A warehouse inbound and outbound management method and system based on Internet of Things devices

Abstract

The present invention provides an inbound and outbound warehouse management method and system based on Internet of Things devices, which belongs to the technical field of warehouse management, and specifically includes: determining the storage location of different types of goods based on the storage data of different types of goods, and using the storage location and warehouse environment data during the storage process as a basis, using an AI model to determine similar stored goods of different types of goods, based on the inbound and outbound warehouse data of similar stored goods, determining the expiration data of electronic tags of different similar stored goods, and using the expiration data to determine the failure risk goods and label failure probability among different types of goods, determining the inbound and outbound warehouse planning data of the failure risk goods in different unit time periods in the current time period, and using the inbound and outbound warehouse planning data and the label failure probability to determine the Internet of Things monitoring method for inbound and outbound warehouse in the current time period, thereby improving the reliability of inbound and outbound warehouse monitoring processing.

Description

Technical Field

[0001] The present invention belongs to the technical field of warehouse management, and in particular relates to a warehouse entry and exit management method and system based on Internet of Things devices. Background Art

[0002] In order to achieve the in-and-out management of medicines and avoid deviations in in-and-out data, existing technical solutions use IoT devices such as RFID tags and cameras to realize the identification and processing of in-and-out data. Specific invention patent applications CN202411347768.6 "An Intelligent Freight Logistics Management System and Method" and CN202010813576.5 "An Electric Safety Tool Cabinet and Management System Based on IoT RFID Technology" both provide technical solutions for in-and-out identification and management based on IoT devices. However, the above technical solutions have the following technical defects:

[0003] When using electronic tags for inbound and outbound management, the electronic tags of the goods may not be accurately read due to physical pressure, poor reading distance, humidity or temperature in the storage environment, which inevitably makes it difficult to meet the accuracy requirements of inbound and outbound data.

[0004] In response to the above technical problems, the present application specifically provides a method and system for warehouse inbound and outbound management based on Internet of Things devices. Summary of the Invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0006] In the first aspect, the present application provides a method for managing inbound and outbound storage based on IoT devices, specifically comprising:

[0007] S1 determines the inbound and outbound plan data for different types of goods in the current period based on the inbound and outbound plan, and combines the similarity of the radio frequency signals of the electronic tags of different types of goods to determine if the identification deviation risk for the current period is within the preset risk range, then proceeds to the next step;

[0008] S2 determines the storage locations of different types of goods based on the storage data of the different types of goods, and uses the storage locations and warehouse environment data during the storage process as a basis to determine similar stored goods of different types of goods using an AI model;

[0009] S3 determines the expiration data of the electronic tags of different similar stored goods based on the in-and-out data of the similar stored goods, and uses the expiration data to determine the expiration risk goods and tag expiration probability of different types of goods;

[0010] S4 determines the inbound and outbound planning data of the goods with failure risk in different unit periods in the current period, and uses the inbound and outbound planning data and the label failure probability to determine the Internet of Things monitoring method for the inbound and outbound of the current period.

[0011] The beneficial effects of the present invention are:

[0012] Based on the inbound and outbound planning data of different types of goods in the current period and the similarity of the radio frequency signals of the electronic tags, it is determined whether the identification deviation risk of the current period is within the preset risk range. This not only takes into account the difference in the risk of identification deviation of goods due to the deviation in the inbound and outbound quantities in the current period, but also takes into account the difference in the risk of identification deviation of different types of goods due to the relatively similar radio frequency signals of the electronic tags. This achieves accurate assessment of identification deviation risks from multiple dimensions, and also ensures the reliability of IoT monitoring and processing of inbound and outbound goods in periods with higher identification deviation risks.

[0013] By utilizing the inbound and outbound planning data of goods with failure risks and the probability of label failure, the IoT monitoring method for inbound and outbound storage in the current period is determined. This fully considers the inbound and outbound quantity of goods with failure risks in the current period and the probability of label failure, ensuring the reliability of monitoring and processing in periods with higher failure risks. At the same time, it also avoids the energy consumption or high-complexity technical problems of monitoring and processing in periods with lower failure risks, further improving the reliability of inbound and outbound storage management.

