Label generation method, article warehousing method and device

Abstract

The embodiment of the invention provides a label generation method, and an article warehousing method and device. The tag generation method comprises the steps of firstly, in response to a received target order for a target article, obtaining article information of the target article, then, based on the article information, judging whether the target article is associated with an article tag, then, in response to determining that the target article is not associated with the article tag, identifying the article information based on a keyword, according to the method and the device, the identification result corresponding to the article information is obtained, and finally the article label corresponding to the target article is generated based on the identification result corresponding to the article information, so that the article label of the target article can be determined when the target order is received, and the accuracy and the generation efficiency of the article label are improved.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the fields of computer technology and Internet technology, and in particular, to a method for generating labels, a method and apparatus for item warehousing. Background Art

[0002] With the rapid development of the logistics industry in China, in the logistics industry, due to the huge scale of commodities and the complexity of category attributes, the current master data information of commodities fails to be maintained and improved in a timely manner, and even in some cases, the dangerous goods level of the actual warehoused commodities cannot be determined based on the commodity name. As a result, the logistics side cannot manage and monitor the warehousing of dangerous goods well, leading to some dangerous goods being put into ordinary warehouses, which poses a risk of being inspected and punished by fire control.

[0003] Currently, to determine whether a commodity is a dangerous good, it is necessary to manually inspect each commodity, which may result in low judgment efficiency and inaccuracy. Consequently, dangerous goods may not be detected during the warehouse receipt inspection, leading to warehousing and posing a safety hazard. Moreover, if the commodity inspection is not timely, it will cause the supplier's delivery to be rejected by the warehouse, affecting the customer experience. Summary of the Invention

[0004] Embodiments of the present disclosure provide a method for generating labels, a method and apparatus for item warehousing, an electronic device, and a computer-readable medium.

[0005] In a first aspect, an embodiment of the present disclosure provides a method for generating labels. The method includes: in response to receiving a target order for a target item, obtaining the item information of the target item; based on the item information, determining whether the target item is associated with an item label; in response to determining that the target item is not associated with an item label, identifying the item information based on keywords to obtain an identification result corresponding to the item information; and generating an item label corresponding to the target item based on the identification result corresponding to the item information.

[0006] In some embodiments, generating an item label corresponding to the target item based on the identification result corresponding to the item information includes: in response to determining that the identification result indicates that the item information includes keywords, generating a dangerous goods label indicating that the target item is a dangerous good; or, in response to determining that the identification result indicates that the item information does not include keywords, generating a non-dangerous goods label indicating that the target item is a non-dangerous good.

[0007] In some embodiments, the keywords include a first preset word and a second preset word; and, in response to determining that the target item is not associated with an item label, the item information is identified based on the keywords to obtain an identification result corresponding to the item information, including: in response to determining that the target item is not associated with an item label, the item information is identified based on the first preset word to obtain an initial identification result corresponding to the item information; in response to determining that the initial identification result indicates that the item information includes the first preset word, the item information is identified based on the second preset word to obtain an identification result corresponding to the item information.

[0008] In some embodiments, the method further includes: in response to generating an item label corresponding to the target item, establishing an association relationship between the item label and the target item; in response to determining that the item label is a dangerous item label, obtaining multiple candidate warehouse information corresponding to multiple candidate warehouses; based on the preset warehouse conditions corresponding to the dangerous item label and the multiple candidate warehouse information, selecting a target warehouse corresponding to the target order from the multiple candidate warehouses; sending the target order to the target warehouse so that the target warehouse performs a processing operation on the target order.

[0009] In a second aspect, an embodiment of the present disclosure provides an item warehousing method, the method including: in response to receiving a target order for a target item, obtaining the item information of the target item; based on the item information of the target item, obtaining an item label associated with the target item; in response to determining that the item label is a non-dangerous item label, predicting a predicted label corresponding to the target item based on a label prediction model and the item information; performing a warehousing operation corresponding to the target item based on the predicted label corresponding to the target item.

[0010] In some embodiments, performing the warehousing operation of the target item based on the predicted label corresponding to the target item includes: in response to determining that the predicted label is a suspected dangerous item label, presenting a prompt pop-up window corresponding to the target item; determining a real-time label of the target item based on the identification operation input through the prompt pop-up window; in response to receiving a determination that the real-time label is a non-dangerous item label, performing the warehousing operation of the target item.

[0011] In some embodiments, performing the warehousing operation of the target item based on the predicted label corresponding to the target item further includes: in response to receiving a determination that the real-time label is a dangerous item label, updating the item label of the target item to a dangerous item label; obtaining real-time warehouse information and determining whether the real-time warehouse information meets the preset warehouse conditions corresponding to the dangerous item label; in response to determining that the real-time warehouse information meets the preset warehouse conditions corresponding to the dangerous item label, performing the warehousing operation of the target item.

[0012] In some embodiments, the method further includes: in response to determining that the real-time warehouse information does not meet the preset warehouse conditions corresponding to the dangerous item label, performing a rejection operation on the target item.

[0013] In some embodiments, when performing the warehousing operation of the target item based on the predicted label corresponding to the target item, it further includes: in response to determining that the predicted label is a non-hazardous item label, performing the warehousing operation of the target item.

[0014] In a third aspect, an embodiment of the present disclosure provides a label generation device, which includes: an acquisition module configured to acquire item information of a target item in response to receiving a target order for the target item; a judgment module configured to judge whether the target item is associated with an item label based on the item information; an identification module configured to, in response to determining that the target item is not associated with an item label, identify the item information based on keywords to obtain an identification result corresponding to the item information; and a generation module configured to generate an item label corresponding to the target item based on the identification result corresponding to the item information.

[0015] In some embodiments, the generation module is further configured to: in response to determining that the identification result indicates that the item information includes keywords, generate a hazardous item label indicating that the target item is a hazardous item; or, in response to determining that the identification result indicates that the item information does not include keywords, generate a non-hazardous item label indicating that the target item is a non-hazardous item.

[0016] In some embodiments, the keywords include a first preset word and a second preset word; and the identification module is further configured to: in response to determining that the target item is not associated with an item label, identify the item information based on the first preset word to obtain an initial identification result corresponding to the item information; and in response to determining that the initial identification result indicates that the item information includes the first preset word, identify the item information based on the second preset word to obtain an identification result corresponding to the item information.

