A living AI intelligent tallying system and method

Abstract

The present invention relates to the field of intelligent tallying technology, and specifically, to a living AI intelligent tallying system and method. It includes a priority sorting unit, a high, medium and low allocation unit, an adaptation position unit, and a shortest distance unit. The storage position module of the present invention calculates the position allocation score using the calculated position adaptability and the calculated commodity priority score, compares the position allocation scores in turn and selects the highest position allocation score, and then uses the small position corresponding to the highest position allocation score as the best storage position for the sorted high-priority commodity. By calculating the storage position adaptability of the commodity, it is ensured that the high-priority commodity is placed in the most suitable position. The storage position with high adaptability means that the commodity can be picked quickly, thereby improving the storage efficiency of the overall warehouse, and at the same time allocating high-priority commodities to the best storage position, so as to more reasonably utilize the warehouse space, reduce space waste, and improve the overall space utilization of the warehouse.

Description

Technical Field

[0001] The present invention relates to the field of intelligent tallying technology, and in particular to a living AI intelligent tallying system and method. Background Art

[0002] Living AI intelligent tallying uses artificial intelligence (AI) and computer vision technology to automatically identify, classify, inspect and manage goods in the warehouse, and uses cameras, sensors and machine learning algorithms to monitor and optimize the warehouse's inventory and goods placement in real time. When tallying a large number of differently packaged goods, due to the different types, volumes, weights, sales volumes and shelf lives of the goods, different types of goods have different priorities. If large-volume priority goods are stored in the warehouse and require specific storage conditions, the living AI cannot accurately store the priority goods in a reasonable space and the best storage location, but only arranges the goods in order of priority. This increases the waste of warehouse space and reduces the overall space utilization of the warehouse. In order to avoid the inability of living AI to accurately store priority goods in a reasonable space and the best storage location, which leads to a reduction in the overall space utilization of the warehouse, we provide a living AI intelligent tallying system and method. Summary of the invention

[0003] The purpose of the present invention is to provide a living AI intelligent tallying system and method to solve the problems raised in the above background technology.

[0004] To achieve the above-mentioned object, one of the objects of the present invention is to provide a living AI intelligent tally system, including a priority sorting unit, a high, medium and low allocation unit, an adaptation position unit and a shortest distance unit;

[0005] The priority sorting unit uses the living AI to obtain relevant commodity data and relevant warehouse information, and calculates commodity priority scores, and then classifies and sorts the commodities into high, medium, and low priority categories according to the commodity priority scores;

[0006] The high, medium and low allocation unit is used to receive data from the priority sorting unit, divide the overall capacity of the warehouse into high, medium and low frequency areas, and then allocate the classified high, medium and low priority commodities to the corresponding high, medium and low frequency areas respectively and divide them into small location areas with the same capacity;

[0007] The adaption position unit is used to receive the data in the priority sorting unit and calculate the adaptability of the small position and the high-priority commodity in the sorting, calculate the position allocation score by using the calculated position adaptability and the data in the priority sorting unit, compare the position allocation scores in turn and select the highest position allocation score, then obtain the small position corresponding to the highest position allocation score, and use the corresponding small position as the best storage position for the sorted high-priority commodity;

[0008] The shortest distance unit is used to receive the storage position in the adaptation position unit, the sorted high-priority goods and the shipping port position in the priority sorting unit and calculate the shortest distance from the sorted high-priority goods to the shipping port position;

[0009] As a further improvement of the present technical solution, the priority sorting unit includes a priority score module and a classification sorting module;

[0010] The priority score module uses live AI to obtain relevant product data and relevant warehouse information, uses linear regression to establish a priority scoring model based on the relevant product data, extracts the product's sales volume, profit, shelf life, and expiration risk data from the relevant product data, and inputs the sales volume, profit, shelf life, and expiration risk data into the priority scoring model. The priority scoring model outputs sales volume weight, profit weight, shelf life weight, and expiration risk weight data, and uses sales volume, profit, shelf life, expiration risk data and sales volume weight, profit weight, shelf life weight, and expiration risk weight data to calculate the product priority score. By comprehensively considering factors such as sales volume, profit, shelf life, and expiration risk, the importance of the product can be more accurately assessed. The inventory level of high-priority products can be reasonably controlled based on their sales data and profit contribution, reducing the risk of inventory backlog and shortage;

[0011] The classification and sorting module is used to receive the commodity priority scores in the priority score module, classify different types of commodities in the relevant commodity data into high, medium and low priority categories according to the commodity priority scores, and then sort the classified high-priority commodities, medium-priority commodities and low-priority commodities from high to low priority according to the commodity priority scores, and place high-priority commodities in more accessible locations to improve picking efficiency. Low-priority commodities can be placed in more distant locations or infrequently used areas to optimize the overall picking process.

