Intelligent unmanned warehouse management system and method

Abstract

The invention discloses an intelligent unmanned warehouse management system and method, relates to the technical field of warehouse management, and solves the technical problem that the rationality of cargo storage is relatively low due to the fact that an existing automatic warehouse management system cannot distribute corresponding storage positions to cargos according to the storage characteristics of the cargos. The data acquisition module is used for acquiring environment data; the shelf classification module is used for acquiring historical environment data and generating a storage environment label according to the historical environment data; geometric features and load-carrying features of each goods shelf storage rack are obtained; generating a storage label corresponding to each storage shelf according to the geometrical characteristics and the load characteristics; the goods distribution module is used for generating storable goods shelves and corresponding optimization coefficients according to the goods geometric data, the goods weight data and the storage labels; generating an environment safety coefficient according to the storage condition data and a storage environment label corresponding to the storable goods shelf; selecting and storing selected goods shelves according to the environment safety coefficient and the optimization coefficient; and the cargo storage rationality is improved.

Description

Technical Field

[0001] The present application belongs to the field of warehouse management technology, and relates to warehouse management technology, specifically to a smart unmanned warehouse management system and method. Background Art

[0002] Smart warehousing refers to the use of modern information technology to carry out intelligent transformation and upgrading of all aspects of warehousing, and realize the automation, intelligence and refined management of warehousing operations. Through the Internet of Things technology, real-time tracking and monitoring of goods can be achieved, and big data and artificial intelligence technologies can be used to optimize warehouse layout, predict inventory needs, and improve picking efficiency, thereby improving overall warehouse operation efficiency and service levels.

[0003] The prior art (an invention patent with announcement number CN114047746B) discloses an automatic warehouse management method without human intervention, which method includes the following steps: pre-dividing the warehouse into a storage area and a preparation area, the storage area includes a plurality of goods outbound units, the preparation area includes a plurality of goods inbound units, the goods outbound units and the goods inbound units respectively form a matrix grid storage structure comprising a plurality of columns, specifically, each column of the goods outbound units is configured with a plurality of first storage parts arranged in sequence, and each column of the goods inbound units is configured with a plurality of second storage parts arranged in sequence, the goods in the goods outbound units and the goods in the goods inbound units are respectively associated with and correspond to the columns of the matrix grid; the storage process of the goods in the goods outbound units and the goods inbound units are configured to be put in one by one from the end of the column to the head of the column, and the shipping process is configured to be taken out one by one from the head of the column to the end of the column. This method configures cargo monitoring systems at the head and tail of the warehouse matrix column, which can realize automatic triggering of scheduling based on cargo demand. This process does not require human intervention and has a low error rate, which significantly improves cargo handling efficiency.

[0004] The above-mentioned automatic warehouse management method monitors the head of the row in the preparation area and the tail of the row in the storage area; stores the goods in order, thereby improving the efficiency of storing goods; however, there are various storage shelves in large warehouses, and the stored goods are various. The storage environments corresponding to the storage shelves in different areas are different. If the goods are stored by the above-mentioned method, the goods may be stored on inappropriate shelves, resulting in a waste of storage space, or the goods may be stored in an inappropriate environment, causing the goods to be damaged by the environment; which leads to a low rationality of goods storage; therefore, a smart unmanned warehouse management system and method are needed. Summary of the invention

[0005] The present application aims to solve at least one of the technical problems existing in the prior art; to this end, the present application proposes an intelligent unmanned warehouse management system and method for solving the technical problem that the existing automatic warehouse management system cannot allocate corresponding storage locations to goods according to the storage characteristics of the goods, resulting in low rationality of goods storage. The present application generates storage environment labels and storage labels corresponding to each storage shelf by analyzing the historical environment of each storage shelf, as well as the geometric data and load data of each shelf; and solves the above problems by selecting appropriate storage shelves according to the storage conditions of the goods.

[0006] To achieve the above-mentioned purpose, the first aspect of the present application provides a smart unmanned warehouse management system, including: a data acquisition module, a shelf classification module, a goods distribution module, an alarm module and a database;

[0007] The data acquisition module is used to obtain environmental data collected in real time by data acquisition devices installed on each storage shelf;

[0008] The shelf classification module: obtains a number of historical environmental data of a number of storage shelves, generates a fluctuation coefficient of each storage shelf according to the historical environmental data; generates a storage environment label corresponding to the storage shelf according to the fluctuation coefficient; and

[0009] Obtaining the geometric features and load characteristics of each shelf storage rack; generating storage labels corresponding to each storage rack according to the geometric features and load characteristics;

[0010] The cargo allocation module: obtains storage condition data, cargo geometry data and cargo weight data of the cargo to be stored; generates storable shelves and corresponding optimization coefficients according to the cargo geometry data, cargo weight data and storage labels; generates environmental safety coefficients according to the storage condition data and the storage environment labels corresponding to the storable shelves; selects the selected storage shelf according to the environmental safety coefficient and the optimization coefficient;

[0011] The alarm module: acquires the environmental data of each storage shelf in real time; extracts the environmental temperature and environmental humidity in the environmental data, generates a temperature alarm signal when the environmental temperature is higher than the temperature upper limit or lower than the temperature lower limit of the stored goods stored on the corresponding storage shelf; generates a humidity alarm signal when the environmental humidity is higher than the humidity upper limit or lower than the humidity lower limit of the stored goods stored on the corresponding storage shelf; and issues an alarm according to the alarm signal; the alarm signal includes a temperature alarm signal and a humidity alarm signal.

[0012] The present application obtains several historical environmental data of several storage shelves, generates the fluctuation coefficient of each storage shelf according to the historical environmental data; generates the storage environment label corresponding to the storage shelf according to the fluctuation coefficient; obtains the geometric characteristics and load characteristics of each shelf storage rack; generates the storage label corresponding to each storage shelf according to the geometric characteristics and load characteristics; obtains the storage condition data, cargo geometry data and cargo weight data of the goods to be stored; generates the storable shelves and the corresponding optimization coefficient according to the cargo geometry data, cargo weight data and storage labels; generates the environmental safety factor according to the storage condition data and the storage environment label corresponding to the storable shelves; selects the selected storage shelf according to the environmental safety factor and the optimization coefficient; the present embodiment generates the storage environment label and storage label of each storage shelf through the historical environmental data of each storage shelf and its corresponding geometric characteristics and load characteristics; selects the appropriate storage shelf for storage according to the storage conditions of the goods to be stored, thereby increasing the rationality of the goods storage.

[0013] Preferably, generating the fluctuation coefficient of each storage shelf according to historical environmental data includes:

[0014] Acquire a number of historical environmental data of a number of storage shelves; pre-process the historical environmental data to obtain qualified environmental data;

[0015] Obtaining the air exchange rate from several qualified environmental data of each storage rack and normalizing it; obtaining the ambient temperature from several qualified environmental data of each storage rack and normalizing it; obtaining the ambient humidity from several qualified environmental data of each storage rack and normalizing it;

[0016] Obtain the maximum value KD and the minimum value KX of the normalized air exchange rate corresponding to each storage shelf, the maximum value ZDW and the minimum value ZXW of the ambient temperature, and the maximum value ZDS and the minimum value ZXS of the ambient humidity; calculate the fluctuation coefficient corresponding to each storage shelf through the formula BX=max(KD-KX, ZDW-ZXW, ZDS-ZXS).