[0017] In some embodiments, the device further includes an establishment module, a selection module, and a sending module; the establishment module is configured to: in response to generating an item label corresponding to the target item, establish an association relationship between the item label and the target item; the acquisition module is further configured to: in response to determining that the item label is a hazardous item label, acquire multiple pieces of candidate warehouse information corresponding to multiple candidate warehouses; the selection module is configured to: select a target warehouse corresponding to the target order from the multiple candidate warehouses based on the preset warehouse conditions corresponding to the hazardous item label and the multiple pieces of candidate warehouse information; and the sending module is configured to: send the target order to the target warehouse so that the target warehouse performs a processing operation on the target order.

[0018] Fourthly, an embodiment of the present disclosure provides an article warehousing device, which includes: an acquisition module configured to acquire article information of a target article in response to receiving a target order for the target article; and based on the article information of the target article, acquire an article label associated with the target article; a prediction module configured to, in response to determining that the article label is a non-hazardous article label, predict a prediction label corresponding to the target article based on a label prediction model and the article information; an execution module configured to perform a warehousing operation corresponding to the target article based on the prediction label corresponding to the target article.

[0019] In some embodiments, the execution module is further configured to: in response to determining that the prediction label is a suspected hazardous article label, present a prompt pop-up window corresponding to the target article; determine a real-time label of the target article based on an identification operation input through the prompt pop-up window; and in response to receiving a determination that the real-time label is a non-hazardous article label, perform a warehousing operation on the target article.

[0020] In some embodiments, the execution module is further configured to: in response to receiving a determination that the real-time label is a hazardous article label, update the article label of the target article to a hazardous article label; acquire real-time warehouse information, and determine whether the real-time warehouse information meets a preset warehouse condition corresponding to the hazardous article label; and in response to determining that the real-time warehouse information meets the preset warehouse condition corresponding to the hazardous article label, perform a warehousing operation on the target article.

[0021] In some embodiments, the execution module is further configured to: in response to determining that the real-time warehouse information does not meet the preset warehouse condition corresponding to the hazardous article label, perform a rejection operation on the target article.

[0022] In some embodiments, the execution module is further configured to: in response to determining that the prediction label is a non-hazardous article label, perform a warehousing operation on the target article.

[0023] Fifthly, an embodiment of the present disclosure provides an electronic device, which includes: one or more processors; a storage device storing one or more programs thereon; when the one or more programs are executed by the one or more processors, the one or more processors implement the label generation method or the article warehousing method described in any embodiment of the first aspect or the second aspect.

[0024] Sixthly, an embodiment of the present disclosure provides a computer-readable medium storing a computer program thereon, and when the computer program is executed by a processor, it implements the label generation method or the article warehousing method described in any embodiment of the first aspect or the second aspect.

[0025] The label generation method provided by the embodiments of the present disclosure. The above-mentioned execution entity first responds to receiving a target order for a target item, obtains the item information of the target item, then based on the item information, determines whether the target item is associated with an item label. After that, in response to determining that the target item is not associated with an item label, it identifies the item information based on keywords to obtain an identification result corresponding to the item information. Finally, based on the identification result corresponding to the item information, it generates an item label corresponding to the target item. It can determine the item label of the target item when receiving the target order, implement the investigation of potential dangerous items at the upstream retail end, enable label recognition of the target item at the order end, and determine whether the target item is a dangerous item before entering the warehouse. It can prevent the target item that is a dangerous item from entering an unqualified warehouse, and there is no need for manual inspection of the target item, improving the accuracy and generation efficiency of the item label. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Other features, objects, and advantages of the present disclosure will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0027] Figure 1 is an exemplary system architecture diagram to which an embodiment of the present disclosure can be applied;

[0028] Figure 2 is a flowchart of an embodiment of the label generation method according to the present disclosure;

[0029] Figure 3 is a flowchart of another embodiment of the label generation method according to the present disclosure;

[0030] Figure 4 is a flowchart of an embodiment of sending the target order to the target warehouse according to the present disclosure;

[0031] Figure 5 is a flowchart of an embodiment of the item warehousing method according to the present disclosure;

[0032] Figure 6 is a flowchart of an embodiment of performing the warehousing operation of the target item according to the present disclosure;

[0033] Figure 7 is a schematic structural diagram of an embodiment of the label generation device according to the present disclosure;

[0034] Figure 8 is a schematic structural diagram of an embodiment of the item warehousing device according to the present disclosure;

[0035] Figure 9 is a schematic structural diagram of an electronic device suitable for implementing the embodiments of the present disclosure. Detailed Implementation Manner

[0036] The following further elaborates on the present disclosure in conjunction with the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are merely used to explain the relevant disclosure and do not limit the disclosure. Additionally, it should be noted that for ease of description, only parts related to the relevant disclosure are shown in the drawings.

[0037] It should be noted that, without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other. The following will elaborate on the present disclosure in detail with reference to the drawings and embodiments.

[0038] Figure 1 An exemplary system architecture 100 of a tag generation method, an item warehousing method, and a device to which the embodiments of the present disclosure can be applied is shown.

[0039] As Figure 1 shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide a communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0040] Users can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, etc. The terminal devices 101, 102, 103 may be user terminal devices, on which various client applications can be installed, such as, for example, image applications, video applications, search applications, financial applications, etc.

[0041] The terminal devices 101, 102, 103 may be various electronic devices with a display screen and supporting receiving server messages, including but not limited to smart phones, tablet computers, e - book readers, electronic players, laptop computers, and desktop computers, etc., and may be devices equipped with an image acquisition device for image acquisition.

[0042] The terminal devices 101, 102, 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices. When the terminal devices 101, 102, 103 are software, they can be installed in the above - listed electronic devices. It can be implemented as multiple software or software modules (such as multiple software modules for providing distributed services), or can be implemented as a single software or software module. No specific limitation is made here.

[0043] Terminal devices 101, 102, and 103 can receive a target order for a target item and obtain item information corresponding to the target item from server 105. Terminal devices 101, 102, and 103 can then determine whether the target item is associated with an item tag based on the item information. If it is determined that the target item is not associated with an item tag, the item information is identified based on keywords to obtain an identification result corresponding to the item information. Finally, based on the identification result corresponding to the item information, an item tag corresponding to the target item is generated.