[0012] As a further improvement of the technical solution, the implementation principle of calculating the commodity priority score in the priority score module is as follows:

[0013] Get sales ,profit , Shelf Life , Expiration risk Data and sales weight , Profit Weight , warranty period weight , Expired Risk Weight The data is used to calculate the product priority score, and the calculated product priority score is obtained. , the specific algorithm formula is:

[0014] ;

[0015] Among them, this formula is used to calculate the commodity priority score. By calculating the commodity priority score, the storage location of the commodity in the warehouse can be determined, and high-priority commodities can be placed in a location that is easier to pick, thereby improving picking efficiency and speeding up order processing.

[0016] As a further improvement of the present technical solution, the high, medium and low priority allocation unit is used to receive the high, medium and low priority goods classified in the classification and sorting module and the high, medium and low priority goods sorted, receive the relevant warehouse information and relevant product data in the priority score module, divide the overall capacity of the warehouse into high-frequency, medium-frequency and low-frequency areas on average, and allocate the classified high, medium and low priority goods to the high-frequency, medium-frequency and low-frequency areas corresponding to the warehouse respectively, and then divide the high-frequency, medium-frequency and low-frequency area capacities into small location areas with the same capacity respectively. By dividing the warehouse into areas with different frequencies, the storage location can be optimized according to the circulation frequency of the goods. High-frequency goods are stored in more easily accessible locations, medium-frequency goods are stored in suboptimal locations, and low-frequency goods are stored in less visited locations. This strategy can reduce picking time and improve overall storage and picking efficiency.

[0017] As a further improvement of the technical solution, the adaptation position unit includes an adaptation degree module and a storage position module;

[0018] The adaptability module is used to receive the relevant commodity data and relevant warehouse information in the priority score module, extract the volume, weight, small location area capacity, and small location adaptation conditions of the sorted high-priority commodities from the relevant commodity data and the relevant warehouse information, and calculate the location adaptability by using the volume, weight, small location area capacity, and small location adaptation conditions of the sorted high-priority commodities, calculate the adaptability of each small location with the sorted high-priority commodity, and consider whether the small location area capacity meets the special storage conditions. By considering the small location area capacity, it can be ensured that each commodity is stored in a suitable space to avoid space waste. In particular, for commodities with larger volumes or commodities requiring specific storage conditions, a suitable storage location can maximize space utilization and avoid empty or inappropriate space;

[0019] The storage location module is used to receive the location fitness calculated in the fitness module and the sorted high-priority goods, receive the product priority score calculated in the priority score module, and use the calculated location fitness and the calculated product priority score to calculate the location allocation score, compare the location allocation scores in turn, select the highest location allocation score, and then use the small location corresponding to the highest location allocation score as the best storage location for the sorted high-priority goods, and obtain the corresponding coordinates based on the best storage location. By calculating the storage location fitness of each product, it can be ensured that high-priority goods are placed in the most suitable location. A storage location with a high degree of fitness means that these goods can be picked and processed more quickly, thereby improving the storage efficiency of the overall warehouse. At the same time, high-priority goods are allocated to the best storage location, so that warehouse space is used more reasonably. This optimization can reduce space waste and improve the overall space utilization of the warehouse.

[0020] As a further improvement of the technical solution, the implementation principle of calculating the position adaptability in the adaptability module is as follows:

[0021] First obtain the volume of high-priority items in sorting ,weight , Small location area capacity , Small location adaptation conditions And calculate the position fitness, get the calculated position fitness , the specific algorithm formula is:

[0022] ;

[0023] in, It's a small location The capacity, and It is a high priority item in the sorting volume (in cubic meters) and weight (in kilograms), Is whether the small location j meets the special storage conditions of the sorted high-priority goods. This formula is used to calculate the location suitability. By calculating the location suitability, it can ensure that each product is allocated to a suitable storage space to avoid space waste, ensure that each area is effectively used, and improve the space utilization of the warehouse.

