain cargo are both smaller than the rated load volume and rated load weight of a certain handling robot, then the handling robot is used as the handling robot corresponding to the cargo, thereby obtaining the handling robots corresponding to each cargo in the primary storage warehouse.

[0026] A2. Calculate the load compatibility δ between each cargo and the corresponding transportable robot ij , where i represents the number of the cargo, i=1,2,...,n, and j represents the number of the transportable robot, j=1,2,...,m.

[0027] In a specific embodiment of the present invention, the specific process of calculating the load compatibility between each cargo and the corresponding transportable robot is as follows: B1. The volume and weight corresponding to each cargo in the primary storage warehouse are respectively recorded as V i and β i .

[0028] B2. The rated load volume and rated load weight of each transportable robot corresponding to each cargo in the primary storage warehouse are recorded as and

[0029] B3. Calculate the load compatibility δ between each cargo and the corresponding transportable robot ij , Among them, △V and △β represent the volume difference and weight difference of the set reference respectively, a 1 and a 2 They respectively represent the weights of the set volume difference and weight difference corresponding to the load adaptability assessment, and e represents a natural constant.

[0030] In a specific embodiment of the present invention, a 1 The setting value is 50%, a 2 The setting value is 50%.

[0031] A3. The transportable robot with the maximum load adaptability corresponding to each cargo is recorded as the target transport robot. If the current working state of the target transport robot corresponding to a certain cargo is idle, the target transport robot is used as the transport robot assigned to the cargo. If the current working state of the target transport robot corresponding to a certain cargo is busy, the load adaptability of each transportable robot corresponding to the cargo is sorted from large to small, and the first transportable robot whose current working state is idle after sorting is used as the transport robot assigned to the cargo. In this way, the transport robots assigned to each cargo in the first-level storage warehouse are obtained.

[0032] The embodiment of the present invention determines the transport robot corresponding to each cargo by combining the compatibility between the volume and weight of the cargo and the rated load volume and rated load weight of the transport robot, thereby ensuring that the selected transport robot can meet the cargo handling requirements, improving the safety and stability of cargo handling, and avoiding waste of transport resources, thereby improving transport efficiency.

[0033] S2. Extraction of delivery information: Extract the sub-warehouse locations corresponding to each delivery sub-area in the secondary distribution point, move each cargo to the corresponding sub-warehouse location, obtain each cargo to be delivered in each delivery sub-area, extract the rated power, current remaining power, current delivery status and historical delivery information of each delivery robot in each delivery sub-area, and extract the delivery distance between each delivery sub-area and the secondary distribution point.

[0034] It should be noted that the method of transporting each cargo to the corresponding sub-warehouse location is: sending the placement location and distribution sub-area location corresponding to each cargo in the first-level storage warehouse to the corresponding assigned transport robot, and the corresponding assigned transport robot will automatically transport the cargo to the corresponding sub-warehouse location.

[0035] In a specific embodiment of the present invention, the historical delivery information includes the total delivery weight and total delivery distance corresponding to each full charge in history.

[0036] It should also be noted that the rated power, current remaining power and current delivery status of each delivery robot in each delivery sub-area are extracted from the delivery robot management system, the total delivery cargo weight and total delivery distance corresponding to each historical full charge are extracted from the delivery management system, and the delivery distance between each delivery sub-area and the secondary distribution point is extracted from the delivery management system.

[0037] S3. Delivery robot confirmation: confirm the delivery robot assigned to each to-be-delivered cargo in each delivery sub-area.

[0038] In a specific embodiment of the present invention, the specific process of confirming the distribution robot corresponding to each to-be-delivered cargo in each distribution sub-area is as follows: C1. Calculating the power required for each to-be-delivered cargo in each distribution sub-area Wherein, g represents the number of the delivery sub-area, g=1,2,...,p, and u represents the number of the goods to be delivered, u=1,2,...,z.

[0039] In a specific embodiment of the present invention, the specific process of calculating the power required for each to-be-delivered cargo in each delivery sub-area is as follows: D1. Extracting the power required for a unit delivery distance corresponding to a unit cargo weight from the database and recording it as U 0 .

[0040] D2. Extract the weight of each cargo to be delivered in each delivery sub-area from the corresponding weight of each cargo, denoted as β gu .

