Logistics storage monitoring system based on artificial intelligence

Abstract

The invention discloses a logistics storage monitoring system based on artificial intelligence, and the system comprises a warehouse environment collection module, an entering vehicle collection module, a leaving vehicle collection module, a fire-fighting equipment collection module, a goods shelf collection module, a data processing module, and an information sending module. The entering vehicle acquisition module is used for acquiring entering vehicle information, the leaving vehicle acquisition module is used for acquiring leaving vehicle information, the goods shelf acquisition module is used for acquiring goods shelf information, and the fire-fighting equipment acquisition module is used for acquiring fire-fighting equipment information. According to the invention, logistics storage monitoring can be carried out more comprehensively, and goods safety is ensured.

Description

Technical Field

[0001] The present invention relates to the field of monitoring systems, and in particular to a logistics warehousing monitoring system based on artificial intelligence. Background Art

[0002] Warehousing logistics refers to the use of self-built or leased warehouses and sites to store, load, unload, transport and distribute goods. The traditional definition of warehousing logistics is given from the perspective of material storage, but modern "warehousing logistics" is not the traditional "warehouse" or "warehouse management", but warehousing logistics in the context of economic globalization and supply chain integration. In the actual use of warehouses in warehousing logistics, monitoring systems are required to monitor and ensure the safety of goods. However, the logistics warehousing monitoring system in the prior art cannot guarantee the safety of goods well, which has a certain impact on the use of the logistics warehousing monitoring system. Therefore, a logistics warehousing monitoring system based on artificial intelligence is proposed. Summary of the invention

[0003] The purpose of the present invention is to provide a logistics warehousing monitoring system based on artificial intelligence to solve the technical defects proposed by the background technology.

[0004] The present invention is through the following technical scheme: a logistics warehousing monitoring system based on artificial intelligence, including a warehouse environment acquisition module, an entry vehicle acquisition module, a departure vehicle acquisition module, a fire equipment acquisition module, a shelf acquisition module, a data processing module and an information sending module; The warehouse environment collection module is used to collect warehouse environment information, the incoming vehicle collection module is used to collect incoming vehicle information, the outgoing vehicle collection module is used to collect outgoing vehicle information, the shelf collection module is used to collect shelf information, and the fire equipment collection module is used to collect fire equipment information; The data processing module is used to process warehouse environment information, entry vehicle information, exit vehicle information, shelf information and fire equipment information to generate warehouse environment warning information, entry vehicle warning information, exit vehicle warning information, shelf warning information and fire equipment warning information; The information sending module is used to send the warehouse environment warning information, the entering vehicle warning information, the leaving vehicle warning information, the shelf warning information and the fire equipment warning information to a preset receiving terminal after the above information is generated.

[0005] Furthermore, the specific processing process of the warehouse environment warning information is as follows: extracting the collected warehouse environment information, the warehouse environment information including warehouse environment temperature information, warehouse environment humidity information and warehouse environment dust concentration information; Extract the collected warehouse environment temperature information, and when the warehouse environment temperature information is greater than the preset temperature for a preset period of time, a warehouse environment warning information is generated; Extract warehouse environment humidity information. When the warehouse environment humidity information is greater than the preset humidity for a preset period of time, a warehouse environment warning message is generated; The warehouse environment dust concentration information is extracted. When the warehouse environment dust concentration information is greater than the preset concentration for a preset period of time, a warehouse environment warning information is generated.

[0006] Furthermore, the process of acquiring the incoming vehicle warning information is as follows: extracting the collected incoming vehicle information, the incoming vehicle information including the incoming vehicle height information, the incoming vehicle width information, the incoming vehicle weight information and the incoming vehicle license plate information; The height information, width information and weight information of the entering vehicle are processed to obtain the real-time vehicle evaluation parameters, and then the vehicle license plate information is retrieved from the access vehicle database to obtain the preset vehicle evaluation parameters corresponding to the vehicle license plate information; The difference between the real-time vehicle evaluation parameter and the preset vehicle evaluation parameter is calculated to obtain the evaluation difference. When the absolute value of the evaluation difference exceeds the preset range, an entry vehicle warning message is generated.

