An intelligent unattended warehouse management system

Abstract

The present invention relates to the field of warehouse management technology, and specifically to an intelligent unmanned warehouse management system. The present invention obtains the peak trend performance degree according to the quantity change trend of incoming and outgoing goods in the warehouse every day during the time period to be tested, and the quantity difference between outgoing goods and incoming goods every day; obtains the storage cycle degree according to the interval between the incoming time and the outgoing time of each type of goods in the time period to be tested, and obtains the space utilization degree based on the relative size of the size of each type of goods and the size of the storage rack; then uses the peak trend performance degree to adjust the storage cycle degree and the space utilization degree to obtain the storage priority, and matches it with the distance between the storage rack and the warehouse entrance and exit to obtain the adaptive storage rack for each type of incoming goods on the current day. The present invention comprehensively considers the storage cycle and space utilization of each type of goods to improve the management efficiency of the warehouse during the future peak period of goods.

Description

Technical Field

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

[0002] In an unmanned warehouse, the inbound and outbound of goods will register and verify the information on the labels of goods through Radio Frequency Identification (RFID) technology, and transport the goods through automatic handling technologies such as container transfering unit (CTU) and automated guided vehicle (AGV), so as to achieve unmanned handling operations, save manpower and material resources, and improve the transport efficiency of goods.

[0003] Existing methods usually store goods on the storage shelves in the warehouse in a fixed manner according to the types, sizes, etc. of the goods, or directly store the goods at the idle positions on the storage shelves. However, during the peak period of goods access management or in the future peak period, fixed storage and random storage may lead to uneven space utilization. Goods with high-frequency shipments may be stored on storage shelves far from the warehouse entrance and exit, resulting in low access efficiency of goods and increased difficulty in picking up goods, making the warehouse have a contradiction among a large amount of future storage requirements, actual available space and storage efficiency, and leading to low management efficiency during the future peak period of goods in the warehouse. Summary of the Invention

[0004] In order to solve the technical problem that fixed storage and random storage lead to low management effect during the future peak period of goods in the warehouse, the purpose of the present invention is to provide an intelligent unmanned warehouse management system, and the specific technical solutions adopted are as follows:

[0005] The present invention provides an intelligent unmanned warehouse management system, and the system includes:

[0006] A data acquisition module, configured to obtain the inbound time and outbound time of all goods in the warehouse during a to-be-measured time period before the current day, the inbound goods on the current day and the sizes of the storage shelves in the warehouse, and the distances between the storage shelves and the warehouse entrance and exit; the goods have corresponding types;

[0007] A peak trend analysis module, configured to obtain the peak trend performance degree of the to-be-measured time period according to the quantity change trends of the inbound goods and outbound goods in the warehouse every day during the to-be-measured time period, and the quantity difference between the inbound goods and outbound goods every day;

[0008] A management impact analysis module, which is used to obtain the storage cycle degree of each type of goods according to the time interval between the inbound time and the outbound time of each type of goods during the period to be measured; and obtain the space utilization degree of each type of inbound goods on the current day according to the relative size between the size of each type of inbound goods on the current day and the size of the storage racks in the warehouse.

[0009] A storage priority analysis module, which is used to adjust the storage cycle degree and the space utilization degree according to the peak trend performance degree, and obtain the storage priority degree of each type of inbound goods on the current day.

[0010] A warehouse management module, which is used to match the storage priority degree with the distance between the storage racks in the warehouse and the warehouse entrance and exit, and obtain the adapted storage racks for each type of inbound goods before the current day.

[0011] Further, the obtaining of the peak trend performance degree of the period to be measured includes:

[0012] Obtaining a reference peak trend value according to the quantity change trend of the inbound goods and the outbound goods every day during the period to be measured.

[0013] Obtaining an inbound and outbound update rate according to the quantity difference between the inbound goods and the outbound goods every day during the period to be measured and the dispersion degree of the quantity difference.

[0014] Adjusting the reference peak trend value by using the inbound and outbound update rate to obtain the peak trend performance degree of the period to be measured.

[0015] Further, the obtaining of the reference peak trend value includes:

[0016] Recording the total quantity of the inbound goods and the outbound goods every day as the daily goods flux.