[0024] As a further improvement of the technical solution, the implementation principle of calculating the position allocation score in the storage position module is:

[0025] Get the calculated position fit and the calculated item priority score And calculate the location allocation score to get the location allocation score , the specific algorithm formula is:

[0026] ;

[0027] Among them, this formula is used to calculate the location allocation score. Selecting the location with the highest score means that high-priority goods will be stored closer to the picking point (such as the shipping port), which can significantly reduce the walking distance and time of the pickers and improve the picking efficiency.

[0028] As a further improvement of the technical solution, the shortest distance unit is used to receive the storage location in the storage location module, the sorted high-priority goods and the shipping port location in the priority score module, and use the Dijkstra algorithm to find the shortest distance from the sorted high-priority goods to the shipping port location according to the storage location and the shipping port location. By finding the shortest distance from the sorted high-priority goods to the shipping port location, the picking path can be optimized, and the path can be selected more efficiently, thereby reducing the picking time and reducing the overall cost of warehouse operations.

[0029] The implementation steps of using Dijkstra to find the shortest distance from the sorted high-priority goods to the shipping port location are:

[0030] Step ①: Collect storage locations, sorted high-priority products, and shipping port locations, and represent the storage location and shipping port location of each product in the sorted high-priority products as nodes. When two locations can be directly reached, add an edge, and the weight of the edge is the distance between the two locations;

[0031] Step 2: Create a distance array , used to store the shortest distance from the shipping port to each commodity storage location node. Initially, the distance from the shipping port to itself is set to 0, and the distance to all other commodity storage location nodes is set to infinity (indicating that the path has not been found yet);

[0032] Step 3: Create a collection , used to mark whether each commodity storage location node has been visited. Initially, all commodity storage location nodes are marked as unvisited;

[0033] Step ④: Add the shipping port location node to the visited set and use it as the current node. For each adjacent node of the commodity storage location node of the current node, if the distance from the current node to the adjacent node is shorter than the known distance, update the shortest distance of the adjacent node.

[0034] Step 5: Select the node with the smallest distance from the unvisited commodity storage location nodes as the next current node, and repeat the above steps until all commodity storage location nodes have been visited. The array now contains the shortest distance from the shipping port to each product location.

[0035] The second object of the present invention is to provide a method for operating a living AI intelligent tally system including any one of the above, comprising the following method steps:

[0036] S1. The priority sorting unit obtains relevant commodity data and relevant warehouse information, calculates commodity priority scores, and then classifies and sorts commodities into high, medium, and low priority categories according to the commodity priority scores, and evenly divides the overall warehouse capacity in the relevant warehouse information into high, medium, and low frequency areas, and then allocates the classified high, medium, and low priority commodities to the corresponding high, medium, and low frequency areas, respectively, and divides them into small location areas with the same capacity;

[0037] S2. The adaption position unit calculates the adaptability of the small position and the high-priority product of the sorting according to the data of the priority sorting unit, calculates the position allocation score by using the calculated position adaptability, then compares the position allocation scores in turn and selects the highest position allocation score, obtains the small position corresponding to the highest position allocation score, and uses the corresponding small position as the best storage position for the high-priority product of the sorting;

[0038] S3. The shortest distance unit calculates the shortest distance from the sorted high-priority commodity to the shipping port position according to the storage position in the adaptation position unit, the sorted high-priority commodity, and the shipping port position in the priority sorting unit.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. In the living AI intelligent tallying system and method, the storage location module is used to receive the location fitness calculated in the fitness module and the sorted high-priority goods, receive the commodity priority score calculated in the priority score module, and use the calculated location fitness and the calculated commodity priority score to calculate the location allocation score, compare the location allocation scores in turn, select the highest location allocation score, and then use the small location corresponding to the highest location allocation score as the best storage location for the sorted high-priority goods, and obtain the corresponding coordinates according to the best storage location. By calculating the storage location fitness of each commodity, it can be ensured that the high-priority goods are placed in the most suitable location and can be picked and processed more quickly, thereby improving the storage efficiency of the overall warehouse. At the same time, high-priority goods are allocated to the best storage location, so as to make more reasonable use of warehouse space, reduce space waste, and improve the overall space utilization of the warehouse.

