An intelligent monitoring method and system for logistics information

Abstract

The present invention relates to the field of logistics detection technology. More specifically, the present invention relates to an intelligent monitoring method and system for logistics information, which acquires the goods on the pick-up list. For any goods, a multi-objective path planning algorithm is used to acquire a first path and a second path. Among them, the first path is planned according to the size of the storage time, and the second path is planned according to the distance of the storage location. The difference part of the grid sets corresponding to the first path and the second path is acquired as a difference path, and the priority of the nth storage location of the goods is calculated. The present invention can monitor the storage status and pick-up requirements in real time, dynamically adjust the path planning strategy to adapt to the changing storage environment, and improve the warehouse operation efficiency as much as possible while balancing the pick-up efficiency and the storage time of the goods, thereby reducing the possibility of losses caused by too long storage time of the goods.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics detection. More specifically, the present invention relates to an intelligent monitoring method and system for logistics information. Background Art

[0002] Nowadays, with the rapid development of the global economic integration process, modern warehouses, as the hubs of international trade and the nodes of multimodal transport, have become increasingly prominent in their important status. The whole-process and all-round systematic tracking management required by modern logistics activities have become the main service contents of modern warehouses. To enhance the core competitiveness of warehouses, it is necessary not only to further improve the efficiency of warehouse logistics management but also to reduce the cost of warehouse logistics management as much as possible.

[0003] In related technologies, for example, the Chinese patent application document with the publication number CN112305978A discloses a logistics transportation monitoring system based on the Internet of Things. This patent application document discloses that the main control MCU is connected to the transportation monitoring cloud platform through the GSM wireless communication module, and transmits impact event data to the transportation monitoring cloud platform, and the transportation monitoring cloud platform completes the monitoring of the equipment logistics transportation. It can monitor the equipment status during transportation, thereby providing technical support for improving the quality of equipment transportation.

[0004] However, when the current warehouse monitors logistics information, it can only monitor that the shortest path is ensured for warehouse inbound and outbound. However, ensuring the shortest path will cause some goods to accumulate. The longer the goods accumulate, the problems of expiration of the shelf life of some goods and waste of materials will occur.

[0005] Therefore, while monitoring the operation efficiency of the warehouse, the present invention monitors the storage time of goods to reduce the problem of losses caused by excessive storage time of goods. Summary of the Invention

[0006] The present invention provides an intelligent monitoring method and system for logistics information, aiming to solve the problem in related technologies that only the shortest path is ensured for warehouse inbound and outbound, but ensuring the shortest path will cause some goods to accumulate.

[0007] In the first aspect, the present invention provides an intelligent monitoring method for logistics information, including: obtaining the goods on the picking list; for any goods, using a multi-objective path planning algorithm to obtain a first path and a second path, where the first path is planned according to the size of the storage time, and the second path is planned according to the distance of the storage location; obtaining the different part of the grid sets corresponding to the first path and the second path as the different path, and calculating the priority of the nth storage location of the goods , and the calculation formula is: ; in the formula, is the shortest distance value of the grille belonging to the second path in the grille from the th storage location to the difference path, is the storage time of the th storage location, is the picking frequency of the th storage location, is an exponential function of negative correlation, is an adjustment function; the adjustment function is related to the distance between the th storage location and the historical picking path, and the number of times the goods in the corresponding storage location have not been taken out; the priority of the storage location is used to perform weighted path planning on the first path and the second path to obtain the final path.

[0008] Using the above method, according to the picking efficiency and the storage time of the goods, the priority of each storage location is calculated to obtain the final path. The final path balances the picking efficiency and the storage time of the goods when picking, and while picking with high efficiency, it avoids the losses caused by the too long storage time of some goods.

[0009] Further, the priority of the storage location is used to weight the first path and the second path to obtain the final path, including: obtaining the union of the grille sets corresponding to the first path and the second path after removing the difference path; taking the grille set with the warehouse entrance as the first-level qualified grille set, and obtaining the end point of the first-level qualified grille set according to the planning results of the first path and the second path; the grille set with the warehouse exit is the second-level qualified grille set, and the starting point of the second-level qualified grille set is obtained according to the planning results of the first path and the second path; according to the end point of the first-level qualified grille set, the starting point of the second-level qualified grille set and the priority of the corresponding points of each storage location, weighted path planning is performed to obtain a new path, and the new path is used to replace the difference path to obtain the final path.

