Internet of Things equipment-based warehouse entry and exit management method and system

Abstract

The invention provides an in-warehouse and out-warehouse management method and system based on Internet of Things equipment, and belongs to the technical field of warehouse management, and the method specifically comprises the steps: determining the storage positions of different types of goods based on the storage data of different types of goods, and taking the storage positions and the warehouse environment data in the storage process as the basis, the method comprises the following steps: determining similar stored cargoes of different types of cargoes by using an AI model, determining failure data of electronic tags of different similar stored cargoes on the basis of warehouse-in and warehouse-out data of the similar stored cargoes, and determining failure risk cargoes and tag failure probabilities in the different types of cargoes by using the failure data, according to the method, the warehouse-in and warehouse-out plan data of the goods with the failure risk in different unit time periods in the current time period are determined, and the warehouse-in and warehouse-out Internet of Things monitoring mode of the current time period is determined by using the warehouse-in and warehouse-out plan data and the label failure probability, so that the reliability of warehouse-in and warehouse-out monitoring processing is improved.

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, the existing technical solutions use IoT devices such as RFID tags and cameras to realize the identification and processing of in-and-out data. The 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: When using electronic tags for warehouse entry and exit 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 will inevitably make it difficult to meet the accuracy requirements of the warehouse entry and exit data.

[0003] In response to the above technical problems, the present application specifically provides a method and system for warehouse in and out management based on Internet of Things devices. Summary of the invention

[0004] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: In the first aspect, the present application provides a method for managing inbound and outbound storage based on IoT devices, specifically comprising: S1 determines the inbound and outbound planning data of different types of goods in the current period based on the inbound and outbound planning, and when it is determined that the identification deviation risk of the current period is within the preset risk range based on the similarity of the radio frequency signals of the electronic tags of different types of goods, proceeds to the next step; S2 determines the storage locations of different types of goods based on the storage data of different types of goods, and uses the storage locations and warehouse environment data during the storage process as a basis to determine similar storage goods of different types of goods using an AI model; 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 the label expiration probability among different types of goods; 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 inbound and outbound in the current period.

[0005] The beneficial effects of the present invention are: 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 number of inbound and outbound goods 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 similarity of the radio frequency signals of the electronic tags. This achieves accurate assessment of the identification deviation risk from multiple dimensions, and also ensures the reliability of the IoT monitoring and processing of inbound and outbound goods in periods with higher identification deviation risks.

[0006] The IoT monitoring method for inbound and outbound storage in the current period is determined by using the inbound and outbound storage planning data of goods with failure risks and the probability of label failure. The number of inbound and outbound goods with failure risks in the current period and the probability of label failure are fully considered to ensure 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.

[0007] A further technical solution is that the in-and-out inventory planning data includes the in-and-out inventory quantities in different unit time periods in the current time period.

[0008] 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 strengths of the radio frequency signals of the electronic tags of different types of goods.

[0009] A further technical solution is that the method for determining the identification deviation risk of the current period is: Determine the inbound and outbound goods and the quantity of inbound and outbound goods in the current period based on the inbound and outbound planning 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 they are used to identify 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.

[0010] A further technical solution is that the in-and-out goods with similar radio frequency signals are in-and-out goods whose radio frequency signal deviations from other in-and-out goods are within a preset signal deviation range.

[0011] A further technical solution is to determine the identification deviation risk of the current period by the number of identified risk goods and the number of goods entering and leaving the warehouse, specifically including: Obtaining the preset goods quantity interval in which the quantity of the in-and-out goods falls, and using the preset risk mapped by the preset goods quantity interval to determine the basic identification risk of the current period; The product of the proportion of the number of the identified risk goods in the number of the in-and-out goods and the basic identification risk is taken as the identification deviation risk of the current period.

[0012] 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 Internet of Things monitoring of the entry and exit of the warehouse in the current time period is performed using a radio frequency identification device and a 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 a radio frequency identification device.