[0017] Arranging the daily goods fluxes of all days during the period to be measured in time sequence to obtain a flux sequence; obtaining the first-order difference sequence of the flux sequence, and taking the mean value of all elements in the first-order difference sequence as the reference trend value.

[0018] Obtaining the concentration value of the daily goods fluxes of all days during the period to be measured; and obtaining the reference peak trend value according to the reference trend value and the concentration value.

[0019] Further, the obtaining of the inbound and outbound update rate includes:

[0020] Taking the absolute value of the difference between the quantity of the inbound goods and the quantity of the outbound goods every day as the daily inbound and outbound difference.

[0021] Obtaining the dispersion index and the concentration value of the daily inbound and outbound differences of all days during the period to be measured.

[0022] Obtain the inbound and outbound update rate according to the discrete index and the centralized value; both the discrete index and the centralized value have a negative correlation with the inbound and outbound update rate.

[0023] Further, the obtaining of the storage cycle degree of each type of goods includes:

[0024] Take the time interval between the inbound time and the outbound time of each goods as the storage duration of the corresponding goods;

[0025] Calculate the mean value of the storage duration of each type of goods within the time period to be measured as the overall storage time of each type of goods; take the ratio of the maximum value to the minimum value of the storage duration of each type of goods within the time period to be measured as the duration adjustment coefficient of each type of goods;

[0026] Obtain the existence cycle degree of each type of goods according to the duration adjustment coefficient and the overall storage time.

[0027] Further, the obtaining of the space utilization degree of each type of inbound goods on the current day includes:

[0028] Select the storage racks in the warehouse whose sizes are greater than or equal to the sizes of each type of inbound goods on the current day as the analysis storage racks for the corresponding type of inbound goods;

[0029] Normalize the maximum value among the ratios of the volume of each type of inbound goods on the current day to the volume of the analysis storage racks to obtain the space utilization degree of each type of inbound goods on the current day.

[0030] Further, the obtaining of the storage priority of each type of inbound goods on the current day is expressed by the formula:

[0031] ; where is the storage priority of the z-th type of inbound goods on the current day; E is the peak trend performance degree of the time period to be measured; is the storage cycle degree of the z-th type of inbound goods on the current day; is the space utilization degree of the z-th type of inbound goods on the current day; Norm is the normalization function.

[0032] Further, the matching of the storage priority with the distance between the storage racks in the warehouse and the warehouse entrance and exit to obtain the adapted storage racks for each type of inbound goods before the current day includes:

[0033] Normalize the distance between each storage rack in the warehouse and the warehouse entrance and exit to obtain the storage priority of each storage rack;

[0034] Arrange the storage racks corresponding to the storage priorities of each type of incoming goods in the warehouse that are less than or equal to the current day in descending order according to the storage priorities, to obtain the storage sequence of each type of incoming goods for the current day;

[0035] Compare the sizes of each type of incoming goods for the current day with the sizes of the storage racks in the storage sequence in turn, and take the storage rack whose size is initially greater than or equal to the size of each type of incoming goods for the current day in the storage sequence as the suitable storage rack for each type of incoming goods for the current day.

[0036] Further, the adjusting the reference peak trend value by using the incoming and outgoing update rate to obtain the peak trend performance degree of the to-be-detected time period includes:

[0037] Take the sum value of the incoming and outgoing update rate and the constant 1 as the trend adjustment coefficient; perform a weighting process on the reference peak trend value based on the trend adjustment coefficient, and perform a normalization process on the weighting result to obtain the peak trend performance degree of the to-be-detected time period.

[0038] Further, the obtaining the discrete index and the central value of the incoming and outgoing circulation difference for all days within the to-be-detected time period includes:

[0039] Respectively take the variance and the mean value of the incoming and outgoing circulation difference for all days within the to-be-detected time period as the discrete index and the central value of the incoming and outgoing circulation difference in turn.