[0014] A further technical solution is that the inbound and outbound planning data includes the inbound and outbound quantities in different unit time periods in the current time period.

[0015] A further technical solution is that the similarity of the radio frequency signals is determined based on the similarity of the signal transmission frequencies or signal transmission intensities of the radio frequency signals of the electronic tags of different types of goods.

[0016] A further technical solution is that the method for determining the identification deviation risk of the current period is:

[0017] Determine the incoming and outgoing goods and the quantity of incoming and outgoing goods in the current period based on the incoming and outgoing goods plan data of different types of goods in the current period;

[0018] Based on the similarity of the radio frequency signals of the electronic tags of different incoming and outgoing goods, it is determined that there are incoming and outgoing goods with similar radio frequency signals, and these goods are identified as risky goods;

[0019] The identification deviation risk of the current period is determined by the number of the identified risk goods and the number of goods entering and leaving the warehouse.

[0020] A further technical solution is that the incoming and outgoing goods with similar radio frequency signals are incoming and outgoing goods whose radio frequency signal deviations from other incoming and outgoing goods are within a preset signal deviation range.

[0021] A further technical solution is to determine the identification deviation risk of the current period based on the number of identified risk goods and the number of goods entering and leaving the warehouse, specifically including:

[0022] Obtaining the preset cargo quantity interval within which the quantity of the inbound and outbound cargo falls, and determining the basic identified risk for the current period using the preset risk mapped to the preset cargo quantity interval;

[0023] The product of the proportion of the number of the identified risk goods in the number of the inbound and outbound goods and the basic identification risk is used as the identification deviation risk of the current period.

[0024] A further technical solution is that, when the identification deviation risk of the current time period is not within the preset risk range, it is determined whether the identification deviation risk of the current time period is greater than the preset risk threshold. If so, the radio frequency identification device and the camera device are used to perform Internet of Things monitoring of the entry and exit of the warehouse in the current time period. If not, only the radio frequency identification device is used to perform Internet of Things monitoring of the entry and exit of the warehouse in the current time period.

[0025] A further technical solution is that the method for determining the IoT monitoring mode of the in-and-out warehouse in the current period is:

[0026] Based on the inventory planning data of the expired risk goods in different unit time periods, the inventory quantity of expired risk goods in different unit time periods is determined. In combination with the label expiration probability of the expired risk goods, the sum of the label expiration probabilities in different unit time periods is determined.

[0027] Determining a failure risk period in the unit period based on the sum of the tag failure probabilities;

[0028] The Internet of Things monitoring method for inbound and outbound storage in the current period is determined by the number of the failure risk periods.

[0029] A further technical solution is that the failure risk period is a unit period in which the sum of the tag failure probabilities is greater than a preset probability value.

[0030] A further technical solution is to determine the IoT monitoring method for inbound and outbound storage during the current period based on the number of failure risk periods, specifically including:

[0031] When the number of failure risk periods is greater than the preset number of risk periods, the RFID device and the camera device are used to perform IoT monitoring of the entry and exit of the warehouse in the current period;

[0032] When the number of failure risk periods is not greater than the number of preset risk periods, only the radio frequency identification device is used to perform IoT monitoring of inbound and outbound storage during the current period.

[0033] In a second aspect, the present invention provides an inbound and outbound management system based on an Internet of Things device, which adopts the above-mentioned inbound and outbound management method based on an Internet of Things device, specifically comprising:

[0034] Deviation risk assessment module, similarity assessment module, failure probability identification module, monitoring method determination module;

[0035] The bias risk assessment module is responsible for determining whether the identification bias risk of the current period is within the preset risk range;

[0036] The similarity assessment module is responsible for determining similar stored goods of different types of goods using an AI model;

[0037] The failure probability identification module is responsible for determining the failure risk goods and label failure probability among different types of goods;

[0038] The monitoring mode determination module is responsible for determining the IoT monitoring mode for inbound and outbound storage during the current period.

