Intelligent unattended warehouse management system

Abstract

The invention relates to the technical field of warehouse management, in particular to an intelligent unattended warehouse management system. The method comprises the following steps: acquiring peak trend representation degree according to the quantity change trend of warehouse-in goods and warehouse-out goods of a warehouse in a to-be-measured time period every day and the quantity difference between the warehouse-out goods and the warehouse-in goods every day; according to the interval between the warehouse-in time and the warehouse-out time of each type of goods in the to-be-measured time period, the storage periodicity is obtained, and the space utilization degree is obtained based on the relative size of the size of each type of goods and the size of the storage rack; then, the storage periodicity and the space utilization degree are adjusted through the peak trend expression degree to obtain the storage priority, the storage priority is matched with the distance between the storage rack and the warehouse entrance and exit, and the adaptive storage rack of each type of warehousing goods in the current day is obtained. According to the invention, the storage cycle and the space utilization condition of each type of goods are comprehensively considered, and the management efficiency of the warehouse in the future peak period of goods is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse management, and in particular to an intelligent unmanned warehouse management system. Background Art

[0002] In an unmanned warehouse, the entry and exit of goods will be registered and verified by radio frequency identification (RFID) technology on the labels of the goods, and the goods will be transported through automatic handling technologies such as container transfer units (CTU) and automatic guided vehicles (AGV), thereby realizing unmanned handling operations, saving manpower and material resources, and improving the transportation efficiency of goods.

[0003] Existing methods usually store goods on the warehouse's storage racks in a fixed manner according to the type and size of the goods, or directly store the goods in the vacant positions of the storage racks. However, during the peak period of goods storage and retrieval management or future peak periods, fixed storage and random storage may lead to uneven use of space. Frequently shipped goods may be stored on storage racks far away from the warehouse entrance and exit, resulting in low storage and retrieval efficiency and increased difficulty in picking up goods, causing a contradiction between the warehouse's future large storage needs, actual available space and storage efficiency, resulting in low warehouse management efficiency during future peak periods of goods. Summary of the invention

[0004] In order to solve the technical problem that fixed storage and random storage lead to poor management effect during the future peak period of goods in the warehouse, the purpose of the present invention is to provide an intelligent unattended warehouse management system, and the technical solution adopted is as follows: The present invention proposes an intelligent unmanned warehouse management system, the system comprising: The data collection module is used to obtain the warehouse entry and exit times of all goods in the test period before the current day, the size of the warehouse entry goods and the storage racks in the warehouse on the current day, and the distance between the storage racks and the warehouse entrance and exit; the goods have corresponding types; The peak trend analysis module is used to obtain the peak trend performance of the time period to be tested based on the quantity change trend of the incoming and outgoing goods in the warehouse every day during the time period to be tested, as well as the difference in the quantity of the incoming and outgoing goods every day; The management impact analysis module is used to obtain the storage cycle of each type of goods according to the time interval between the storage time and the storage time of each type of goods in the time period to be tested; and to obtain the space utilization of each type of incoming goods on the current day according to the relative size of each type of incoming goods on the current day and the size of the storage racks in the warehouse; A storage priority analysis module, used to adjust the storage period and the space utilization according to the peak trend performance, and obtain the storage priority of each type of incoming goods on the current day; The warehouse management module is used to match the storage priority and the distance between the storage racks in the warehouse and the warehouse entrance and exit to obtain the suitable storage racks for each type of incoming goods before the day.

[0005] Furthermore, obtaining the peak trend performance of the time period to be tested includes: According to the quantity change trend of incoming and outgoing goods every day during the test period, the benchmark peak trend value is obtained; According to the quantity difference between the incoming goods and the outgoing goods every day in the time period to be tested, and the discrete degree of the quantity difference, the incoming and outgoing goods update rate is obtained; The reference peak trend value is adjusted using the warehouse in / out update rate to obtain the peak trend performance of the time period to be measured.

[0006] Further, obtaining the benchmark peak trend value includes: The total number of incoming and outgoing goods per day is recorded as the daily cargo turnover; Arrange the cargo circulation of all days in the time period to be tested in time sequence to obtain a circulation sequence; obtain a first-order difference sequence of the circulation sequence, and use the mean of all elements in the first-order difference sequence as a benchmark trend value; The concentrated value of the cargo circulation volume of all days in the time period to be measured is obtained; and the benchmark peak trend value is obtained according to the benchmark trend value and the concentrated value.