[0040] The present invention has the following beneficial effects:

[0041] In the embodiments of the present invention, in order to improve the management efficiency of the warehouse during the future peak period of goods, the future access peak period is predicted. Specifically: the changing trends of the quantity of incoming goods and outgoing goods every day intuitively reflect the trend of the future peak period of goods. The quantity difference between the outgoing goods and the incoming goods reflects the update speed of the goods, showing the peak phenomenon of goods. Through comprehensive analysis, the peak trend performance degree is obtained; due to different goods demands, there are differences in the storage time of different types of goods in the warehouse, and different goods and storage racks have different sizes. While ensuring that the goods can be completely placed, the space of the storage rack is maximally utilized, so as to obtain the storage cycle degree and space utilization degree of each type of goods; based on the performance degree of the future peak period of goods, the storage strategy of the goods is adaptively adjusted, that is, the peak trend performance degree is used to adjust the storage cycle degree and space utilization degree, and the storage priority of each type of incoming goods 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 the suitable storage rack for each type of incoming goods is obtained. This solution dynamically adjusts the storage cycle degree and space utilization degree according to the performance degree of the peak period of goods, effectively balancing the relationship between the large storage demand, the actual available space and the storage efficiency in the future of the warehouse. It not only optimizes the space utilization and the incoming and outgoing efficiency of the warehouse, but also can reduce the operation cost of the warehouse, improve the response ability to the future peak of goods, and improve the management efficiency of the warehouse during the future peak period of goods. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0043] Figure 1 It is a system structure diagram of a smart unmanned warehouse management system provided by an embodiment of the present invention;

[0044] Figure 2 It is a structure diagram of a peak trend analysis module provided by an embodiment of the present invention;

[0045] Figure 3 It is a schematic diagram of a computer device of a smart unmanned warehouse management device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation manner, structure, features, and effects of a smart unattended warehouse management system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0048] The following specifically describes the specific solution of a smart unattended warehouse management system provided by the present invention in conjunction with the accompanying drawings.

[0049] Embodiment 1:

[0050] Please refer to Figure 1 , which shows a system block diagram of a smart unattended 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.

[0051] The data acquisition module 110 is used to obtain the inbound time and outbound time of all goods within a measurement period before the current day, the inbound goods on the current day and the dimensions of the storage racks in the warehouse, and the distance between the storage racks and the warehouse entrance and exit; the goods have corresponding types.

[0052] In an unattended warehouse, the inbound and outbound of goods will register and verify the information on the labels of the goods through radio frequency identification technology, which ensures the recorded data of the entire life cycle of the goods; automatic handling technologies such as bin robots, shelves, and automated guided vehicles are used to jointly transport the goods, thereby realizing unmanned handling operations, saving manpower and material resources, and improving the transportation efficiency of the goods.

[0053] Specifically, each piece of goods has a label, and the label includes basic information such as the type and name of the goods; the radio frequency identification technology of the unattended warehouse records the inbound time, outbound time, and type of each piece of goods by scanning the label of the goods. Obtain the inbound time and outbound time of all goods within a measurement period before the current day, the dimensions of the inbound goods on the current day, and the distance and dimensions of each storage rack in the warehouse from the warehouse entrance and exit.

[0054] It should be noted that the duration of the time period to be measured is one week, and the implementer can set it according to specific circumstances; the size of the storage rack refers to the size of the storage positions on the storage rack, and the sizes of all storage positions on the same storage rack are usually the same. The size of an object refers to its length, width, and height. The goods and the storage positions on the storage rack are usually regular cuboids. The incoming goods and outgoing goods each day respectively refer to the goods with an incoming time on that day and the goods with an outgoing time on that day.

[0055] The peak trend analysis module 120 is used to obtain the peak trend performance degree of the time period to be measured based on the quantity change trends of the incoming goods and outgoing goods in the warehouse each day during the time period to be measured, as well as the quantity difference between the incoming goods and outgoing goods each day.

[0056] The placement of goods in the warehouse is related to the current incoming and outgoing operations of goods. Due to the different demands of different types of goods in real life and industry, etc., there are differences in the placement time of different types of goods in the warehouse, which further makes the placement of goods in the warehouse closely related to the incoming and outgoing of future goods. The problem solved by this solution is: on the premise of considering a sudden increase in the future quantity of goods, while completing the storage of current goods, reducing the impact on the transportation and storage of future goods.

[0057] Definition of the future goods peak period: In life and industrial scenarios, a large amount of goods are needed, and they are stored and transferred temporarily through the warehouse. According to the subsequent actual demand, the new and old goods are transported through incoming and outgoing operations. That is, the future goods peak period is manifested as a large amount of goods entering and leaving the warehouse. It can be understood that when a large amount of new goods enter the warehouse, a large amount of old goods stored in the warehouse also leave the warehouse. Among them, new goods refer to incoming goods, and old goods refer to outgoing goods.

