Smart warehouse management method based on big data

Abstract

The invention discloses a smart warehouse management method based on big data. The method comprises the following steps of 1, calibrating warehousing position coordinates in a warehousing shelf area within a radius set value based on a warehousing cargo accumulation area as a circle center; step 2, the control equipment detects the edge of the target cargo by using an edge operator, and the target cargo selected in advance is used as a comparison sample to carry out positioning identification confirmation on the target cargo by matching with the sample; 3, after target goods are determined based on the attribute identifiers, all paths from the target goods to the warehousing goods shelf are calculated according to stacking nodes formed by the current coordinate identifier of the target goods and the storage goods stacking area; and 4, selecting an optimal delivery path from all the paths according to set condition requirements, thereby not only improving the accuracy of the positioning information and the identification information in the identification process, but also reducing identification errors and planning errors caused by cargo accumulation and blocking due to positioning errors to a great extent.

Description

Technical Field

[0001] The present invention relates to the technical field of smart warehouse management, and specifically to a smart warehouse management method based on big data. Background Art

[0002] The automated warehousing system is a system that can automatically store goods by sending control commands during the operation process without direct human intervention. It can store the corresponding goods based on the various automated control devices at any position in the storage lanes, and use a variety of sensors to collect data, predict the operating status, and respond to various fault alarms. However, when encountering a large number of goods suddenly influx under certain circumstances, resulting in a large increase and accumulation of stored goods, the partially coordinated control devices of the automated warehousing system cannot work according to the established procedures in a short period of time. It is necessary to manually adjust the accumulated goods while letting each control device operate. However, this will cause the position and operation path of the target goods identified by each control device to be blocked and reflected in the storage and accumulation environment during the identification process. The signal reaching the control device may not be a direct signal, resulting in errors in the propagation distance, causing positioning errors, and forming identification errors and planning errors caused by the accumulation of goods. When the goods that are identified incorrectly are delivered to the corresponding shelves for storage, errors occur, and manual correction and rework are required. Therefore, it is necessary to design a smart warehouse management method based on big data to improve the accuracy and automation of recognition calculations. Summary of the invention

[0003] The purpose of the present invention is to provide a smart warehouse management method based on big data to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a smart warehouse management system based on big data, including a coordinate value determination module, a delivery goods information identification module, and a delivery path calculation and planning module, characterized in that: the coordinate value determination module is used to determine the coordinate values ​​of different positions based on the storage goods stacking area; the delivery goods information identification module is used to confirm the positioning identification of the target goods by matching with a pre-selected comparison sample; the delivery path calculation and planning module is used to calculate the delivery path of the target goods to the storage shelf, and the coordinate value determination module, the delivery goods information identification module, and the delivery path calculation and planning module are interconnected by a network.

[0005] According to the above technical solution, the coordinate value determination module includes: a circle center demarcation module, a radius value setting module, and a coordinate calibration module. The circle center demarcation module is used to determine the center of the circle based on the area where the stored goods are piled up; the radius value setting module is used to select the distance from the shelf to the center of the circle corresponding to the maximum distance, and set the radius of the control device for delivery for the center of the area where the stored goods are piled up; the coordinate calibration module is used to calibrate the storage position coordinates of the storage shelf area within the radius setting value of the area where the stored goods are piled up based on the radius, and the circle center demarcation module, the radius value setting module, and the coordinate calibration module are interconnected in a network.

[0006] According to the above technical solution, the delivery cargo information identification module includes: a cargo information data setting module, an attribute identification association module, a cargo identification matching module, and a cargo coordinate identification determination module. The cargo information data setting module is used to set the cargo information data type of the area where the warehouse cargo is piled up; the attribute identification association module is used to identify and associate the attribute identification of the cargo with the attribute identification of the corresponding distribution control device and the shelf attribute identification; the cargo identification matching module is used to match the target cargo by adopting a local feature unchanged method and perform a first feature match on the target cargo; the cargo coordinate identification determination module is used to determine the coordinate identification of the target cargo according to the edge distance, and the cargo information data setting module, the attribute identification association module, the cargo identification matching module, and the cargo coordinate identification determination module are interconnected in a network.