[0017] Preferably, the pretreatment comprises:

[0018] Extracting several air exchange rates, ambient temperatures and ambient humidity from various historical environmental data; determining whether there are missing data in the air exchange rates, ambient temperatures and ambient humidity; wherein missing data include missing values ​​or missing item units;

[0019] If yes, the historical environment data is discarded;

[0020] If not, the project units corresponding to the environmental items will be converted into standard units, and the project values ​​will be converted into standard values ​​under the standard units; when the standard values ​​corresponding to each environmental item are within a preset range, the preset range is set based on experience; the corresponding historical environmental data will be marked as qualified environmental data; otherwise, the historical environmental data will be discarded.

[0021] Preferably, generating a storage environment label corresponding to the storage shelf according to the fluctuation coefficient includes:

[0022] Obtain the fluctuation coefficient corresponding to each storage shelf; determine whether the fluctuation coefficient is greater than the set fluctuation coefficient threshold;

[0023] If yes, the environment type label corresponding to the storage shelf is marked as a large change environment;

[0024] If not, the environment type label corresponding to the storage rack is marked as a small change environment; and a storage environment label corresponding to the storage rack is generated according to the qualified environment coefficient of the storage rack.

[0025] Preferably, generating the storage environment label corresponding to the storage shelf according to the qualified environment data includes:

[0026] Acquire several qualified environmental data of the storage rack; extract the air exchange rate, environmental temperature and environmental humidity in each qualified environmental data; obtain the occurrence probability of each environmental temperature and mark it as the temperature probability corresponding to the corresponding environmental temperature; and the occurrence probability of each environmental humidity and mark it as the humidity probability corresponding to the environmental humidity;

[0027] The probability of the air exchange rate being greater than the ventilation threshold is obtained and recorded as the ventilation probability; each ambient temperature and its corresponding temperature probability, ambient humidity and its corresponding humidity probability, and ventilation probability are integrated into a storage environment label.

[0028] Preferably, the step of generating storage labels corresponding to each storage shelf according to geometric features and load characteristics includes:

[0029] Extracting shelf height, shelf width and shelf length from the geometric features of each storage shelf;

[0030] Determine whether the shelf height is greater than the preset large-item threshold; if yes, set the height label to large-item; if no, when the shelf height is greater than the preset medium-item threshold, set the threshold to medium-item; otherwise, set the height label to small-item;

[0031] Determine whether the shelf width is greater than the preset large item threshold; if yes, set the width label to large item; if no, when the shelf width is greater than the preset medium item threshold, set the threshold to medium item; otherwise, set the width label to small item;

[0032] Determine whether the length of the storage shelf is greater than a preset large-piece threshold; if so, set the length label to large-piece; otherwise, when the length of the storage shelf is greater than a preset medium-piece threshold, set the threshold to medium-piece; otherwise, set the length label to small-piece;

[0033] Extract the height from the ground LH in the geometric features of each storage shelf and the maximum load DZ in the load-bearing features; calculate the load-bearing safety limit JZ through the formula JZ = DZ × JX × exp(-k1 × LH / C); where k is an adjustment coefficient and 0 < k1 < 1; the specific value is set according to expert experience; C is the set unit size; JX is the correction coefficient; set the load label of the corresponding storage shelf to the load-bearing safety limit;

[0034] Calculate the correction coefficient through the formula JX = 1 - k2 × arctan(∑(ZHSg / S × ZHZg / Z)); where k2 is a proportionality coefficient and 0 < k2 < 2 / π; ZHSg is the inventory duration of the inventory record with storage shelf number g; ZHZg is the weight of the goods in the inventory record with storage shelf number g; g is the number of the inventory record and g = 1, 2,..., G; G is the total number of inventory records; the inventory records of each storage shelf are obtained from the database; S is the set unit duration;

[0035] The storage label includes a height label, a width label, a length label, and a load label.

[0036] Preferably, generate the storage shelves that can hold the goods and their corresponding preference coefficients according to the geometric data of the goods, the weight data of the goods, and the storage label, including:

[0037] Extract the height, width, and length of the goods from the geometric data of the goods;

[0038] Determine whether the height of the goods is less than the medium-piece threshold; if so, set the height label of the goods to small-piece; otherwise, when the height of the goods is less than the medium-piece threshold, set the height label of the goods to medium-piece; otherwise, set the height label of the goods to large-piece;

[0039] Determine whether the width of the goods is less than the medium-piece threshold; if so, set the width label of the goods to small-piece; otherwise, when the width of the goods is less than the medium-piece threshold, set the width label of the goods to medium-piece; otherwise, set the width label of the goods to large-piece;

[0040] Determine whether the length of the goods is less than the medium-piece threshold; if so, set the length label of the goods to small-piece; otherwise, when the length of the goods is less than the medium-piece threshold, set the length label of the goods to medium-piece; otherwise, set the length label of the goods to large-piece;

[0041] Obtain the status label of each storage shelf; the status label includes free and in stock; extract the height label, width label and length label in the storage label corresponding to each storage shelf whose status label is free; mark the storage shelf whose height label is the same level or higher than the height label of the goods, whose width label is the same level or higher than the width label of the goods, and whose length label is the same level or higher than the length label of the goods as the preliminary selection shelf;

[0042] Extract the cargo weight from the cargo weight data; obtain the load safety limit recorded in the load label of each preliminary selected shelf, and record the preliminary selected shelf whose load safety limit is greater than the cargo weight as a shelf that can be stored;

[0043] Extract the shelf height JG, shelf width JK and shelf length JC from the geometric features of the storage shelf;

[0044] The optimization coefficient YX of the corresponding shelf is calculated by the formula YX=β1×exp(-(JG+JK+JC-HG-HK-HC) / C)+β2×exp(-(JZ-HZ) / Z); wherein HG is the height of the goods, HK is the width of the goods, HC is the length of the goods, and HZ is the weight of the goods; β1 and β2 are weight coefficients; the specific values ​​are set according to expert experience; C is the set unit size; and Z is the set unit weight.

[0045] The present application calculates the degree of matching between each optional shelf and the goods to be stored through the above formula, that is, the priority coefficient. The closer the optional shelf size is to the goods size, the higher the corresponding matching degree; the closer the load safety limit of the optional shelf is to the goods weight, the higher the corresponding matching degree, and the larger the corresponding priority coefficient is set, so as to avoid the situation where small-sized goods are stored on large-sized shelves when selecting shelves in the later stage, rationally utilize storage space, and thus improve storage efficiency.