[0058] Therefore, the closer the quantities of the incoming goods and outgoing goods each day during the time period to be measured are, and at the same time, the total quantity of the incoming goods and outgoing goods each day during the time period to be measured shows an increasing trend and the total quantity is larger, the greater the possibility of a goods peak period occurring on the current day.

[0059] The quantity change trends of the incoming goods and outgoing goods each day during the time period to be measured intuitively reflect the trend status of the future goods peak period; the quantity difference between the outgoing goods and incoming goods each day reflects the update speed of the new and old goods in the warehouse every day, showing the goods peak phenomenon. By combining these two factors to analyze the performance of the goods peak period on the current day, the peak trend performance degree is obtained.

[0060] Please refer to Figure 2 , which shows the 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 goods update unit 122, and a peak trend unit 123.

[0061] Trend analysis unit 121: It is used to obtain a reference peak trend value according to the change trend of the quantity of incoming and outgoing goods every day within the time period to be measured.

[0062] Preferably, in some possible implementation manners of the embodiments of the present invention, the method for obtaining the reference peak trend value includes: recording the total quantity of incoming and outgoing goods every day as the daily goods throughput; arranging the daily goods throughputs of all days within the time period to be measured in chronological order to obtain a throughput sequence; obtaining the first-order difference sequence of the throughput sequence, and taking the mean value of all elements in the first-order difference sequence as the reference trend value; obtaining the central value of the daily goods throughputs of all days within the time period to be measured; and obtaining the reference peak trend value according to the reference trend value and the central value.

[0063] The reference trend value reflects the growth trend of the total quantity of incoming and outgoing goods every day within the time period to be measured, and the central value of the goods throughput represents the central level of the total quantity of incoming and outgoing goods of all days within the time period to be measured; if the reference trend value is greater than 0 and the larger it is, the more obvious the increasing trend of the total quantity of incoming and outgoing goods every day within the time period to be measured, and at the same time, the larger the central value of the daily goods throughputs of all days within the time period to be measured, the greater the possibility that the time period to be measured shows a future goods peak period. Therefore, both the reference trend value and the central value are positively correlated with the reference peak trend value.

[0064] The central value of data is used to describe the central tendency of a set of data, and can be measured by statistical indicators such as mean, median and mode. In the embodiments of the present invention, the mean is selected to measure the central value of data, that is, the mean value of the daily goods throughputs of all days within the time period to be measured is used as the central value of the goods throughput; in other embodiments of the present invention, the mean can also be replaced by the median or the mode. The central values of data in other positions in this solution are all measured by the mean.

[0065] In the embodiments of the present invention, the product of the central value of the daily goods throughputs of all days within the time period to be measured and the reference trend value is used as the reference peak trend value. If the reference peak trend value is larger, the goods peak period of the current day after the time period to be measured is more obvious.

[0066] In the embodiments of the present invention, the correlation relationship between the central value of the daily goods throughputs of all days within the time period to be measured, the reference trend value and the reference peak trend value can also be constructed through other basic mathematical operations, which will not be limited and elaborated here.

[0067] Goods update unit 122: It is used to obtain the incoming and outgoing update rate according to the quantity difference between the incoming and outgoing goods every day within the time period to be measured and the degree of dispersion of the quantity difference.

[0068] Preferably, in some possible implementation manners of the embodiments of the present invention, the method for obtaining the inbound and outbound update rate includes: taking the absolute value of the difference between the quantity of inbound goods and the quantity of outbound goods every day as the daily inbound and outbound circulation difference; obtaining the discrete index and the central value of the inbound and outbound circulation differences for all days within the to-be-detected time period; and obtaining the inbound and outbound update rate according to the discrete index and the central value.

[0069] The discrete index of data is used to describe the dispersion degree of a set of data, and can be measured by statistical indexes such as variance, range, standard deviation, and quartile difference. In the embodiments of the present invention, the variance is selected to measure the dispersion degree of data, that is, the variance of the inbound and outbound circulation differences for all days within the to-be-detected time period is taken as the discrete index of the inbound and outbound circulation differences; in other embodiments of the present invention, the variance can also be replaced by the range, standard deviation, or quartile difference. In the embodiments of the present invention, the mean value of the inbound and outbound circulation differences for all days within the to-be-detected time period is taken as the central value of the inbound and outbound circulation differences.