[0041] 2. In the living AI intelligent tallying system and method, the adaptability module is used to receive the relevant commodity data and relevant warehouse information in the priority score module, and extract the volume, weight, small location area capacity, and small location adaptation conditions of the sorted high-priority commodities from the relevant commodity data and the relevant warehouse information, and use the volume, weight, small location area capacity, and small location adaptation conditions of the sorted high-priority commodities to calculate the location adaptability, calculate the adaptability of each small location with the sorted high-priority commodity, and consider whether the small location area capacity meets special storage conditions. By considering the small location area capacity, it can be ensured that each commodity is stored in a suitable space to avoid space waste. The appropriate storage location can maximize space utilization and avoid empty or inappropriate space. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is an overall block diagram of the present invention;

[0043] Figure 2 It is a block diagram of the priority sorting unit of the present invention;

[0044] Figure 3 is a block diagram of an adaptation location unit of the present invention;

[0045] Figure 4 It is a flowchart of the overall method steps of the present invention.

[0046] The meaning of each number in the figure is:

[0047] 1. Priority sorting unit; 11. Priority score module; 12. Classification sorting module;

[0048] 2. High, medium and low allocation units;

[0049] 3. Adaptation position unit; 31. Adaptation degree module; 32. Storage position module; 4. Shortest distance unit. DETAILED DESCRIPTION

[0050] The following will be combined with the accompanying drawings in 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. Example

[0051] See also Figure 1-Figure 4 As shown, one of the purposes of this embodiment is to provide a living AI intelligent tallying system, including a priority sorting unit 1, a high, medium and low allocation unit 2, an adaptation position unit 3, and a shortest distance unit 4;

[0052] Priority sorting unit 1 uses live AI to obtain relevant product data and relevant warehouse information, and calculates product priority scores, and then classifies and sorts the products into high, medium, and low priority categories according to the product priority scores;

[0053] The high, medium and low allocation unit 2 is used to receive the data from the priority sorting unit 1, divide the overall capacity of the warehouse into high, medium and low frequency areas, and then allocate the classified high, medium and low priority goods to the corresponding high, medium and low frequency areas respectively and divide them into small location areas with the same capacity;

[0054] The adaption position unit 3 is used to receive the data in the priority sorting unit 1 and calculate the adaptability of the small position and the high-priority product in the sorting, calculate the position allocation score by using the calculated position adaptability and the data in the priority sorting unit 1, compare the position allocation scores in turn and select the highest position allocation score, then obtain the small position corresponding to the highest position allocation score, and use the corresponding small position as the best storage position for the sorted high-priority product;

[0055] The shortest distance unit 4 is used to receive the storage location in the adaptation location unit 3, the sorted high-priority goods and the shipping port location in the priority sorting unit 1 and calculate the shortest distance from the sorted high-priority goods to the shipping port location.

[0056] The following is a refinement of the above units, see Figure 1-Figure 4 As shown;

[0057] The priority sorting unit 1 includes a priority score module 11 and a classification sorting module 12;

[0058] The priority score module 11 uses live AI to obtain relevant product data and relevant warehouse information, uses linear regression to establish a priority scoring model based on the relevant product data, extracts the sales volume, profit, shelf life, and expiration risk data of the product from the relevant product data, and inputs the sales volume, profit, shelf life, and expiration risk data into the priority scoring model. The priority scoring model outputs sales volume weight, profit weight, shelf life weight, and expiration risk weight data, and uses the sales volume, profit, shelf life, and expiration risk data and the sales volume weight, profit weight, shelf life weight, and expiration risk weight data to calculate the product priority score. By comprehensively considering factors such as sales volume, profit, shelf life, and expiration risk, the importance of the product can be more accurately assessed. The inventory level of high-priority products can be reasonably controlled based on their sales data and profit contribution, reducing the risk of inventory backlog and shortage;

[0059] Relevant product data includes sales volume, profit, shelf life, expiration risk, inventory, volume, and weight;

[0060] The relevant warehouse information includes the overall warehouse capacity, the small location area capacity, and the small location coordinates ( ), small location adaptation conditions (small location Whether the special storage conditions of the product are met ( Express satisfaction, Indicates that it is not satisfied)), the location of the shipping port;