[0007] Furthermore, the process of acquiring the real-time vehicle evaluation parameters is as follows: extract the height information, width information and weight information of the entering vehicle, mark the vehicle height information as H1, mark the entering vehicle width information as H2, mark the entering vehicle weight information as H3, and obtain the real-time vehicle evaluation parameter Hh through the formula H3 / (H1*H2)=Hh.

[0008] Furthermore, the specific processing process of the leaving vehicle warning information is as follows: extracting the collected leaving vehicle information, the leaving vehicle information including real-time vehicle load information, vehicle speed information and leaving vehicle license plate information; First, the license plate information of the departing vehicle is extracted, and the license plate information of the departing vehicle is imported into the database to extract the departing load information corresponding to the license plate information of the departing vehicle; Then, the difference between the real-time vehicle load information and the departure load information is extracted to obtain the weight assessment difference. When the weight assessment difference is greater than the preset value and the vehicle speed information is greater than the preset value, a departure vehicle warning message is generated.

[0009] Furthermore, the specific processing process of the fire equipment warning information is as follows: extracting the collected fire equipment information, the fire equipment information includes the fire area information and the number of fire equipment of a single fire equipment, and then collecting the area information of the logistics storage warehouse; The fire protection area information of a single fire protection device, the number information of fire protection devices and the area information of the logistics storage warehouse are calculated and processed to obtain a comprehensive evaluation parameter. When the comprehensive evaluation parameter is less than a preset value, a fire protection equipment warning message is generated.

[0010] Furthermore, the specific processing process of the comprehensive evaluation parameters is as follows: extract the fire protection area information of a single fire-fighting equipment, the number information of the fire-fighting equipment and the area information of the logistics storage warehouse, mark the fire protection area information of the single fire-fighting equipment as Q1, mark the number information of the fire-fighting equipment as Q2, mark the collected area information of the logistics storage warehouse as Q3, and obtain the comprehensive evaluation parameter Qq through the formula Q1*Q2*α-Q3=Qq, 0.95≤α≤0.99, α is inversely proportional to Q3, and the larger Q3 is, the smaller α is.

[0011] Furthermore, the specific processing process of the shelf warning information is as follows: extracting the collected shelf information, the shelf information including shelf load-bearing information, shelf quantity information and shelf cleaning history information; Extract the shelf load-bearing information, mark it as K, and mark the shelf quantity information as i. When the number of shelf load-bearing information K that is greater than the preset value a1 exceeds one-third of the number of shelves, a shelf warning message is generated; When the load-bearing information K of any shelf is greater than the preset value a2, a shelf warning message is generated; The shelf cleaning history information is processed to obtain the shelf average cleaning interval information. When the shelf average cleaning interval information is greater than a preset value, a shelf warning information is generated.

[0012] Compared with the prior art, the present invention has the following advantages: The present invention generates warehouse environment warning information, entry vehicle warning information, exit vehicle warning information, shelf warning information and fire equipment warning information by processing warehouse environment information, entry vehicle information, exit vehicle information, shelf warning information and fire equipment warning information, so as to realize more comprehensive logistics warehousing monitoring and better ensure the safety of logistics warehousing goods. Through the generated warehouse environment information, when abnormalities are found in the warehouse environment, timely warnings are given to reduce the damage of stored goods caused by abnormal warehouse environment. Through the generated entry vehicle warning information, when abnormalities are found in the vehicles entering by themselves, timely warnings are given to avoid the situation where unauthorized vehicles enter the logistics warehouse. Through the generated exit vehicle warning information, when abnormalities are found in the exiting vehicles, timely warnings are given to avoid the situation where the exiting vehicles steal goods. Through the generated fire equipment warning information, management personnel can be warned of abnormalities in the fire equipment of the storage warehouse. Through the generated shelf warning information, abnormalities in the storage shelves can be found in time to avoid damage to goods caused by abnormalities in the storage shelves, making the system more worthy of popularization and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0014] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiment.