[0070] If the central value of the inbound and outbound circulation difference is smaller, the quantity of inbound goods and the quantity of outbound goods every day within the to-be-detected time period are closer, indicating that the update speed of new and old goods every day is more consistent, that is, the update speed of new and old goods every day is faster, then the inbound and outbound update rate is larger. If the discrete index of the inbound and outbound circulation difference is smaller, it indicates that the difference between the quantity of inbound goods and the quantity of outbound goods for all days within the to-be-detected time period is closer, and the central value of the inbound and outbound circulation difference can more accurately reflect the update speed of new and old goods within the to-be-detected time period.

[0071] Therefore, both the discrete index and the central value of the inbound and outbound circulation difference are negatively correlated with the inbound and outbound update rate. In the embodiments of the present invention, the product of the discrete index and the central value of the inbound and outbound circulation differences for all days within the to-be-detected time period is negatively correlated and normalized to obtain the inbound and outbound update rate. If the inbound and outbound update rate is larger, it means that the workload scheduling within the to-be-detected time period is larger, and the peak performance of goods on the current day after the to-be-detected time period is more obvious.

[0072] It should be noted that in the embodiments of the present invention, first take the opposite number of the product of the discrete index and the central value, and then use this opposite number as the exponent of the exponential function with the natural constant as the base to realize the negative correlation and normalization processing of the above product. In the embodiments of the present invention, normalization methods such as function transformation and maximum-minimum normalization can also be selected, and negative correlation methods such as taking the reciprocal and taking the negative number are not limited herein.

[0073] In the embodiments of the present invention, the correlation relationship between the discrete index and the central value of the inbound and outbound circulation differences for all days within the to-be-detected time period and the inbound and outbound update rate can also be constructed through other basic mathematical operations, which are not limited and elaborated herein.

[0074] Peak trend unit 123: used to adjust the reference peak trend value by using the inbound and outbound update rate to obtain the peak trend performance degree of the to-be-detected time period.

[0075] Adjust the reference peak trend value by using the inbound and outbound update rate, so that the obtained peak trend performance can better reflect the peak performance of goods on the current day after the period to be measured.

[0076] In the embodiment of the present invention, the method for obtaining the peak trend performance is as follows: Take the sum value of the inbound and outbound update rate and the constant 1 as the trend adjustment coefficient; perform weighted processing on the reference peak trend value based on the trend adjustment coefficient, and perform normalization processing on the weighted result to obtain the peak trend performance of the period to be measured. The greater the peak trend performance, the more obvious the peak performance of goods on the current day.

[0077] It should be noted that in the embodiment of the present invention, the Norm function is selected for normalization processing, and other normalization methods such as function transformation and maximum-minimum normalization can also be selected.

[0078] The management impact analysis module 130 is used to obtain the storage period degree of each type of goods according to the time interval between the inbound time and the outbound time of each type of goods within the period to be measured; obtain the space utilization degree of each type of inbound goods on the current day according to the relative size between the size of each type of inbound goods on the current day and the size of the storage shelves in the warehouse.

[0079] The goods stored in the warehouse will undergo subsequent outbound operations according to demand. Different goods demands will cause differences in the storage time of different types of goods in the warehouse. The storage time of the same type of goods is relatively consistent. The storage period degree is obtained by analyzing the storage duration of each type of goods within the period to be measured.

[0080] Preferably, in some possible implementation manners of the embodiment of the present invention, the method for obtaining the existence period degree includes: taking the time interval between the inbound time and the outbound time of each goods as the storage duration of the corresponding goods; calculating the average value of the storage duration of each type of goods within the period to be measured as the overall storage time of each type of goods; taking the ratio of the maximum value to the minimum value of the storage duration of each type of goods within the period to be measured as the duration adjustment coefficient of each type of goods; obtaining the existence period degree of each type of goods according to the duration adjustment coefficient and the overall storage time.

[0081] It should be noted that the goods within the period to be measured refer to the goods whose inbound time and outbound time are both within the period to be measured.