[0007] Further, obtaining the update rate of inbound and outbound storage includes: The absolute value of the difference between the quantity of incoming goods and outgoing goods each day is taken as the daily inflow and outflow circulation difference; Obtaining the discrete index and the concentrated value of the inflow and outflow differences of all days in the time period to be tested; The in-and-out update rate is obtained according to the discrete index and the concentrated value; the discrete index and the concentrated value are both negatively correlated with the in-and-out update rate.

[0008] Furthermore, obtaining the storage period of each type of goods includes: The time interval between the storage time and the storage time of each product is taken as the storage time of the corresponding product; Calculate the average storage time of each type of goods in the time period to be tested as the overall storage time of each type of goods; take the ratio of the maximum value to the minimum value of the storage time of each type of goods in the time period to be tested as the time adjustment coefficient of each type of goods; The existence period of each type of goods is obtained according to the duration adjustment coefficient and the overall storage time.

[0009] Furthermore, the obtaining of the space utilization of each type of incoming goods on the current day includes: Select the storage racks in the warehouse whose size is greater than or equal to the size of each type of incoming goods on the current day, and record them as the analysis storage racks of the corresponding type of incoming goods; The maximum value of the ratio of the volume of each type of incoming goods on the current day to the volume of the analysis storage rack is normalized to obtain the space utilization of each type of incoming goods on the current day.

[0010] Furthermore, the storage priority of each type of incoming goods on the current day is obtained using the formula: ; In the formula, is the storage priority of the zth type of incoming goods on the current day; E is the peak trend performance of the time period to be tested; is the storage period of the zth type of incoming goods on the current day; is the space utilization of the zth type of incoming goods on the current day; Norm is the normalization function.

[0011] Furthermore, the storage priority and the distance between the storage rack in the warehouse and the warehouse entrance and exit are matched to obtain the adapted storage rack for each type of incoming goods before the current day, including: Normalize the distance between each storage rack and the warehouse entrance and exit to obtain the placement priority of each storage rack; Arrange the storage racks corresponding to the storage priority of each type of incoming goods in the warehouse that is less than or equal to the storage priority of each type of incoming goods on the current day in descending order of the storage priority to obtain a storage sequence of each type of incoming goods on the current day; The size of each type of incoming goods on the current day is compared with the size of the storage racks in the storage sequence in turn, and the storage rack in the storage sequence whose size is greater than or equal to the size of each type of incoming goods on the current day is used as the suitable storage rack for each type of incoming goods on the current day.

[0012] Further, the method of adjusting the reference peak trend value by using the warehouse in / out update rate to obtain the peak trend performance of the time period to be measured includes: The sum of the inventory update rate and the constant 1 is used as the trend adjustment coefficient; the benchmark peak trend value is weighted based on the trend adjustment coefficient, and the weighted result is normalized to obtain the peak trend expression degree of the time period to be measured.

[0013] Furthermore, the step of obtaining the discrete index and the concentrated value of the inflow and outflow flow difference of all days in the time period to be tested includes: The variance and mean of the in-and-out circulation difference of all days in the time period to be tested are respectively used as the discrete index and the central value of the in-and-out circulation difference.

[0014] The present invention has the following beneficial effects: In an embodiment of the present invention, in order to improve the management efficiency of the warehouse for future cargo peak periods, the future storage and access peak periods are predicted, specifically: the daily quantity change trend of incoming and outgoing cargo directly reflects the trend of the future cargo peak period, the quantity difference between outgoing cargo and incoming cargo reflects the update speed of cargo, and shows the cargo peak phenomenon, and a comprehensive analysis is performed to obtain the peak trend expression degree; different cargo demands will lead to different storage times of different types of cargo in the warehouse, and different cargoes and storage racks have different sizes, while ensuring that the cargo is completely put down, the space of the storage rack is maximized, thereby obtaining the storage period and space utilization of each type of cargo; the storage strategy of the cargo is adaptively adjusted based on the performance degree of the future cargo peak period, that is, the storage period and space utilization are adjusted using the peak trend expression degree, and the storage priority of each type of incoming cargo on the current day is obtained; the storage priority is matched with the distance between the storage rack in the warehouse and the warehouse entrance and exit, and an adapted storage rack for each type of incoming cargo is obtained. This solution uses the performance of cargo peak periods to dynamically adjust the storage cycle and space utilization, effectively balancing the relationship between the warehouse's future large storage needs, actual available space and storage efficiency. It not only optimizes the warehouse's space utilization and warehousing efficiency, but also reduces the warehouse's operating costs, improves the ability to respond to future cargo peaks, and improves the warehouse's management efficiency during future cargo peak periods. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A system structure diagram of an intelligent unmanned warehouse management system provided by an embodiment of the present invention; Figure 2 A structural diagram of a peak trend analysis module provided by an embodiment of the present invention; Figure 3A schematic diagram of computer equipment for an intelligent unmanned warehouse management device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation method, structure, features and effects of a smart unattended warehouse management system proposed by the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The specific scheme of a smart unmanned warehouse management system provided by the present invention is described in detail below with reference to the accompanying drawings.