[0044] Alternatively, the terminal devices 101, 102, and 103 may also receive a target order for a target item and obtain the item information of the target item from the server 105. Then, the terminal devices 101, 102, and 103 obtain the item tag associated with the target item based on the item information of the target item, and if it is determined that the item tag is a non-dangerous item tag, the predicted tag corresponding to the target item is predicted based on the tag prediction model and the item information, and finally, the warehousing operation corresponding to the target item is performed based on the predicted tag corresponding to the target item.

[0045] The server 105 may be a server providing various services, such as a backend server that receives a request sent by a terminal device that establishes a communication connection with the server 105. The backend server may receive and analyze the request sent by the terminal device and generate a processing result.

[0046] It should be noted that the server can be hardware or software. When the server is hardware, it can be various electronic devices that provide various services to the terminal device. When the server is software, it can be implemented as multiple software or software modules that provide various services to the terminal device, or it can be implemented as a single software or software module that provides various services to the terminal device. No specific limitation is made here.

[0047] It should be noted that the label generation method and item warehousing method provided in the embodiments of the present disclosure can be executed by the terminal devices 101 , 102 , and 103 , and accordingly, the label generation device and item warehousing device can be set in the terminal devices 101 , 102 , and 103 .

[0048] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0049] refer to Figure 2 , a flowchart 200 of an embodiment of a label generation method according to the present disclosure is shown. The label generation method comprises the following steps:

[0050] Step 210 , in response to receiving a target order for a target item, obtaining item information of the target item.

[0051] In this step, the execution entity (such as the terminal devices 101, 102, 103 in Figure 1 ) on which the label generation method runs can receive a target order for a target item input by the user, and then can obtain the item information of the target item from the server through the network. The item information may include the attribute information of the target item, and the attribute information may represent the description information of the target item, such as the usage description information, appearance description information, etc. of the target item. The item information may include the SKU (Stock Keeping Unit) code of the target item.

[0052] Alternatively, the above-mentioned execution entity can perform data analysis on the target item and extract the item information corresponding to the target item. The item information may include the attribute information of the target item, and the attribute information may represent the description information of the target item, such as the usage description information, appearance description information, etc. of the target item. The item information may include the SKU (Stock Keeping Unit) code of the target item.

[0053] Step 220: Based on the item information, determine whether the target item is associated with an item label.

[0054] In this step, after the above-mentioned execution entity obtains the item information of the target item, it can send a label acquisition request to the server according to the item information to determine whether the target item is associated with an item label. If the server returns the item label of the target item to the above-mentioned execution entity based on the label acquisition request, it is determined that the target item is associated with an item label; if the data returned by the server to the above-mentioned execution entity based on the label acquisition request is empty, it is determined that the target item is not associated with an item label.

[0055] Step 230: In response to determining that the target item is not associated with an item label, identify the item information based on keywords to obtain an identification result corresponding to the item information.

[0056] In this step, if the above-mentioned execution entity determines through the data returned by the server that the target item is not associated with an item label, it can obtain the keywords for screening items. The keywords may include multiple keywords corresponding to dangerous items, such as flammable, explosive, combustion-supporting, spontaneous combustion, perfume, battery, engine oil, etc. The keywords may be multiple words set by the staff according to needs or real-time regulations.

[0057] The above-mentioned execution entity can compare the item information of the target item with the keyword to determine whether the item information includes the keyword, so as to identify the item information, and determine the recognition result corresponding to the item information according to the judgment result. If it is determined through comparison that the item information includes the keyword, the recognition result corresponding to the item information can be that it is determined that the item information includes the keyword and the target item is identified as a dangerous item; if it is determined through comparison that the item information does not include the keyword, the recognition result corresponding to the item information can be that it is determined that the item information does not include the keyword and the target item is identified as a non-dangerous item.

[0058] Step 240, generate an item label corresponding to the target item based on the recognition result corresponding to the item information.

[0059] In this step, after the above-mentioned execution entity obtains the recognition result corresponding to the item information, it can determine the item category to which the target item belongs according to the recognition result corresponding to the item information, and generate an item label corresponding to the target item according to the item category.

[0060] That is, if it is determined that the item category to which the target item belongs is a dangerous item, an item label corresponding to the target item is generated according to this item category, and this item label is a dangerous item label; if it is determined that the item category to which the target item belongs is a non-dangerous item, an item label corresponding to the target item is generated according to this item category, and this item label is a non-dangerous item label.

[0061] As an optional implementation, the above step 240, generating an item label corresponding to the target item based on the recognition result corresponding to the item information, may include the following steps: in response to determining that the recognition result indicates that the item information includes the keyword, generate a dangerous item label indicating that the target item is a dangerous item; or, in response to determining that the recognition result indicates that the item information does not include the keyword, generate a non-dangerous item label indicating that the target item is a non-dangerous item.

[0062] Specifically, the above-mentioned execution entity compares the keyword with the item information of the target item. If it is determined that the recognition result indicates that the item information includes the keyword, a dangerous item label indicating that the target item is a dangerous item is generated; if it is determined that the recognition result indicates that the item information does not include the keyword, a non-dangerous item label indicating that the target item is a non-dangerous item is generated.

[0063] In this implementation, by generating different item labels based on different recognition results, it is possible to determine the label of the target item when the target item receives an order, and determine whether the target item belongs to a dangerous item.

[0064] The label generation method provided by the embodiments of the present disclosure. The above-mentioned execution subject first responds to receiving a target order for a target item, obtains the item information of the target item, then determines whether the target item is associated with an item label based on the item information, and then, in response to determining that the target item is not associated with an item label, identifies the item information based on keywords to obtain an identification result corresponding to the item information, and finally generates an item label corresponding to the target item based on the identification result corresponding to the item information. It is possible to determine the item label of the target item when receiving the target order, implement the investigation of potential dangerous items at the upstream retail end, enable label identification of the target item at the order end, and determine whether the target item is a dangerous item before entering the warehouse, which can prevent the target item that is a dangerous item from entering an unqualified warehouse, and there is no need for manual inspection of the target item, improving the accuracy and generation efficiency of the item label.