[0015] like Figure 1 As shown, this embodiment provides a technical solution: a logistics warehousing monitoring system based on artificial intelligence, including a warehouse environment collection module, an entry vehicle collection module, a departure vehicle collection module, a fire equipment collection module, a shelf collection module, a data processing module and an information sending module; The warehouse environment collection module is used to collect warehouse environment information, the entry vehicle collection module is used to collect entry vehicle information, the exit vehicle collection module is used to collect exit vehicle information, the shelf collection module is used to collect shelf information, and the fire equipment collection module is used to collect fire equipment information; The data processing module is used to process warehouse environment information, entry vehicle information, exit vehicle information, shelf information and fire equipment information to generate warehouse environment warning information, entry vehicle warning information, exit vehicle warning information, shelf warning information and fire equipment warning information; The information sending module is used to send the warehouse environment warning information, the entering vehicle warning information, the leaving vehicle warning information, the shelf warning information and the fire equipment warning information to a preset receiving terminal after the above information is generated; The present invention generates warehouse environment warning information, entry vehicle warning information, exit vehicle warning information, shelf warning information and fire equipment warning information by processing warehouse environment information, entry vehicle information, exit vehicle information, shelf warning information and fire equipment warning information, thereby realizing more comprehensive logistics warehousing monitoring and better ensuring the safety of logistics warehousing goods. Through the generated warehouse environment information, when abnormalities are found in the warehouse environment, timely warnings are given to reduce the damage of stored goods caused by abnormal warehouse environment. Through the generated entry vehicle warning information, when abnormalities are found in the vehicles entering the warehouse, timely warnings are given to avoid unauthorized vehicles entering the logistics warehouse. Through the generated exit vehicle warning information, when abnormalities are found in the exiting vehicles, timely warnings are given to avoid the situation where the exiting vehicles steal the goods. Through the generated fire equipment warning information, management personnel can be warned of abnormalities in the fire equipment of the storage warehouse. Through the generated shelf warning information, abnormalities in the storage shelves can be found in time to avoid damage to goods caused by abnormalities in the storage shelves.

[0016] The specific processing process of the warehouse environment warning information is as follows: extracting the collected warehouse environment information, which includes warehouse environment temperature information, warehouse environment humidity information and warehouse environment dust concentration information; The collected warehouse environment temperature information is extracted. When the warehouse environment temperature information is greater than the preset temperature for a preset period of time, a warehouse environment warning message is generated. At this time, the warehouse environment warning message indicates that the warehouse temperature is abnormal and needs to be adjusted to ensure the safety of the goods. Extract warehouse environment humidity information. When the warehouse environment humidity information is greater than the preset humidity for a preset period of time, a warehouse environment warning message is generated. At this time, the warehouse environment warning message indicates that the warehouse humidity is abnormal and needs to be adjusted to ensure the safety of the goods. The warehouse environment dust concentration information is extracted. When the warehouse environment dust concentration information is greater than the preset concentration for more than the preset time, a warehouse environment warning message is generated. At this time, the warehouse environment warning message indicates that the warehouse dust concentration is abnormal and needs to be adjusted to ensure the safety of the goods.

[0017] The process of obtaining the incoming vehicle warning information is as follows: extracting the collected incoming vehicle information, the incoming vehicle information including the incoming vehicle height information, the incoming vehicle width information, the incoming vehicle weight information and the incoming vehicle license plate information; The height information, width information and weight information of the entering vehicle are processed to obtain the real-time vehicle evaluation parameters, and then the vehicle license plate information is retrieved from the access vehicle database to obtain the preset vehicle evaluation parameters corresponding to the vehicle license plate information; The difference between the real-time vehicle evaluation parameter and the preset vehicle evaluation parameter is calculated to obtain the evaluation difference. When the absolute value of the evaluation difference exceeds the preset range, an entry vehicle warning message is generated; Through the above process, vehicles entering the storage warehouse can be verified, and abnormal vehicles such as cloned vehicles entering the storage warehouse can be discovered in time.

[0018] The process of obtaining the real-time vehicle evaluation parameters is as follows: extract the height information, width information and weight information of the entering vehicle, mark the vehicle height information as H1, mark the entering vehicle width information as H2, and mark the entering vehicle weight information as H3, and obtain the real-time vehicle evaluation parameter Hh through the formula H3 / (H1*H2)=Hh; The real-time vehicle evaluation parameter Hh obtained through the above process can ensure the accuracy of the identification of the entering vehicle.