[0010] Further, the adjustment function is related to the distance between the th storage location and the historical picking path, and the number of times the goods in the corresponding storage location have not been taken out, including: the adjustment function of the th storage location ; where is the average value of the sum of the minimum distance values between the path when picking historical goods and the th storage location, is the number of times the goods have not been taken out in the storage location, is an exponential function of negative correlation.

[0011] And setting the adjustment function is to adjust The weight in obtaining the preference level solves the problem that each calculation of the preference level is between storage time and efficiency, making it difficult to obtain the maximum value, resulting in the goods stored in the storage location with the longest storage time staying there for too long.

[0012] Further, the calculation method of the storage time of the storage location includes: obtaining the inbound time of each good entering the stored goods, calculating the difference between the inbound time of each good and the current time, selecting the time with the largest difference from them, and normalizing the time with the largest difference to obtain the storage time of the corresponding storage location.

[0013] Using the time with the largest difference as the storage time can more accurately understand the goods stored in the storage location for the longest time. In the calculation, it further improves the preference level of the storage location, thus greatly avoiding the losses caused by some goods staying in storage for too long.

[0014] Further, the calculation method of the storage time of the storage location also includes: obtaining the inbound time of each good entering the stored goods, calculating the average value of the sum of the differences between the inbound time of each good and the current time, and normalizing the average value to obtain the storage time of the corresponding storage location.

[0015] Further, for any good, using the multi-objective path planning algorithm to obtain the first path and the second path, it also includes: performing grid processing on the entire warehouse.

[0016] Performing grid processing on the warehouse facilitates the planning of the picking path and improves the planning efficiency.

[0017] Further, the number of times the goods in the storage location are not taken out includes: obtaining the average value of the number of times the goods in the storage location are not taken out and normalizing it to obtain the number of times the goods in the storage location are not taken out.

[0018] Further, the number of times the goods in the storage location are not taken out includes: calculating the sum of the number of times each good in the storage location is not taken out and normalizing it to obtain the number of times the goods in the storage location are not taken out.

[0019] Using this method, compared with the average value, the number of times the goods are not taken out in the storage location is more. Therefore, when the value of the adjustment function becomes larger, the weight occupied by the storage time is greater, the preference level of this storage location becomes larger, and the problem of goods loss caused by the goods being stacked for too long is avoided.

[0020] In the second aspect of the present invention, a logistics information intelligent monitoring system is also provided, including a processor and a memory. The memory stores a computer program, and the processor executes the computer program to implement the logistics information intelligent monitoring method described in any one of the above.

[0021] Beneficial effects: The warehousing status and picking requirements can be monitored in real time, and the path planning strategy can be dynamically adjusted to adapt to the changing warehousing environment. When balancing the picking efficiency and the warehousing time of goods, the operating efficiency of the warehouse is improved as much as possible, thereby reducing the possibility of losses caused by excessive storage time of goods. Description of the Drawings

[0022] By referring to the accompanying drawings and reading the following detailed description, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown by way of example and not limitation, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0023] Figure 1 It is a flowchart schematically showing the calculation of the priority of warehousing positions according to an embodiment of the present invention. Detailed Embodiments

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0025] In one embodiment, when the current warehouse monitors logistics information, it can only monitor the shortest path for warehouse inbound and outbound. However, ensuring the shortest path may lead to the accumulation of some goods, and the longer the goods are accumulated, the problems of expiration of the shelf life of some goods and waste of materials will occur. Therefore, the present invention monitors the warehousing time of goods while monitoring the operating efficiency of the warehouse, reducing the problem of losses caused by excessive storage time of goods.

[0026] As Figure 1 shown, the following will describe the detailed embodiments of the present invention in detail with reference to this drawing.

[0027] Step S101: Obtain the goods on the picking list.

[0028] In one embodiment, the goods on the picking list include, but are not limited to, one type of goods, and may be other types of goods. For example, the picking list may include three types of goods: electronic products, household appliances, and office supplies, or may only include one type of goods, such as electronic products. Further, the picking list may also involve more specific goods. For example, the picking list may be mobile phones, refrigerators, or printers, etc.