[0061] The implementation principle of calculating the commodity priority score in the priority score module 11 is:

[0062] Get sales volume ,profit , Shelf Life , Expiration risk Data and sales weight , Profit Weight , warranty period weight , Expired Risk Weight The data is used to calculate the product priority score, and the calculated product priority score is obtained. , the specific algorithm formula is:

[0063] ;

[0064] Among them, this formula is used to calculate the commodity priority score. By calculating the commodity priority score, the storage location of the commodity in the warehouse can be determined, and high-priority commodities can be placed in a location that is easier to pick, thereby improving picking efficiency and speeding up order processing;

[0065] Suppose we have the following data:

[0066] Sales: 1000; Profit: $2000; Remaining shelf life: 30 days; Expiration risk: Low (set to 0.1);

[0067] And assume the weights are as follows:

[0068] Sales volume weight: 0.4; Profit weight: 0.3; Shelf life weight: 0.2; Expiration risk weight: 0.1;

[0069] The calculated item priority score is:

[0070] ;

[0071] The classification and sorting module 12 is used to receive the commodity priority scores in the priority score module 11, classify different types of commodities in the relevant commodity data into high, medium and low priority categories according to the commodity priority scores, and then sort the classified high priority commodities, medium priority commodities and low priority commodities from high to low according to the commodity priority scores, and place high priority commodities in a more accessible location to improve picking efficiency, and low priority commodities can be placed in a farther location or an infrequently used area to optimize the overall picking process;

[0072] For example:

[0073] When the product priority score is greater than or equal to the set product priority score A, the product is classified as a high-priority product, and then the high-priority products are sorted according to the product priority score;

[0074] When the product priority score is less than the set product priority score A, and the product priority score is greater than or equal to the set product priority score B, the product is classified as a medium priority product, and then the medium priority products are sorted according to the product priority score;

[0075] When the product priority score is less than the set product priority score B, the product is classified as a low-priority product, and then the low-priority products are sorted according to the product priority score;

[0076] The high, medium and low priority allocation unit 2 is used to receive the high, medium and low priority goods classified in the classification and sorting module 12 and the high, medium and low priority goods sorted, receive the relevant warehouse information and relevant product data in the priority score module 11, divide the overall capacity of the warehouse into high-frequency, medium-frequency and low-frequency areas on average, and allocate the classified high, medium and low priority goods to the high-frequency, medium-frequency and low-frequency areas corresponding to the warehouse respectively, and then divide the high-frequency, medium-frequency and low-frequency area capacities into small location areas with the same capacity respectively. By dividing the warehouse into areas with different frequencies, the storage location can be optimized according to the circulation frequency of the goods. High-frequency goods are stored in more easily accessible locations, medium-frequency goods are stored in suboptimal locations, and low-frequency goods are stored in less visited locations. This strategy can reduce picking time and improve overall storage and picking efficiency.

[0077] The adaptation location unit 3 includes an adaptation degree module 31 and a storage location module 32;

[0078] The adaptability module 31 is used to receive the relevant commodity data and relevant warehouse information in the priority score module 11, extract the volume, weight, small location area capacity, and small location adaptation conditions of the sorted high-priority commodities from the relevant commodity data and the relevant warehouse information, and calculate the location adaptability by using the volume, weight, small location area capacity, and small location adaptation conditions of the sorted high-priority commodities, calculate the adaptability of each small location with the sorted high-priority commodity, and consider whether the small location area capacity meets the special storage conditions. By considering the small location area capacity, it can be ensured that each commodity is stored in a suitable space to avoid space waste. In particular, for commodities with larger volumes or commodities requiring specific storage conditions, a suitable storage location can maximize space utilization and avoid empty or inappropriate space.