[0041] D3. The distribution distance between each distribution sub-area and the secondary distribution point is recorded as L g .

[0042] D4. Calculate the power required for each delivery item in each delivery sub-area

[0043] C2. Calculate the current available power of each delivery robot in each delivery sub-area Wherein, r represents the number of the delivery robot, r=1,2,...,q.

[0044] In a specific embodiment of the present invention, the specific process of calculating the current available power of each delivery robot in each delivery sub-area is as follows: E1, extracting the total delivery weight and total delivery distance corresponding to each full charge in history from the historical delivery information, and calculating the power loss abnormality index ω of each delivery robot in each delivery sub-area gr .

[0045] In a specific embodiment of the present invention, the specific process of calculating the power consumption abnormality index of each delivery robot in each delivery sub-area is as follows: F1, the total delivery cargo weight and total delivery distance corresponding to each delivery robot in each delivery sub-area after each full charge in history are recorded as β grx and L grx , where x represents the number of each full charge, x=1,2,...,y.

[0046] F2. Calculate the actual power consumption U of each delivery robot in each delivery sub-area after each full charge in history grx , U grx =U 0 *β grx *L grx .

[0047] F3. Record the rated power of each delivery robot in each delivery sub-area as

[0048] F4. Calculate the power loss abnormality index ω of each delivery robot in each delivery sub-area gr , Among them, △U 损 It represents the power loss deviation of the setting reference, and y represents the number of full charges.

[0049] E2. Extract the power loss corresponding to the abnormal power loss index per unit from the database and record it as U 损 .

[0050] E3. Record the current remaining power of each delivery robot in each delivery sub-area as

[0051] E4. Calculate the current available power of each delivery robot in each delivery sub-area

[0052] C3. Compare the power required for each cargo to be delivered in each delivery sub-area with the currently available power of each delivery robot. When the currently available power of a delivery robot is greater than the power required for a cargo to be delivered, the delivery robot will be used as the deliverable robot for the cargo to be delivered, thereby obtaining the deliverable robots corresponding to the cargo to be delivered in each delivery sub-area.

[0053] C4. Calculate the power adaptability between each to-be-delivered item in each delivery sub-area and the corresponding delivery robot ξ gus , where s represents the number of the deliverable robot, s = 1, 2, ..., w.

[0054] It should be noted that the specific process of calculating the power adaptability between each to-be-delivered cargo in each delivery sub-area and each corresponding delivery robot is as follows: the current available power of each delivery robot corresponding to each to-be-delivered cargo in each delivery sub-area is recorded as

[0055] Calculate the power adaptability between each delivery item in each delivery sub-area and the corresponding delivery robot ξ gus , Wherein, △U″ represents the available power difference of the set reference.

[0056] C5. Confirm the delivery robots assigned to the goods to be delivered in each delivery sub-area in the same manner as the confirmation method for the handling robots assigned to the goods in the primary storage warehouse.

[0057] The embodiment of the present invention analyzes whether the current available power of the delivery robot can meet the power required for the delivery of corresponding goods, and analyzes the abnormal power consumption of the delivery robot in combination with the historical delivery information of the delivery robot, and finally confirms the delivery robot corresponding to each cargo to be delivered, thereby improving the accuracy of the analysis of the current available power of the delivery robot, avoiding affecting the timely delivery of goods, reducing additional delivery costs, and meeting customers' requirements for reliability and timeliness of delivery services, thereby avoiding customer loss or increased complaints.

[0058] S4. Delivery saturation analysis: extract the number of goods to be delivered in each delivery sub-area in each historical monitoring period, the number of deliveries by each delivery robot, the delivery distance and delivery time corresponding to each delivery, analyze the delivery saturation corresponding to each delivery sub-area, and provide feedback.

[0059] It should be noted that the number of goods to be delivered in each delivery sub-area in each historical monitoring period is extracted from the delivery management system, and the number of deliveries by each delivery robot, the delivery distance and delivery time corresponding to each delivery are all extracted from the delivery robot management system.

[0060] In a specific embodiment of the present invention, the specific process of analyzing the distribution saturation corresponding to each distribution sub-area is as follows: G1. Accumulate the number of goods to be delivered in each distribution sub-area in each historical monitoring period to obtain the total number of delivered goods in each distribution sub-area, recorded as τ g .