[0029] Step S102: Plan the picking paths respectively according to the warehousing time and the distance to obtain the first path and the second path.

[0030] In one embodiment, for any cargo, if the same storage location does not meet the requirement of picking up the same cargo and multi-location picking is needed, the multi-objective path planning algorithm can be used to obtain the first path and the second path. Among them, the first path is planned according to the size of the storage time, and the second path is planned according to the distance of the storage location. Specifically, the first path is planned in descending order of the storage time of each storage location, and the second path is planned in ascending order of the distance of the storage location.

[0031] In one embodiment, the target path planning algorithm can be a multi-objective A* path planning method, or other swarm optimization intelligent algorithms, etc. And before path planning, the warehouse needs to be gridded. Among them, the grid size can be adjusted by the implementer according to the specific implementation scenario. For example, the grid size is , etc.

[0032] And gridding is an important step in path planning, for the following reasons: The form of information processed by a computer is mainly binary, and gridding is exactly to convert environmental information into a binary form that is easy for a computer to understand and process. Specifically, gridding divides the working environment of an AGV (Automated Guided Vehicle) into unit segments and represents them with equal-sized squares (grids). This representation enables environmental information to be stored and calculated in the form of a digital matrix, facilitating subsequent path search and planning; the gridded environmental map can be searched and planned using various path planning algorithms. Since the environmental information has been simplified and discretized, the search space is limited to a finite number of grid cells, thereby improving the planning efficiency. In addition, gridding also enables the path planning algorithm to process multiple grid cells in parallel, further improving the planning speed.

[0033] It should be noted that for any cargo, picking up the cargo according to the first path can meet the requirement of first-in, first-out of the cargo. However, the distances of the storage locations of the cargo are different, so picking up the cargo according to the first path will result in a slow picking efficiency. And picking up the cargo according to the second path only considers the storage location closest to the cargo. Therefore, the picking efficiency is the highest. However, according to the method of the second path, some cargo will accumulate in the storage location for a long time, resulting in the possibility of losses. Therefore, the present invention takes into account the picking efficiency and the storage time and adopts the following steps to plan a new path.

[0034] Step S103: Obtain the different part of the grid sets corresponding to the first path and the second path as the differential path.

[0035] Specifically, obtain the grille sets corresponding to the first path and the second path, and take the difference between the two grille sets, that is, the non-overlapping part of the two grille sets. First, take the union of the two grille sets, and then each grille set excludes the intersection part of the two grille sets to obtain two complementary sets. The union of the two complementary sets can be combined into a new grille set, and the new grille set is the difference path.

[0036] After obtaining the difference path, the greater the difference between the difference paths, that is, the more grilles in the new grille set, the more the current efficiency and storage time need to be balanced. If the difference is smaller, that is, the fewer grilles in the new grille set, it means that the two paths are more similar, and the better the balance effect of the picking efficiency and storage time of the current goods.

[0037] When the balance effect of the picking efficiency and storage time is not good, it needs to be adjusted. When adjusting, it is necessary to consider the storage time of the storage location selected by the current difference path. Among them, the shorter the storage time, the more priority should be given to the grilles corresponding to the second path in the difference path; if the storage time is too long, the grilles corresponding to the first path in the difference path should be given more priority. Therefore, the present solution assigns priority values to each storage location and performs local new path planning.

[0038] Step S104: Calculate the priority of the storage location storing the goods.

[0039] In one embodiment, the priority of the storage location of the goods is calculated, and the calculation formula is: . In the formula, is the priority of the th storage location of the goods, is the th shortest distance value from the storage location to the grilles belonging to the second path in the difference path, is the th storage time of the storage location, is the th frequency of picking the goods at the storage location, exp(-) is an exponential function of negative correlation, is an adjustment function; among them, the adjustment function is related to the distance between the th storage location and the historical picking path and the number of times the goods in the corresponding storage location have not been taken out.

[0040] In the above formula, The smaller the value, the closer the storage location corresponding to the goods is to the second path with high efficiency, and the higher the priority of the storage location. And The larger the value, the longer the storage time of the goods in the current storage location may be. In order to comply with the first-in, first-out principle, The larger the value, the higher the priority of the storage location. The larger the value, the higher the picking frequency of the storage location, which means the goods may enter and leave quickly, and the residence time of the goods in the storage location is short. Therefore, the priority of the storage location is low. The smaller the value, the lower the picking frequency of the storage location, which means the storage location is not frequently picked. Therefore, the residence time of the goods in the storage location is long, and the goods in this storage location may face the problem of loss, thus increasing the priority of the storage location.