[0007] According to the above technical solution, the delivery path calculation planning module includes: an absolute path sum calculation module, a path collection module, a node connection search module, and an optimal path iterative update module. The absolute path sum calculation module is used to calculate the sum of the absolute paths from the initial starting point to the storage position coordinates in the horizontal movement; the path collection module is used to form a path collection with the acquired paths that can be from the initial node to the terminal node; the node connection search module is used to search based on the connection between the initial node and the terminal node; the optimal path iterative update module is used to update and iterate the optimal path among all the paths according to the set conditions, and the absolute path sum calculation module, the path collection module, the node connection search module, and the optimal path iterative update module are interconnected in a network.

[0008] According to the above technical solution, the smart warehouse management method based on big data includes the following steps:

[0009] Step 1: Calibrate the storage position coordinates based on the storage goods accumulation area as the center of the circle and the storage shelf area within the radius setting value;

[0010] Step 2: The control device detects the edge of the target goods using an edge operator, takes the pre-selected target goods as a comparison sample, and confirms the location and identification of the target goods by matching with the sample;

[0011] Step 3: After the target goods are determined based on the attribute identifiers, all paths from the target goods to the storage shelves are calculated according to the current coordinate identifiers of the target goods and the accumulation nodes formed by the storage goods accumulation area;

[0012] Step 4: Select the optimal delivery route from all routes according to the set conditions.

[0013] According to the above technical solution, the method for determining the radius value based on the warehouse goods accumulation area as the center of the circle includes:

[0014] Step 11: Determine the center of the circle based on the area where the stored goods are piled up, and calculate the overall delivery time of the delivery control device according to the distance from each shelf to the center of the circle and the weight of the goods stored on the shelf to the speed of the delivery device;

[0015] Step 12: By setting an optimal delivery time threshold based on the input cost of the control device, the calculated overall delivery time of the control device is compared and screened according to the threshold;

[0016] Step 13: The shelves corresponding to the delivery time exceeding the set optimal delivery time threshold are eliminated, and the shelves corresponding to the delivery time equal to or less than the optimal delivery time threshold are retained, and the shelves corresponding to the reserved delivery time are sorted from large to small according to the distance from the center of the circle;

[0017] Step 14: Select the distance from the shelf to the center of the circle corresponding to the maximum distance as the center of the area where the stored goods are accumulated to set the radius of the control device for delivery, and based on this radius, calibrate the storage position coordinates of the storage shelf area within the radius setting value of the area where the stored goods are accumulated.

[0018] According to the above technical solution, the method for locating and confirming the target goods by matching with the sample includes:

[0019] Step 21: The data type of the cargo information for the storage cargo accumulation area includes: cargo number, cargo type, and attribute identification of cargo distribution shelf coordinate information. The attribute identification of the cargo is associated with the attribute identification of the corresponding distribution control device and the shelf attribute identification. When any one of the attribute identifications is identified, the attribute identification information data of the other two are obtained synchronously.

[0020] Step 22: The control device identifies the delivered goods, uses the edge operator to detect the edge of the target goods piled up in the warehouse goods, uses the pre-selected target goods as a template, locates the target goods by matching with the template, matches the target goods by adopting the method of keeping the local features unchanged, performs the first feature matching on the target goods, and calculates the similarity between the target goods and the target goods template;

[0021] Step 23: Perform a second match on the result obtained in the first step, identify the regional edge information data corresponding to the target goods under the warehouse goods pile, calculate the edge distance, and determine the coordinate identification of the target goods based on the edge distance.