[0065] See Figure 3 , Figure 3 which shows a flowchart of another embodiment of the label generation method. The label generation method may include the following steps:

[0066] Step 310, in response to receiving a target order for a target item, obtain the item information of the target item.

[0067] In this step, step 310 is the same as Figure 2 step 210 in the embodiment shown, and details are not described here.

[0068] Step 320, based on the item information, determine whether the target item is associated with an item label.

[0069] In this step, step 320 is the same as Figure 2 step 220 in the embodiment shown, and details are not described here.

[0070] Step 330, in response to determining that the target item is not associated with an item label, identify the item information based on a first preset word to obtain an initial identification result corresponding to the item information.

[0071] Among them, the keywords may include a first preset word and a second preset word. The first preset word may include words representing the types of items, such as erasers, perfumes, etc. The second preset word may include words representing the ingredients of items, such as alcohol, flammable, explosive, etc. The second preset word is more detailed than the first preset word and describes more detailed information. In addition, the second preset word can also be divided into different categories, which may include prohibited items category and restricted items category. The second preset words corresponding to the prohibited items category and the restricted items category are different. The second preset word corresponding to the prohibited items category may include flammable, explosive, combustion-supporting ingredients, spontaneous combustion caused by external force, and information words corresponding to such items; the second preset word corresponding to the restricted items category may include information words corresponding to items such as perfumes, storage batteries, engine oils, etc. that need to be stored uniformly and follow the due storage rules.

[0072] In this step, after the above-mentioned execution entity determines that the target item is not associated with an item label, it can compare the item information of the target item with the first preset word to determine whether the item information includes the first preset word, so as to identify the item information, and determine the initial recognition result corresponding to the item information according to the judgment result. If it is determined through comparison that the item information includes the first preset word, the initial recognition result corresponding to the item information may be to determine that the item information includes the first preset word and identify the target item as a dangerous item; if it is determined through comparison that the item information does not include the first preset word, the recognition result corresponding to the item information may be to determine that the item information does not include the first preset word and identify the target item as a non-dangerous item.

[0073] Step 340, in response to determining that the initial recognition result indicates that the item information includes the first preset word, identify the item information based on the second preset word to obtain the recognition result corresponding to the item information.

[0074] In this step, if the above-mentioned execution entity determines through recognition that the initial recognition result indicates that the item information includes the first preset word, it is necessary to further identify the item information according to the second preset word to determine whether the target item is a dangerous item. The above-mentioned execution entity can compare the item information of the target item with the second preset word to determine whether the item information includes the second preset word, so as to identify the item information, and determine the recognition result corresponding to the item information according to the judgment result.

[0075] If it is determined through comparison that the item information includes the second preset word, the recognition result corresponding to the item information may be that it is determined that the item information includes the second preset word, and the target item is recognized as a dangerous item. Moreover, the above-mentioned execution entity may further determine whether the target item belongs to a prohibited item or a restricted item according to the second preset word included in the item information, that is, if the item information includes the second preset word corresponding to a prohibited item, it is determined that the target item is a prohibited item; if the item information includes the second preset word corresponding to a restricted item, it is determined that the target item is a restricted item.

[0076] If it is determined through comparison that the item information does not include the second preset word, the recognition result corresponding to the item information may be that it is determined that the item information does not include the second preset word, and the target item is recognized as a non-dangerous item.

[0077] Step 350, based on the recognition result corresponding to the item information, generate an item label corresponding to the target item.

[0078] In this step, step 350 is the same as Figure 2 step 240 in the illustrated embodiment, and will not be elaborated here.

[0079] In this embodiment, by dividing the keywords into the first preset word and the second preset word, and using the first preset word and the second preset word to perform two-fold recognition on the item information, the recognition result of the item information can be made more accurate, and the accuracy of the item label can be improved.

[0080] Refer to Figure 4 , Figure 4 shows a flowchart 400 of an embodiment of sending a target order to a target warehouse. The above label generation method may further include the following steps:

[0081] Step 410, in response to generating an item label corresponding to the target item, establish an association relationship between the item label and the target item.

[0082] In this step, after generating the item label of the target item, the above-mentioned execution entity may establish an association relationship between the item label and the target item to realize the associated storage between the item label and the target item.

[0083] Step 420, in response to determining that the item label is a dangerous item label, obtain multiple candidate warehouse information corresponding to multiple candidate warehouses.

[0084] In this step, if the above-mentioned execution entity determines that the item label is a dangerous item label, it may read the information of multiple candidate warehouses to obtain multiple candidate warehouse information corresponding to multiple candidate warehouses. The candidate warehouse information may characterize the warehouse qualifications of the candidate warehouses, and may include the warehouse type, storage scale, storage rules, etc. of the candidate warehouses.

[0085] Step 430: Based on the preset warehouse conditions corresponding to the dangerous goods label and the multiple candidate warehouse information, select the target warehouse corresponding to the target order from the multiple candidate warehouses.

[0086] In this step, after the above-mentioned execution entity determines that the target item is a dangerous good, it can obtain the preset warehouse conditions corresponding to the dangerous goods label, and different dangerous goods labels may also correspond to different preset warehouse conditions. If the dangerous goods label indicates that the target item is a prohibited item, the preset warehouse conditions corresponding to the prohibited item can be obtained, and the preset warehouse conditions may include that it must enter a special warehouse for management and is not allowed to enter the general warehouse for management; if the dangerous goods label indicates that the target item is a restricted item, the preset warehouse conditions corresponding to the restricted item can be obtained, and the preset warehouse conditions may include that it can enter the comprehensive warehouse, but needs to be stored uniformly and follow the due storage rules, and each area is centrally managed in the operation plan.

[0087] The above-mentioned execution entity can screen the multiple candidate warehouse information according to the preset warehouse conditions corresponding to the dangerous goods label, determine a candidate warehouse information that meets the preset warehouse conditions, and determine the candidate warehouse corresponding to the candidate warehouse information as the target warehouse corresponding to the target order, thereby realizing the selection of the target warehouse corresponding to the target order from the multiple candidate warehouses.

[0088] Step 440: Send the target order to the target warehouse so that the target warehouse performs a processing operation on the target order.

[0089] In this step, after the above-mentioned execution entity determines the target warehouse, it can send the received target order to the target warehouse so that the target warehouse receives the target order and performs procurement and subsequent processing operations on the target order.