[0046] Preferably, generating an environmental safety factor according to the storage condition data and the storage environment labels corresponding to the storable shelves includes:

[0047] When the storage condition data is "none", the environmental safety factor is set to "0", that is, AX = 0;

[0048] Otherwise, extract the temperature storage condition, humidity storage condition and ventilation storage condition in the storage condition data; extract the temperature upper limit and temperature lower limit in the temperature storage condition, and the humidity upper limit and humidity lower limit in the humidity storage condition;

[0049] Extract each ambient temperature and its corresponding temperature probability from the storage environment label corresponding to the storage shelf with the environment type label of small change environment; mark the ambient temperature greater than the upper temperature limit as the high-risk temperature GWi1, and mark its corresponding temperature probability as WGI1; mark the ambient temperature lower than the lower temperature limit as the low-risk temperature DWi2; and mark its corresponding temperature probability as WGi2; i1 is the number of the high-risk temperature of the corresponding storage shelf; i2 is the number of the low-risk temperature of the corresponding storage shelf;

[0050] Extract each ambient humidity and its corresponding humidity probability in the storage environment label; mark the ambient humidity greater than the upper humidity limit as high-risk humidity GSj1, and mark its corresponding humidity probability as SGj1; mark the ambient humidity below the lower humidity limit as low-risk humidity DSj2; and mark its corresponding humidity probability as SGj2; j1 is the number of the high-risk humidity of the corresponding storage shelf; j2 is the number of the low-risk humidity of the corresponding storage shelf;

[0051] The temperature safety factor AW is calculated by the formula AW = ∑[WGi1×exp(-|GWi1-WS|)]+∑[DWi2×exp(-|DWi2-WX|)]; where WS is the upper temperature limit and WX is the lower temperature limit; i1 = 1, 2, ..., m1; m1 is the total number of high-risk temperatures; i2 = 1, 2, ..., m2; m2 is the total number of low-risk temperatures;

[0052] The humidity safety factor AS is calculated by the formula AS = ∑[SGj1×exp(-|SS-GSj1|)]+∑[SGj2×exp(-|DSj2-SX|)]; where SS is the upper limit of humidity and SX is the lower limit of humidity; j1 = 1, 2, ..., n1; n1 is the total number of high-risk humidity; j2 = 1, 2, ..., n2; n2 is the total number of low-risk humidity;

[0053] Extract the ventilation probability TG in the storage environment label; when the ventilation storage condition is ventilation, the ventilation safety factor is AT=TG; otherwise, the ventilation safety factor is AT=1-TG;

[0054] The environmental safety factor AX of the corresponding storage rack is calculated by the formula AX=α1×AW+α2×AS+α3×AT; wherein α1, α2 and α3 are weight coefficients, and the specific values ​​are set according to expert experience.

[0055] Preferably, storing the selected shelves according to the environmental safety factor and the optimization factor comprises:

[0056] Obtain the environmental safety factor AX and the optimization factor YX of each shelf that can be stored;

[0057] The comprehensive selection coefficient XQ is calculated by the formula XQ=γ1×YX+γ2×AX; wherein γ1 and γ2 are weight coefficients, and the specific values ​​are set according to expert experience; the storable shelf with the largest comprehensive selection coefficient is selected as the storage selected shelf; and the status label of the storage selected shelf is set to inventory.

[0058] Another aspect of the present invention provides a smart unmanned warehouse management method, comprising the following steps:

[0059] Step 1: Obtain historical environmental data of each storage shelf, generate a fluctuation coefficient based on the historical environmental data; generate a storage environment label corresponding to the storage shelf based on the fluctuation coefficient;

[0060] Step 2: Obtain the geometric characteristics and load characteristics of each shelf storage rack; generate storage labels corresponding to each storage shelf according to the geometric characteristics and load characteristics;

[0061] Step 3: Obtain storage condition data, cargo geometry data and cargo weight data of the cargo to be stored; generate storage shelves and corresponding optimization coefficients based on the cargo geometry data, cargo weight data and storage labels;

[0062] Step 4: Generate an environmental safety factor based on the storage condition data and the storage environment labels corresponding to the storable shelves;

[0063] Step 5: Select the storage shelf according to the environmental safety factor and the optimization factor;

[0064] Step 6: Acquire the environmental data of each storage shelf in real time; extract the ambient temperature and ambient humidity in the environmental data, and generate a temperature alarm signal when the ambient temperature is higher than the upper temperature limit or lower temperature limit of the stored goods stored on the corresponding storage shelf; generate a humidity alarm signal when the ambient humidity is higher than the upper humidity limit or lower humidity limit of the stored goods stored on the corresponding storage shelf; the environmental data includes the ambient temperature and the ambient humidity.

[0065] This application will issue an alarm based on whether the real-time environmental data meets the storage conditions of the goods, so that relevant personnel can transfer the goods to the appropriate shelves in time to reduce the loss of goods.

[0066] Compared with the prior art, the beneficial effects of this application are:

[0067] 1. The present application generates storage environment labels corresponding to each storage shelf according to the historical environment data corresponding to each storage shelf; obtains the geometric characteristics and load characteristics of each storage shelf; generates storage labels corresponding to each storage shelf according to the geometric characteristics and load characteristics of each storage shelf; generates storable shelves and corresponding optimization coefficients according to cargo geometry data, cargo weight data and storage labels; generates environmental safety factors according to storage condition data and storage environment labels corresponding to storable shelves; selects storage shelves according to the environmental safety factors and optimization coefficients; enables selection of suitable storage shelves for storage according to the storage conditions of the cargo to be stored, thereby increasing the rationality of cargo storage.

[0068] 2. This application sets a preference coefficient. The closer the optional shelf size is to the cargo size, the higher the corresponding matching degree will be. The closer the load safety limit of the optional shelf is to the cargo weight, the higher the corresponding matching degree will be. The larger the corresponding preference coefficient will be set. This will avoid the situation where large shelves are used to store small goods when selecting shelves in the future, and will make rational use of storage space to improve storage efficiency.

[0069] 3. This application issues an alarm based on whether the real-time environmental data meets the storage conditions of the goods, so that relevant personnel can transfer the goods to appropriate shelves in time to reduce the loss of goods. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0071] Figure 1 This is a schematic diagram of the principle of an intelligent unmanned warehouse management system in this application;

[0072] Figure 2 This is a step diagram of a smart unmanned warehouse management method in this application;

[0073] Figure 3 A diagram of the steps for comprehensive selection coefficient generation in this application. DETAILED DESCRIPTION

[0074] The technical solution of the present application will be described clearly and completely below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0075] See also Figure 1 , the first aspect of the present application provides a smart unmanned warehouse management system, including: a data acquisition module, a shelf classification module, a goods allocation module, an alarm module and a database;

[0076] Data acquisition module: obtains environmental data collected in real time by data acquisition devices set on each storage shelf; environmental data includes environmental temperature, environmental humidity and air exchange rate; data acquisition devices include temperature acquisition devices, humidity acquisition devices and air flow acquisition devices, and the air exchange rate is the air exchange rate of the corresponding area of ​​the storage shelf, which can be specifically calculated by the formula ACH=3600×Q / V, where Q is the air flow collected per unit time; V is the volume of the corresponding storage shelf; the environmental temperature is the environmental temperature of the corresponding storage shelf area, and the environmental humidity is the environmental humidity of the corresponding storage shelf area; the storage shelves in this embodiment are all one-layer shelves, and the shelves can be spliced ​​left and right and up and down to form multi-layer shelves; or the multi-layer shelves are divided into multiple areas, each area only contains part or all of a layer of storage area, and the area is recorded as a storage shelf;

