Big data logistics warehouse management system

Abstract

The invention discloses a logistics warehouse management system based on big data, and belongs to the technical field of logistics management. The method is used for solving the technical problems that the warehousing states of different warehousing positions cannot be supervised and analyzed according to each batch of logistics warehousing and data expansion and utilization of different dimensions cannot be implemented in the existing scheme. The warehousing states of different warehousing positions are supervised and analyzed according to each batch of logistics warehousing storage, so that periodic local warehousing state supervision can be implemented on different warehousing positions in a logistics warehouse; reliable local data support can be provided for subsequent adjustment management analysis of different storage positions and overall saturation condition analysis of the logistics warehouse; according to the method, the periodic local storage state supervision results of different storage positions in the early stage are expanded and utilized, and reliable data support can be provided for subsequent dynamic management prompts of different storage positions while the overall filling stability conditions of the different storage positions can be analyzed and classified.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and in particular to a logistics warehousing management system of big data. Background Art

[0002] Logistics warehousing refers to the link in the supply chain that is responsible for the storage, management and distribution of logistics goods. It involves logistics transportation, warehouse management, inventory control and other aspects, aiming to achieve effective logistics operations and goods circulation.

[0003] The existing logistics warehousing management solutions cannot supervise and analyze the storage status of different storage locations based on each batch of logistics entering the warehouse, and implement data expansion and utilization in different dimensions, resulting in the inability to timely and efficiently obtain the local storage status of different storage locations in the logistics warehouse and the overall filling stability of different storage locations. Summary of the invention

[0004] The purpose of the present invention is to provide a big data logistics warehousing management system to solve the technical problem that the existing solutions cannot supervise and analyze the storage status of different storage locations according to each batch of logistics entering the warehouse and implement data expansion and utilization in different dimensions.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A big data logistics warehousing management system, including a logistics in-and-out local monitoring and analysis module, which is used to implement periodic data monitoring and statistics on the in-and-out data of the logistics warehouse and implement data analysis and dynamic prompts on the throughput change status of the corresponding period; including: Monitor the incoming objects in the logistics warehouse within the preset evaluation period. When collecting data on the scanned incoming objects, number and mark the incoming objects in the same batch in the order of scanning time. The scanning technology is used to obtain the cargo number, cargo time, cargo location and corresponding cargo volume of different incoming objects. The cargo number, cargo number, cargo time, cargo location and corresponding cargo volume of all incoming objects in the same batch constitute the incoming monitoring data and are uploaded to the management platform in real time. After all incoming objects of the batch have been scanned, the total number of incoming objects of the batch is counted, and the total number of incoming objects is compared and analyzed with the total number of transported objects recorded during the transportation of the batch; If the total number of incoming goods is equal to the total number of goods transported, a normal incoming goods label is generated and the data statistics of the next process are started; otherwise, an abnormal incoming goods label is generated and the corresponding batch incoming goods are marked as abnormal batch incoming goods, and an alarm is issued to prompt the staff to intervene for review; When monitoring the storage status of different storage locations in the logistics warehouse during the same period according to the normal warehouse entry labels, the storage status of different storage locations in the logistics warehouse is calculated according to the real-time warehouse entry objects and real-time warehouse exit objects to obtain the filling degree μ corresponding to different storage locations; Compare and analyze the filling degree with the filling warning value to obtain the storage of normal labels and normal warehouses, the storage of slightly abnormal labels and slightly abnormal warehouses, and the storage of severely abnormal labels and severely abnormal warehouses; The overall monitoring and analysis module for storage location filling is used to evaluate the overall filling stability of storage locations based on the filling conditions of different storage locations in each batch, and dynamically adjust the subsequent filling of objects in different storage locations based on the evaluation results.