[0039] Other features and advantages will be described in the following description. The objectives and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0040] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The above and other features and advantages of the present invention will become more apparent by describing in detail example embodiments thereof with reference to the accompanying drawings;

[0042] Figure 1 It is a flow chart of a method for managing inbound and outbound inventory based on IoT devices;

[0043] Figure 2 is a flow chart of the method for determining the risk of deviation during the current period;

[0044] Figure 3 is a flow chart of a method for determining similar storage of goods;

[0045] Figure 4 It is a flow chart of a method for determining goods at risk of failure in goods;

[0046] Figure 5 It is a flow chart of a method for determining the IoT monitoring method for inbound and outbound storage during the current period;

[0047] Figure 6 It is a framework diagram of an inbound and outbound warehouse management system based on IoT devices. DETAILED DESCRIPTION

[0048] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this specification without creative work should fall within the scope of protection of this specification.

[0049] In this application, the failure risk of the electronic tags of the goods in the current period is used to determine the Internet of Things monitoring strategy for the current period. When there are more goods with a higher risk of failure, the radio frequency identification device and the camera device are used to perform Internet of Things monitoring of the entry and exit of the warehouse in the current period. When there are fewer goods with a higher risk of failure, only the radio frequency identification device is used to perform Internet of Things monitoring of the entry and exit of the warehouse in the current period.

[0050] Based on the similarity of the radio frequency signals of the electronic tags of the inbound and outbound goods in different unit time periods in the current period, it is determined that there are inbound and outbound goods with similar radio frequency signals, and they are used as identified risk goods. When either the number of inbound and outbound goods in the current period or the number of identified risk goods is not within the corresponding preset goods quantity range, it is determined that the identification deviation risk of the current period is not within the preset range.

[0051] Similar stored goods are other goods in the warehouse whose distance from the storage location of the goods is less than a preset distance and whose storage time deviation is less than a preset time deviation. Specifically, the distance from the storage location of the goods and the storage time deviation are used as inputs of the AI model, and the storage similarity coefficient output by the AI model is used to determine whether the other goods are similarly stored goods of the goods.

[0052] The average value of the percentage of invalid electronic tags of different similar stored goods is used to determine the label failure probability of the corresponding goods. When the label failure probability is greater than 0.2, the goods are determined to be at risk of failure.

[0053] According to the number of goods with failure risk entering and leaving the warehouse in the current period and the failure probability of the labels of goods with different failure risks, the sum of the failure probabilities of the labels of goods with different failure risks is determined. When the sum of the failure probabilities of the labels of goods with different failure risks is greater than the preset probability threshold, the radio frequency identification device and the camera device are used to perform Internet of Things monitoring of the entry and exit of the warehouse in the current period. When the sum of the failure probabilities of the labels of goods with different failure risks is not greater than the preset probability threshold, only the radio frequency identification device is used to perform Internet of Things monitoring of the entry and exit of the warehouse in the current period.

[0054] Example 1 Figure 1 As shown, in the first aspect, the present application provides a method for managing inbound and outbound storage based on IoT devices, specifically comprising:

[0055] S1 determines the inbound and outbound plan data for different types of goods in the current period based on the inbound and outbound plan, and combines the similarity of the radio frequency signals of the electronic tags of different types of goods to determine if the identification deviation risk for the current period is within the preset risk range, then proceeds to the next step;

[0056] Furthermore, the inbound and outbound planning data includes the inbound and outbound quantities in different unit time periods in the current time period.

[0057] Specifically, the similarity of the radio frequency signals is determined based on the similarity of the signal transmission frequencies or signal transmission intensities of the radio frequency signals of the electronic tags of different types of goods.

[0058] It should be noted that if Figure 2 As shown, the method for determining the identification deviation risk of the current period is:

[0059] Determine the incoming and outgoing goods and the quantity of incoming and outgoing goods in the current period based on the incoming and outgoing goods plan data of different types of goods in the current period;

[0060] Based on the similarity of the radio frequency signals of the electronic tags of different incoming and outgoing goods, it is determined that there are incoming and outgoing goods with similar radio frequency signals, and these goods are identified as risky goods;

[0061] The identification deviation risk of the current period is determined by the number of the identified risk goods and the number of goods entering and leaving the warehouse.