[0077] Shelf classification module: obtains several historical environmental data of several storage shelves through a database connected thereto, the historical environmental data being the historical environmental data recorded for the corresponding storage shelves, including air exchange rate, ambient temperature and ambient humidity; generates a fluctuation coefficient of each storage shelf according to the historical environmental data; the fluctuation coefficient is the fluctuation of the environmental data of the corresponding area of ​​each storage shelf; generates a storage environment label corresponding to the storage shelf according to the fluctuation coefficient; and,

[0078] Obtaining the geometric features and load characteristics of each shelf storage rack; generating storage labels corresponding to each storage rack according to the geometric features and load characteristics;

[0079] Goods allocation module: obtain storage condition data, goods geometry data and goods weight data of goods to be stored; generate storable shelves and corresponding optimization coefficients according to goods geometry data, goods weight data and storage labels; storable shelves are storage shelves whose size and load capacity meet the storage conditions of corresponding stored goods; the optimization coefficient is the suitability of the corresponding storable shelves and the stored goods; generate environmental safety factors according to storage condition data and storage environment labels corresponding to storable shelves; the environmental safety factor can be understood as the probability that the corresponding environmental data of the storable shelves meet the storage conditions of goods within the storage time; select the selected storage shelves according to the environmental safety factor and the optimization coefficient;

[0080] Alarm module: acquires environmental data of each storage shelf in real time; extracts ambient temperature and ambient humidity from the environmental data, generates a temperature alarm signal when the ambient temperature is higher than the upper temperature limit or lower temperature limit of the stored goods stored on the corresponding storage shelf; generates a humidity alarm signal when the ambient humidity is higher than the upper humidity limit or lower humidity limit of the stored goods stored on the corresponding storage shelf; issues an alarm based on the alarm signal; the alarm signal includes a temperature alarm signal and a humidity alarm signal.

[0081] This embodiment obtains several historical environmental data of several storage shelves, generates the fluctuation coefficient of each storage shelf according to the historical environmental data; generates the storage environment label corresponding to the storage shelf according to the fluctuation coefficient; obtains the geometric characteristics and load characteristics of each shelf storage rack; generates the storage label corresponding to each storage shelf according to the geometric characteristics and load characteristics; obtains the storage condition data, cargo geometry data and cargo weight data of the goods to be stored; generates the storable shelves and the corresponding optimization coefficient according to the cargo geometry data, cargo weight data and storage labels; generates the environmental safety factor according to the storage condition data and the storage environment label corresponding to the storable shelves; selects the selected storage shelf according to the environmental safety factor and the optimization coefficient; this embodiment generates the storage environment label and storage label of each storage shelf through the historical environmental data of each storage shelf and its corresponding geometric characteristics and load characteristics; selects the appropriate storage shelf for storage according to the storage conditions of the goods to be stored, thereby increasing the rationality of the goods storage.

[0082] Generating the fluctuation coefficient of each storage rack according to the historical environmental data includes: obtaining a number of historical environmental data of a number of storage racks; preprocessing the historical environmental data to obtain qualified environmental data;

[0083] Obtaining the air exchange rate from several qualified environmental data of each storage rack and normalizing it; obtaining the ambient temperature from several qualified environmental data of each storage rack and normalizing it; obtaining the ambient humidity from several qualified environmental data of each storage rack and normalizing it;

[0084] Obtain the maximum value KD and the minimum value KX of the normalized air exchange rate corresponding to each storage shelf, the maximum value ZDW and the minimum value ZXW of the ambient temperature, and the maximum value ZDS and the minimum value ZXS of the ambient humidity; calculate the fluctuation coefficient corresponding to each storage shelf through the formula BX=max(KD-KX, ZDW-ZXW, ZDS-ZXS); this embodiment obtains the difference between the maximum and minimum values ​​of ambient humidity, ambient temperature and air exchange rate in the historical environmental data through the above formula, and generates the fluctuation coefficient of each shelf. The larger the fluctuation coefficient, the more unstable at least one of the environmental data is; it is not suitable for storing goods with storage conditions.

[0085] Preprocessing includes: extracting several air exchange rates, ambient temperatures and ambient humidity from various historical environmental data; determining whether there are missing data in the air exchange rates, ambient temperatures and ambient humidity; wherein missing data includes missing values ​​or missing item units;

[0086] If yes, the historical environment data is discarded;

[0087] If not, the project units corresponding to the environmental items will be converted into standard units, and the project values ​​will be converted into standard values ​​under the standard units; when the standard values ​​corresponding to each environmental item are within the preset range; the preset range is set according to experience; the preset range is a normal range set in advance, such as the preset range of temperature can be set to -10 degrees to 30 degrees, etc.; the corresponding historical environmental data will be marked as qualified environmental data; otherwise, the historical environmental data will be discarded; environmental data that is not within the preset range is abnormal data, which may be caused by abnormal collection equipment; so it will not be considered.

[0088] Generating a storage environment label corresponding to the storage shelf according to the fluctuation coefficient includes: obtaining the fluctuation coefficient corresponding to each storage shelf; judging whether the fluctuation coefficient is greater than a set fluctuation coefficient threshold;

[0089] If yes, the environment type label corresponding to the storage shelf is marked as a large-variation environment; a large-variation environment is a storage shelf with an unstable storage environment, such as a shelf near a window or a vent, which is greatly affected by the external environment, or has a changeable environment, which leads to an unstable storage environment for the goods; therefore, it is not suitable to store goods with specific storage conditions;

[0090] If not, the environment type label corresponding to the storage shelf is marked as a small change environment; and the storage environment label of the corresponding storage shelf is generated according to the qualified environment coefficient of the storage shelf. A small change environment is a storage shelf with a stable storage environment, specifically a storage shelf with basically no change in ambient temperature, ambient humidity, air exchange rate, etc., or a storage shelf with changes within a small range, which can be used to store goods with corresponding environmental conditions.

[0091] Generating the storage environment label of the corresponding storage shelf according to the qualified environment data includes: obtaining several qualified environment data of the storage shelf; it is worth noting that the qualified environment data considered in the generation of the storage environment label in this embodiment is the qualified environment data within the time range of the storage time of the goods this time; the specific time range is set according to experience, and can be set to 10 days, 7 days, etc.; it is convenient to consider the impact of seasonal changes on the storage environment of each storage shelf; extract the air exchange rate, ambient temperature and ambient humidity in each qualified environment data; obtain the occurrence probability of each ambient temperature and mark it as the temperature probability corresponding to the corresponding ambient temperature; and the occurrence probability of each ambient humidity is marked as the humidity probability of the corresponding ambient humidity; specifically, obtain the number of ambient temperatures with the same value, divide the number by the total number of qualified environment data in the corresponding storage shelf, and the total number of ambient temperatures to obtain the occurrence probability of the corresponding ambient temperature; obtain the number of ambient humidity with the same value, divide the number by the total number of qualified environment data in the corresponding storage shelf, and the total number of ambient humidity to obtain the occurrence probability of the corresponding ambient humidity;

[0092] In another embodiment, the ambient temperatures are first grouped, and then the total number of ambient data in each group is divided by the total number of qualified ambient data in the corresponding storage shelf to obtain the occurrence probability of the corresponding ambient temperature; for example, the ambient temperatures in 28-29 are grouped together; it can be understood that the historical ambient data is collected regularly, and the interval time of each collection is set according to experience. The shorter the interval time is set, the more accurate the range of each ambient data corresponding to the storage environment tag is;

[0093] The probability of the air exchange rate being greater than the ventilation threshold is obtained and recorded as the ventilation probability; each ambient temperature and its corresponding temperature probability, ambient humidity and its corresponding humidity probability, and ventilation probability are integrated into a storage environment label.