[0013] A further technical solution is that the method for determining the IoT monitoring mode of in-and-out storage in the current period is: The in-and-out quantity of the goods with failure risk in different unit time periods is determined based on the in-and-out planning data of the goods with failure risk in different unit time periods, and the sum of the label failure probabilities in different unit time periods is determined based on the label failure probabilities of the goods with different failure risks; 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.

[0014] 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.

[0015] A further technical solution is to determine the IoT monitoring method of the in-and-out warehouse in the current period according to the number of the failure risk periods, specifically including: When the number of failure risk periods is greater than the number of preset 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 in-and-out storage during the current period.

[0016] In a second aspect, the present invention provides an inbound and outbound warehouse management system based on an Internet of Things device, which adopts the above-mentioned inbound and outbound warehouse management method based on an Internet of Things device, specifically comprising: Deviation risk assessment module, similarity assessment module, failure probability identification module, monitoring method determination module; The deviation risk assessment module is responsible for determining whether the identification deviation 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.

[0017] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0018] 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

[0019] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings; Figure 1 It is a flow chart of a method for managing inbound and outbound storage based on IoT devices; Figure 2 is a flow chart of a method for determining the risk of identification deviations for the current period; Figure 3 It is a flow chart of a method for determining similar storage of goods; Figure 4 It is a flow chart of a method for determining goods at risk of failure in goods; Figure 5 It is a flow chart of a method for determining the IoT monitoring method for inbound and outbound storage in the current period; Figure 6 It is a framework diagram of an inbound and outbound warehouse management system based on IoT devices. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0021] In the present 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.

[0022] 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 time 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 any of the number of inbound and outbound goods in the current time period and the number of identified risk goods are not within the corresponding preset goods quantity range, it is determined that the identification deviation risk of the current time period is not within the preset range.

[0023] 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.

[0024] The failure probability of the corresponding goods is determined by taking the average value of the percentage of failure numbers of electronic tags of different similar stored goods. When the failure probability of the label is greater than 0.2, the goods are determined to be at risk of failure.

[0025] According to the number of goods with failure risk entering and leaving the warehouse in the current period and the failure probability of labels of goods with different failure risks, the sum of the failure probabilities of labels of goods with different failure risks is determined. When the sum of the failure probabilities of labels of goods with different failure risks is greater than a preset probability threshold, the Internet of Things monitoring of the entry and exit of the warehouse in the current period is performed using the radio frequency identification device and the camera device. When the sum of the failure probabilities of 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 the Internet of Things monitoring of the entry and exit of the warehouse in the current period.

[0026] 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: S1 determines the inbound and outbound planning data of different types of goods in the current period based on the inbound and outbound planning, and when it is determined that the identification deviation risk of the current period is within the preset risk range based on the similarity of the radio frequency signals of the electronic tags of different types of goods, proceeds to the next step; Furthermore, the inbound and outbound inventory planning data includes the inbound and outbound quantities in different unit time periods in the current time period.

[0027] Specifically, the similarity of the radio frequency signals is determined based on the similarity of the signal transmission frequencies or signal transmission strengths of the radio frequency signals of the electronic tags of different types of goods.

[0028] It should be noted that if Figure 2 As shown, the method for determining the identification deviation risk of the current period is: Determine the inbound and outbound goods and the quantity of inbound and outbound goods in the current period based on the inbound and outbound planning 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 they are used to identify 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.

[0029] 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.

[0030] Optionally, 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, specifically including: Obtaining the preset goods quantity interval in which the quantity of the in-and-out goods falls, and using the preset risk mapped by the preset goods quantity interval to determine the basic identification risk of the current period; The product of the proportion of the number of the identified risk goods in the number of the in-and-out goods and the basic identification risk is taken as the identification deviation risk of the current period.

[0031] In one of the embodiments, 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 a radio frequency identification device and a 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 a radio frequency identification device.