[0090] In this implementation manner, after determining that the target item of the target order is a dangerous good, the preset warehouse conditions are used to determine the target warehouse that can process the target order, preventing dangerous goods from being stored in unqualified warehouses, accurately distinguishing whether the target item can be stored in the warehouse, which warehouses it can be stored in, avoiding the risks caused by the storage of dangerous goods in unmatched warehouses, avoiding potential fire hazards, reducing losses and negative public opinion, and also reducing or even avoiding the waste of freight and customer experience caused by the rejection of the goods delivered by the supplier to the warehouse due to non-compliance with the rules for the storage of dangerous goods.

[0091] Reference Figure 5 , shows a flowchart 500 of an embodiment of the item warehousing method according to the present disclosure. The item warehousing method includes the following steps:

[0092] Step 510: In response to receiving a target order for a target item, obtain the item information of the target item.

[0093] In this step, the execution entity (such as the terminal devices 101, 102, 103 in Figure 1 ) on which the item warehousing method runs can obtain the item information of the target item from the server through the network after receiving the target order for the target item. The item information may include the attribute information of the target item, and the attribute information may characterize the description information of the target item, such as the usage description information, appearance description information, etc. of the target item. The item information may include the SKU (Stock Keeping Unit) code of the target item.

[0094] Alternatively, the above execution entity can perform data analysis on the target item and extract the item information corresponding to the target item. The item information may include the attribute information of the target item, and the attribute information may characterize the description information of the target item, such as the usage description information, appearance description information, etc. of the target item. The item information may include the SKU (Stock Keeping Unit) code of the target item.

[0095] Step 520: Obtain the item label associated with the target item based on the item information of the target item.

[0096] In this step, after the above execution entity obtains the item information of the target item, it can send a label acquisition request to the server according to the item information. The server returns the item label of the target item to the above execution entity based on the label acquisition request, so as to obtain the item label associated with the target item.

[0097] Step 530: In response to determining that the item label is a non-hazardous item label, predict the prediction label corresponding to the target item based on the label prediction model and the item information.

[0098] In this step, after the above execution entity obtains the item label associated with the target item, if it determines that the item label is a non-hazardous item label, it can further use the label prediction model to process the item information of the target item. Input the item information into the label prediction model. The label prediction model performs information processing and label prediction on the item information and outputs the prediction label corresponding to the target item. The prediction label may include a non-hazardous item label or a suspected hazardous item label.

[0099] Among them, the above label prediction model may be an online sequential extreme learning machine with self-learning ability. By using the item information labeled with hazardous item labels and non-hazardous item labels to train the model, in the initial training stage, there are N0 arbitrary training samples (X i , t i) Using the idea of traditional ELM, the model of a single-hidden-layer feedforward neural network (SLFN) with L hidden nodes and a non-linear mapping as the activation function g(x), and during the online learning phase, the final results of suspected dangerous items can be collected and continuously learned. While maintaining the prediction of suspected dangerous items, the algorithm model is adjusted, that is, the algorithm parameters can be continuously learned and adjusted using the previous prediction results to achieve the continuous self-learning and adjustment of the label prediction model.

[0100] Step 540: Based on the predicted label corresponding to the target item, perform the warehousing operation corresponding to the target item.

[0101] In this step, after the above-mentioned execution entity obtains the predicted label corresponding to the target item, it can determine the warehousing operation corresponding to the predicted label according to the predicted label. Different predicted labels can correspond to different warehousing operations, and the warehousing operation can be an inspection and judgment operation and an inspection and warehousing operation before warehousing the target item corresponding to the predicted label.

[0102] As an optional implementation manner, the above step 540, based on the predicted label corresponding to the target item, performing the warehousing operation corresponding to the target item, may include the following steps: In response to determining that the predicted label is a non-dangerous item label, perform the warehousing operation of the target item.

[0103] Specifically, after the above-mentioned execution entity obtains the predicted label, if it determines that the predicted label of the target item is a non-dangerous item label, it determines that the target item is a non-dangerous item, and can directly perform the warehousing operation of the target item, scan the item information of the target item, and when the received quantity or the shelving quantity is greater than 0, it is determined that the warehousing operation of the target item has been completed.

[0104] In this implementation manner, by further determining that the target item is a non-dangerous item when the predicted label is still a non-dangerous item label, the warehousing operation of the target item can be directly performed, ensuring the accuracy of the warehousing of non-dangerous items.

[0105] For the item warehousing method provided by the embodiments of the present disclosure, the above-mentioned execution entity first responds to receiving a target order for a target item, obtains the item information of the target item, then based on the item information of the target item, obtains the item label associated with the target item, and then in response to determining that the item label is a non-dangerous item label, predicts the predicted label corresponding to the target item based on the label prediction model and the item information, and finally based on the predicted label corresponding to the target item, performs the warehousing operation corresponding to the target item. It can further judge the target item that has been associated with the item label, and can further judge the item determined to be a non-dangerous item label, making the identification of items with non-dangerous item labels more accurate, ensuring the accuracy of the judgment of non-dangerous items, and thus ensuring the safety of the warehousing of non-dangerous items.

[0106] Reference Figure 6 , Figure 6 FIG. 600 shows a flowchart of an embodiment of performing the warehousing operation of the target item, i.e., the above step 540. Based on the predicted label corresponding to the target item, performing the warehousing operation corresponding to the target item may include the following steps:

[0107] Step 610, in response to determining that the predicted label is a suspected dangerous item label, present a prompt pop-up window corresponding to the target item.

[0108] In this step, after the above-mentioned execution entity obtains the predicted label, if it is determined that the predicted label of the target item is a suspected dangerous item label, when scanning the item information of the target item, a prompt pop-up window corresponding to the target item will be presented. This prompt pop-up window can be used to prompt that the target item is a suspected dangerous item and it is necessary to check whether it is a dangerous item. For example, the prompt pop-up window can be: This item is a suspected dangerous item. Dangerous items need to be rejected! Please check whether it is a dangerous item.

[0109] The above prompt pop-up window may include a prompt message and two selectable selection controls. The selection controls may include a "Yes" control and a "No" control. After the staff checks the target item, they can select the "Yes" control or the "No" control according to the check result.