[0094] This embodiment generates the probability of occurrence of environmental data corresponding to each storage shelf through the qualified environmental data corresponding to each storage shelf with relatively small environmental changes in the recent period, and integrates the corresponding occurrence probabilities into storage environment labels; this facilitates the subsequent generation of corresponding environmental safety factors according to the storage conditions of the goods to be stored.

[0095] Generate storage labels corresponding to each storage shelf according to geometric features and load characteristics, including: extracting shelf height, shelf width and shelf length from the geometric features of each storage shelf;

[0096] Determine whether the shelf height is greater than the preset large-item threshold; if yes, set the height label to large-item; if no, when the shelf height is greater than the preset medium-item threshold, set the threshold to medium-item; otherwise, set the height label to small-item;

[0097] Determine whether the width of the storage rack is greater than a preset large-piece threshold; if so, set the width label to large-piece; otherwise, when the width of the storage rack is greater than the preset medium-piece threshold, set the threshold to medium-piece; otherwise, set the width label to small-piece;

[0098] Determine whether the length of the storage rack is greater than a preset large-piece threshold; if so, set the length label to large-piece; otherwise, when the length of the storage rack is greater than the preset medium-piece threshold, set the threshold to medium-piece; otherwise, set the length label to small-piece;

[0099] Extract the height from the ground LH in the geometric features of each storage rack and the maximum load DZ in the load-bearing features; calculate the load-bearing safety limit JZ through the formula JZ = DZ × JX × exp(-k1 × LH / C); where k is an adjustment coefficient and 0 < k1 < 1; the specific value is set according to expert experience; C is the set unit size; JX is the correction coefficient; set the load label of the corresponding storage rack to the load-bearing safety limit; in this embodiment, the load-bearing safety limits of each storage rack are calculated through the above formula; the higher the distance of the storage rack from the ground, the greater the probability of danger when placing heavy objects, and the corresponding load-bearing safety limit needs to be set smaller than the maximum load when the storage rack is placed on the ground; that is, the higher the height of the same specification of the storage rack, the corresponding load-bearing safety limit is reduced accordingly; the specific reduction degree can be adjusted for the adjustment coefficient k1.

[0100] Calculate the correction coefficient through the formula JX = 1 - k2 × arctan(∑(ZHSg / S × ZHZg / Z)); where k2 is a proportionality coefficient and 0 < k2 < 2 / π; ZHSg is the inventory duration of the inventory record with the storage rack number g; ZHZg is the weight of the goods in the inventory record with the storage rack number g; g is the number of the inventory record, and g = 1, 2,..., G; G is the total number of inventory records; the inventory records of each storage rack are obtained from the database; S is the set unit duration; the specific value of the set unit duration S is set according to expert experience; in this embodiment, the set unit duration S is set to 10 min; it can be understood that the time unit involved in this embodiment is all min;

[0101] The longer the use time of the storage rack and the heavier the stored goods, the greater the aging or wear of the storage rack, and its corresponding load-bearing capacity will become smaller accordingly; in this embodiment, the correction coefficients of each storage rack are calculated through the above formula. The longer the use time of the storage rack and the heavier the stored goods, the greater the aging or wear of the storage rack, and the smaller the correction factor is set, thereby making the load-bearing safety limit value smaller;

[0102] The storage label includes a height label, a width label, a length label, and a load label.

[0103] This embodiment adjusts the original maximum load of the storage shelf according to the usage of the storage shelf and the setting height of the storage shelf to obtain a load safety limit; so that the load safety limit corresponding to each storage shelf is more in line with the specific situation of each storage shelf; and avoids the situation where the load capacity decreases due to aging of the storage shelf, which in turn causes the stored goods to fall.

[0104] Generate storage shelves and their corresponding optimization coefficients based on cargo geometry data, cargo weight data and storage labels, including:

[0105] Extract cargo height, cargo width and cargo length from cargo geometric data;

[0106] Determine whether the height of the cargo is less than the medium-sized piece threshold; if so, set the cargo height label to small piece; if not, when the cargo height is less than the medium-sized piece threshold, set the cargo height label to medium piece; otherwise, set the cargo height label to large piece;

[0107] Determine whether the cargo width is less than the medium piece threshold; if yes, set the cargo width label to small piece; if no, set the cargo width label to medium piece when the cargo width is less than the medium piece threshold; otherwise, set the cargo width label to large piece;

[0108] Determine whether the cargo length is less than the medium piece threshold; if yes, set the cargo length label to small piece; if no, set the cargo length label to medium piece when the cargo length is less than the medium piece threshold; otherwise, set the cargo length label to large piece;

[0109] Obtain the status label of each storage shelf; the status label includes free and in stock; extract the height label, width label and length label in the storage label corresponding to each storage shelf whose status label is free; mark the storage shelves whose height label is at the same level or higher than the height label of the goods, whose width label is at the same level or higher than the width label of the goods, and whose length label is at the same level or higher than the length label of the goods as preliminary selection shelves; it can be understood that in the process of goods storage, the top surface of the goods is generally determined, so that the height of the goods is generally determined, but the width and length of the goods may not be clear, that is, it is possible that the width of the goods corresponds to the width of the shelf, or it may correspond to the length of the shelf; therefore, in the case that the width and length of the goods are unclear, the storage shelves whose height label is at the same level or higher than the height label of the goods, whose width label is at the same level or higher than the length label of the goods, and whose length label is at the same level or higher than the width label of the goods can also be marked as preliminary selection shelves;

[0110] Extract the cargo weight from the cargo weight data; obtain the load safety limit recorded in the load label of each preliminary selected shelf, and record the preliminary selected shelf whose load safety limit is greater than the cargo weight as a shelf that can be stored;

[0111] Extract the shelf height JG, shelf width JK and shelf length JC from the geometric features of the storage shelf;

[0112] The optimization coefficient YX of the corresponding shelf is calculated by the formula YX=β1×exp(-(JG+JK+JC-HG-HK-HC) / C)+β2×exp(-(JZ-HZ) / Z); wherein HG is the height of the goods, HK is the width of the goods, HC is the length of the goods, and HZ is the weight of the goods; β1 and β2 are weight coefficients; the specific values ​​are set according to expert experience; C is the set unit size; Z is the set unit weight; in this embodiment, the set unit size C is set to 10 cm, and the set unit weight Z is set to 1 kg; it can be understood that in this embodiment, the units of length involved are cm, and the units of weight involved are kg,

[0113] This embodiment calculates the degree of matching between each optional shelf and the goods to be stored through the above formula, that is, the optimization coefficient. The closer the optional shelf size is to the goods size, the higher the corresponding matching degree is; the closer the load safety limit of the optional shelf is to the goods weight, the higher the corresponding matching degree is, and the larger the corresponding optimization coefficient is set, so as to avoid the situation where small-sized goods are stored on large-sized shelves when selecting shelves in the later stage, reasonably use the storage space, and thus improve the storage efficiency.