[0062] Furthermore, the inbound and outbound goods with similar radio frequency signals are inbound and outbound goods whose radio frequency signal deviations from other inbound and outbound goods are within a preset signal deviation range.

[0063] Optionally, determining the identification deviation risk for the current period based on the number of identified risk goods and the number of inbound and outbound goods may include:

[0064] Obtaining the preset cargo quantity interval within which the quantity of the inbound and outbound cargo falls, and determining the basic identified risk for the current period using the preset risk mapped to the preset cargo quantity interval;

[0065] The product of the proportion of the number of the identified risk goods in the number of the inbound and outbound goods and the basic identification risk is used as the identification deviation risk of the current period.

[0066] In one embodiment, when the identification deviation risk of the current time period is not within the preset risk range, it is determined whether the identification deviation risk of the current time period is greater than the preset risk threshold. If so, the Internet of Things monitoring of the entry and exit of the warehouse in the current time period is performed using the radio frequency identification device and the camera device. If not, the Internet of Things monitoring of the entry and exit of the warehouse in the current time period is performed only using the radio frequency identification device.

[0067] Optionally, the method for determining the identification deviation risk of the current period is:

[0068] Determine the incoming and outgoing goods and the quantity of incoming and outgoing goods in the current period based on the incoming and outgoing goods plan data of different types of goods in the current period;

[0069] Based on the similarity of the radio frequency signals of the electronic tags of the incoming and outgoing goods in different unit time periods in the current period, it is determined that there are incoming and outgoing goods with similar radio frequency signals, and these goods are identified as risk goods;

[0070] The risk identification period in the unit period is determined according to the number of inbound and outbound goods and the number of identified risk goods in different unit periods, and the identification deviation risk of the current period is determined by the proportion of the number of risk identification periods in the current period.

[0071] Specifically, the risk identification period is a unit period in which either the quantity of goods entering and leaving the warehouse or the quantity of goods identified as risky does not meet the requirements.

[0072] Optionally, the method for determining the identification deviation risk of the current period is:

[0073] S11 determines the incoming and outgoing goods and the quantity of the incoming and outgoing goods in the current period based on the incoming and outgoing plan data of different types of goods in the current period;

[0074] Optionally, the above step S11 includes the following contents:

[0075] S111 uses the inbound and outbound planning data of different types of goods in the current period to determine the inbound and outbound goods and the quantity of inbound and outbound goods in the current period. If the quantity of inbound and outbound goods in the current period does not meet the requirements, the RFID device and the camera device are used to perform IoT monitoring of the inbound and outbound goods in the current period. If the quantity of inbound and outbound goods in the current period meets the requirements, the process proceeds to step S112.

[0076] If, based on the quantity of inbound and outbound goods in different unit time periods in the current period, it is determined in step S112 that there is a unit time period in which the quantity of inbound and outbound goods is greater than the preset quantity of goods, the process proceeds to step S113; if, on the other hand, there is no unit time period in which the quantity of inbound and outbound goods is greater than the preset quantity of goods, the process proceeds to step S12;

[0077] S113 When the quantity of goods entering and leaving the warehouse is greater than the preset quantity of goods and the proportion of the quantity in the unit time period in the current time period is greater than the proportion of the quantity in the preset time period, the Internet of Things monitoring of the goods entering and leaving the warehouse in the current time period is performed using the radio frequency identification device and the camera device. When the quantity of goods entering and leaving the warehouse is greater than the preset quantity of goods and the proportion of the quantity in the unit time period in the current time period is not greater than the proportion of the quantity in the preset time period, go to step S12.

[0078] S12 determines, based on the similarity of the radio frequency signals of the electronic tags of the incoming and outgoing goods in different unit time periods within the current period, that there are incoming and outgoing goods with similar radio frequency signals, and identifies them as risky goods, and determines the identification deviation coefficients for the different unit time periods according to the number of incoming and outgoing goods in the different unit time periods and the number of identified risky goods;

[0079] Optionally, the above step S12 includes the following contents:

[0080] S121: Based on the similarity of the radio frequency signals of the electronic tags of the incoming and outgoing goods in different unit time periods in the current time period, if it is determined that there are no incoming and outgoing goods with similar radio frequency signals in different unit time periods, then the RFID device is used to perform IoT monitoring of the incoming and outgoing goods in the current time period. If there are incoming and outgoing goods with similar radio frequency signals, the process proceeds to step S122.