[0041] In one embodiment, the calculation method of the storage time of each storage location includes: obtaining the inbound time of each good entering the storage, calculating the difference between the inbound time of each good and the current time, selecting the time with the largest difference, and normalizing the time with the largest difference to obtain the storage time of the corresponding storage location.

[0042] In another embodiment, the calculation method of the storage time of each storage location further includes: obtaining the inbound time of each good entering the storage, calculating the average value of the sum of the differences between the inbound time of each good and the current time, and normalizing the average value to obtain the storage time of the corresponding storage location.

[0043] In one embodiment, the calculation method of the picking frequency of the storage location is as follows: counting the ratio of the number of goods taken out in the storage location to the total amount of goods stored in the storage location within a preset time. The preset time can be 2 hours, 4 hours, 5 hours, etc., which can be determined according to the actual situation. In addition, it should be noted that for the same type of goods, the total amount of goods stored in each storage location is the same.

[0044] Exemplarily, if within one hour, the number of goods taken out from the first storage location is X1 pieces and the total amount of goods in the first storage location is X2 pieces, then the frequency of the first storage location is X1 / X2. If the number of goods taken out from the second storage location is Y1 pieces and the total amount of goods in the second storage location is Y2 pieces, then the frequency of the second storage location is Y1 / Y2; if X1 / X2 is less than Y1 / Y2, it means that compared with the first storage location, the second storage location is frequently picked. Therefore, the residence time of the goods in the second storage location is short, and the residence time of the goods in the first storage location is long. So, the priority of the first storage location is increased.

[0045] Rather, in multiple pickings, each priority calculation is between storage time and efficiency, and it is difficult to obtain the maximum value, resulting in the storage location with the longest storage time having been stored for too long. Therefore, an adjustment function is set , to adjust the weight in obtaining the priority.

[0046] In one embodiment, the The calculation formula of the adjustment function for one storage location is as follows: . In the formula, is the average value of the sum of the minimum distance values between the path when taking goods historically and the th storage location, is the number of times the goods have not been taken out in the storage location.

[0047] Among them, The smaller the value of, it means that the distance from the current storage location is relatively close during each historical goods-taking process, and thus the corresponding weight should be greater. The larger the value of, it indicates that the number of times the goods in the storage location have not been taken out is relatively large, which means that the storage time of the goods in this storage location is relatively long, and thus the corresponding weight is greater.

[0048] In one embodiment, the number of times the goods in the storage location have not been taken out includes: obtaining the average value of the number of times the goods in the storage location have not been taken out, and performing normalization processing on it to obtain the number of times the goods in the storage location have not been taken out.

[0049] In another embodiment, the number of times the goods in the storage location have not been taken out includes: calculating the sum of the number of times each good in the storage location has not been taken out, and performing normalization processing on it to obtain the number of times the goods in the storage location have not been taken out. Thus, the priority of each storage location can be obtained.

[0050] Step S105: Determine the final path for taking goods according to the priority of the storage location.

[0051] In one embodiment, weighted path planning is performed on the first path and the second path by using the priority of the storage location to obtain the final path. Specifically, obtain the union of the grid sets corresponding to the first path and the second path after removing the different paths; regard the grid set with the warehouse entrance as the first qualified grid set, and obtain the end point of the first qualified grid set according to the planning results of the first path and the second path; regard the grid set with the warehouse exit as the second qualified grid set, and obtain the starting point of the second qualified grid set according to the planning results of the first path and the second path; perform weighted path planning (the multi-objective A* path planning method can be used) according to the end point of the first qualified grid set, the starting point of the second qualified grid set, and the priority of each corresponding point of the storage location to obtain a new path, and replace the different path with the new path to obtain the final path. Use the first qualified grid set and the second qualified grid set as the end point and the starting point, and at the same time consider the priority of each corresponding point of the storage location to perform weighted path planning.