[0082] In a specific implementation manner of the embodiment of the present invention, the existence period degree of each type of goods is expressed by the formula:

[0083]

[0084] In the formula, is the storage cycle degree of the z-th type of goods during the period to be measured; is the storage duration of the u-th piece of the z-th type of goods during the period to be measured; G is the set composed of the z-th type of goods during the period to be measured; is the duration adjustment coefficient of the z-th type of goods during the period to be measured; is the overall storage time of the z-th type of goods during the period to be measured; Norm is the normalization function.

[0085] It should be noted that reflects the overall level of the storage duration of the z-th type of goods during the period to be measured; when is smaller, it indicates that the storage durations of all the z-th type of goods are more consistent, represents the overall level of the storage duration of the z-th type of goods more accurately, the required adjustment degree is smaller, that is, is closer to ; when it is larger, it indicates that the storage durations of all the z-th type of goods vary greatly, is not sufficient to represent the overall level of the storage duration of the z-th type of goods, the required adjustment degree is larger, and the storage cycle degree is larger.

[0086] After the goods are stored on the storage shelves in the warehouse through intelligent machine transportation after being put into storage, due to the different sizes of different goods and different storage shelves, when placing the goods, it is necessary to fully consider the space utilization of the storage shelves by the goods, and the space of the storage shelves is maximally utilized when the goods can be completely placed.

[0087] Preferably, in some possible implementation manners of the embodiments of the present invention, the method for obtaining the space utilization degree includes: selecting the storage shelves in the warehouse whose sizes are greater than or equal to the sizes of each type of incoming goods on the current day, and recording them as the analysis storage shelves for the corresponding type of incoming goods; normalizing the maximum value among the ratios of the volumes of each type of incoming goods on the current day to the volumes of the analysis storage shelves to obtain the space utilization degree of each type of incoming goods on the current day.

[0088] Only the storage positions of the storage shelves whose sizes are greater than or equal to the sizes of each type of incoming goods on the current day can place the corresponding type of incoming goods, and the space of the storage shelves can be utilized, and it makes sense to analyze the space utilization of the storage shelves by the goods. Analyze the best space utilization of each type of incoming goods on the current day for the storage shelves by using the analysis storage shelves whose volumes are closest to the volumes of each type of incoming goods to obtain the space utilization degree of each type of incoming goods on the current day.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] In the embodiment of the present invention, the storage priority is expressed by the formula:

[0094]

[0095] 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.

[0096] The warehouse management module 150 is used to match the storage priority with the distance between the storage racks in the warehouse and the warehouse entrance and exit, so as to obtain the suitable storage racks for each type of incoming goods before the current day.

[0097] Normalize the distance between each storage rack in the warehouse and the warehouse entrance and exit to obtain the storage priority of each storage rack; arrange the storage racks corresponding to the storage priorities 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, so as to obtain the storage sequence of each type of incoming goods on the current day; compare the sizes of each type of incoming goods on the current day with the sizes of the storage racks in the storage sequence in turn, and use the storage rack whose size is initially greater than or equal to the size of each type of incoming goods on the current day as the suitable storage rack for each type of incoming goods on the current day.

[0098] The storage priority refers to the priority of each storage rack for placing goods, and the storage priority refers to the priority of each type of incoming goods on the current day for being stored in the storage rack. By matching the storage priority with the storage priority, the storage rack for each type of goods to be placed is confirmed. The specific matching process is as follows:

[0099] Judge whether there is a storage priority equal to the storage priority of each type of incoming goods on the current day among the storage priorities of all storage racks in the warehouse. If so, use the storage rack corresponding to this storage priority as the suitable storage rack for each type of incoming goods on the current day; if not, select the storage racks with storage priorities less than or equal to the storage priority of each type of incoming goods on the current day for analysis, and through sacrificing a small amount of transportation distance and taking into account the transportation of future goods, obtain the storage sequence of each type of incoming goods; the storage rack whose size is initially greater than or equal to the size of each type of incoming goods on the current day in the storage sequence, while ensuring that the goods can be completely stored in the storage rack, minimizes the transportation distance of future goods, and use this storage rack as the suitable storage rack for each type of incoming goods.