[0079] The implementation principle of calculating the position adaptability in the adaptability module 31 is as follows:

[0080] First obtain the volume of high-priority items in sorting ,weight , Small location area capacity , Small location adaptation conditions And calculate the position fitness, get the calculated position fitness , the specific algorithm formula is:

[0081] ;

[0082] in, It's a small location The capacity, and It is a high priority item in the sorting volume (in cubic meters) and weight (in kilograms), Is whether the small location j meets the special storage conditions of the sorted high-priority goods. This formula is used to calculate the location suitability. By calculating the location suitability, it can ensure that each product is allocated to a suitable storage space to avoid space waste, ensure that each area is effectively used, and improve the space utilization rate of the warehouse;

[0083] The storage location module 32 is used to receive the location fitness calculated in the fitness module 31 and the sorted high-priority goods, receive the commodity priority score calculated in the priority score module 11, and use the calculated location fitness and the calculated commodity priority score to calculate the location allocation score, compare the location allocation scores in turn, select the highest location allocation score, and then use the small location corresponding to the highest location allocation score as the best storage location for the sorted high-priority goods, and obtain the corresponding coordinates according to the best storage location. By calculating the storage location fitness of each commodity, it can be ensured that the high-priority goods are placed in the most suitable location. The storage location with high fitness means that these goods can be picked and processed more quickly, thereby improving the storage efficiency of the overall warehouse. At the same time, the high-priority goods are allocated to the best storage location, so as to make more reasonable use of the warehouse space. This optimization can reduce space waste and improve the overall space utilization of the warehouse.

[0084] The implementation principle of calculating the position allocation score in the storage position module 32 is:

[0085] Get the calculated position fit and the calculated item priority score And calculate the location allocation score to get the location allocation score , the specific algorithm formula is:

[0086] ;

[0087] Among them, this formula is used to calculate the location allocation score. Selecting the location with the highest score means that high-priority goods will be stored closer to the picking point (such as the shipping port), which can significantly reduce the walking distance and time of the pickers and improve the picking efficiency.

[0088] Example: Suppose the high frequency area is divided into three small locations:

[0089] Small location 1:

[0090] Coordinates (10,20,5); Capacity =30 (m³); Special conditions =1;

[0091] Small location 2:

[0092] Coordinates (15,20,5); Capacity =25 (m³); Special conditions =0;

[0093] Small location 3:

[0094] Coordinates (20,20,5); Capacity =20 (m³); Special conditions =1;

[0095] The highest item in the sorted high priority items :

[0096] volume =1 (m³); weight =5 (kg);

[0097] ;

[0098] Calculate the positional fitness:

[0099] Small location 1: ;

[0100] Small location 2: ;

[0101] Small location 3: ;

[0102] Calculate the location-allocation score:

[0103] Small location 1: ;

[0104] Small location 2: ;

[0105] Small location 3: ;

[0106] Calculate the position allocation score of small position 1, so the high priority items are sorted Should be assigned to small position 1, position is ( , , );

[0107] Find out the best storage location for the sorted medium priority items and the sorted low priority items. The same as the above operation method is used to replace the sorted high priority items with the sorted medium priority items or the sorted low priority items to obtain the best storage location for the sorted medium priority items and the sorted low priority items.

[0108] The shortest distance unit 4 is used to receive the storage location in the storage location module 32, the sorted high-priority goods and the shipping port location in the priority score module 11, and use the Dijkstra algorithm to find the shortest distance from the sorted high-priority goods to the shipping port location according to the storage location and the shipping port location. By finding the shortest distance from the sorted high-priority goods to the shipping port location, the picking path can be optimized, and the path can be selected more efficiently, thereby reducing the picking time and reducing the overall cost of warehouse operations.

[0109] The implementation steps of using Dijkstra to find the shortest distance from the sorted high-priority goods to the shipping port location are:

[0110] Step ①: Collect storage locations, sorted high-priority products, and shipping port locations, and represent the storage location and shipping port location of each product in the sorted high-priority products as nodes. When two locations can be directly reached, add an edge, and the weight of the edge is the distance between the two locations;

[0111] Step 2: Create a distance array , used to store the shortest distance from the shipping port to each commodity storage location node. Initially, the distance from the shipping port to itself is set to 0, and the distance to all other commodity storage location nodes is set to infinity (indicating that the path has not been found yet);

[0112] Step 3: Create a collection , used to mark whether each commodity storage location node has been visited. Initially, all commodity storage location nodes are marked as unvisited;

[0113] Step ④: Add the shipping port location node to the visited set and use it as the current node. For each adjacent node of the commodity storage location node of the current node, if the distance from the current node to the adjacent node is shorter than the known distance, update the shortest distance of the adjacent node.

[0114] Step 5: Select the node with the smallest distance from the unvisited commodity storage location nodes as the next current node, and repeat the above steps until all commodity storage location nodes have been visited. The array now contains the shortest distance from the shipping port to each product location.