[0032] Optionally, the method for determining the identification deviation risk of the current period is: Determine the inbound and outbound goods and the quantity of inbound and outbound goods in the current period based on the inbound and outbound planning data of different types of goods in the current period; 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 time period, it is determined that there are inbound and outbound goods with similar radio frequency signals, and these goods are used to identify risk goods; The risk identification period in the unit period is determined according to the number of in-and-out 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.

[0033] Specifically, the risk identification period is a unit period in which any one of the quantity of goods entering and leaving the warehouse and the quantity of goods with identified risks does not meet the requirements.

[0034] Optionally, the method for determining the identification deviation risk of the current period is: S11 determines the inbound and outbound goods and the quantity of the inbound and outbound goods in the current period based on the inbound and outbound planning data of different types of goods in the current period; Optionally, the above step S11 includes the following contents: S111 determines the in-and-out goods and the quantity of the in-and-out goods in the current period based on the in-and-out planning data of different types of goods in the current period. When the quantity of the in-and-out goods in the current period does not meet the requirement, the RFID device and the camera device are used to perform IoT monitoring of the in-and-out goods in the current period. When the quantity of the in-and-out goods in the current period meets the requirement, the process proceeds to step S112. S112 determines that if there is a unit time period in which the number of in-and-out goods is greater than the preset number of goods based on the number of in-and-out goods in different unit time periods in the current time period, the process proceeds to step S113; if there is no unit time period in which the number of in-and-out goods is greater than the preset number of goods, the process proceeds to step S12; 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 entry and exit of 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, proceed to step S12.

[0035] S12 determines that there are goods entering and leaving the warehouse with similar radio frequency signals based on the similarity of the radio frequency signals of the electronic tags of the goods entering and leaving the warehouse in different unit time periods in the current time period, and uses them as the goods to be identified as risk goods, and determines the identification deviation coefficients of different unit time periods according to the number of goods entering and leaving the warehouse in different unit time periods and the number of goods to be identified as risk goods; Optionally, the above step S12 includes the following contents: S121: Based on the similarity of the radio frequency signals of the electronic tags of the in-and-out goods in different unit time periods in the current time period, if it is determined that there are no in-and-out goods with similar radio frequency signals in different unit time periods, the radio frequency identification device is used to perform IoT monitoring of the in-and-out goods in the current time period. If there are in-and-out goods with similar radio frequency signals, the process proceeds to step S122; S122 regards the in-and-out goods with similar radio frequency signals as the identified risk goods, obtains the total number of the identified risk goods in the current period, and when the total number of the identified risk goods in the current period does not meet the requirement, uses the radio frequency identification device and the camera device to perform the Internet of Things monitoring of the in-and-out goods in the current period. When the total number of the identified risk goods in the current period meets the requirement, proceeds to step S123; 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, proceed 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, proceed to step S125; S124: When the number of identified risk goods is greater than the preset number of risk goods, and the proportion of the number in a unit time period is greater than the proportion of the number in the preset time period, the RFID device and the camera device are used to perform IoT monitoring of the entry and exit of the current time period; when the number of identified risk goods is greater than the preset number of risk goods, and the proportion of the number in a unit time period is not greater than the proportion of the number in the preset time period, the process proceeds to step S125; 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 risky goods in different unit time periods. When the proportion of unit time periods in which the identification deviation coefficient does not meet the requirements is greater than the proportion of the 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 unit time periods in which the identification deviation coefficient does not meet the requirements is not greater than the proportion of the preset time periods, proceed to step S13.

[0036] S13 determines the identification deviation risk of the current period by averaging the identification deviation coefficients of different unit periods.

[0037] S2 determines the storage locations of different types of goods based on the storage data of different types of goods, and uses the storage locations and warehouse environment data during the storage process as a basis to determine similar storage goods of different types of goods using an AI model; Furthermore, the warehouse environment data during the storage process includes the number of time periods in different temperature ranges and humidity ranges.