[0020] Embodiment 1: See also Figure 1 , which shows a system block diagram of an intelligent unmanned warehouse management system provided by an embodiment of the present invention, the system includes: a data acquisition module 110, a peak trend analysis module 120, a management impact analysis module 130, a storage priority analysis module 140, and a warehouse management module 150.

[0021] The data collection module 110 is used to obtain the warehouse entry and exit times of all goods in the test time period before the current day, the size of the current day's incoming goods and the storage racks in the warehouse, and the distance between the storage racks and the warehouse entrance and exit; the goods have corresponding types.

[0022] In an unmanned warehouse, the information on the labels of goods entering and leaving the warehouse will be registered and verified through wireless radio frequency identification technology. This process ensures the record data of the entire life cycle of the goods. Goods are transported together through automatic handling technologies such as material box robot shelves and automatic guided vehicles, thereby realizing unmanned handling operations, saving manpower and material resources, and improving the transportation efficiency of goods.

[0023] Specifically, each piece of cargo has a label, which includes basic information such as cargo type and cargo name; the wireless radio frequency identification technology of the unmanned warehouse records the entry time, exit time and type of each cargo by scanning the cargo label. The entry time and exit time of all cargo in the time period to be tested before the current day are obtained, the size of the cargo entering the warehouse on the current day, and the distance between each storage rack in the warehouse and the warehouse entrance and exit and the size of the storage rack.

[0024] It should be noted that the duration of the time period to be tested is one week, which can be set by the implementer according to the specific situation; the size of the storage rack refers to the size of the storage position of the storage rack, and the size of all storage positions of the same storage rack is usually the same, and the size of the object refers to the length, width and height of the object. The storage position of the goods and the storage rack is usually a regular rectangular parallelepiped. The incoming goods and outgoing goods of each day refer to the goods that enter the warehouse on that day and the goods that leave the warehouse on that day respectively.

[0025] The peak trend analysis module 120 is used to obtain the peak trend performance of the time period to be tested according to the quantity change trend of the incoming and outgoing goods in the warehouse every day during the time period to be tested, and the quantity difference between the incoming and outgoing goods every day.

[0026] The placement of goods in the warehouse is related to the current goods in and out of the warehouse. Different types of goods have different needs in real life and industry, which leads to different storage times for different types of goods. As a result, the placement of goods in the warehouse is also closely related to the future goods in and out of the warehouse. The problem solved by this solution is: considering the sudden increase in the amount of goods in the future, while completing the storage of current goods, the impact on the transportation and storage of future goods is reduced.

[0027] Definition of the future cargo peak period: A large amount of cargo is needed in daily life and industrial scenarios, and warehouses are used for short-term storage and transfer. According to subsequent actual needs, new and old cargo are operated through inbound and outbound storage. That is, the future cargo peak period is manifested as a large amount of cargo entering and leaving the warehouse. It can be understood that when a large amount of new cargo enters the warehouse, a large amount of old cargo stored in the warehouse is also shipped out. Among them, new cargo refers to incoming cargo, and old cargo refers to outbound cargo.

[0028] Therefore, when the number of incoming goods and outgoing goods every day in the time period to be tested is closer, and the total number of incoming goods and outgoing goods every day in the time period to be tested is increasing and the total number is larger, the possibility of a peak period of goods on the current day is greater.

[0029] The quantity change trend of incoming and outgoing goods during the test period directly reflects the trend of future peak period of goods. The difference between the quantity of outgoing and incoming goods reflects the speed of updating new and old goods in the warehouse every day, showing the peak phenomenon of goods. Combining these two factors, the performance of the current day's peak period of goods is analyzed to obtain the peak trend performance degree.

[0030] See also Figure 2 , which shows a structural diagram of a peak trend analysis module provided by an embodiment of the present invention. The peak trend analysis module includes: a trend analysis unit 121, a cargo update unit 122, and a peak trend unit 123.