[0006] Preferably, when monitoring the incoming objects of the logistics warehouse within the same evaluation period, the outgoing objects are numbered and marked in the order of scanning time; and the cargo number, outgoing time, outgoing location and corresponding outgoing object volume of different outgoing objects are obtained through scanning technology. The outgoing number, cargo number, outgoing time, outgoing location and corresponding outgoing object volume of all outgoing objects of the same batch constitute the outgoing monitoring data and are uploaded to the management platform in real time.

[0007] Preferably, the filling degree μ corresponding to different storage locations is calculated by the formula μ=(Vc+Vin-Vout) / VB; wherein Vc is the total volume of objects already stored in the storage location, Vin is the volume of incoming objects to be stored in the storage location, Vout is the volume of outgoing objects to be shipped out of the storage location, and VB is the total capacity of objects that can be stored in the storage location.

[0008] Preferably, the warehouse location corresponding to the filling degree not greater than the filling warning value is marked as a normal warehouse and a storage normal label is generated; The warehouse location corresponding to the filling degree greater than the filling warning value but not greater than the filling warning value M is marked as a slightly abnormal warehouse and a storage slightly abnormal label is generated; the value range of M is (1, 2); The warehouse location corresponding to the filling degree greater than the filling warning value M is marked as a severely abnormal warehouse and a storage severely abnormal label is generated.

[0009] Preferably, the working steps of the storage location filling overall monitoring and analysis module include: Different storage locations in the logistics warehouse are numbered and marked as j, j=1, 2, 3, ..., m; m is a positive integer; all normal storage labels, slightly abnormal storage labels and severely abnormal storage labels in different storage locations are counted to obtain the total number of normal labels Zzj, the total number of slightly abnormal labels Qyj and the total number of severely abnormal labels Zyj; the first filling support φ1 and the second filling support φ2 corresponding to different storage locations are calculated by the formula φ1=Zzj / (Zzj+Zzj+Zyj) and φ2=Zyj / (Zzj+Zzj+Zyj).

[0010] Preferably, all storage locations are sorted in descending order according to the sizes of all first filling support values ​​and second filling support values ​​to obtain the first filling sorting set and the second filling sorting set, and the top K storage locations in the first filling sorting set and the second filling sorting set are marked as the first selected warehouse and the second selected warehouse, respectively, and alarm prompts for dynamic adjustment of logistics filling are implemented for several first selected warehouses and second selected warehouses.

[0011] Preferably, the overall monitoring and analysis module for logistics in and out of the warehouse is used to analyze the overall saturation situation of the logistics warehouse and issue dynamic alarm prompts by integrating the storage status of different storage locations in the logistics warehouse during the same period.

[0012] Preferably, the total number of all normal storage tags B1, the total number of slightly abnormal storage tags B2, and the total number of severely abnormal storage tags B3 that appear in real time in different storage locations in the logistics warehouse are counted, the values ​​of the marked data are extracted, and the saturation β of the logistics warehouse in the same period is calculated by the formula β=(a1×B1+a2×B2+a3×B3) / (B1+B2+B3); wherein a1, a2, and a3 are different state influencing factors, 0<a1<a2<a3<1, a1+a2+a3=1; The overall saturation of logistics warehouses in the same period is classified according to the saturation level.

[0013] Preferably, if the saturation is not greater than the saturation normal threshold, a saturation normal label is generated; If the saturation is greater than the saturation normal threshold and not greater than the saturation abnormal threshold, a saturation mild abnormal label is generated; If the saturation is greater than the saturation anomaly threshold, a saturation severe anomaly label is generated; The saturated normal label, saturated mild abnormal label or saturated severe abnormal label constitutes the saturation analysis data and is uploaded to the management platform in real time. According to the saturated mild abnormal label or saturated severe abnormal label in the saturation analysis data, the abnormal overall saturation situation of the logistics warehouse is warned with the corresponding degree of abnormality.

[0014] Compared with the existing solutions, the present invention achieves the following beneficial effects: The present invention supervises and analyzes the storage status of different storage locations according to the storage of each batch of logistics entering the warehouse, and can implement periodic local storage status supervision of different storage locations in the logistics warehouse. At the same time, it can also provide reliable local data support for subsequent adjustment management analysis of different storage locations and overall saturation analysis of the logistics warehouse, thereby improving the diversity and reliability of the expanded utilization of each batch of logistics warehousing supervision data.