[0110] To prevent accidental touch, the selection result of the prompt pop-up window does not block the warehousing process. To increase the alert frequency and reduce the risk of misoperation by the staff without affecting the receiving efficiency, the frequency of the prompt pop-up window is controlled at the task order dimension. Under the same order, when scanning the SKU code of the same target item, the prompt pop-up window is presented only once.

[0111] Step 620, based on the recognition operation input through the prompt pop-up window, determine the real-time label of the target item.

[0112] In this step, the above-mentioned execution entity can receive the recognition operation input by the staff through the prompt pop-up window. This recognition operation can be the selection operation of the staff on the "Yes" control or the "No" control, and the real-time label of the target item can be determined according to this recognition operation. If the recognition operation is the selection operation of the staff on the "Yes" control, it is determined that the real-time label of the target item is a dangerous item label; if the recognition operation is the selection operation of the staff on the "No" control, it is determined that the real-time label of the target item is a non-dangerous item label.

[0113] Step 630, in response to receiving the determination that the real-time label is a non-dangerous item label, perform the warehousing operation of the target item.

[0114] In this step, if the above-mentioned execution entity determines that the real-time label is a non-hazardous item label, it can directly perform the warehousing operation of the target item, scan the item information of the target item, and when the received quantity or the shelving quantity is greater than 0, it is determined that the warehousing operation of the target item has been completed.

[0115] In this implementation, by manually inspecting the target item corresponding to the predicted label as a suspected hazardous item label, the item label of the target item is further verified, so that the item label of the target item can be more accurate.

[0116] Continue to refer to Figure 6 , the above step 540, based on the predicted label corresponding to the target item, performs the warehousing operation corresponding to the target item, and may further include the following steps:

[0117] Step 640, in response to receiving the determination that the real-time label is a hazardous item label, update the item label of the target item to a hazardous item label.

[0118] In this step, if the above-mentioned execution entity determines that the real-time label is a hazardous item label, it can update the item label associated with the target item and update the associated item label to a hazardous item label.

[0119] Step 650, obtain the real-time warehouse information and determine whether the real-time warehouse information meets the preset warehouse conditions corresponding to the hazardous item label.

[0120] In this step, the above-mentioned execution entity can read the real-time warehouse information of the warehouse to obtain the real-time warehouse information, and this real-time warehouse information can characterize the warehouse qualification of the warehouse at the current time.

[0121] After the above-mentioned execution entity determines that the target item is a hazardous item, it can obtain the preset warehouse conditions corresponding to the hazardous item label, and different preset warehouse conditions can also correspond to the hazardous item label. If the hazardous item label indicates that the target item is a prohibited item, the preset warehouse conditions corresponding to the prohibited item can be obtained, and the preset warehouse conditions can include that it must enter a special warehouse for management and is not allowed to enter the general warehouse for management; if the hazardous item label indicates that the target item is a restricted item, the preset warehouse conditions corresponding to the restricted item can be obtained, and the preset warehouse conditions can include that it can enter the comprehensive warehouse, but needs to be stored uniformly and follow the due storage rules, and each area of the operation plan is centrally managed.

[0122] The above-mentioned execution entity can compare the preset warehouse conditions corresponding to the hazardous item label with the real-time warehouse information to determine whether the real-time warehouse information meets the preset warehouse conditions corresponding to the hazardous item label.

[0123] Step 660: In response to determining that the real-time warehouse information meets the preset warehouse conditions corresponding to the dangerous goods label, perform the warehousing operation of the target item.

[0124] In this step, if the above-mentioned execution entity determines through judgment that the real-time warehouse information meets the preset warehouse conditions corresponding to the dangerous goods label, it is determined that the warehouse can be used to store the target item, and the warehousing operation of the target item can be directly performed. Scan the item information of the target item. When the received quantity or the shelving quantity is greater than 0, it is determined that the warehousing operation of the target item has been completed.

[0125] In this implementation, by judging the warehouse information of the target item belonging to dangerous goods, it is determined whether the warehouse currently has the warehouse qualification to store the target item, avoiding the situation that dangerous goods are still stored in the warehouse after the warehouse loses the qualification to store dangerous goods, avoiding potential fire hazards, and reducing losses and negative public opinion.

[0126] Continue to refer to Figure 6 , the above-mentioned step 540: Based on the predicted label corresponding to the target item, perform the warehousing operation corresponding to the target item, and may further include the following steps:

[0127] Step 670: In response to determining that the real-time warehouse information does not meet the preset warehouse conditions corresponding to the dangerous goods label, perform the rejection operation of the target item.

[0128] In this step, if the above-mentioned execution entity determines through judgment that the real-time warehouse information does not meet the preset warehouse conditions corresponding to the dangerous goods label, it is determined that the warehouse has lost the warehouse qualification to store dangerous goods, and it is necessary to perform the rejection operation on the target item to prevent the target item from being warehoused.

[0129] In this implementation, by performing the rejection operation of the target item when it is determined that the real-time warehouse information does not meet the preset warehouse conditions corresponding to the dangerous goods label, it is avoided to store dangerous goods in a warehouse that has already lost its qualification, improving the safety and accuracy of the warehousing of dangerous goods.

[0130] Refer to Figure 7 , as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a label generation device. This device embodiment corresponds to Figure 2 the method embodiment shown.

[0131] As shown in Figure 7 , the label generation device 700 of this embodiment may include: an acquisition module 710, a judgment module 720, an identification module 730, and a generation module 740.

[0132] Among them, the acquisition module 710 is configured to obtain the item information of the target item in response to receiving a target order for the target item;

[0133] A determination module 720, configured to determine whether a target item is associated with an item label based on item information;

[0134] An identification module 730, configured to, in response to determining that the target item is not associated with an item label, identify the item information based on keywords to obtain an identification result corresponding to the item information;

[0135] A generation module 740, configured to generate an item label corresponding to the target item based on the identification result corresponding to the item information.

[0136] In some alternative implementation manners of this embodiment, the generation module 740 is further configured to: in response to determining that the identification result indicates that the item information includes keywords, generate a dangerous item label indicating that the target item is a dangerous item; or, in response to determining that the identification result indicates that the item information does not include keywords, generate a non - dangerous item label indicating that the target item is a non - dangerous item.