[0031] The trend analysis unit 121 is used to obtain a benchmark peak trend value according to the quantity change trend of incoming and outgoing goods every day in the time period to be tested.

[0032] Preferably, in some possible implementation modes of the embodiments of the present invention, the method for obtaining the benchmark peak trend value includes: recording the total number of incoming and outgoing goods every day as the daily cargo circulation volume; arranging the cargo circulation volumes of all days in the time period to be measured in time series to obtain a circulation volume sequence; obtaining a first-order difference sequence of the circulation volume sequence, and taking the mean of all elements in the first-order difference sequence as the benchmark trend value; obtaining the concentrated value of the cargo circulation volumes of all days in the time period to be measured; and obtaining the benchmark peak trend value based on the benchmark trend value and the concentrated value.

[0033] The benchmark trend value reflects the growth trend of the total number of incoming and outgoing goods every day in the time period to be tested, and the concentration value of the goods circulation represents the concentration level of the total number of incoming and outgoing goods on all days in the time period to be tested; if the benchmark trend value is greater than 0 and the larger it is, the more obvious the increasing trend of the total number of incoming and outgoing goods on all days in the time period to be tested is, and at the same time, the larger the concentration value of the goods circulation on all days in the time period to be tested is, the greater the possibility that the time period to be tested will show a future cargo peak period. Therefore, the benchmark trend value and the concentration value are positively correlated with the benchmark peak trend value.

[0034] The central value of data is used to describe the central tendency of a group of data, which can be measured by statistical indicators such as mean, median and mode. In the embodiment of the present invention, the mean is used to measure the central value of data, that is, the mean of the cargo flow volume of all days in the time period to be measured is used as the central value of the cargo flow volume; in other embodiments of the present invention, the mean can also be replaced by the median or mode. The central values ​​of other location data in this solution are all measured by the mean.

[0035] In the embodiment of the present invention, the product of the concentrated value of the cargo flow volume of all days in the time period to be measured and the reference trend value is used as the reference peak trend value. The larger the reference peak trend value, the more obvious the cargo peak period of the current day after the time period to be measured is.

[0036] In the embodiment of the present invention, the correlation between the concentrated value of the cargo circulation volume of all days in the measured time period and the benchmark trend value and the benchmark peak trend value can also be constructed through other basic mathematical operations, which are not limited or elaborated here.

[0037] The goods updating unit 122 is used to obtain the update rate of the goods entering and leaving the warehouse according to the quantity difference between the goods entering the warehouse and the goods leaving the warehouse every day in the time period to be tested, and the discrete degree of the quantity difference.

[0038] Preferably, in some possible implementation modes of the embodiments of the present invention, the method for obtaining the warehouse-in and -out update rate includes: taking the absolute value of the difference between the quantity of warehouse-in goods and warehouse-out goods each day as the daily warehouse-in circulation difference; obtaining the discrete index and the concentrated value of the warehouse-in circulation difference of all days in the test time period; and obtaining the warehouse-in and -out update rate according to the discrete index and the concentrated value.

[0039] The discrete index of data is used to describe the degree of discreteness of a set of data, which can be measured by statistical indicators such as variance, range, standard deviation and interquartile range. In the embodiment of the present invention, variance is used to measure the degree of discreteness of data, that is, the variance of the inflow and outflow difference of all days in the time period to be measured is used as the discrete index of the inflow and outflow difference; in other embodiments of the present invention, variance can also be replaced by range, standard deviation or interquartile range. In the embodiment of the present invention, the mean of the inflow and outflow difference of all days in the time period to be measured is used as the concentrated value of the inflow and outflow difference.

[0040] If the concentration value of the in-and-out circulation difference is smaller, the quantity of incoming and outgoing goods in the test time period is closer, which means that the update speed of new and old goods is more consistent every day, that is, the faster the update speed of new and old goods is, the greater the update rate of in-and-out. If the discrete index of the in-and-out circulation difference is smaller, it means that the quantity difference of incoming and outgoing goods on all days in the test time period is closer, and the concentration value of the in-and-out circulation difference can more accurately reflect the update speed of new and old goods in the test time period.

[0041] Therefore, the discrete index and the concentrated value of the in-and-out circulation difference are negatively correlated with the in-and-out update rate. In the embodiment of the present invention, the product of the discrete index and the concentrated value of the in-and-out circulation difference of all days in the time period to be measured is negatively correlated and normalized to obtain the in-and-out update rate. If the in-and-out update rate is larger, it means that the workload scheduling in the time period to be measured is larger, and the peak period of goods on the current day after the time period to be measured is more obvious.