[0019] The specific processing process of the leaving vehicle warning information is as follows: extracting the collected leaving vehicle information, the leaving vehicle information includes real-time vehicle load information, vehicle speed information and leaving vehicle license plate information; First, the license plate information of the departing vehicle is extracted, and the license plate information of the departing vehicle is imported into the database to extract the departing load information corresponding to the license plate information of the departing vehicle; Then, the difference between the real-time vehicle load information and the departure load information is extracted to obtain the weight assessment difference. When the weight assessment difference is greater than a preset value and the vehicle speed information is greater than a preset value, a departure vehicle warning message is generated; Through the above process, the departing vehicle can be verified, and it can be discovered in time whether the departing vehicle is loaded with cargo exceeding the standard weight.

[0020] The specific processing process of the fire equipment warning information is as follows: extract the collected fire equipment information, which includes the fire area information and the number of fire equipment of a single fire equipment, and then collect the area information of the logistics storage warehouse; The fire protection area information of a single fire protection equipment, the number of fire protection equipment and the area information of the logistics storage warehouse are calculated and processed to obtain a comprehensive evaluation parameter. When the comprehensive evaluation parameter is less than a preset value, a fire protection equipment warning message is generated; Through the above process, it is possible to timely understand whether the fire-fighting equipment in the storage warehouse is sufficient.

[0021] The specific processing process of the comprehensive evaluation parameters is as follows: extract the fire protection area information of a single fire-fighting equipment, the number of fire-fighting equipment and the area information of the logistics storage warehouse, mark the fire protection area information of a single fire-fighting equipment as Q1, mark the number of fire-fighting equipment as Q2, and mark the collected area information of the logistics storage warehouse as Q3. Through the formula Q1*Q2*α-Q3=Qq, the comprehensive evaluation parameter Qq is obtained, 0.95≤α≤0.99, α is inversely proportional to Q3, and the larger Q3 is, the smaller α is.

[0022] The specific processing process of the shelf warning information is as follows: extracting the collected shelf information, the shelf information includes shelf load-bearing information, shelf quantity information and shelf cleaning history information; Extract the shelf load-bearing information, mark it as K, and mark the shelf quantity information as i. When the number of shelf load-bearing information K that is greater than the preset value a1 exceeds one-third of the number of shelves, a shelf warning message is generated; When the load-bearing information K of any shelf is greater than the preset value a2, a shelf warning message is generated; The shelf cleaning history information is processed to obtain the shelf average cleaning interval information. When the shelf average cleaning interval information is greater than a preset value, a shelf warning letter is generated; Through the above-generated shelf warning information, management personnel can promptly discover abnormalities in the shelves of the storage warehouse and perform maintenance in a timely manner to avoid damage to goods caused by shelf abnormalities.

[0023] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0024] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0025] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A logistics warehousing monitoring system based on artificial intelligence, characterized in that: It includes warehouse environment collection module, entry vehicle collection module, exit vehicle collection module, fire equipment collection module, shelf collection module, data processing module and information sending module; The warehouse environment collection module is used to collect warehouse environment information, the incoming vehicle collection module is used to collect incoming vehicle information, the outgoing vehicle collection module is used to collect outgoing vehicle information, the shelf collection module is used to collect shelf information, and the fire equipment collection module is used to collect fire equipment information; The data processing module is used to process warehouse environment information, entry vehicle information, exit vehicle information, shelf information and fire equipment information to generate warehouse environment warning information, entry vehicle warning information, exit vehicle warning information, shelf warning information and fire equipment warning information; The information sending module is used to send the warehouse environment warning information, the entering vehicle warning information, the leaving vehicle warning information, the shelf warning information and the fire equipment warning information to a preset receiving terminal after the above information is generated.

2. According to claim 1, a logistics warehousing monitoring system based on artificial intelligence is characterized in that: The specific processing process of the warehouse environment warning information is as follows: extracting the collected warehouse environment information, the warehouse environment information includes warehouse environment temperature information, warehouse environment humidity information and warehouse environment dust concentration information; Extract the collected warehouse environment temperature information, and when the warehouse environment temperature information is greater than the preset temperature for a preset period of time, a warehouse environment warning information is generated; Extract warehouse environment humidity information. When the warehouse environment humidity information is greater than the preset humidity for a preset period of time, a warehouse environment warning message is generated; The warehouse environment dust concentration information is extracted. When the warehouse environment dust concentration information is greater than the preset concentration for a preset period of time, a warehouse environment warning information is generated.