[0100] It should be noted that in this solution, the ratio obtained by taking the distance from each storage rack in the warehouse to the warehouse entrance and exit as the numerator and the maximum value among the distances from all storage racks in the warehouse to the warehouse entrance and exit as the denominator is used as the result of the normalization process of the distance from the storage rack to the warehouse entrance and exit. The distances from different storage racks in the warehouse to the warehouse entrance and exit are different, that is, the storage priorities of all storage racks are not equal. If the storage priority of a certain type of goods on the current day is less than the storage priorities of all storage racks, or the sizes of a certain type of incoming goods are all larger than the sizes of their storage sequences, then in the order of increasing storage priority, the sizes of a certain type of goods are compared with the sizes of all storage racks in the warehouse in turn, and the storage rack whose size is first greater than or equal to the size of the incoming goods of this type is used as the suitable storage rack for the goods of this type.

[0101] Through the bin robot shelves and the automatic guided vehicle, each type of goods on the current day is automatically transported and placed at the storage position of the suitable storage rack for the goods of this type.

[0102] This solution dynamically adjusts the storage cycle and space utilization during the peak period of goods, effectively balancing the relationship among the large storage requirements, the actual available space, and the storage efficiency of the warehouse in the future. It not only optimizes the space utilization and the inbound and outbound efficiency of the warehouse, but also can reduce the operation cost of the warehouse, improve the response ability to the future peak of goods, and enhance the management efficiency of the warehouse during the future peak period of goods.

[0103] So far, the present invention is completed.

[0104] Embodiment 2:

[0105] Figure 3 It is a schematic diagram of a computer device for a smart unmanned warehouse management device provided by an embodiment of the present invention. Exemplarily, as Figure 3 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. Among them, when the processor 202 executes the computer program 203, the computer device can execute any one of the smart unmanned warehouse management systems introduced above.

[0106] In addition, an embodiment of the present application also protects a device, which may include a memory and a processor. Among them, an executable program code is stored in the memory, and the processor is used to call and execute the executable program code to execute a smart unmanned warehouse management system provided by an embodiment of the present application.

[0107] In this embodiment, the device can be divided into functional modules according to the above method examples. For example, it can correspond to each functional module, 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 illustrative, only a logical function division, and there can be other division methods in actual implementation.

[0108] It should be understood that the device provided in this embodiment is used to execute the above-mentioned intelligent unattended warehouse management system, so the same effect as the above implementation method can be achieved.

[0109] In the case of adopting an integrated unit, the device can include a processing module and a storage module. Among them, when the device is applied to a device, the processing module can be used to control and manage the actions of the device. The storage module can be used to support the device to execute mutual program codes, etc.

[0110] Among them, the processing module can be a processor or a controller, which can implement or execute various exemplary logical blocks, modules, and circuits described in combination with the disclosure of this application. The processor can also be a combination that realizes computing functions, such as including a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc. The storage module can be a memory.

[0111] Embodiment 3:

[0112] This embodiment also provides a computer-readable storage medium, in which computer program code is stored. When the computer program code runs on a computer, it causes the computer to execute the above-related method steps to implement an intelligent unattended warehouse management system provided in the above embodiment.

[0113] Among them, the device and the computer-readable storage medium provided in this embodiment are both 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 elaborated here.