[0042] It should be noted that in the embodiment of the present invention, the inverse of the product of the discrete index and the concentrated value is first taken, and then the inverse is used as the exponent of the exponential function with the natural constant as the base, so as to achieve negative correlation and normalization of the above product. In the embodiment of the present invention, normalization methods such as function conversion and maximum and minimum normalization can also be selected, and negative correlation methods such as taking the inverse and taking the negative number can also be selected, which are not limited here.

[0043] In the embodiment of the present invention, other basic mathematical operations can also be used to construct the correlation between the discrete index of the in-and-out circulation difference of all days in the test time period and the concentrated value and the in-and-out warehouse update rate, which is not limited or elaborated here.

[0044] The peak trend unit 123 is used to adjust the reference peak trend value by using the storage update rate to obtain the peak trend performance of the time period to be measured.

[0045] The benchmark peak trend value is adjusted using the warehouse in and out update rate so that the obtained peak trend performance can better reflect the peak performance of the goods on the current day after the time period to be tested.

[0046] In the embodiment of the present invention, the peak trend performance degree is obtained by: taking the sum of the warehouse in and out update rate and the constant 1 as the trend adjustment coefficient; weighting the benchmark peak trend value based on the trend adjustment coefficient, normalizing the weighted result, and obtaining the peak trend performance degree of the time period to be tested. The greater the peak trend performance degree, the more obvious the peak performance of the goods on the current day.

[0047] It should be noted that in the embodiment of the present invention, the Norm function is selected for normalization processing, and normalization methods such as function conversion and maximum and minimum normalization can also be selected.

[0048] The management impact analysis module 130 is used to obtain the storage period of each type of goods based on the time interval between the entry time and the exit time of each type of goods during the time period to be tested; and to obtain the space utilization of each type of incoming goods on the current day based on the relative size of each type of incoming goods on the current day and the size of the storage racks in the warehouse.

[0049] The goods stored in the warehouse will be subsequently shipped out based on demand. Different goods demands will lead to different storage times for different types of goods in the warehouse. The storage time of the same type of goods is relatively consistent. The storage cycle is obtained by analyzing the storage time of each type of goods during the test time period.

[0050] Preferably, in some possible implementation modes of the embodiments of the present invention, the method for obtaining the existence periodicity includes: taking the time interval between the storage time and the storage time of each cargo as the storage time of the corresponding cargo; calculating the mean storage time of each type of cargo in the time period to be measured as the overall storage time of each type of cargo; taking the ratio of the maximum value to the minimum value of the storage time of each type of cargo in the time period to be measured as the time adjustment coefficient of each type of cargo; and obtaining the existence periodicity of each type of cargo according to the time adjustment coefficient and the overall storage time.

[0051] It should be noted that the goods within the time period to be tested refer to the goods whose entry time and exit time are both within the time period to be tested.

[0052] In a specific implementation of the embodiment of the present invention, the existence period of each type of goods is expressed by the formula:

[0053] In the formula, is the storage period of the zth type of goods in the time period to be tested; is the storage time of the u-th item of the z-th type of goods in the time period to be tested; G is the set of goods of the z-th type in the time period to be tested; is the duration adjustment coefficient of the zth type of goods in the time period to be tested; is the overall storage time of the zth type of goods during the time period to be tested; Norm is the normalization function.

[0054] It should be noted that Reflects the overall level of storage time of the zth type of goods during the testing period; The smaller the time, the more consistent the storage time of all goods of the zth type. The more accurately the overall level representing the storage time of the zth type of goods is, The smaller the degree of adjustment required, i.e. The closer ; The larger the value, the greater the difference in storage time of all goods of type z. It is not enough to represent the overall level of storage time of type z goods. The greater the degree of adjustment required, the longer the storage period. The bigger.

[0055] After entering the warehouse, the goods are transported intelligently by machines and stored on storage racks in the warehouse. Since different goods have different sizes on different storage racks, it is necessary to fully consider the space utilization of the goods on the storage racks when placing the goods, and ensure that the space of the storage racks is maximized when the goods are completely put down.

[0056] Preferably, in some possible implementation modes of the embodiments of the present invention, the method for obtaining space utilization includes: selecting storage racks in the warehouse whose dimensions are greater than or equal to the dimensions of each type of incoming goods on the current day, and recording them as analysis storage racks for the corresponding type of incoming goods; normalizing the maximum value of the ratio of the volume of each type of incoming goods on the current day to the volume of the analysis storage rack, to obtain the space utilization of each type of incoming goods on the current day.