[0022] According to the above technical solution, the method steps for calculating all paths from the target goods to the storage shelves include the following process:

[0023] Step 31: Based on the determined current position coordinates of the target goods, the initial starting point (x0, y0) is identified, and based on the calibrated storage position coordinates (x1, y1) is the end point. The distribution space of the target goods is a horizontal movement space, so the sum S of the absolute paths from the initial starting point to the storage position coordinates in the horizontal movement is calculated, that is, S=n*|x0-x1|+m*|y0-y1|, where n and m are balance constants for weight setting of the balance horizontal and vertical coordinate values, and n+m=1, x0 is the horizontal coordinate value of the target goods in the storage goods accumulation area at the initial starting point, y0 is the vertical coordinate value of the target goods in the storage goods accumulation area at the initial starting point, x1 is the horizontal coordinate value of the storage position coordinates calibrated in the storage goods accumulation area, and y1 is the vertical coordinate value of the storage position coordinates calibrated in the storage goods accumulation area;

[0024] Step 32: Setting the influencing factors based on the calculated path, that is, the speed of the dispatched control device will be reduced to the set turning threshold speed when turning, and the optimal speed defined based on the delivery of goods during the straight-line operation, and the influence of the goods dispatched by the control device on the speed;

[0025] Step 33: Based on the path formed from the coordinates of the initial starting point to the coordinates of the storage location, the coordinates of the initial starting point and the coordinates of the storage location are regarded as nodes, and all paths formed from the initial node to the terminal node are calculated, that is, the paths obtained from the initial node to the terminal node are formed into a path set.

[0026] According to the above technical solution, the calculation of all paths formed from the initial node to the terminal node in step 33 includes: setting the travel logic of the control device operation path, defining the travel direction of the delivery point as from the initial node to the terminal node, and from the terminal node to the initial node when returning. The travel logic between the control devices is stored in the attribute identifier. When two control devices interact, they will identify each other's attribute identifiers. In the travel path, the control devices will interact with each other. In the case of mutual interaction, it is determined whether to give way to the other party. When giving way is required, based on the terminal node, travel direction and path planning coordinate node of the interacting control device, stay away from the nearest path point in the other party's travel direction during the interaction, and at the same time ensure that there is no travel problem in the own travel path after giving way. When a travel problem occurs, retreat to the second closest path node during the interaction, and determine whether the conditions are met;

[0027] The search is performed based on the connection between the initial node and the terminal node. The nodes that are searched for interaction from the initial node to the terminal node are connected one by one. The connection length function L from the initial node to the terminal node is calculated. The feasibility degree K of the search direction of the path is calculated using the sum S of the absolute paths from the initial starting point to the storage position coordinates in the horizontal motion and the connection length function L from the initial node to the terminal node, that is, K = (L + S) * θ, where θ is the degree coefficient. When (L + S) changes due to the change of L caused by the continuous traversal of the passing nodes, when K increases with the increase of (L + S), the current path traversal range is reduced. When K increases with the decrease of (L + S), the current path traversal range is increased. Based on this, all paths formed from the initial node to the terminal node are searched, and all paths constitute a path set.

[0028] According to the above technical solution, the method for the control device for calculating each delivery to select the optimal delivery path from all paths according to the set conditions is: set the conditions that the optimal delivery path needs to meet, that is, the delivery time and the delivery path are the shortest, or the delivery cost is the least, calculate the path from the initial node to the terminal node according to the condition, and add the calculation result to the path set corresponding to the condition, and sort and output the paths iteratively calculated according to the conditions that meet the conditions in the path set, and compare the paths that meet the set conditions with each other, that is, after each calculation iteration, compare the optimal paths of the current path and the existing paths, when the optimal path of the current calculation iteration is better than the optimal path obtained before, the current path is judged to be the optimal path, the previously saved optimal path is updated, and the parameters associated with the current optimal path are updated, and the loop iteration is performed until the set number of iterations is completed. When all paths are updated and iterated according to the set conditions, the calculation iteration of the optimal path from the initial node to the terminal node is completed.