[0038] Specifically, Figure 3 As shown, the method for determining similar stored goods of the goods is: Determine the distance similarity coefficient between the storage location of the goods and the storage locations of other goods in the warehouse; Determine the deviation rate of the number of time periods in different temperature intervals and humidity intervals according to the number of time periods in different temperature intervals and humidity intervals during the storage of the goods, and determine the environmental similarity coefficient based on the average value of the deviation rate of the number of time periods in different temperature intervals and humidity intervals; 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.

[0039] 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.

[0040] 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.

[0041] 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 the label expiration probability among different types of goods; Optionally, the expiration data of the electronic tags of similar stored goods includes the expiration number of the electronic tags of similar stored goods.

[0042] Specifically, Figure 4 As shown, the method for determining the goods with failure risk among the goods is: Determine the failure probability of the electronic tags of different similar stored goods by the ratio of the failure number to the identification number of the electronic tags of different similar stored goods; Determining similarity weight coefficients of different similar stored goods based on the storage similarity coefficients of different similar stored goods and the goods; Based on the failure probability of the electronic tags of different similar stored goods and the average value of similar weight coefficients, 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.

[0043] Furthermore, when the expiration probability of the label of the goods is greater than a preset expiration probability, the goods are determined to be goods with expiration risk.

[0044] In another embodiment, the method for determining the goods with failure risk among the goods is: Determine the failure probability of the electronic tags of different similar stored goods by the ratio of the failure number to the identification number of the electronic tags of different similar stored goods; Using the failure probability to identify similar risk goods among the similar stored goods; 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.

[0045] Optionally, the method for determining the goods with failure risk among the goods is: Based on the number of invalid electronic tags of different similar stored goods, if it is determined that there is no invalid electronic tag for different similar stored goods, then it is determined that the goods do not belong to the invalid risk goods; When there are similar stored goods with invalid electronic tags: Similar stored goods with expired electronic tags are regarded as expired stored goods. When the proportion of the expired stored goods in the similar stored goods meets the requirement: Obtaining the invalidation number of the electronic tags in different invalid storage goods, and when the invalidation number of the electronic tags in different invalid storage goods is less than the preset invalidation number, determining that the goods do not belong to the invalidation risk goods; When there are expired goods with expired electronic tags whose expired number is not less than the preset expired number: Obtain the ratio of the number of failures to the number of identifications of the electronic tags in different failed stored goods, determine the failure probability of the electronic tags of different similar stored goods, and when there is no failed stored goods whose failure probability of the electronic tags does not meet the requirements, determine that the goods do not belong to the failed risk goods; When the proportion of the quantity of the failed stored goods in the similar stored goods does not meet the requirements or there are failed stored goods whose failure probability of electronic tags does not meet the requirements: 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 probabilities of the electronic tags of the failed stored goods with similarity weight coefficients greater than the preset weight coefficients all meet the requirements, it is determined that the goods do not belong to the failed risk goods; When there are similarity weight coefficients greater than the preset weight coefficients of electronic tags whose failure probability does not meet the requirements for failed storage goods: Based on the failure probability of the electronic tags of different similar stored goods and the average value of similar weight coefficients, 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.

[0046] 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 inbound and outbound in the current period.

[0047] Specifically, Figure 5 As shown, the method for determining the IoT monitoring mode of in-and-out storage in the current period is: The in-and-out quantity of the goods with failure risk in different unit time periods is determined based on the in-and-out planning data of the goods with failure risk in different unit time periods, and the sum of the label failure probabilities in different unit time periods is determined based on the label failure probabilities of the goods with different failure risks; 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.

[0048] 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.

[0049] In another embodiment, determining the IoT monitoring method of the warehouse entry and exit in the current period according to the number of the failure risk periods specifically includes: When the number of failure risk periods is greater than the number of preset 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 in-and-out storage during the current period.