[0061] G2. Calculate the delivery fullness θ of the delivery robots in each delivery sub-area based on the number of deliveries of each delivery robot in each historical monitoring cycle, the delivery distance and delivery time corresponding to each delivery, g .

[0062] It should be noted that the specific process of calculating the delivery fullness of the delivery robots in each delivery sub-area is as follows: the number of deliveries of each delivery robot in each delivery sub-area in each historical monitoring period is accumulated to obtain the total number of deliveries of each delivery sub-area in each historical monitoring period, and recorded as μ gf , where f represents the number of the monitoring period, f=1,2,...,l.

[0063] The delivery distance and delivery time corresponding to each delivery of each delivery robot in each delivery sub-area in each historical monitoring period are respectively accumulated twice to obtain the total delivery distance and total delivery time of each delivery sub-area in each historical monitoring period, and are recorded as and T gf .

[0064] Calculate the delivery fullness θ of the delivery robot in each delivery sub-area g , Among them, μ′, L″ and T′ represent the reference delivery times, delivery distance and delivery time, respectively. 1 , b 2 and b 3 They respectively represent the weights of the set number of deliveries, delivery distance and delivery time corresponding to the delivery fullness assessment, and l represents the number of monitoring cycles.

[0065] In a specific embodiment of the present invention, b 1 The setting value of b is 35%, 2 The setting value of b is 30%, 3 The setting value is 35%.

[0066] G3. Calculate the distribution saturation corresponding to each distribution sub-area Among them, τ′ and θ′ represent the number of delivery goods and the delivery fullness of the set reference, respectively, a 3 and a 4 They respectively represent the weights of the set number of delivered goods and the delivery fullness corresponding to the delivery saturation assessment.

[0067] In a specific embodiment of the present invention, a 3 The setting value is 50%, a 4 The setting value is 50%.

[0068] The embodiment of the present invention analyzes the delivery fullness of the delivery robot by combining the total number of delivered goods and the number of deliveries of the delivery robot, the delivery distance and delivery time corresponding to each delivery, and thus analyzes the delivery saturation corresponding to each delivery sub-area, thereby providing effective data support for decision-making, improving the accuracy and effectiveness of decision-making, and at the same time making reasonable adjustments and optimizations based on actual conditions.

[0069] Reference Figure 2 As shown, the second aspect of the present invention provides an intelligent scheduling system for multi-robot collaborative operations, including: a handling robot confirmation module, a delivery information extraction module, a delivery robot confirmation module, a database and a delivery saturation analysis module.

[0070] The handling robot confirmation module is connected to the delivery information extraction module, the delivery information extraction module is connected to the delivery robot confirmation module, the delivery robot confirmation module is connected to the database, and the delivery information extraction module is connected to the delivery saturation analysis module.

[0071] The handling robot confirmation module is used to extract the placement position, volume, weight and distribution sub-area position corresponding to each cargo in the primary storage warehouse in the target area, and to extract the rated load volume, rated load weight and current working status of each handling robot in the primary storage warehouse, and to confirm the handling robot assigned to each cargo in the primary storage warehouse.

[0072] The delivery information extraction module is used to extract the sub-warehouse positions corresponding to each delivery sub-area in the secondary distribution point, transport each cargo to the corresponding sub-warehouse position, obtain each cargo to be delivered in each delivery sub-area, extract the rated power, current remaining power, current delivery status and historical delivery information of each delivery robot in each delivery sub-area, and extract the delivery distance between each delivery sub-area and the secondary distribution point.

[0073] The delivery robot confirmation module is used to confirm the delivery robot assigned to each to-be-delivered cargo in each delivery sub-area.

[0074] The database is used to store the power required for unit delivery distance corresponding to unit cargo weight, and to store the power loss corresponding to the unit power loss abnormality index.

[0075] The data sources in the database of this embodiment are shown in Table 1 below.

[0076] Table 1

[0077]

[0078] The delivery saturation analysis module is used to extract the number of goods to be delivered in each delivery sub-area in each historical monitoring period, the number of deliveries by each delivery robot, the delivery distance and delivery time corresponding to each delivery, analyze the delivery saturation corresponding to each delivery sub-area, and provide feedback.