[0081] In step S122, incoming and outgoing goods with similar radio frequency signals are identified as risky goods, and the total number of identified risky goods in the current period is obtained. If the total number of identified risky goods in the current period does not meet the requirement, the RFID device and the camera device are used to perform IoT monitoring of incoming and outgoing goods in the current period. If the total number of identified risky goods in the current period meets the requirement, the process proceeds to step S123.

[0082] S123: When there is a unit time period in which the number of identified risk goods is greater than the preset number of risk goods, the process proceeds to step S124; when there is no unit time period in which the number of identified risk goods is greater than the preset number of risk goods, the process proceeds to step S125;

[0083] S124: When the number of identified risky goods is greater than the preset number of risky goods and the proportion of the number per unit time period is greater than the preset number of time period, the RFID device and the camera device are used to perform IoT monitoring of the entry and exit of the warehouse in the current time period. When the number of identified risky goods is greater than the preset number of risky goods and the proportion of the number per unit time period is not greater than the preset number of time period, the process proceeds to step S125.

[0084] S125 determines the identification deviation coefficients of different unit time periods according to the number of goods entering and leaving the warehouse and the number of identified risk goods in different unit time periods. When the proportion of the number of unit time periods in which the identification deviation coefficient does not meet the requirements is greater than the proportion of the number of preset time periods, the Internet of Things monitoring of the entry and exit of the current time period is performed using the radio frequency identification device and the camera device. When the proportion of the number of unit time periods in which the identification deviation coefficient does not meet the requirements is not greater than the proportion of the number of preset time periods, proceed to step S13.

[0085] S13 determines the identification deviation risk of the current period by averaging the identification deviation coefficients of different unit periods.

[0086] S2 determines the storage locations of different types of goods based on the storage data of the different types of goods, and uses the storage locations and warehouse environment data during the storage process as a basis to determine similar stored goods of different types of goods using an AI model;

[0087] Furthermore, the warehouse environment data during the storage process includes the number of time periods within different temperature ranges and humidity ranges.

[0088] Specifically, such as Figure 3 As shown, the method for determining similar stored goods of the goods is:

[0089] Determining a distance similarity coefficient between the storage locations of the goods and other goods based on the distance between the storage location of the goods and the storage locations of other goods in the warehouse;

[0090] determining a deviation rate of the number of time periods in different temperature and humidity ranges according to the number of time periods in which the goods are stored, and determining an environmental similarity coefficient based on an average value of the deviation rates of the number of time periods in different temperature and humidity ranges;

[0091] Based on the distance similarity coefficient and the environment similarity coefficient, an AI model is used to determine the storage similarity coefficient between the cargo and other cargoes, and the storage similarity coefficient is used to determine whether the other cargoes are similarly stored cargoes to the cargo.

[0092] Furthermore, the distance similarity coefficient is the product of the distance to the storage location of other goods in the warehouse and a preset proportional factor.

[0093] It should also be noted that when the storage similarity coefficient between the other goods and the goods is greater than a preset similarity coefficient, the goods are determined to be similarly stored goods.

[0094] S3 determines the expiration data of the electronic tags of different similar stored goods based on the in-and-out data of the similar stored goods, and uses the expiration data to determine the expiration risk goods and tag expiration probability of different types of goods;

[0095] Optionally, the expiration data of the electronic tags of similar stored goods includes the number of expiration dates of the electronic tags of similar stored goods.

[0096] Specifically, such as Figure 4 As shown, the method for determining the goods with failure risk among the goods is:

[0097] Determine the failure probability of the electronic tags of different similar stored goods based on the ratio of the failure number to the identification number of the electronic tags of different similar stored goods;

[0098] Determining similarity weight coefficients of different similar stored goods based on the storage similarity coefficients of different similar stored goods and the goods;

[0099] Based on the average value of the failure probability of the electronic tags of different similar stored goods and the similarity weight coefficient, the failure probability of the tag of the goods is determined, and the failure probability of the tag is used to determine whether the goods are failure-risk goods.