[0057] Only the storage positions of the storage racks whose size is greater than or equal to that of each type of incoming goods on the current day can be used to store the incoming goods of that type, and the space of the storage racks can be used. It is meaningful to analyze the space utilization of the goods on the storage racks. The analysis storage racks that are closest to the volume of each type of incoming goods are used to analyze the best space utilization of the storage racks for the incoming goods of that type, and the space utilization of each type of incoming goods on the current day is obtained.

[0058] It should be noted that in this solution, the sizes of goods of the same type are the same; the size of the storage rack is larger than the size of the goods, which means that the length, width and height of the storage position of the storage rack are larger than the corresponding length, width and height of the goods; the size of the storage rack is smaller than or equal to the size of the storage rack, and the analysis method is the same as that of the size of the storage rack is larger than the size of the storage rack; the goods entering the warehouse on the current day refer to the goods whose entry time is on the current day.

[0059] The storage priority analysis module 140 is used to adjust the storage period and space utilization according to the peak trend performance, and obtain the storage priority of each type of incoming goods on the current day.

[0060] The placement strategy of the current day's goods in this plan is to avoid possible peak cargo situations in the future. When placing goods, it is necessary to consider the storage time of the goods and the utilization rate of the goods on the storage racks, and use the current day's peak performance of goods, that is, the peak trend performance, to adjust the storage cycle and space utilization. Under the premise of a sudden increase in the amount of goods in the future, while completing the storage of the current day's goods, the impact on the transportation and storage of future goods can be reduced.

[0061] When the peak trend performance degree of the time period to be tested is greater, it means that the peak period of the goods on the current day is more obvious. In order to reduce the impact of the goods on the transportation and storage of future goods, it is necessary to place the goods with longer storage cycles on the storage racks that are more remote from the warehouse entrance and exit, so as to facilitate the transportation and storage of future goods; when the peak trend performance degree is smaller, in order to make full use of the storage rack resources on the current day, it is necessary to maximize the use of the storage rack space. Therefore, if the peak trend performance degree is greater, the storage cycle degree should be focused on; if the peak trend performance degree is smaller, the space utilization should be focused on.

[0062] In the embodiment of the present invention, the storage priority is expressed by the formula:

[0063] In the formula, is the storage priority of the zth type of incoming goods on the current day; E is the peak trend performance of the time period to be tested; is the storage period of the zth type of incoming goods on the current day; is the space utilization of the zth type of incoming goods on the current day; Norm is the normalization function. It should be noted that the storage cycle of each type of incoming goods on the current day refers to the storage cycle obtained by the time interval between the incoming time and the outgoing time of the same type of goods in the time period to be tested.

[0064] The warehouse management module 150 is used to match the storage priority and the distance between the storage racks in the warehouse and the warehouse entrance and exit to obtain the suitable storage racks for each type of incoming goods before the current day.

[0065] The distance between each storage rack in the warehouse and the warehouse entrance and exit is normalized to obtain the storage priority of each storage rack; the storage racks corresponding to the storage priority less than or equal to the storage priority of each type of incoming goods on the current day are arranged in order from large to small to obtain the storage sequence of each type of incoming goods on the current day; the size of each type of incoming goods on the current day is compared with the size of the storage racks in the storage sequence in turn, and the storage rack whose size in the storage sequence is greater than or equal to the size of each type of incoming goods on the current day for the first time is used as the adapted storage rack for each type of incoming goods on the current day.

[0066] The placement priority refers to the priority of each storage rack where goods are placed, and the storage priority refers to the priority of each type of incoming goods on the current day to be stored on the storage rack. By matching the placement priority with the storage priority, the storage rack where each type of goods is placed is determined. The specific matching process is as follows: Determine whether there is a storage priority among all the storage racks in the warehouse that is equal to the storage priority of each type of incoming goods on the current day. If so, use the storage rack corresponding to the placement priority as the adaptive storage rack for each type of incoming goods on the current day; if not, select the storage rack with a placement priority less than or equal to the storage priority of each type of incoming goods on the current day for analysis, and obtain the storage sequence of each type of incoming goods by sacrificing a small amount of transportation distance and taking into account the transportation of future goods; the storage rack whose size in the storage sequence is first greater than or equal to the size of each type of incoming goods on the current day, while ensuring that the goods can be stored intact on the storage rack, minimizes the transportation distance of future goods, and uses the storage rack as the adaptive storage rack for each type of incoming goods.