[0050] Optionally, the method for determining the IoT monitoring mode of inbound and outbound storage in the current period is: The total quantity of the expired risk goods in the current period is determined by the inbound and outbound planning data of the expired risk goods in different unit periods. When the total quantity of the expired risk 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. When the total quantity of goods at risk of expiration in the current period meets the requirements: When the total number of expired risk goods in the current period is less than the preset number of expired goods, only the RFID device is used to perform IoT monitoring of the in and out of the warehouse in the current period; 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: Based on the in-and-out quantity of expired risk goods in different unit time periods in the current period, when the in-and-out quantity of expired risk goods is determined to be greater than the preset in-and-out quantity per unit time period: The unit time period in which the number of in-and-out goods with failure risk is greater than the preset number of in-and-out goods is used as the detection risk period. When the number of detection risk periods does not meet the requirements, the RFID device and the camera device are used to perform IoT monitoring of the in-and-out goods in the current period. When the in-and-out quantity of goods without failure risk is greater than the preset in-and-out quantity per unit period or the quantity of the detection risk period meets the requirements: Determine the in-and-out quantity of goods with failure risk in different unit time periods, and determine the sum of the label failure probabilities in different unit time periods in combination with the label failure probabilities of goods with different failure risks. When determining that there is a failure risk period in the unit time period based on the sum of the label failure probabilities: When the number of failure risk periods in the current period does not meet the requirement, the RFID device and the camera device are used to perform IoT monitoring of the entry and exit of the current period; 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: The average value of the sum of the tag failure probabilities in different unit time periods is taken as the probability average value, and the Internet of Things monitoring method for inbound and outbound storage in the current time period is determined using the probability average value.

[0051] Optionally, the Internet of Things monitoring method for inbound and outbound storage in the current period is determined by using the probability average value, specifically including: When the probability average value 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; 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.

[0052] Embodiment 2 The second aspect, as Figure 6 As shown, the present invention provides an inbound and outbound warehouse management system based on an Internet of Things device, which adopts the above-mentioned inbound and outbound warehouse management method based on an Internet of Things device, specifically comprising: Deviation risk assessment module, similarity assessment module, failure probability identification module, monitoring method determination module; The deviation risk assessment module is responsible for determining whether the identification deviation 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.

[0053] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0054] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0055] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A warehouse entry and exit management method based on Internet of Things 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, and combine the similarities of the radio frequency signals of the electronic tags of different types of goods to determine that the identification deviation risk of the current period is within the preset risk range, and then 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 storage goods of different types of goods using an AI model; Based on the in-and-out data of the similar stored goods, the expiration data of the electronic tags of different similar stored goods are determined, and the expiration risk goods and the probability of label expiration in different types of goods are determined using the expiration data; Determine the inbound and outbound planning data of the goods with failure risk in different unit time 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 inbound and outbound in the current period.

2. The method for managing inbound and outbound storage based on IoT devices as claimed in claim 1, characterized in that: 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 storage based on IoT devices as claimed in claim 1, characterized in that: The similarity of the radio frequency signals is determined based on the similarity of the signal transmission frequencies or signal transmission strengths of the radio frequency signals of the electronic tags of different types of goods.

4. The method for managing inbound and outbound storage based on IoT devices as claimed in claim 1, characterized in that: The method for determining the identification deviation risk of the current period is: Determine the inbound and outbound goods and the quantity of inbound and outbound goods in the current period based on the inbound and outbound planning 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 they are used to identify 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 as claimed in 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 storage based on IoT devices as claimed in claim 1, characterized in that: 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 storage based on IoT devices as claimed in claim 1, characterized in that: The method for determining the IoT monitoring mode of the in-and-out warehouse in the current period is: The in-and-out quantity of the goods with failure risk in different unit time periods is determined based on the in-and-out planning data of the goods with failure risk in different unit time periods, and the sum of the label failure probabilities in different unit time periods is determined based on the label failure probabilities of the goods with different failure risks; 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 as claimed in 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 as claimed in claim 7, characterized in that: The IoT monitoring method for inbound and outbound storage in the current period is determined by the number of the failure risk periods, specifically including: When the number of failure risk periods is greater than the number of preset 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 in-and-out 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 deviation risk assessment module is responsible for determining whether the identification deviation 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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