[0115] Usage process:

[0116] The adaptability module 31 is used to receive the relevant commodity data and relevant warehouse information in the priority score module 11, extract the volume, weight, small location area capacity, and small location adaptation conditions of the sorted high-priority commodities from the relevant commodity data and the relevant warehouse information, and calculate the location adaptability, calculate the adaptability of each small location with the sorted high-priority commodity, and pass the calculated location adaptability to the storage location module 32. The storage location module 32 is used to receive the location adaptability calculated in the adaptability module 31 and the sorted high-priority commodities, receive the commodity priority score calculated in the priority score module 11, and use the calculated location adaptability The location allocation score is calculated based on the calculated commodity priority score, the location allocation scores are compared in turn, the highest location allocation score is selected, and the small location corresponding to the highest location allocation score is obtained, and the corresponding small location is used as the optimal storage location for the sorted high-priority commodities, and the storage location is then passed to the shortest distance unit 4. The shortest distance unit 4 is used to receive the storage location in the storage location module 32, the sorted high-priority commodities and the shipping port location in the priority score module 11, and use the Dijkstra algorithm to find the shortest distance from the sorted high-priority commodities to the shipping port location according to the storage location and the shipping port location.

[0117] The second object of the present invention is to provide a method for operating the above-mentioned living AI intelligent tally system, comprising the following method steps:

[0118] S1, the priority sorting unit 1 obtains relevant commodity data and relevant warehouse information, and calculates the commodity priority score, and then classifies and sorts the commodities into high, medium and low priority according to the commodity priority score, and divides the overall warehouse capacity in the relevant warehouse information into high, medium and low frequency areas on average, and then allocates the classified high, medium and low priority commodities to the corresponding high, medium and low frequency areas respectively and divides them into small location areas with the same capacity;

[0119] S2, the adaption position unit 3 calculates the adaptability of the small position and the high-priority product of the sorting according to the data of the priority sorting unit 1, calculates the position allocation score by using the calculated position adaptability, then compares the position allocation scores in turn and selects the highest position allocation score, and obtains the small position corresponding to the highest position allocation score, and uses the corresponding small position as the best storage position for the sorted high-priority product;

[0120] S3. The shortest distance unit 4 calculates the shortest distance from the sorted high-priority commodity to the shipping port position according to the storage position in the adaptation position unit 3, the sorted high-priority commodity and the shipping port position in the priority sorting unit 1.