[0015] The present invention can analyze and classify the overall filling stability of different storage locations by expanding and utilizing the periodic local storage status supervision results of different storage locations in the early stage, and can also provide reliable data support for the subsequent dynamic management prompts of different storage locations, thereby improving the timeliness and reliability of management adjustments of different storage locations, avoiding uneven and unreasonable material filling at different storage locations, and improving the overall effect of supervision prompts on resource utilization of storage locations.

[0016] The present invention can analyze and classify the overall saturation situation of logistics warehouses and issue real-time alarms by expanding and utilizing the periodic local storage status supervision results of different storage locations in the early stage, thereby realizing data calculation and analysis as well as dynamic alarms from the overall storage status, thereby improving the supervision effect of the overall saturation situation of logistics warehousing and the risk prediction prompt effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below in conjunction with the accompanying drawings.

[0018] Figure 1 This is a module block diagram of a big data logistics warehousing management system of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary operation and maintenance personnel in this field without creative work are within the scope of protection of the present invention.

[0020] Example 1: Figure 1 As shown, the present invention is a big data logistics warehousing management system, including a logistics in-and-out local monitoring and analysis module and a management platform; The local monitoring and analysis module for logistics in and out of the warehouse is used to implement periodic data monitoring and statistics on the in and out of the logistics warehouse and implement data analysis and dynamic prompts on the throughput change status of the corresponding period; including: The objects entering the logistics warehouse are monitored within a preset evaluation period. The unit of the evaluation period is hours or days, which can be customized according to the real-time busyness of the logistics warehouse. When implementing data statistics on the objects scanned into the warehouse, the objects entering the warehouse in the same batch are numbered in the order of scanning time and marked as i, i=1, 2, 3, ..., n; n is a positive integer; The cargo number, cargo time, cargo location and corresponding cargo volume of different incoming cargoes are obtained through scanning technology. Scanning technology includes but is not limited to laser scanner and camera scanning. The cargo number, cargo number, cargo time, cargo location and corresponding cargo volume of all incoming cargoes in the same batch constitute the incoming cargo monitoring data and are uploaded to the management platform in real time; The same batch can be logistics items transported on the same transport vehicle; the cargo number is obtained by scanning the QR code attached to the surface of the incoming object, the unit of the incoming time is accurate to seconds, and the incoming location refers to the specific shelf location stored in the logistics warehouse after scanning; After all incoming objects of the batch have been scanned, the total number of incoming objects of the batch is counted, and the total number of incoming objects is compared and analyzed with the total number of transported objects recorded during the transportation of the batch; If the total number of incoming goods is equal to the total number of goods transported, a normal incoming goods label is generated and the data statistics of the next process are started; otherwise, an abnormal incoming goods label is generated and the corresponding batch incoming goods are marked as abnormal batch incoming goods, and an alarm is issued to prompt the staff to intervene for review; By statistically analyzing the total number of all incoming objects in the same batch, data traceability support can be provided for subsequent object loss. And, when monitoring the incoming objects of the logistics warehouse within the same evaluation period, the outgoing objects are numbered in the order of scanning time and marked as q, q=1, 2, 3, ..., v; v is a positive integer; Also, the cargo number, cargo time, cargo location and corresponding cargo volume of different outbound objects are obtained through scanning technology. The outbound number, cargo number, cargo time, cargo location and corresponding cargo volume of all outbound objects of the same batch constitute outbound monitoring data and are uploaded to the management platform in real time; When the storage status of different storage locations in the logistics warehouse in the same period is supervised according to the normal labels of incoming warehouses, and the storage status of different storage locations in the logistics warehouse is calculated and analyzed according to the real-time incoming objects and real-time outgoing objects, the filling degree μ corresponding to different storage locations is calculated by the formula μ=(Vc+Vin-Vout) / VB; in the formula, Vc is the total volume of objects stored in the storage location, Vin is the volume of incoming objects to be stored in the storage location, Vout is the volume of outgoing objects to be out of the storage location, and VB is the total storage capacity of objects that can be stored in the storage location; It should be noted that the filling degree is a numerical value used to evaluate and classify the storage status of different storage locations by integrating and calculating all monitoring data of inbound and outbound storage in the same period after the logistics warehouse implements the storage behavior of incoming goods. The larger the filling degree, the worse the storage status of the corresponding storage location, that is, the storage location has stored more objects, and the corresponding future storage capacity is poor. The warehouse location corresponding to the filling degree not greater than the filling warning value is marked as a normal warehouse and a storage normal label is generated; The warehouse location corresponding to the filling degree greater than the filling warning value but not greater than the filling warning value M is marked as a slightly abnormal warehouse and a storage slightly abnormal label is generated; the value range of M is (1, 2); The warehouse location corresponding to the filling degree greater than the filling warning value M is marked as a severely abnormal warehouse and a storage severely abnormal label is generated; In the embodiment of the present invention, the storage status of different storage locations is supervised and analyzed according to the storage of each batch of logistics entering the warehouse, so that periodic local storage status supervision can be implemented for different storage locations in the logistics warehouse. At the same time, reliable local data support can be provided for the subsequent adjustment management analysis of different storage locations and the overall saturation analysis of the logistics warehouse, thereby improving the diversity and reliability of the expanded utilization of each batch of logistics warehousing supervision data.