[0114] Generate an environmental safety factor based on the storage condition data and the storage environment label corresponding to the storable shelf, including: when the storage condition data is "none", the environmental safety factor is set to "0", that is, AX = 0; that is, storage can be carried out under any environmental conditions without considering the impact of the environment, so the corresponding environmental safety factor is set to "0";

[0115] Otherwise, extract the temperature storage condition, humidity storage condition and ventilation storage condition in the storage condition data; extract the temperature upper limit and temperature lower limit in the temperature storage condition, and the humidity upper limit and humidity lower limit in the humidity storage condition;

[0116] Extract each ambient temperature and its corresponding temperature probability from the storage environment label corresponding to the storage shelf with the environment type label of small change environment; mark the ambient temperature greater than the upper temperature limit as the high-risk temperature GWi1, and mark its corresponding temperature probability as WGI1; mark the ambient temperature lower than the lower temperature limit as the low-risk temperature DWi2; and mark its corresponding temperature probability as WGi2; i1 is the number of the high-risk temperature of the corresponding storage shelf; i2 is the number of the low-risk temperature of the corresponding storage shelf;

[0117] Extract each ambient humidity and its corresponding humidity probability in the storage environment label; mark the ambient humidity greater than the upper humidity limit as high-risk humidity GSj1, and mark its corresponding humidity probability as SGj1; mark the ambient humidity below the lower humidity limit as low-risk humidity DSj2; and mark its corresponding humidity probability as SGj2; j1 is the number of the high-risk humidity of the corresponding storage shelf; j2 is the number of the low-risk humidity of the corresponding storage shelf;

[0118] The temperature safety factor AW is calculated by the formula AW = ∑[WGi1×exp(-|GWi1-WS|)]+∑[DWi2×exp(-|DWi2-WX|)]; where WS is the upper temperature limit and WX is the lower temperature limit; i1 = 1, 2, ..., m1; m1 is the total number of high-risk temperatures; i2 = 1, 2, ..., m2; m2 is the total number of low-risk temperatures;

[0119] This embodiment calculates the temperature safety factor of the corresponding storage shelf through the above formula. When the recent ambient temperature is higher than the upper temperature limit of the corresponding goods to be stored, or lower than the lower temperature limit of the goods to be stored, the greater the probability is, the greater the possibility of a temperature that is not suitable for storing goods, and the smaller the corresponding temperature safety factor is set.

[0120] The humidity safety factor AS is calculated by the formula AS = ∑[SGj1×exp(-|SS-GSj1|)]+∑[SGj2×exp(-|DSj2-SX|)]; where SS is the upper limit of humidity and SX is the lower limit of humidity; j1 = 1, 2, ..., n1; n1 is the total number of high-risk humidity; j2 = 1, 2, ..., n2; n2 is the total number of low-risk humidity;

[0121] In this embodiment, the humidity safety factor of the corresponding storage shelf is calculated by the above formula. When the recent ambient humidity is higher than the upper humidity limit of the corresponding goods to be stored, or lower than the lower humidity limit of the goods to be stored, the greater the probability is, the greater the possibility of humidity being unsuitable for storing goods, and the smaller the corresponding humidity safety factor is set.

[0122] Extract the ventilation probability TG in the storage environment label; when the ventilation storage condition is ventilation, the ventilation safety factor is AT=TG; otherwise, the ventilation safety factor is AT=1-TG;

[0123] The environmental safety factor AX of the corresponding storage rack is calculated by the formula AX=α1×AW+α2×AS+α3×AT; wherein α1, α2 and α3 are weight coefficients, and the specific values ​​are set according to expert experience.

[0124] This embodiment uses the above formula to perform weighted summation of the safety factors of various environmental factors to obtain the environmental safety factor. The larger the environmental safety factor, the smaller the probability that the corresponding goods to be stored will be damaged by the environment when stored on the storage shelf; the more suitable it is for the storage of such goods.

[0125] See also Figure 3 , store selected shelves according to environmental safety factor and optimization factor;, including:

[0126] Obtain the environmental safety factor AX and the optimization factor YX of each shelf that can be stored;

[0127] The comprehensive selection coefficient XQ is calculated by the formula XQ=γ1×YX+γ2×AX; wherein γ1 and γ2 are weight coefficients, and the specific values ​​are set according to expert experience; γ2>γ1 set in this embodiment; that is, priority is given to the suitability of the storage environment; and then saving storage space is considered; the storable shelf with the largest comprehensive selection coefficient is selected as the storage selected shelf; and the status label of the storage selected shelf is set to inventory.

[0128] This embodiment comprehensively considers the matching degree between the goods and the storage shelf sizes, as well as the environmental safety factor of the storage shelf pair, that is, performs weighted summation of the optimization coefficient and the environmental safety factor to obtain a comprehensive selection coefficient; the storage shelf pair with the highest comprehensive selection coefficient is selected to store the corresponding goods; by setting different weight coefficients, goods are stored according to user needs. For example, when there are many shelves and few goods, γ2>γ1 can be set; when there are many goods and few shelves, γ1>γ2 can be set.

[0129] See also Figure 2 Another aspect of the present invention provides a smart unmanned warehouse management method, comprising the following steps:

[0130] Step 1: Obtain historical environmental data of each storage shelf, generate a fluctuation coefficient based on the historical environmental data; generate a storage environment label corresponding to the storage shelf based on the fluctuation coefficient;

[0131] Step 2: Obtain the geometric characteristics and load characteristics of each shelf storage rack; generate storage labels corresponding to each storage shelf according to the geometric characteristics and load characteristics;

[0132] Step 3: Obtain storage condition data, cargo geometry data and cargo weight data of the cargo to be stored; generate storage shelves and corresponding optimization coefficients based on the cargo geometry data, cargo weight data and storage labels;

[0133] Step 4: Generate an environmental safety factor based on the storage condition data and the storage environment labels corresponding to the storable shelves;

[0134] Step 5: Select the storage shelf according to the environmental safety factor and the optimization factor;

[0135] Step 6: Acquire the environmental data of each storage shelf in real time; extract the ambient temperature and ambient humidity in the environmental data, and generate a temperature alarm signal when the ambient temperature is higher than the upper temperature limit or lower temperature limit of the stored goods stored on the corresponding storage shelf; generate a humidity alarm signal when the ambient humidity is higher than the upper humidity limit or lower humidity limit of the stored goods stored on the corresponding storage shelf; the environmental data includes the ambient temperature and the ambient humidity.

[0136] This embodiment can also issue an alarm based on whether the real-time environmental data meets the storage conditions of the goods, so that relevant personnel can transfer the goods to appropriate shelves in time to reduce the loss of goods.