[0029] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention, by providing a coordinate value determination module, a delivery goods information identification module, and a delivery path calculation and planning module, calibrates the storage position coordinates based on the storage goods stacking area as the center to the storage shelf area within the radius setting value; the control device detects the edge of the target goods using an edge operator, takes the pre-selected target goods as a comparison sample, and confirms the positioning identification of the target goods by matching with the sample, and then after determining the target goods based on each attribute identification, calculates all paths from the target goods to the storage shelf according to the current coordinate identification of the target goods and the stacking node formed by the storage goods stacking area; finally, the optimal delivery path is selected from all the paths according to the set conditions, which not only improves the accuracy of the positioning information and identification information in the identification process, but also greatly reduces the identification errors and planning errors caused by the accumulation of goods due to positioning errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0031] Figure 1 A schematic diagram of the module composition of a smart warehouse management system based on big data provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0032] 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.

[0033] Embodiment 1: This embodiment can be applied to the scenario of goods delivery management by automated warehouse control equipment. The method can be executed by a smart warehouse management system based on big data provided by this embodiment. The method specifically includes the following steps:

[0034] Step 1: Calibrate the storage position coordinates based on the storage goods accumulation area as the center of the circle and the storage shelf area within the radius setting value;

[0035] Step 2: The control device detects the edge of the target goods using an edge operator, takes the pre-selected target goods as a comparison sample, and confirms the location and identification of the target goods by matching with the sample;

[0036] Step 3: After the target goods are determined based on the attribute identifiers, all paths from the target goods to the storage shelves are calculated according to the current coordinate identifiers of the target goods and the accumulation nodes formed by the storage goods accumulation area;

[0037] Step 4: Select the optimal delivery route from all routes according to the set conditions.

[0038] In an embodiment of the present invention, a method for determining a radius value based on a warehouse cargo accumulation area as a circle center includes:

[0039] Step 11: Determine the center of the circle based on the area where the stored goods are piled up, and calculate the overall delivery time of the delivery control device according to the distance from each shelf to the center of the circle and the weight of the goods stored on the shelf to the speed of the delivery device;

[0040] Step 12: By setting an optimal delivery time threshold based on the input cost of the control device, the calculated overall delivery time of the control device is compared and screened according to the threshold;

[0041] Step 13: The shelves corresponding to the delivery time exceeding the set optimal delivery time threshold are eliminated, and the shelves corresponding to the delivery time equal to or less than the optimal delivery time threshold are retained, and the shelves corresponding to the reserved delivery time are sorted from large to small according to the distance from the center of the circle;

[0042] Step 14: Select the distance from the shelf to the center of the circle corresponding to the maximum distance as the center of the area where the stored goods are accumulated to set the radius of the control device for delivery, and based on this radius, calibrate the storage position coordinates of the storage shelf area within the radius setting value of the area where the stored goods are accumulated.

[0043] In an embodiment of the present invention, a method for locating and confirming target goods by matching with a sample includes:

[0044] Step 21: The cargo information data setting for the area where the stored cargo is piled up includes: cargo number, cargo type, and attribute identification of cargo distribution shelf coordinate information. The attribute identification of the cargo is associated with the attribute identification of the corresponding distribution control device and the attribute identification of the shelf. When any one of the attribute identifications is identified, the attribute identification information data of the other two are obtained synchronously;

[0045] Step 22: The control device identifies the delivered goods, uses the edge operator to detect the edge of the target goods piled up in the warehouse goods, uses the pre-selected target goods as a template, locates the target goods by matching with the template, matches the target goods by adopting the method of keeping the local features unchanged, performs the first feature matching on the target goods, and calculates the similarity between the target goods and the target goods template;

[0046] Step 23: Perform a second match on the result obtained in the first step, identify the regional edge information data corresponding to the target goods under the warehouse goods pile, calculate the edge distance, and determine the coordinate identification of the target goods based on the edge distance.