[0137] In some alternative implementation manners of this embodiment, the keywords include a first preset word and a second preset word; and the identification module 730 is further configured to: in response to determining that the target item is not associated with an item label, identify the item information based on the first preset word to obtain an initial identification result corresponding to the item information; in response to determining that the initial identification result indicates that the item information includes the first preset word, identify the item information based on the second preset word to obtain an identification result corresponding to the item information.

[0138] In some alternative implementation manners of this embodiment, the apparatus further includes an establishment module, a selection module, and a sending module; the establishment module is configured to: in response to generating an item label corresponding to the target item, establish an association relationship between the item label and the target item; the acquisition module 710 is further configured to: in response to determining that the item label is a dangerous item label, acquire multiple pieces of candidate warehouse information corresponding to multiple candidate warehouses; the selection module is configured to: based on a preset warehouse condition corresponding to the dangerous item label and the multiple pieces of candidate warehouse information, select a target warehouse corresponding to the target order from the multiple candidate warehouses; the sending module is configured to: send the target order to the target warehouse so that the target warehouse performs a processing operation on the target order.

[0139] The label generation device provided by the above embodiments of the present disclosure. First, the execution entity responds to receiving a target order for a target item, obtains the item information of the target item, then based on the item information, determines whether the target item is associated with an item label. After that, in response to determining that the target item is not associated with an item label, it identifies the item information based on keywords to obtain an identification result corresponding to the item information. Finally, based on the identification result corresponding to the item information, it generates an item label corresponding to the target item. It can determine the item label of the target item when receiving the target order, implement the investigation of potential dangerous items at the upstream retail end, enable the target item to achieve label recognition at the order end, and determine whether the target item is a dangerous item before entering the warehouse, which can prevent the target item that is a dangerous item from entering an unqualified warehouse, eliminating the need for manual inspection of the target item, and improving the accuracy and generation efficiency of the item label.

[0140] Reference Figure 8 , as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of an item warehousing device. This device embodiment corresponds to Figure 5 the method embodiment shown.

[0141] As Figure 8 shown, the item warehousing device 800 of this embodiment may include: an acquisition module 810, a prediction module 820, and an execution module 830.

[0142] Among them, the acquisition module 810 is configured to respond to receiving a target order for a target item, obtain the item information of the target item; and obtain the item label associated with the target item based on the item information of the target item;

[0143] The prediction module 820 is configured to, in response to determining that the item label is a non-dangerous item label, predict the prediction label corresponding to the target item based on the label prediction model and the item information;

[0144] The execution module 830 is configured to perform the warehousing operation corresponding to the target item based on the prediction label corresponding to the target item.

[0145] In some optional implementation manners of this embodiment, the execution module 830 is further configured to: in response to determining that the prediction label is a suspected dangerous item label, present a prompt pop-up window corresponding to the target item; determine the real-time label of the target item based on the recognition operation input through the prompt pop-up window; and in response to receiving that the determined real-time label is a non-dangerous item label, perform the warehousing operation of the target item.

[0146] In some alternative implementation manners of this embodiment, the execution module 830 is further configured to: in response to receiving a determination that the real-time label is a dangerous goods label, update the item label of the target item to a dangerous goods label; obtain real-time warehouse information, and determine whether the real-time warehouse information meets the preset warehouse conditions corresponding to the dangerous goods label; in response to determining that the real-time warehouse information meets the preset warehouse conditions corresponding to the dangerous goods label, perform the warehousing operation of the target item.

[0147] In some alternative implementation manners of this embodiment, the execution module 830 is further configured to: in response to determining that the real-time warehouse information does not meet the preset warehouse conditions corresponding to the dangerous goods label, perform the rejection operation of the target item.

[0148] In some alternative implementation manners of this embodiment, the execution module 830 is further configured to: in response to determining that the predicted label is a non-dangerous goods label, perform the warehousing operation of the target item.

[0149] For the item warehousing device provided in the above embodiments of the present disclosure, the above execution entity first responds to receiving a target order for a target item, obtains the item information of the target item, then based on the item information of the target item, obtains the item label associated with the target item, and then in response to determining that the item label is a non-dangerous goods label, predicts the predicted label corresponding to the target item based on the label prediction model and the item information, and finally based on the predicted label corresponding to the target item, performs the warehousing operation corresponding to the target item. It can further judge the target item with an associated item label, and can further judge the item determined to be a non-dangerous goods label, making the identification of non-dangerous goods labels more accurate, ensuring the accuracy of non-dangerous goods judgment, and thus ensuring the safety of non-dangerous goods warehousing.

[0150] Those skilled in the art can understand that the above device further includes some other well-known structures, such as a processor, a memory, etc. In order not to unnecessarily obscure the embodiments of the present disclosure, these well-known structures are not shown in Figure 8 it.

[0151] It should be noted that in the technical solution of the present disclosure, in terms of the collection, collection, update, analysis, processing, use, transmission, storage, etc. of the user's personal information, it complies with the provisions of relevant laws and regulations, is used for legal purposes, and does not violate public order and good customs. Necessary measures are taken for the user's personal information to prevent illegal access to the user's personal information data, and to maintain the security of the user's personal information, network security and national security.

[0152] Next, refer to Figure 9, which shows a schematic structural diagram of an electronic device 900 suitable for implementing the embodiments of the present disclosure. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as smart screens, laptop computers, PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 9 The terminal device shown is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present disclosure.

[0153] As Figure 9 shown, the electronic device 900 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 901, which may perform various appropriate actions and processes according to the programs stored in the read-only memory (ROM) 902 or the programs loaded from the storage device 908 into the random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the electronic device 900 are also stored. The processing device 901, the ROM 902, and the RAM 903 are connected to each other through a bus 904. The input / output (I / O) interface 905 is also connected to the bus 904.

[0154] Generally, the following devices may be connected to the I / O interface 905: an input device 906 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 907 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 908 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 909. The communication device 909 may allow the electronic device 900 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 9 the electronic device 900 with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. More or fewer devices may be alternatively implemented or had. Figure 9 Each block shown in

[0155] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes program code for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from the network via a communication device 909, or installed from a storage device 908, or installed from a ROM 902. When the computer program is executed by a processing device 901, the above-described functions defined in the methods of the embodiments of the present disclosure are performed. It should be noted that the computer-readable medium in the embodiments of the present disclosure can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the embodiments of the present disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the embodiments of the present disclosure, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, and the computer-readable signal medium can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0156] Computer program code for performing the operations of the embodiments of the present disclosure may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or, it may be connected to an external computer (e.g., connected through the Internet using an Internet service provider).