[0079] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. An intelligent scheduling method for multi-robot collaborative operation, characterized in that: include: S1. Confirmation of handling robots: Extract the placement, volume, weight and distribution sub-area location of each cargo in the primary storage warehouse of the target area, extract the rated volume, rated weight and current working status of each handling robot in the primary storage warehouse, and confirm the handling robot assigned to each cargo in the primary storage warehouse; Calculate the load compatibility between each cargo and the corresponding transportable robot , ,in, and Respectively represent the volume difference and weight difference of the set reference, and They represent the weights of the set volume difference and weight difference corresponding to the load adaptation evaluation. represents a natural constant; and Respectively represent the rated load volume and rated load weight of each transportable robot corresponding to each cargo; and Respectively indicate the volume and weight of each cargo; Indicates the number of the goods. , Indicates the number of the robot that can be transported. ; S2. Extraction of delivery information: Extract the sub-warehouse locations corresponding to each delivery sub-area in the secondary distribution point, move each cargo to the corresponding sub-warehouse location, obtain each cargo to be delivered in each delivery sub-area, extract the rated power, current remaining power, current delivery status and historical delivery information of each delivery robot in each delivery sub-area, and extract the delivery distance between each delivery sub-area and the secondary distribution point; S3, delivery robot confirmation: confirm the delivery robot assigned to each to-be-delivered cargo in each delivery sub-area; Extract the total weight of delivered goods and total delivery distance after each full charge in history; calculate the required power based on the unit weight and delivery distance, and calculate the power loss abnormality index through historical delivery information, and then confirm the matching of the delivery robot; S4. Delivery saturation analysis: extract the number of goods to be delivered in each delivery sub-area in each historical monitoring period, the number of deliveries by each delivery robot, the delivery distance and delivery time corresponding to each delivery, analyze the delivery saturation corresponding to each delivery sub-area, and provide feedback.

2. The intelligent scheduling method for multi-robot collaborative operation according to claim 1, characterized in that: The specific method of confirming the handling robot corresponding to each cargo in the primary storage warehouse is: A1. Compare the volume and weight of each cargo in the primary storage warehouse with the rated volume and rated weight of each transport robot. If the volume and weight of a cargo are both smaller than the rated volume and rated weight of a transport robot, then the transport robot is used as the transportable robot corresponding to the cargo. Thus, the transportable robots corresponding to the cargo in the primary storage warehouse are obtained. A2. Calculate the load compatibility between each cargo and the corresponding transportable robots ; A3. The transportable robot with the maximum load adaptability corresponding to each cargo is recorded as the target transport robot. If the current working state of the target transport robot corresponding to a certain cargo is idle, the target transport robot is used as the transport robot assigned to the cargo. If the current working state of the target transport robot corresponding to a certain cargo is busy, the load adaptability of each transportable robot corresponding to the cargo is sorted from large to small, and the first transportable robot whose current working state is idle after sorting is used as the transport robot assigned to the cargo. In this way, the transport robots assigned to each cargo in the first-level storage warehouse are obtained.

3. The intelligent scheduling method for multi-robot collaborative operation according to claim 1, characterized in that: The historical delivery information includes the total delivery weight and total delivery distance corresponding to each full charge in history.

4. The intelligent scheduling method for multi-robot collaborative operation according to claim 3 is characterized in that: The specific process of confirming the distribution robot corresponding to each to-be-delivered cargo in each distribution sub-area is as follows: C1. Calculate the power required for each delivery item in each delivery sub-area ,in, The number representing the delivery sub-area. , Indicates the number of the goods to be delivered. ; C2. Calculate the current available power of each delivery robot in each delivery sub-area ,in, Indicates the number of the delivery robot. ; C3. Compare the power required for each item to be delivered in each delivery sub-area with the current available power of each delivery robot. When the current available power of a delivery robot is greater than the power required for a item to be delivered, the delivery robot is used as the delivery robot for the item to be delivered, thereby obtaining the delivery robots corresponding to the items to be delivered in each delivery sub-area; C4. Calculate the power compatibility between each delivery item in each delivery sub-area and the corresponding delivery robot ,in, Indicates the number of the robot that can be delivered. ; C5. Confirm the delivery robots assigned to the goods to be delivered in each delivery sub-area in the same manner as the confirmation method for the handling robots assigned to the goods in the primary storage warehouse.