[0047] In an embodiment of the present invention, the method steps for calculating all paths from target goods to storage shelves include the following process:

[0048] Step 31: Based on the determined current position coordinates of the target goods, the initial starting point (x0, y0) is identified, and based on the calibrated storage position coordinates (x1, y1) is the end point. The distribution space of the target goods is a horizontal movement space, so the sum S of the absolute paths from the initial starting point to the storage position coordinates in the horizontal movement is calculated, that is, S=n*|x0-x1|+m*|y0-y1|, where n and m are balance constants for weight setting of the balance horizontal and vertical coordinate values, and n+m=1, x0 is the horizontal coordinate value of the target goods in the storage goods accumulation area at the initial starting point, y0 is the vertical coordinate value of the target goods in the storage goods accumulation area at the initial starting point, x1 is the horizontal coordinate value of the storage position coordinates calibrated in the storage goods accumulation area, and y1 is the vertical coordinate value of the storage position coordinates calibrated in the storage goods accumulation area;

[0049] Step 32: Setting the influencing factors based on the calculated path, that is, the speed of the dispatched control device will be reduced to the set turning threshold speed when turning, and the optimal speed defined based on the delivery of goods during the straight-line operation, and the influence of the goods dispatched by the control device on the speed;

[0050] Step 33: Based on the path formed from the coordinates of the initial starting point to the coordinates of the storage location, the coordinates of the initial starting point and the coordinates of the storage location are regarded as nodes, and all paths formed from the initial node to the terminal node are calculated, that is, the paths obtained from the initial node to the terminal node are formed into a path set.

[0051] Exemplarily, calculating all paths formed from the initial node to the terminal node includes: setting the travel logic of the control device operation path, defining the travel direction of the delivery point as from the initial node to the terminal node, and from the terminal node to the initial node when returning, the travel logic between the control devices is stored in the attribute identifier, and the two control devices will identify each other's attribute identifier when interacting, and the control devices will interact with each other in the travel path. In the case of mutual interaction, it is determined whether to give way to the other party. When giving way is required, based on the terminal node, travel direction and path planning coordinate node of the interacting control device, stay away from the nearest path point in the other party's travel direction when interacting, and at the same time ensure that there is no travel problem on the own travel path after giving way. When a travel problem occurs, retreat to the second closest path node during the interaction, and determine whether the conditions are met;

[0052] Furthermore, the search is performed based on the connection between the initial node and the terminal node, and the interactive nodes from the initial node to the terminal node are used to connect successively, and the connection length function L from the initial node to the terminal node is calculated. The feasibility degree K of the search direction of the path is calculated using the sum S of the absolute path from the initial starting point to the storage position coordinates in the horizontal movement and the connection length function L from the initial node to the terminal node, that is, K = (L+S)*θ, θ is the degree coefficient, when (L+S) changes due to the change of L caused by the continuous traversal of the passing nodes, when K increases with the increase of (L+S), the current path traversal range is reduced, and when K increases with the decrease of (L+S), the current path traversal range is increased, based on this, all paths formed from the initial node to the terminal node are searched, and all paths constitute a path set.

[0053] In an embodiment of the present invention, a method for calculating the control device for each delivery to select the optimal delivery path from all paths according to set conditions is as follows: set the conditions that the optimal delivery path needs to meet, that is, the delivery time and the delivery path are the shortest, or the delivery cost is the least, calculate the path from the initial node to the terminal node according to the condition, and add the calculation result to the path set corresponding to the condition, and sort and output the paths iteratively calculated according to the conditions that meet the conditions in the path set, and compare the paths that meet the set conditions with each other, that is, after each calculation iteration, compare the optimal paths of the current path and the existing paths, when the optimal path of the current calculation iteration is better than the optimal path obtained before, the current path is judged to be the optimal path, the previously saved optimal path is updated, and the parameters associated with the current optimal path are updated, and the loop iteration is performed until the set number of iterations is completed. When all paths are updated and iterated according to the set conditions, the calculation iteration of the optimal path from the initial node to the terminal node is terminated.