[0100] Furthermore, when the expiration probability of the label of the goods is greater than a preset expiration probability, the goods are determined to be expiration-risk goods.

[0101] In another embodiment, the method for determining the goods with failure risk among the goods is:

[0102] Determine the failure probability of the electronic tags of different similar stored goods based on the ratio of the failure number to the identification number of the electronic tags of different similar stored goods;

[0103] Using the failure probability to identify similar risk goods among the similar stored goods;

[0104] Based on the proportion of the similar risk goods in the similar stored goods, the label failure probability of the goods is determined, and the label failure probability is used to determine whether the goods are failure risk goods.

[0105] Optionally, the method for determining the goods with failure risk among the goods is:

[0106] Based on the number of expired electronic tags of different similar stored goods, if it is determined that no electronic tags of different similar stored goods have expired, then the goods are determined not to be at risk of expiration;

[0107] When there are similar stored goods with expired electronic tags:

[0108] Similar stored goods with expired electronic tags are considered expired stored goods. When the proportion of the expired stored goods in the similar stored goods meets the requirements:

[0109] Obtaining the expiration number of the electronic tags in different expired stored goods, and when the expiration number of the electronic tags in the different expired stored goods is less than a preset expiration number, determining that the goods do not belong to the expired risk goods;

[0110] When there are expired goods with the number of expired electronic tags not less than the preset expired number:

[0111] Obtain the ratio of the number of failed electronic tags to the number of identified electronic tags in different failed stored goods, and determine the failure probability of the electronic tags of different similar stored goods. If there are no failed stored goods whose electronic tags have failure probabilities that do not meet the requirements, then the goods are determined not to be at risk of failure.

[0112] When the proportion of the number of the expired stored goods among the similar stored goods does not meet the requirements or there are expired stored goods whose electronic tags have an expiration probability that does not meet the requirements:

[0113] Based on the storage similarity coefficients of different similar stored goods and the goods, similarity weight coefficients of different similar stored goods are determined. When the failure probability of the electronic tags of the failed stored goods with similarity weight coefficients greater than the preset weight coefficients all meets the requirements, it is determined that the goods do not belong to the failure risk goods;

[0114] When there are goods stored with similar weight coefficients greater than the preset weight coefficients and the failure probability of the electronic tags does not meet the requirements:

[0115] Based on the average value of the failure probability of the electronic tags of different similar stored goods and the similarity weight coefficient, the failure probability of the tag of the goods is determined, and the failure probability of the tag is used to determine whether the goods are failure-risk goods.

[0116] S4 determines the inbound and outbound planning data of the goods with failure risk in different unit periods in the current period, and uses the inbound and outbound planning data and the label failure probability to determine the Internet of Things monitoring method for the inbound and outbound of the current period.

[0117] Specifically, such as Figure 5 As shown, the method for determining the IoT monitoring mode of inbound and outbound storage in the current period is:

[0118] Based on the inventory planning data of the expired risk goods in different unit time periods, the inventory quantity of expired risk goods in different unit time periods is determined. In combination with the label expiration probability of the expired risk goods, the sum of the label expiration probabilities in different unit time periods is determined.

[0119] Determining a failure risk period in the unit period based on the sum of the tag failure probabilities;

[0120] The Internet of Things monitoring method for inbound and outbound storage in the current period is determined by the number of the failure risk periods.

[0121] Furthermore, the failure risk period is a unit period in which the sum of the tag failure probabilities is greater than a preset probability value.

[0122] In another embodiment, determining the IoT monitoring method for inbound and outbound storage during the current period based on the number of failure risk periods specifically includes:

[0123] When the number of failure risk periods is greater than the preset number of risk periods, the RFID device and the camera device are used to perform IoT monitoring of the entry and exit of the warehouse in the current period;

[0124] When the number of failure risk periods is not greater than the number of preset risk periods, only the radio frequency identification device is used to perform IoT monitoring of inbound and outbound storage during the current period.