[0137] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0138] How this application works:

[0139] The present application obtains several historical environmental data of several storage shelves, generates the fluctuation coefficient of each storage shelf according to the historical environmental data; generates the storage environment label corresponding to the storage shelf according to the fluctuation coefficient; obtains the geometric characteristics and load characteristics of each storage shelf; generates the storage label corresponding to each storage shelf according to the geometric characteristics and load characteristics; obtains the storage condition data, cargo geometry data and cargo weight data of the goods to be stored; generates the storable shelves and the corresponding optimization coefficient according to the cargo geometry data, cargo weight data and storage labels; generates the environmental safety factor according to the storage condition data and the storage environment label corresponding to the storable shelves; selects the storage option according to the environmental safety factor and the optimization coefficient The system can determine the storage shelf; and obtain the environmental data of each storage shelf in real time; extract the environmental temperature and environmental humidity in the environmental data, and generate a temperature alarm signal when the environmental temperature is higher than the upper temperature limit or lower temperature limit of the storage goods stored on the corresponding storage shelf; generate a humidity alarm signal when the environmental humidity is higher than the upper humidity limit or lower humidity limit of the storage goods stored on the corresponding storage shelf; and give an alarm according to the alarm signal; this embodiment generates storage environment labels and storage labels of each storage shelf through the historical environmental data of each storage shelf and its corresponding geometric characteristics and load characteristics; selects suitable storage shelves for storage according to the storage conditions of the goods to be stored, thereby increasing the rationality of goods storage.

[0140] The above embodiments are only used to illustrate the technical method of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical method of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present application.

Claims

1. A smart unmanned warehouse management system, comprising: The data collection module, shelf classification module, goods allocation module, alarm module and database are characterized in that the data collection module: obtains environmental data collected in real time by data collection devices set on each storage shelf; The shelf classification module: obtains a number of historical environmental data of a number of storage shelves, generates a fluctuation coefficient of each storage shelf according to the historical environmental data; generates a storage environment label corresponding to the storage shelf according to the fluctuation coefficient; and Obtaining the geometric features and load characteristics of each shelf storage rack; generating storage labels corresponding to each storage rack according to the geometric features and load characteristics; The cargo allocation module: obtains storage condition data, cargo geometry data and cargo weight data of the cargo to be stored; generates storable shelves and corresponding optimization coefficients according to the cargo geometry data, cargo weight data and storage labels; generates environmental safety coefficients according to the storage condition data and the storage environment labels corresponding to the storable shelves; selects the selected storage shelf according to the environmental safety coefficient and the optimization coefficient; The alarm module: acquires the environmental data of each storage shelf in real time; extracts the environmental temperature and environmental humidity in the environmental data, generates a temperature alarm signal when the environmental temperature is higher than the temperature upper limit or lower than the temperature lower limit of the stored goods stored on the corresponding storage shelf; generates a humidity alarm signal when the environmental humidity is higher than the humidity upper limit or lower than the humidity lower limit of the stored goods stored on the corresponding storage shelf; and issues an alarm according to the alarm signal; the alarm signal includes a temperature alarm signal and a humidity alarm signal.

2. According to claim 1, the intelligent unmanned warehouse management system is characterized in that: Generating the fluctuation coefficient of each storage shelf according to historical environmental data includes: Acquire a number of historical environmental data of a number of storage shelves; pre-process the historical environmental data to obtain qualified environmental data; Obtaining the air exchange rate from several qualified environmental data of each storage rack and normalizing it; obtaining the ambient temperature from several qualified environmental data of each storage rack and normalizing it; obtaining the ambient humidity from several qualified environmental data of each storage rack and normalizing it; Obtain the maximum value KD and the minimum value KX of the normalized air exchange rate corresponding to each storage shelf, the maximum value ZDW and the minimum value ZXW of the ambient temperature, and the maximum value ZDS and the minimum value ZXS of the ambient humidity; calculate the fluctuation coefficient corresponding to each storage shelf through the formula BX=max(KD-KX, ZDW-ZXW, ZDS-ZXS).

3. The intelligent unmanned warehouse management system according to claim 2 is characterized in that: The pre-processing comprises: Extracting several air exchange rates, ambient temperatures and ambient humidity from various historical environmental data; determining whether there are missing data in the air exchange rates, ambient temperatures and ambient humidity; wherein missing data include missing values ​​or missing item units; If yes, the historical environment data is discarded; If not, the project units corresponding to the environmental items will be converted into standard units, and the project values ​​will be converted into standard values ​​under the standard units; when the standard values ​​corresponding to each environmental item are within the set preset range, the corresponding historical environmental data will be marked as qualified environmental data; otherwise, the historical environmental data will be discarded.

4. The intelligent unmanned warehouse management system according to claim 2 is characterized in that: Generating a storage environment label corresponding to a storage rack according to a fluctuation coefficient includes: Obtaining the fluctuation coefficient corresponding to each storage rack; determining whether the fluctuation coefficient is greater than a set fluctuation coefficient threshold; If so, marking the environment type label corresponding to the storage rack as a large-change environment; If not, marking the environment type label corresponding to the storage rack as a small-change environment; and generating a storage environment label for the corresponding storage rack according to the qualified environment coefficient of the storage rack.

5. The intelligent unmanned warehouse management system according to claim 4 is characterized in that: Generating the storage environment label for the corresponding storage rack according to qualified environment data includes: Obtaining a number of qualified environment data of the storage rack; extracting the air exchange rate, ambient temperature, and ambient humidity from each qualified environment data; obtaining the occurrence probability of each ambient temperature and marking it as the temperature probability corresponding to the ambient temperature, and obtaining the occurrence probability of each ambient humidity and marking it as the humidity probability corresponding to the ambient humidity; Obtaining the occurrence probability that the air exchange rate is greater than the ventilation threshold and recording it as the ventilation probability; integrating each ambient temperature with its corresponding temperature probability, each ambient humidity with its corresponding humidity probability, and the ventilation probability into a storage environment label.

6. The intelligent unmanned warehouse management system according to claim 1 is characterized in that: Generating storage labels corresponding to each storage rack according to geometric features and load characteristics includes: Extracting the rack height, rack width, and rack length from the geometric features of each storage rack; Determining whether the rack height is greater than a preset large-piece threshold; if so, setting the height label as large-piece; if not, when the rack height is greater than a preset medium-piece threshold, then setting the threshold as medium-piece; otherwise, setting the height label as small-piece; Determining whether the rack width is greater than a preset large-piece threshold; if so, setting the width label as large-piece; if not, when the rack width is greater than a preset medium-piece threshold, then setting the threshold as medium-piece; otherwise, setting the width label as small-piece; Determining whether the rack length is greater than a preset large-piece threshold; if so, setting the length label as large-piece; if not, when the rack length is greater than a preset medium-piece threshold, then setting the threshold as medium-piece; otherwise, setting the length label as small-piece; Extracting the height from the ground LH in the geometric features of each storage rack and the maximum load DZ in the load characteristics; calculating the load safety limit JZ through the formula JZ = DZ × JX × exp(-k1 × LH / C); where k is an adjustment coefficient and 0 < k1 < 1; C is a set unit size; JX is a correction coefficient; setting the load label of the corresponding storage rack as the load safety limit; Calculating the correction coefficient through the formula JX = 1 - k2 × arctan(∑(ZHSg / S × ZHZg / Z)); where k2 is a proportionality coefficient and 0 < k2 < 2 / π; ZHSg is the inventory duration of the inventory record with storage rack number g; ZHZg is the weight of the goods in the inventory record with storage rack number g; g is the number of the inventory record and g = 1, 2,..., G; G is the total number of inventory records; the inventory records of each storage rack are obtained from the database; S is a set unit duration; The storage label includes a height label, a width label, a length label, and a load label.