[0054] Embodiment 2: Embodiment 2 of the present invention provides a smart warehouse management system based on big data for executing a smart warehouse management method based on big data. Figure 1 A schematic diagram of the module composition of a smart warehouse management system based on big data provided in the second embodiment of the present invention, such as Figure 1 As shown, the system includes:

[0055] A coordinate value determination module, used to determine the coordinate values ​​of different locations based on the storage goods accumulation area;

[0056] A delivery cargo information identification module is used to identify and locate the target cargo by matching it with a pre-selected comparison sample;

[0057] The delivery path calculation and planning module is used to calculate the delivery path from the target goods to the storage shelf.

[0058] In some embodiments of the present invention, the coordinate value determination module includes:

[0059] The circle center determination module is used to determine the circle center based on the area where the stored goods are piled up;

[0060] The radius value setting module is used to select the distance from the shelf to the center of the circle corresponding to the maximum distance, and set the radius of the control device for delivery for the center of the area where the stored goods are piled;

[0061] The coordinate calibration module is used to calibrate the storage position coordinates of the storage shelf area within the set radius value of the storage goods accumulation area based on the radius.

[0062] In some embodiments of the present invention, the delivery cargo information identification module includes:

[0063] A cargo information data setting module is used to set the cargo information data type of the storage cargo accumulation area;

[0064] An attribute identification association module is used to associate the attribute identification of the goods with the attribute identification of the corresponding distribution control device and the attribute identification of the shelf;

[0065] The cargo identification and matching module is used to match the target cargo by adopting the method of keeping the local features unchanged and to perform the first feature matching on the target cargo;

[0066] The cargo coordinate identification determination module is used to determine the coordinate identification of the target cargo according to the edge distance.

[0067] In some embodiments of the present invention, the delivery path calculation planning module includes:

[0068] The absolute path sum calculation module is used to calculate the absolute path sum of the coordinates from the initial starting point to the storage position in the horizontal motion;

[0069] A path collection module is used to form a path collection from the acquired paths from the initial node to the terminal node;

[0070] A node connection search module, used to search based on the connection between the initial node and the terminal node;

[0071] The optimal path iterative update module is used to iterate and update the optimal path among all paths according to the set conditions.

[0072] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0073] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A smart warehouse management system based on big data, including a coordinate value determination module, a delivery goods information identification module, and a delivery path calculation and planning module, characterized in that: The coordinate value determination module is used to determine the coordinate values ​​of different positions based on the storage goods stacking area; the delivery goods information identification module is used to confirm the positioning identification of the target goods by matching with the pre-selected comparison samples; the delivery path calculation planning module is used to calculate the delivery path of the target goods to the storage shelf. The coordinate value determination module, the delivery goods information identification module, and the delivery path calculation planning module are interconnected in a network.

2. According to the big data-based smart warehouse management system of claim 1, it is characterized by: The coordinate value determination module includes: a circle center delineation module, a radius value setting module, and a coordinate calibration module. The circle center delineation module is used to determine the circle center based on the area where the stored goods are piled up; the radius value setting module is used to select the distance from the shelf to the circle center corresponding to the maximum distance, and set the radius for the control device to perform delivery for the circle center of the area where the stored goods are piled up; the coordinate calibration module is used to calibrate the storage position coordinates of the storage shelf area within the radius setting value of the area where the stored goods are piled up based on the radius, and the circle center delineation module, the radius value setting module, and the coordinate calibration module are interconnected in a network.

3. According to claim 2, a smart warehouse management system based on big data is characterized in that: The delivery cargo information identification module includes: a cargo information data setting module, an attribute identification association module, a cargo identification matching module, and a cargo coordinate identification determination module. The cargo information data setting module is used to set the cargo information data type of the area where the warehouse cargo is piled up; the attribute identification association module is used to identify and associate the attribute identification of the cargo with the attribute identification of the corresponding distribution control device and the shelf attribute identification; the cargo identification matching module is used to match the target cargo by adopting a method of keeping local features unchanged, and perform a first feature match on the target cargo; the cargo coordinate identification determination module is used to determine the coordinate identification of the target cargo according to the edge distance, and the cargo information data setting module, the attribute identification association module, the cargo identification matching module, and the cargo coordinate identification determination module are interconnected in a network.