[0125] Optionally, the method for determining the IoT monitoring mode for inbound and outbound storage during the current period is:

[0126] The total quantity of expired risk goods in the current period is determined using the inbound and outbound planning data for expired risk goods in different unit time periods. If the total quantity of expired risk goods in the current period does not meet the requirements, the inbound and outbound IoT monitoring of the current period is performed using radio frequency identification devices and cameras.

[0127] When the total quantity of goods at risk of failure in the current period meets the requirements:

[0128] When the total number of expired goods in the current period is less than the preset number of expired goods, only the RFID device is used to monitor the entry and exit of the goods in the current period.

[0129] When the total quantity of goods at risk of failure in the current period is not less than the preset quantity of goods at risk of failure:

[0130] Based on the inbound and outbound quantities of goods with expiration risk in different unit time periods within the current period, when the inbound and outbound quantities of goods with expiration risk are determined to be greater than the preset inbound and outbound quantities per unit time period:

[0131] The unit time period in which the number of inbound and outbound goods with failure risk exceeds the preset inbound and outbound number is regarded as the detection risk period. If the number of detection risk period does not meet the requirement, the RFID device and camera device are used to conduct IoT monitoring of the inbound and outbound goods in the current period.

[0132] When the inbound and outbound quantity of goods without failure risk is greater than the preset inbound and outbound quantity per period or the quantity of the detection risk period meets the requirements:

[0133] Determine the inbound and outbound quantities of goods with failure risks in different unit time periods, and determine the sum of the label failure probabilities in different unit time periods based on the label failure probabilities of the goods with different failure risks. When determining that a failure risk period exists in the unit time period based on the sum of the label failure probabilities:

[0134] When the number of failure risk periods in the current period does not meet the requirements, the RFID device and the camera device are used to perform IoT monitoring of the entry and exit of the current period;

[0135] When it is determined based on the sum of the tag failure probabilities that there is no failure risk period in the unit period or when the number of failure risk periods in the current period meets the requirement:

[0136] The average value of the sum of the tag failure probabilities in different unit time periods is used as the probability average value, and the probability average value is used to determine the Internet of Things monitoring method for the entry and exit of the current period.

[0137] Optionally, the method of using the probability average value to determine the IoT monitoring method for the inbound and outbound storage during the current period specifically includes:

[0138] When the probability average is greater than the preset probability setting value, the RFID device and the camera device are used to perform IoT monitoring of the entry and exit of the warehouse in the current period;

[0139] When the probability average value is not greater than the preset probability setting value, only the radio frequency identification device is used to perform IoT monitoring of the entry and exit of the warehouse in the current period.

[0140] In the second aspect of embodiment 2, Figure 6 As shown, the present invention provides an inbound and outbound management system based on IoT devices, which adopts the above-mentioned inbound and outbound management method based on IoT devices, specifically including:

[0141] Deviation risk assessment module, similarity assessment module, failure probability identification module, monitoring method determination module;

[0142] The bias risk assessment module is responsible for determining whether the identification bias risk of the current period is within the preset risk range;

[0143] The similarity assessment module is responsible for determining similar stored goods of different types of goods using an AI model;

[0144] The failure probability identification module is responsible for determining the failure risk goods and label failure probability among different types of goods;

[0145] The monitoring mode determination module is responsible for determining the IoT monitoring mode for inbound and outbound storage during the current period.

[0146] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, apparatus, and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant details, refer to the descriptions of the method embodiments.

[0147] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0148] The foregoing description is merely one or more embodiments of this specification and is not intended to limit this specification. It will be apparent to those skilled in the art that various modifications and variations may be made to one or more embodiments of this specification. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of one or more embodiments of this specification are intended to be within the scope of the claims of this specification.

Claims

1. A method for managing inbound and outbound inventory based on IoT devices, characterized in that: Specifically include: Based on the inbound and outbound plans, determine the inbound and outbound plan data for different types of goods in the current period. Combined with the similarity of the radio frequency signals of the electronic tags of different types of goods, if it is determined that the identification deviation risk of the current period is within the preset risk range, proceed to the next step. Determine the storage locations of different types of goods based on the storage data of different types of goods, and use the storage locations and warehouse environment data during the storage process as a basis to determine similar stored goods of different types of goods using an AI model; Based on the inbound and outbound data of the similar stored goods, determining the expiration data of the electronic tags of different similar stored goods, and using the expiration data to determine the expiration risk goods and the expiration probability of the tags among different types of goods; Determine the inbound and outbound planning data of the goods with failure risk in different unit periods in the current period, and use the inbound and outbound planning data and the label failure probability to determine the Internet of Things monitoring method for the inbound and outbound of the current period.