[0067] It should be noted that this solution uses the distance from each storage rack in the warehouse to the warehouse entrance and exit as the numerator and the maximum value of the distances from all storage racks in the warehouse to the warehouse entrance and exit as the denominator to obtain the ratio as the result of the normalization of the distances from the storage rack to the warehouse entrance and exit. The distances between different storage racks in the warehouse and the warehouse entrance and exit are different, that is, the placement priorities of all storage racks are not the same. If the storage priority of a certain type of goods on the current day is less than the placement priority of all storage racks, or the size of a certain type of incoming goods is greater than the size of its storage sequence, the size of a certain type of goods will be compared with the size of all storage racks in the warehouse in order from small to large placement priority, and the storage rack whose size is greater than or equal to the size of the incoming goods of this type for the first time will be used as the adapted storage rack for the certain type of goods.

[0068] Each type of goods for the current day is automatically transported and placed in the storage location of the storage rack that is suitable for that type of goods through the box robot rack and the automatic guided vehicle.

[0069] This solution uses the peak period of cargo to dynamically adjust the storage cycle and space utilization, effectively balancing the relationship between the warehouse's large storage needs in the future, the actual available space and storage efficiency. It not only optimizes the warehouse's space utilization and warehousing efficiency, but also reduces the warehouse's operating costs, improves the ability to respond to future cargo peaks, and improves the warehouse's management efficiency during future cargo peaks.

[0070] So far, the present invention is completed.

[0071] Embodiment 2: Figure 3 A computer device schematic diagram of a smart unattended warehouse management device provided by an embodiment of the present invention. For example, Figure 3 As shown, the computer device includes: a memory 201, a processor 202, and a computer program 203 stored in the memory 201 and running on the processor 202, wherein when the processor 202 executes the computer program 203, the computer device can execute any one of the intelligent unattended warehouse management systems introduced above.

[0072] In addition, an embodiment of the present application also protects a device, which may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to execute an intelligent unattended warehouse management system provided by an embodiment of the present application.

[0073] In this embodiment, the functional modules of the device can be divided according to the above method example. For example, each functional module can be corresponded, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0074] It should be understood that the device provided in this embodiment is used to execute the above-mentioned intelligent unattended warehouse management system, and thus can achieve the same effect as the above-mentioned implementation method.

[0075] In the case of an integrated unit, the device may include a processing module and a storage module. When the device is applied to a device, the processing module may be used to control and manage the actions of the device. The storage module may be used to support the device to execute mutual program codes, etc.

[0076] The processing module may be a processor or a controller, which may implement or execute various exemplary logic blocks, modules and circuits disclosed in the present application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc. The storage module may be a memory.

[0077] Embodiment 3: This embodiment also provides a computer-readable storage medium, which stores computer program code. When the computer program code runs on a computer, the computer executes the above-mentioned related method steps to implement an intelligent unmanned warehouse management system provided by the above embodiment.

[0078] Among them, the device and computer-readable storage medium provided in this embodiment are used to execute the corresponding system provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding system provided above, and will not be repeated here.

[0079] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0080] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0081] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A smart unattended warehouse management system, characterized in that: The system includes: The data collection module is used to obtain the warehouse entry and exit times of all goods in the test period before the current day, the size of the warehouse entry goods and the storage racks in the warehouse on the current day, and the distance between the storage racks and the warehouse entrance and exit; the goods have corresponding types; The peak trend analysis module is used to obtain the peak trend performance of the time period to be tested based on the quantity change trend of the incoming and outgoing goods in the warehouse every day during the time period to be tested, as well as the difference in the quantity of the incoming and outgoing goods every day; The management impact analysis module is used to obtain the storage cycle of each type of goods according to the time interval between the storage time and the storage time of each type of goods in the time period to be tested; and to obtain the space utilization of each type of incoming goods on the current day according to the relative size of each type of incoming goods on the current day and the size of the storage racks in the warehouse; A storage priority analysis module, used to adjust the storage period and the space utilization according to the peak trend performance, and obtain the storage priority of each type of incoming goods on the current day; The warehouse management module is used to match the storage priority and the distance between the storage racks in the warehouse and the warehouse entrance and exit to obtain the suitable storage racks for each type of incoming goods before the day.