4. The smart warehouse management system based on big data according to claim 3 is characterized by: The delivery path calculation planning module includes: an absolute path sum calculation module, a path collection module, a node connection search module, and an optimal path iterative update module. The absolute path sum calculation module is used to calculate the sum of the absolute paths from the initial starting point to the storage position coordinates in the horizontal movement; the path collection module is used to form a path collection with the acquired paths that can be from the initial node to the terminal node; the node connection search module is used to search based on the connection between the initial node and the terminal node; the optimal path iterative update module is used to update and iterate the optimal path among all the paths according to the set conditions. The absolute path sum calculation module, the path collection module, the node connection search module, and the optimal path iterative update module are interconnected in a network.

5. According to the smart warehouse management system based on big data of claim 4, a smart warehouse management method based on big data is implemented, characterized in that: The method comprises the following steps: Step 1: Calibrate the storage position coordinates based on the storage goods accumulation area as the center of the circle and the storage shelf area within the radius setting value; Step 2: The control device detects the edge of the target goods using an edge operator, takes the pre-selected target goods as a comparison sample, and confirms the location and identification of the target goods by matching with the sample; Step 3: After the target goods are determined based on the attribute identifiers, all paths from the target goods to the storage shelves are calculated according to the current coordinate identifiers of the target goods and the accumulation nodes formed by the storage goods accumulation area; Step 4: Select the optimal delivery route from all routes according to the set conditions.

6. The smart warehouse management method based on big data according to claim 5 is characterized by: The method for determining the radius value based on the warehouse goods accumulation area as the center of the circle includes: Step 11: Determine the center of the circle based on the area where the stored goods are piled up, and calculate the overall delivery time of the delivery control device according to the distance from each shelf to the center of the circle and the weight of the goods stored on the shelf to the speed of the delivery device; Step 12: By setting an optimal delivery time threshold based on the input cost of the control device, the calculated overall delivery time of the control device is compared and screened according to the threshold; Step 13: The shelves corresponding to the delivery time exceeding the set optimal delivery time threshold are eliminated, and the shelves corresponding to the delivery time equal to or less than the optimal delivery time threshold are retained, and the shelves corresponding to the reserved delivery time are sorted from large to small according to the distance from the center of the circle; Step 14: Select the distance from the shelf to the center of the circle corresponding to the maximum distance as the center of the area where the stored goods are accumulated to set the radius of the control device for delivery, and based on this radius, calibrate the storage position coordinates of the storage shelf area within the radius setting value of the area where the stored goods are accumulated.

7. The smart warehouse management method based on big data according to claim 6 is characterized by: The method for locating and confirming the target goods by matching with the sample includes: Step 21: The data type of the cargo information for the storage cargo accumulation area includes: cargo number, cargo type, and attribute identification of cargo distribution shelf coordinate information. The attribute identification of the cargo is associated with the attribute identification of the corresponding distribution control device and the shelf attribute identification. When any one of the attribute identifications is identified, the attribute identification information data of the other two are obtained synchronously. Step 22: The control device identifies the delivered goods, uses the edge operator to detect the edge of the target goods piled up in the warehouse goods, uses the pre-selected target goods as a template, locates the target goods by matching with the template, matches the target goods by adopting the method of keeping the local features unchanged, performs the first feature matching on the target goods, and calculates the similarity between the target goods and the target goods template; Step 23: Perform a second match on the result obtained in the first step, identify the regional edge information data corresponding to the target goods under the warehouse goods pile, calculate the edge distance, and determine the coordinate identification of the target goods based on the edge distance.