[0157] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0158] The units involved in the embodiments described in the present application may be implemented in software or in hardware. The described units may also be provided in a processor. For example, it may be described as: a processor includes an acquisition module, a judgment module, an identification module, and a generation module, or, a processor includes an acquisition module, a prediction module, and an execution module, where the names of these modules do not constitute a limitation on the module itself in some cases.

[0159] As another aspect, the present application also provides a computer-readable medium. The above computer-readable medium may be included in the above electronic device; or it may exist alone and not be assembled into the electronic device. The above computer-readable medium carries one or more programs. When the above one or more programs are executed by the electronic device, the electronic device is caused to: in response to receiving a target order for a target item, obtain item information of the target item; based on the item information, determine whether the target item is associated with an item label; in response to determining that the target item is not associated with an item label, identify the item information based on keywords to obtain an identification result corresponding to the item information; based on the identification result corresponding to the item information, generate an item label corresponding to the target item. Alternatively, the electronic device is caused to: in response to receiving a target order for a target item, obtain item information of the target item; based on the item information of the target item, obtain an item label associated with the target item; in response to determining that the item label is a non-hazardous item label, predict a predicted label corresponding to the target item based on a label prediction model and the item information; based on the predicted label corresponding to the target item, perform a warehousing operation corresponding to the target item.

[0160] The above description is only a preferred embodiment of the present disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are mutually replaced with the (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure to form a technical solution.

Claims

1. A label generation method, the method comprising: Upon receiving a target order for a target item, obtaining item information of the target item; Based on the item information, determining whether the target item is associated with an item label; Upon determining that the target item is not associated with an item label, identifying the item information based on keywords to obtain an identification result corresponding to the item information; Based on the identification result corresponding to the item information, generating an item label corresponding to the target item.

2. The method according to claim 1, wherein, The generating an item label corresponding to the target item based on the identification result corresponding to the item information includes: Upon determining that the identification result indicates that the item information includes the keywords, generating a dangerous item label indicating that the target item is a dangerous item; or, Upon determining that the identification result indicates that the item information does not include the keywords, generating a non - dangerous item label indicating that the target item is a non - dangerous item.

3. The method according to any one of claims 1 or 2, wherein The keywords include a first preset word and a second preset word; and the identifying the item information based on keywords to obtain an identification result corresponding to the item information upon determining that the target item is not associated with an item label includes: Upon determining that the target item is not associated with an item label, identifying the item information based on the first preset word to obtain an initial identification result corresponding to the item information; Upon determining that the initial identification result indicates that the item information includes the first preset word, identifying the item information based on the second preset word to obtain an identification result corresponding to the item information.

4. The method according to any one of claims 1 - 3, the method further comprising: Upon generating an item label corresponding to the target item, establishing an association relationship between the item label and the target item; Upon determining that the item label is a dangerous item label, obtaining multiple pieces of candidate warehouse information corresponding to multiple candidate warehouses; Based on the preset warehouse conditions corresponding to the dangerous item label and the multiple pieces of candidate warehouse information, selecting a target warehouse corresponding to the target order from the multiple candidate warehouses; Sending the target order to the target warehouse so that the target warehouse performs a processing operation on the target order.

5. An item warehousing method, the method comprising: Upon receiving a target order for a target item, obtaining item information of the target item; Based on the item information of the target item, obtaining an item label associated with the target item; Upon determining that the item label is a non - dangerous item label, predicting a predicted label corresponding to the target item based on a label prediction model and the item information; Based on the predicted label corresponding to the target item, performing a warehousing operation corresponding to the target item.

6. The method according to claim 5, wherein, The performing a warehousing operation corresponding to the target item based on the predicted label corresponding to the target item includes: Upon determining that the predicted label is a suspected dangerous item label, presenting a prompt pop - up window corresponding to the target item; Based on the identification operation input through the prompt pop - up window, determining a real - time label of the target item. In response to receiving a determination that the real-time label is a non-hazardous item label, perform the warehousing operation of the target item.

7. The method according to claim 6, wherein The performing the warehousing operation of the target item based on the predicted label corresponding to the target item further includes: In response to receiving a determination that the real-time label is a hazardous item label, update the item label of the target item to the hazardous item label; Obtain real-time warehouse information, and determine whether the real-time warehouse information meets the preset warehouse conditions corresponding to the hazardous item label; In response to determining that the real-time warehouse information meets the preset warehouse conditions corresponding to the hazardous item label, perform the warehousing operation of the target item.

8. The method according to claim 7, the method further includes: In response to determining that the real-time warehouse information does not meet the preset warehouse conditions corresponding to the hazardous item label, perform the rejection operation of the target item.

9. The method according to claim 5, wherein The performing the warehousing operation of the target item based on the predicted label corresponding to the target item further includes: In response to determining that the predicted label is a non-hazardous item label, perform the warehousing operation of the target item.

10. A label generation device, the device includes: An acquisition module, configured to acquire the item information of the target item in response to receiving a target order for the target item; A judgment module, configured to judge whether the target item is associated with an item label based on the item information; An identification module, configured to, in response to determining that the target item is not associated with an item label, identify the item information based on keywords to obtain an identification result corresponding to the item information; A generation module, configured to generate an item label corresponding to the target item based on the identification result corresponding to the item information.

11. An item warehousing device, the device includes: An acquisition module, configured to acquire the item information of the target item in response to receiving a target order for the target item; Based on the item information of the target item, acquire the item label associated with the target item; A prediction module, configured to, in response to determining that the item label is a non-hazardous item label, predict a predicted label corresponding to the target item based on a label prediction model and the item information; An execution module, configured to perform the warehousing operation corresponding to the target item based on the predicted label corresponding to the target item.

12. An electronic device, including: One or more processors; A storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method according to any one of claims 1-9.

13. A computer-readable medium having a computer program stored thereon, characterized in that, The computer program, when executed by a processor, implements the method according to any one of claims 1-9.

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