2. According to claim 1, the intelligent unattended warehouse management system is characterized in that: The step of obtaining the peak trend performance of the time period to be tested includes: According to the quantity change trend of incoming and outgoing goods every day during the test period, obtain the benchmark peak trend value; According to the quantity difference between the incoming goods and the outgoing goods every day in the time period to be tested, and the discrete degree of the quantity difference, the incoming and outgoing goods update rate is obtained; The reference peak trend value is adjusted using the warehouse in / out update rate to obtain the peak trend performance of the time period to be measured.

3. According to claim 2, the intelligent unattended warehouse management system is characterized in that: The step of obtaining the benchmark peak trend value includes: The total number of incoming and outgoing goods per day is recorded as the daily cargo turnover; Arrange the cargo circulation of all days in the time period to be tested in time sequence to obtain a circulation sequence; obtain a first-order difference sequence of the circulation sequence, and use the mean of all elements in the first-order difference sequence as a benchmark trend value; The concentrated value of the cargo circulation volume of all days in the time period to be measured is obtained; and the benchmark peak trend value is obtained according to the benchmark trend value and the concentrated value.

4. The intelligent unattended warehouse management system according to claim 2 is characterized in that: The obtaining of the update rate of inbound and outbound storage includes: The absolute value of the difference between the quantity of incoming goods and outgoing goods each day is taken as the daily inflow and outflow circulation difference; Obtaining the discrete index and the concentrated value of the inflow and outflow differences of all days in the time period to be tested; The in-and-out update rate is obtained according to the discrete index and the concentrated value; the discrete index and the concentrated value are both negatively correlated with the in-and-out update rate.

5. The intelligent unattended warehouse management system according to claim 1 is characterized in that: The step of obtaining the storage period of each type of goods includes: The time interval between the storage time and the storage time of each product is taken as the storage time of the corresponding product; Calculate the average storage time of each type of goods in the time period to be tested as the overall storage time of each type of goods; take the ratio of the maximum storage time to the minimum storage time of each type of goods in the time period to be tested as the time adjustment coefficient of each type of goods; The existence period of each type of goods is obtained according to the duration adjustment coefficient and the overall storage time.

6. The intelligent unattended warehouse management system according to claim 1 is characterized in that: The obtaining of the space utilization of each type of incoming goods on the current day includes: Select the storage racks in the warehouse whose size is greater than or equal to the size of each type of incoming goods on the current day, and record them as the analysis storage racks of the corresponding type of incoming goods; The maximum value of the ratio of the volume of each type of incoming goods on the current day to the volume of the analysis storage rack is normalized to obtain the space utilization of each type of incoming goods on the current day.

7. The intelligent unattended warehouse management system according to claim 1 is characterized in that: The formula for obtaining the storage priority of each type of incoming goods on the current day is: ; In the formula, is the storage priority of the zth type of incoming goods on the current day; E is the peak trend performance of the time period to be tested; is the storage period of the zth type of incoming goods on the current day; is the space utilization of the zth type of incoming goods on the current day; Norm is the normalization function.

8. The intelligent unattended warehouse management system according to claim 1 is characterized in that: The storage priority and the distance between the storage rack in the warehouse and the warehouse entrance and exit are matched to obtain the storage rack suitable for each type of incoming goods before the current day, including: Normalize the distance between each storage rack and the warehouse entrance and exit to obtain the placement priority of each storage rack; Arrange the storage racks corresponding to the storage priority of each type of incoming goods in the warehouse that is less than or equal to the storage priority of each type of incoming goods on the current day in descending order of the storage priority to obtain a storage sequence of each type of incoming goods on the current day; The size of each type of incoming goods on the current day is compared with the size of the storage racks in the storage sequence in turn, and the storage rack in the storage sequence whose size is greater than or equal to the size of each type of incoming goods on the current day is used as the suitable storage rack for each type of incoming goods on the current day.

9. The intelligent unattended warehouse management system according to claim 2 is characterized in that: The step of adjusting the reference peak trend value by using the storage in / out update rate to obtain the peak trend performance of the time period to be tested includes: The sum of the inventory update rate and the constant 1 is used as the trend adjustment coefficient; the benchmark peak trend value is weighted based on the trend adjustment coefficient, and the weighted result is normalized to obtain the peak trend expression degree of the time period to be measured.

10. The intelligent unmanned warehouse management system according to claim 4 is characterized in that: The step of obtaining the discrete index and the concentrated value of the inflow and outflow differences of all days in the time period to be tested includes: The variance and mean of the in-and-out circulation difference of all days in the time period to be tested are respectively used as the discrete index and the central value of the in-and-out circulation difference.

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