8. The smart warehouse management method based on big data according to claim 7 is characterized by: The method steps for calculating all paths from the target goods to the storage shelves include the following process: Step 31: Based on the determined current position coordinates of the target goods, the initial starting point (x0, y0) is identified, and based on the calibrated storage position coordinates (x1, y1) is the end point. The distribution space of the target goods is a horizontal movement space, so the sum S of the absolute paths from the initial starting point to the storage position coordinates in the horizontal movement is calculated, that is, S=n*|x0-x1|+m*|y0-y1|, where n and m are balance constants for weight setting of the balance horizontal and vertical coordinate values, and n+m=1, x0 is the horizontal coordinate value of the target goods in the storage goods accumulation area at the initial starting point, y0 is the vertical coordinate value of the target goods in the storage goods accumulation area at the initial starting point, x1 is the horizontal coordinate value of the storage position coordinates calibrated in the storage goods accumulation area, and y1 is the vertical coordinate value of the storage position coordinates calibrated in the storage goods accumulation area; Step 32: Setting the influencing factors based on the calculated path, that is, the speed of the dispatched control device will be reduced to the set turning threshold speed when turning, and the optimal speed defined based on the delivery of goods during the straight-line operation, and the influence of the goods dispatched by the control device on the speed; Step 33: Based on the path formed from the coordinates of the initial starting point to the coordinates of the storage location, the coordinates of the initial starting point and the coordinates of the storage location are regarded as nodes, and all paths formed from the initial node to the terminal node are calculated, that is, the paths obtained from the initial node to the terminal node are formed into a path set.

9. The smart warehouse management method based on big data according to claim 8 is characterized by: The calculation of all paths formed from the initial node to the terminal node in step 33 includes: setting the travel logic of the control device operation path, defining the travel direction of the delivery point as from the initial node to the terminal node, and from the terminal node to the initial node when returning, the travel logic between the control devices is stored in the attribute identifier, and the two control devices will identify each other's attribute identifier when interacting, and the control devices will interact with each other in the travel path. In the case of mutual interaction, it is determined whether to give way to the other party. When giving way is required, based on the terminal node, travel direction and path planning coordinate node of the interacting control device, it is away from the nearest path point in the direction of interaction of the other party, and at the same time, it is ensured that there is no travel problem in the own travel path after giving way. When a travel problem occurs, retreat to the second closest path node during interaction, and determine whether the conditions are met; The search is performed based on the connection between the initial node and the terminal node. The nodes that are searched for interaction from the initial node to the terminal node are connected one by one. The connection length function L from the initial node to the terminal node is calculated. The feasibility degree K of the search direction of the path is calculated using the sum S of the absolute paths from the initial starting point to the storage position coordinates in the horizontal motion and the connection length function L from the initial node to the terminal node, that is, K = (L + S) * θ, where θ is the degree coefficient. When (L + S) changes due to the change of L caused by the continuous traversal of the passing nodes, when K increases with the increase of (L + S), the current path traversal range is reduced. When K increases with the decrease of (L + S), the current path traversal range is increased. Based on this, all paths formed from the initial node to the terminal node are searched, and all paths constitute a path set.

10. The smart warehouse management method based on big data according to claim 9 is characterized in that: The method for the control device for calculating each delivery to select the optimal delivery path from all paths according to the set conditions is as follows: set the conditions that the optimal delivery path needs to meet, that is, the delivery time and the delivery path are the shortest, or the delivery cost is the least, calculate the path from the initial node to the terminal node according to the condition, and add the calculation result to the path set corresponding to the condition, and sort and output the paths iteratively calculated according to the conditions that meet the conditions in the path set, and compare the paths that meet the set conditions with each other, that is, after each calculation iteration, compare the optimal paths of the current path and the existing paths, when the optimal path of the current calculation iteration is better than the optimal path obtained before, the current path is judged to be the optimal path, the previously saved optimal path is updated, and the parameters associated with the current optimal path are updated, and the loop iteration is performed until the set number of iterations is completed. When all paths are updated and iterated according to the set conditions, the calculation iteration of the optimal path from the initial node to the terminal node is completed.

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