[0052] According to the above steps, the warehousing status and picking requirements can be monitored in real time, and the path planning strategy can be dynamically adjusted to adapt to the changing warehousing environment. When balancing the picking efficiency and the warehousing time of goods, the operation efficiency of the warehouse is improved as much as possible, thereby reducing the possibility of losses caused by excessive storage time of goods.

[0053] The present invention also provides an intelligent logistics information monitoring system. The system includes a processor and a memory, and the memory stores computer program instructions, which when executed by the processor implement an intelligent logistics information monitoring method according to the first aspect of the present invention.

[0054] The system also includes other components well-known to those skilled in the art such as a communication bus and a communication interface, and their settings and functions are known in the art, so they will not be described herein again.

[0055] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as, for example, resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium can be part of the device or accessible or connectable to the device. Any application or module described in the present invention can be implemented using computer-readable / executable instructions that can be stored or otherwise maintained by such a computer-readable medium.

[0056] In the description of this specification, the meanings of "a plurality" and "several" are at least two, for example, two, three or more, etc., unless otherwise clearly and specifically defined.

[0057] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as falling within the scope described in this specification.

[0058] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention.

Claims

1. An intelligent monitoring method for logistics information, characterized in that Including: Obtain the goods on the picking list; For any goods, use the multi-objective path planning algorithm to obtain the first path and the second path, wherein the first path is planned according to the size of the storage time, and the second path is planned according to the distance of the storage location; Obtain the different part of the grille sets corresponding to the first path and the second path as the different path, and calculate the priority of the nth storage location of the goods. The calculation formula is: ； Wherein, is the shortest distance value of the grille belonging to the second path in the grille from the th storage location to the difference path, is the storage time of the th storage location, is the picking frequency of the th storage location, is an adjustment function, is an exponential function with negative correlation; The calculation method for the frequency of the storage location being picked includes: counting the ratio of the number of goods taken out in the storage location to the total amount of goods stored in the storage location within a preset time; The calculation method for the storage time of the storage location includes: obtaining the warehousing time when each good enters the warehouse, calculating the difference between the warehousing time of each good and the current time, selecting the time with the largest difference therefrom, and normalizing the time with the largest difference to obtain the storage time corresponding to the storage location; The adjustment function of the th storage location; where is the average value of the sum of the minimum distance values between the path when taking goods historically and the th storage location, is the number of times the goods have not been taken out in the storage location, is an exponential function with negative correlation; The number of times the goods in the storage location are not taken out includes: obtaining the average value of the number of times the goods in the storage location are not taken out, and normalizing it to obtain the number of times the goods in the storage location are not taken out; Use the priority of the storage location to perform weighted path planning on the first path and the second path to obtain the final path, including: obtaining the union of the grid sets corresponding to the first path and the second path after removing the difference path; taking the grid set with the warehouse entrance as the first-level qualified grid set, and obtaining the end point of the first-level qualified grid set according to the planning results of the first path and the second path; the grid set with the warehouse exit is the second-level qualified grid set, and obtaining the starting point of the second-level qualified grid set according to the planning results of the first path and the second path; perform weighted path planning according to the end point of the first-level qualified grid set, the starting point of the second-level qualified grid set, and the priority of each point corresponding to the storage location to obtain a new path, and replace the difference path with the new path to obtain the final path.

2. The intelligent monitoring method for logistics information according to claim 1, characterized in that The calculation method for the storage time of the storage location further includes: Obtain the warehousing time when each good enters the warehouse, calculate the average value of the sum of the differences between the warehousing time of each good and the current time, and normalize the average value to obtain the storage time corresponding to the storage location.

3. The intelligent monitoring method for logistics information according to claim 1, wherein For any goods, using the multi-objective path planning algorithm to obtain the first path and the second path further includes: Perform grid processing on the entire warehouse.

4. The intelligent monitoring method for logistics information according to claim 1, wherein The calculation method for the number of times the goods in the storage location are not taken out can also be replaced with: Calculate the sum of the number of times each good in the storage location is not taken out, and normalize it to obtain the number of times the goods in the storage location are not taken out.

5. An intelligent logistics information monitoring system, characterized in that, Including a processor and a memory, the memory stores a computer program, and the processor executes the computer program to implement the intelligent monitoring method for logistics information as described in any one of claims 1-4.

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