7. The intelligent unmanned warehouse management system according to claim 1 is characterized in that: Generate storage shelves and their corresponding optimization coefficients based on cargo geometry data, cargo weight data and storage labels, including: Extract cargo height, cargo width and cargo length from cargo geometric data; Determine whether the height of the cargo is less than the medium-sized piece threshold; if so, set the cargo height label to small piece; if not, when the cargo height is less than the medium-sized piece threshold, set the cargo height label to medium piece; otherwise, set the cargo height label to large piece; Determine whether the cargo width is less than the medium piece threshold; if yes, set the cargo width label to small piece; if no, set the cargo width label to medium piece when the cargo width is less than the medium piece threshold; otherwise, set the cargo width label to large piece; Determine whether the cargo length is less than the medium piece threshold; if yes, set the cargo length label to small piece; if no, set the cargo length label to medium piece when the cargo length is less than the medium piece threshold; otherwise, set the cargo length label to large piece; Obtain the status label of each storage shelf; the status label includes free and in stock; extract the height label, width label and length label in the storage label corresponding to each storage shelf whose status label is free; mark the storage shelf whose height label is the same level or higher than the height label of the goods, whose width label is the same level or higher than the width label of the goods, and whose length label is the same level or higher than the length label of the goods as the preliminary selection shelf; Extract the cargo weight from the cargo weight data; obtain the load safety limit recorded in the load label of each preliminary selected shelf, and record the preliminary selected shelf whose load safety limit is greater than the cargo weight as a storage shelf; Extract the shelf height JG, shelf width JK and shelf length JC from the geometric features of the storage shelf; The optimization coefficient YX of the corresponding shelf is calculated by the formula YX=β1×exp(-(JG+JK+JC-HG-HK-HC) / C)+β2×exp(-(JZ-HZ) / Z); wherein HG is the height of the goods, HK is the width of the goods, HC is the length of the goods, and HZ is the weight of the goods; β1 and β2 are weight coefficients; C is the set unit size; and Z is the set unit weight.

8. The intelligent unmanned warehouse management system according to claim 1 is characterized in that: The environmental safety factor is generated according to the storage condition data and the storage environment labels corresponding to the storable shelves, including: When the storage condition data is "none", the environmental safety factor is set to "0", that is, AX = 0; Otherwise, extract the temperature storage condition, humidity storage condition and ventilation storage condition in the storage condition data; extract the temperature upper limit and temperature lower limit in the temperature storage condition, and the humidity upper limit and humidity lower limit in the humidity storage condition; Extract each ambient temperature and its corresponding temperature probability from the storage environment label corresponding to the storage shelf with the environment type label of small change environment; mark the ambient temperature greater than the upper temperature limit as the high-risk temperature GWi1, and mark its corresponding temperature probability as WGI1; mark the ambient temperature lower than the lower temperature limit as the low-risk temperature DWi2; and mark its corresponding temperature probability as WGi2; i1 is the number of the high-risk temperature of the corresponding storage shelf; i2 is the number of the low-risk temperature of the corresponding storage shelf; Extract each ambient humidity and its corresponding humidity probability in the storage environment label; mark the ambient humidity greater than the upper humidity limit as high-risk humidity GSj1, and mark its corresponding humidity probability as SGj1; mark the ambient humidity below the lower humidity limit as low-risk humidity DSj2; and mark its corresponding humidity probability as SGj2; j1 is the number of the high-risk humidity of the corresponding storage shelf; j2 is the number of the low-risk humidity of the corresponding storage shelf; The temperature safety factor AW is calculated by the formula AW = ∑[WGi1×exp(-|GWi1-WS|)]+∑[DWi2×exp(-|DWi2-WX|)]; where WS is the upper temperature limit and WX is the lower temperature limit; i1 = 1, 2, ..., m1; m1 is the total number of high-risk temperatures; i2 = 1, 2, ..., m2; m2 is the total number of low-risk temperatures; The humidity safety factor AS is calculated by the formula AS = ∑[SGj1×exp(-|SS-GSj1|)]+∑[SGj2×exp(-|DSj2-SX|)]; where SS is the upper limit of humidity and SX is the lower limit of humidity; j1 = 1, 2, ..., n1; n1 is the total number of high-risk humidity; j2 = 1, 2, ..., n2; n2 is the total number of low-risk humidity; Extract the ventilation probability TG in the storage environment label; when the ventilation storage condition is ventilation, the ventilation safety factor is AT=TG; otherwise, the ventilation safety factor is AT=1-TG; The environmental safety factor AX of the corresponding storage rack is calculated by the formula AX=α1×AW+α2×AS+α3×AT; wherein α1, α2 and α3 are weight coefficients.

9. The intelligent unmanned warehouse management system according to claim 1, characterized in that: The storage of selected shelves according to the environmental safety factor and the optimization factor comprises: Obtain the environmental safety factor AX and the optimization factor YX of each shelf that can be stored; The comprehensive selection coefficient XQ is calculated by the formula XQ=γ1×YX+γ2×AX; wherein γ1 and γ2 are weight coefficients; the storable shelf with the largest comprehensive selection coefficient is selected as the storage selected shelf; and the status label of the storage selected shelf is set to inventory.

10. A smart unmanned warehouse management method, applied to the operation of a smart unmanned warehouse management system according to any one of claims 1 to 9; characterized in that: The following steps are involved: Step 1: Obtain historical environmental data of each storage shelf, generate a fluctuation coefficient based on the historical environmental data; generate a storage environment label corresponding to the storage shelf based on the fluctuation coefficient; Step 2: Obtain the geometric characteristics and load characteristics of each shelf storage rack; generate storage labels corresponding to each storage shelf according to the geometric characteristics and load characteristics; Step 3: Obtain storage condition data, cargo geometry data and cargo weight data of the cargo to be stored; Generate storage shelves and corresponding optimization coefficients based on cargo geometry data, cargo weight data and storage labels; Step 4: Generate an environmental safety factor based on the storage condition data and the storage environment labels corresponding to the storable shelves; Step 5: Select the storage shelf according to the environmental safety factor and the optimization factor; Step 6: Acquire the environmental data of each storage shelf in real time; extract the ambient temperature and ambient humidity in the environmental data, and generate a temperature alarm signal when the ambient temperature is higher than the upper temperature limit or lower temperature limit of the stored goods stored on the corresponding storage shelf; generate a humidity alarm signal when the ambient humidity is higher than the upper humidity limit or lower humidity limit of the stored goods stored on the corresponding storage shelf; the environmental data includes the ambient temperature and the ambient humidity.

Images