3. The artificial intelligence-based logistics warehousing monitoring system according to claim 1 is characterized in that: The acquisition process of the incoming vehicle warning information is as follows: extracting the collected incoming vehicle information, the incoming vehicle information including the incoming vehicle height information, the incoming vehicle width information, the incoming vehicle weight information and the incoming vehicle license plate information; The height information, width information and weight information of the entering vehicle are processed to obtain the real-time vehicle evaluation parameters, and then the vehicle license plate information is retrieved from the access vehicle database to obtain the preset vehicle evaluation parameters corresponding to the vehicle license plate information; The difference between the real-time vehicle evaluation parameter and the preset vehicle evaluation parameter is calculated to obtain the evaluation difference. When the absolute value of the evaluation difference exceeds the preset range, an entry vehicle warning message is generated.

4. The artificial intelligence-based logistics warehousing monitoring system according to claim 3 is characterized by: The process of acquiring the real-time vehicle evaluation parameters is as follows: extract the height information, width information and weight information of the entering vehicle, mark the vehicle height information as H1, mark the entering vehicle width information as H2, and mark the entering vehicle weight information as H3, and obtain the real-time vehicle evaluation parameter Hh through the formula H3 / (H1*H2)=Hh.

5. The artificial intelligence-based logistics warehousing monitoring system according to claim 1 is characterized in that: The specific processing process of the leaving vehicle warning information is as follows: extracting the collected leaving vehicle information, the leaving vehicle information including real-time vehicle load information, vehicle speed information and leaving vehicle license plate information; First, the license plate information of the departing vehicle is extracted, and the license plate information of the departing vehicle is imported into the database to extract the departing load information corresponding to the license plate information of the departing vehicle; Then, the difference between the real-time vehicle load information and the departure load information is extracted to obtain the weight assessment difference. When the weight assessment difference is greater than the preset value and the vehicle speed information is greater than the preset value, a departure vehicle warning message is generated.

6. The artificial intelligence-based logistics warehousing monitoring system according to claim 1 is characterized by: The specific processing process of the fire equipment warning information is as follows: extracting the collected fire equipment information, the fire equipment information includes the fire area information and the number of fire equipment of a single fire equipment, and then collecting the area information of the logistics storage warehouse; The fire protection area information of a single fire protection device, the number information of fire protection devices and the area information of the logistics storage warehouse are calculated and processed to obtain a comprehensive evaluation parameter. When the comprehensive evaluation parameter is less than a preset value, a fire protection equipment warning message is generated.

7. The artificial intelligence-based logistics warehousing monitoring system according to claim 1, characterized in that: The specific processing process of the comprehensive evaluation parameters is as follows: extract the fire protection area information of a single fire-fighting equipment, the number information of fire-fighting equipment and the area information of the logistics storage warehouse, mark the fire protection area information of a single fire-fighting equipment as Q1, mark the number information of fire-fighting equipment as Q2, and mark the collected area information of the logistics storage warehouse as Q3. Through the formula Q1*Q2*α-Q3=Qq, the comprehensive evaluation parameter Qq is obtained, 0.95≤α≤0.99, α is inversely proportional to Q3, and the larger Q3 is, the smaller α is.

8. The artificial intelligence-based logistics warehousing monitoring system according to claim 1, characterized in that: The specific processing process of the shelf warning information is as follows: extracting the collected shelf information, the shelf information includes shelf load-bearing information, shelf quantity information and shelf cleaning history information; Extract the shelf load-bearing information, mark it as K, and mark the shelf quantity information as i. When the number of shelf load-bearing information K that is greater than the preset value a1 exceeds one-third of the number of shelves, a shelf warning message is generated; When the load-bearing information K of any shelf is greater than the preset value a2, a shelf warning message is generated; The shelf cleaning history information is processed to obtain the shelf average cleaning interval information. When the shelf average cleaning interval information is greater than a preset value, a shelf warning information is generated.

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