[0114] It should be noted that the above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0115] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0116] The above are only the preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A smart unattended warehouse management system, characterized in that, The system includes: A data acquisition module, which is used to obtain the inbound time and outbound time of all goods within a period to be measured before the current day, the inbound goods on the current day and the dimensions of the storage racks in the warehouse, and the distance between the storage racks and the warehouse entrance and exit; the goods have corresponding types; A peak trend analysis module, which is used to obtain the peak trend performance degree of the period to be measured according to the quantity change trend of inbound and outbound goods in the warehouse every day within the period to be measured, and the quantity difference between inbound and outbound goods every day, including: Obtaining a reference peak trend value according to the quantity change trend of inbound and outbound goods every day within the period to be measured; Obtaining an inbound / outbound update rate according to the quantity difference between inbound and outbound goods every day within the period to be measured and the dispersion degree of the quantity difference; Adjusting the reference peak trend value by using the inbound / outbound update rate to obtain the peak trend performance degree of the period to be measured; The obtaining of the reference peak trend value includes: Recording the total quantity of inbound and outbound goods every day as the daily goods throughput; Arranging the daily goods throughputs of all days within the period to be measured in chronological order to obtain a throughput sequence; obtaining the first-order difference sequence of the throughput sequence, and taking the mean value of all elements in the first-order difference sequence as the reference trend value; Taking the mean value of the daily goods throughputs of all days within the period to be measured as the concentration value of the goods throughput; taking the product of the concentration value of the daily goods throughputs of all days within the period to be measured and the reference trend value as the reference peak trend value; The obtaining of the inbound / outbound update rate includes: Taking the absolute value of the difference between the quantity of inbound and outbound goods every day as the daily inbound / outbound difference; Taking the variance of the daily inbound / outbound differences of all days within the period to be measured as the dispersion index of the inbound / outbound difference, and taking the mean value of the daily inbound / outbound differences of all days within the period to be measured as the concentration value of the inbound / outbound difference; Performing negative correlation and normalization processing on the product of the dispersion index and the concentration value of the daily inbound / outbound differences of all days within the period to be measured to obtain the inbound / outbound update rate; A management impact analysis module, which is used to obtain the storage period degree of each type of goods according to the time interval between the inbound time and the outbound time of each type of goods within the period to be measured; and obtain the space utilization degree of the inbound goods of each type on the current day according to the relative size between the dimensions of the inbound goods of each type on the current day and the dimensions of the storage racks in the warehouse; The adjusting of the reference peak trend value by using the inbound / outbound update rate to obtain the peak trend performance degree of the period to be measured includes: Taking the sum value of the inbound / outbound update rate and the constant 1 as the trend adjustment coefficient; performing weighted processing on the reference peak trend value based on the trend adjustment coefficient, and performing normalization processing on the weighted result to obtain the peak trend performance degree of the period to be measured; A storage priority analysis module, which is used to adjust the storage period degree and the space utilization degree according to the peak trend performance degree to obtain the storage priority of the inbound goods of each type on the current day; The obtaining of the storage priority of the inbound goods of each type on the current day is expressed by the formula: P z = Norm(E × T z + (1 - E) × V z ); where, P z is the storage priority of the z-th type of incoming goods on the current day; E is the peak trend performance degree of the period to be measured; T z is the storage cycle degree of the z-th type of incoming goods on the current day; V z is the space utilization degree of the z-th type of incoming goods on the current day; Norm is the normalization function; The storage period degree of each type of goods is expressed by the formula: Where, T z is the storage cycle degree of the z-th type of goods during the period to be measured; A z,u is the storage duration of the u-th piece of the z-th type of goods during the period to be measured; G is the set composed of the z-th type of goods during the period to be measured; is the duration adjustment coefficient of the z-th type of goods during the period to be measured; is the overall storage time of the z-th type of goods during the period to be measured; Norm is the normalization function; Obtaining the space utilization rate of each type of incoming goods for the current day includes: Selecting the storage racks in the warehouse whose sizes are greater than or equal to the sizes of each type of incoming goods for the current day, and denoting them as the analysis storage racks for the corresponding type of incoming goods; Normalizing the maximum value among the ratios of the volumes of each type of incoming goods for the current day to the volumes of the analysis storage racks to obtain the space utilization rate of each type of incoming goods for the current day; A warehouse management module for matching the storage priority with the distance between the storage racks in the warehouse and the warehouse entrance and exit to obtain the adapted storage racks for each type of incoming goods before the current day.

2. The intelligent unattended warehouse management system according to claim 1, characterized in that, The matching of the storage priority with the distance between the storage racks in the warehouse and the warehouse entrance and exit to obtain the adapted storage racks for each type of incoming goods before the current day includes: Normalizing the distance between each storage rack in the warehouse and the warehouse entrance and exit to obtain the item placement priority of each storage rack; Arranging the storage racks corresponding to the item placement priorities that are less than or equal to the storage priorities of each type of incoming goods for the current day in the warehouse in descending order of the item placement priority to obtain the storage sequence of each type of incoming goods for the current day; Sequentially comparing the sizes of each type of incoming goods for the current day with the sizes of the storage racks in the storage sequence, and taking the storage rack whose size is first greater than or equal to the size of each type of incoming goods for the current day in the storage sequence as the adapted storage rack for each type of incoming goods for the current day.

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