[0121] The above shows and describes the basic principles, 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 are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A living AI intelligent tally system, characterized by: It includes a priority sorting unit (1), a high, medium and low allocation unit (2), an adaptation position unit (3) and a shortest distance unit (4); The priority sorting unit (1) uses the living AI to obtain relevant commodity data and relevant warehouse information, and calculates commodity priority scores, and then classifies and sorts the commodities into high, medium, and low priority categories according to the commodity priority scores; The priority sorting unit (1) comprises a priority score module (11) and a classification sorting module (12); The priority score module (11) uses live AI to obtain relevant product data and relevant warehouse information, uses linear regression to establish a priority scoring model based on the relevant product data, extracts sales volume, profit, shelf life, and expiration risk data of the product from the relevant product data and inputs them into the priority scoring model, the priority scoring model outputs weight data, and uses the sales volume, profit, shelf life, expiration risk data and weight data to calculate the product priority score; The implementation principle of calculating the commodity priority score in the priority score module (11) is as follows: Get sales ,profit , Shelf Life , Expiration risk Data and sales weight , Profit Weight , warranty period weight , Expired Risk Weight The data is used to calculate the product priority score, and the calculated product priority score is obtained. , the specific algorithm formula is: ； The classification and sorting module (12) is used to receive the commodity priority scores in the priority score module (11), classify the commodities into high, medium and low priority categories according to the commodity priority scores, and then sort the classified high priority commodities, medium priority commodities and low priority commodities from high to low priority according to the commodity priority scores; The high, medium and low priority allocation unit (2) is used to receive data from the priority sorting unit (1), divide the overall capacity of the warehouse into high, medium and low frequency areas, and then allocate the classified high, medium and low priority commodities to the corresponding high, medium and low frequency areas respectively and divide them into small location areas with the same capacity; The adaption position unit (3) is used to receive the data in the priority sorting unit (1) and calculate the position adaptability of the small position and the sorted high-priority goods, calculate the position allocation score using the calculated position adaptability and the data in the priority sorting unit (1), compare the position allocation scores in turn and select the highest position allocation score, then obtain the small position corresponding to the highest position allocation score, and use the corresponding small position as the best storage position for the sorted high-priority goods; The adaptation position unit (3) comprises an adaptation degree module (31) and a storage position module (32); The adaptability module (31) is used to receive the relevant commodity data and relevant warehouse information in the priority score module (11), extract the volume, weight, small location area capacity, and small location adaptation conditions of the sorted high-priority commodities from the relevant commodity data and the relevant warehouse information, and calculate the location adaptability, calculate the location adaptability of each small location and the sorted high-priority commodity, and consider whether the small location area capacity meets the special storage conditions; The implementation principle of calculating the position adaptability in the adaptability module (31) is: First obtain the volume of the sorted high-priority products ,weight , Small location area capacity , Small location adaptation conditions And calculate the position fitness, get the calculated position fitness , the specific algorithm formula is: ； in, is the capacity of small position j, and is the volume and weight of the sorted high-priority item i, is whether small position j meets the special storage conditions of the sorted high-priority goods; The storage location module (32) is used to receive the location fitness calculated by the fitness module (31) and the ranked high-priority products, receive the product priority score calculated by the priority score module (11), and use the calculated location fitness and the calculated product priority score to calculate the location allocation score, compare the location allocation scores in turn, select the highest location allocation score, and then obtain the small location corresponding to the highest location allocation score, use the corresponding small location as the best storage location for the ranked high-priority products, and obtain the corresponding coordinates based on the best storage location; The implementation principle of calculating the position allocation score in the storage position module (32) is: Get the calculated position fit and the calculated item priority score And calculate the location allocation score to get the location allocation score , the specific algorithm formula is: ； The shortest distance unit (4) is used to receive the storage position in the adaptation position unit (3), the sorted high-priority goods and the delivery port position in the priority sorting unit (1) and calculate the shortest distance from the sorted high-priority goods to the delivery port position.

2. The living AI intelligent tally system according to claim 1 is characterized by: The high, medium and low priority allocation unit (2) is used to receive the high, medium and low priority commodities classified in the classification and sorting module (12) and the high, medium and low priority commodities sorted, receive the relevant warehouse information and relevant commodity data in the priority score module (11), divide the overall capacity of the warehouse into high-frequency, medium-frequency and low-frequency areas on average, and allocate the classified high, medium and low priority commodities to the high-frequency, medium-frequency and low-frequency areas corresponding to the warehouse respectively, and then divide the capacity of the high-frequency, medium-frequency and low-frequency areas into small location areas with the same capacity.

3. The living AI intelligent tally system according to claim 1 is characterized by: The shortest distance unit (4) is used to receive the storage location in the storage location module (32), the sorted high-priority goods and the shipping port location in the priority score module (11), and use the Dijkstra algorithm to find the shortest distance from the sorted high-priority goods to the shipping port location based on the storage location and the shipping port location.

4. A method for operating a living AI intelligent tally system according to any one of claims 1 to 3, characterized in that: The method comprises the following steps: S1. Priority sorting unit (1) obtains relevant commodity data and relevant warehouse information, calculates commodity priority scores, and then classifies and sorts commodities into high, medium, and low priority categories according to the commodity priority scores, and evenly divides the overall warehouse capacity in the relevant warehouse information into high, medium, and low frequency areas, and then allocates the classified high, medium, and low priority commodities to the corresponding high, medium, and low frequency areas, respectively, and divides them into small location areas with the same capacity; S2, the location adaptation unit (3) calculates the degree of adaptability between the small location and the high-priority product of the sorting according to the data of the priority sorting unit (1), calculates the location allocation score using the calculated location adaptability, then compares the location allocation scores in turn and selects the highest location allocation score, obtains the small location corresponding to the highest location allocation score, and uses the corresponding small location as the best storage location for the high-priority product of the sorting; S3, the shortest distance unit (4) calculates the shortest distance from the sorted high-priority goods to the shipping port location according to the storage location in the adaptation location unit (3), the sorted high-priority goods and the shipping port location in the priority sorting unit (1).

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