2. The method for managing inbound and outbound inventory based on IoT devices according to claim 1, wherein: The inbound and outbound inventory planning data includes the inbound and outbound inventory quantities in different unit time periods in the current time period.

3. The method for managing inbound and outbound inventory based on IoT devices according to claim 1, wherein: The similarity of the radio frequency signals is determined based on the similarity of the signal transmission frequencies or signal transmission intensities of the radio frequency signals of the electronic tags of different types of goods.

4. The method for managing inbound and outbound inventory based on IoT devices according to claim 1, wherein: The method for determining the identification deviation risk of the current period is: Determine the incoming and outgoing goods and the quantity of incoming and outgoing goods in the current period based on the incoming and outgoing goods plan data of different types of goods in the current period; Based on the similarity of the radio frequency signals of the electronic tags of different incoming and outgoing goods, it is determined that there are incoming and outgoing goods with similar radio frequency signals, and these goods are identified as risky goods; The identification deviation risk of the current period is determined by the number of the identified risk goods and the number of goods entering and leaving the warehouse.

5. The method for managing inbound and outbound storage based on IoT devices according to claim 4, characterized in that: The inbound and outbound goods with similar radio frequency signals are inbound and outbound goods whose radio frequency signal deviations from other inbound and outbound goods are within a preset signal deviation range.

6. The method for managing inbound and outbound inventory based on IoT devices according to claim 1, wherein: When the identification deviation risk of the current time period is not within the preset risk range, determine whether the identification deviation risk of the current time period is greater than the preset risk threshold. If so, use the radio frequency identification device and the camera device to perform Internet of Things monitoring of the entry and exit of the current time period. If not, only use the radio frequency identification device to perform Internet of Things monitoring of the entry and exit of the current time period.

7. The method for managing inbound and outbound inventory based on IoT devices according to claim 1, wherein: The method for determining the IoT monitoring mode for inbound and outbound storage during the current period is as follows: Based on the inventory planning data of the expired risk goods in different unit time periods, the inventory quantity of expired risk goods in different unit time periods is determined. In combination with the label expiration probability of the expired risk goods, the sum of the label expiration probabilities in different unit time periods is determined. Determining a failure risk period in the unit period based on the sum of the tag failure probabilities; The Internet of Things monitoring method for inbound and outbound storage in the current period is determined by the number of the failure risk periods.

8. The method for managing inbound and outbound storage based on IoT devices according to claim 7, characterized in that: The failure risk period is a unit period in which the sum of the tag failure probabilities is greater than a preset probability value.

9. The method for managing inbound and outbound storage based on IoT devices according to claim 7, wherein: The IoT monitoring method for inbound and outbound storage during the current period is determined based on the number of failure risk periods, specifically including: When the number of failure risk periods is greater than the preset number of risk periods, the RFID device and the camera device are used to perform IoT monitoring of the entry and exit of the warehouse in the current period; When the number of failure risk periods is not greater than the number of preset risk periods, only the radio frequency identification device is used to perform IoT monitoring of inbound and outbound storage during the current period.

10. A warehouse in and out management system based on Internet of Things devices, characterized in that: The method for managing inbound and outbound storage based on an Internet of Things device according to any one of claims 1 to 9 specifically comprises: Deviation risk assessment module, similarity assessment module, failure probability identification module, monitoring method determination module; The bias risk assessment module is responsible for determining whether the identification bias risk of the current period is within the preset risk range; The similarity assessment module is responsible for determining similar stored goods of different types of goods using an AI model; The failure probability identification module is responsible for determining the failure risk goods and label failure probability among different types of goods; The monitoring mode determination module is responsible for determining the IoT monitoring mode for inbound and outbound storage during the current period.

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