5. The intelligent scheduling method for multi-robot collaborative operation according to claim 4 is characterized in that: The specific process of calculating the required power for each to-be-delivered item in each delivery sub-area is as follows: D1. Extract the power required for unit delivery distance corresponding to unit cargo weight from the database and record it as ; D2. Extract the weight of each cargo to be delivered in each delivery sub-area from the corresponding weight of each cargo, and record it as ; D3. The distribution distance between each distribution sub-area and the secondary distribution point is recorded as ; D4. Calculate the power required for each delivery item in each delivery sub-area , .

6. The intelligent scheduling method for multi-robot collaborative operation according to claim 5, characterized in that: The specific process of calculating the current available power of each delivery robot in each delivery sub-area is as follows: E1. Extract the total weight of delivered goods and total delivery distance corresponding to each full charge in the history from the historical delivery information, and calculate the power loss abnormality index of each delivery robot in each delivery sub-area ; E2. Extract the power loss corresponding to the abnormal power loss index per unit from the database and record it as ; E3. Record the current remaining power of each delivery robot in each delivery sub-area as ; E4. Calculate the current available power of each delivery robot in each delivery sub-area , .

7. The intelligent scheduling method for multi-robot collaborative operation according to claim 6, characterized in that: The specific process of calculating the abnormal power consumption index of each delivery robot in each delivery sub-area is as follows: F1. The total weight of delivered goods and the total delivery distance corresponding to each delivery robot in each delivery sub-area after each full charge are recorded as and ,in, Indicates the number of each full charge. ; F2. Calculate the actual power consumption of each delivery robot in each delivery sub-area after each full charge in history , ; F3. Record the rated power of each delivery robot in each delivery sub-area as ; F4. Calculate the abnormal power consumption index of each delivery robot in each delivery sub-area , ,in, Indicates the power loss deviation of the set reference. Indicates the number of full charges.

8. The intelligent scheduling method for multi-robot collaborative operation according to claim 4, characterized in that: The specific process of analyzing the distribution saturation corresponding to each distribution sub-area is as follows: G1. Accumulate the number of goods to be delivered in each distribution sub-area in each historical monitoring period to obtain the total number of delivered goods in each distribution sub-area, recorded as ; G2. Calculate the delivery fullness of the delivery robots in each delivery sub-area based on the number of deliveries of each delivery robot in each historical monitoring cycle, the delivery distance and delivery time corresponding to each delivery, ; G3. Calculate the distribution saturation corresponding to each distribution sub-area , ,in, and They respectively represent the number of delivery goods and the delivery fullness of the set reference. and They respectively represent the weights of the set number of delivered goods and the delivery fullness corresponding to the delivery saturation assessment.

9. The intelligent scheduling method for multi-robot collaborative operation according to claim 1, characterized in that: The above-mentioned intelligent scheduling method involves an intelligent scheduling system for multi-robot collaborative operation, and the intelligent scheduling system for multi-robot collaborative operation includes: The handling robot confirmation module is used to extract the placement location, volume, weight and distribution sub-area location of each cargo in the primary storage warehouse of the target area, and extract the rated load volume, rated load weight and current working status of each handling robot in the primary storage warehouse, and confirm the handling robot corresponding to each cargo in the primary storage warehouse; The distribution information extraction module is used to extract the sub-warehouse location corresponding to each distribution sub-area in the secondary distribution point, transport each cargo to the corresponding sub-warehouse location, obtain each cargo to be distributed in each distribution sub-area, extract the rated power, current remaining power, current distribution status and historical distribution information of each distribution robot in each distribution sub-area, and extract the distribution distance between each distribution sub-area and the secondary distribution point; A delivery robot confirmation module is used to confirm the delivery robot assigned to each delivery sub-area for each cargo to be delivered; A database is used to store the power required for unit delivery distance corresponding to unit cargo weight, and the power loss corresponding to the unit power loss abnormality index; The delivery saturation analysis module is used to extract the number of goods to be delivered in each delivery sub-area in each historical monitoring period, the number of deliveries by each delivery robot, the delivery distance and delivery time corresponding to each delivery, analyze the delivery saturation corresponding to each delivery sub-area, and provide feedback.

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