[0021] Embodiment 2: A storage location filling overall monitoring and analysis module is used to evaluate the overall filling stability of the storage location according to the filling conditions of different storage locations in each batch, and dynamically adjust the subsequent object filling of different storage locations according to the evaluation results; including: Different storage locations in the logistics warehouse are numbered and marked as j, j=1, 2, 3, ..., m; m is a positive integer; all normal storage tags, slightly abnormal storage tags and severely abnormal storage tags in different storage locations are counted to obtain the total number of normal tags Zzj, the total number of slightly abnormal tags Qyj and the total number of severely abnormal tags Zyj; the first filling support φ1 and the second filling support φ2 corresponding to different storage locations are calculated by the formula φ1=Zzj / (Zzj+Zzj+Zyj) and φ2=Zyj / (Zzj+Zzj+Zyj); According to the sizes of all the first filling support degrees and the second filling support degrees, all the storage locations are sorted in descending order to obtain the first filling sorted set and the second filling sorted set, and the first K storage locations in the first filling sorted set and the second filling sorted set are marked as the first selected warehouse and the second selected warehouse respectively and uploaded to the management platform in real time. K is a positive integer. The specific value can be customized according to the actual application scenario and the total number of storage locations in the logistics warehouse, and alarm prompts for dynamic adjustment of logistics filling are implemented for several first selected warehouses and second selected warehouses.

[0022] In the embodiment of the present invention, by expanding and utilizing the periodic local storage status supervision results of different storage locations in the early stage, the overall filling stability of different storage locations can be analyzed and classified, and reliable data support can be provided for the subsequent dynamic management prompts of different storage locations, thereby improving the timeliness and reliability of management adjustments of different storage locations, avoiding uneven and unreasonable material filling at different storage locations, and improving the overall effect of supervision prompts on resource utilization of storage locations.

[0023] Embodiment 3: A logistics in-and-out overall monitoring and analysis module is used to analyze the overall saturation of the logistics warehouse and issue dynamic alarm prompts based on the storage status of different storage locations in the logistics warehouse during the same period; including: The total number of all normal storage tags B1, the total number of slightly abnormal storage tags B2, and the total number of severely abnormal storage tags B3 that appear in real time in different storage locations in the logistics warehouse are counted, the values ​​of the marked data are extracted, and the saturation β of the logistics warehouse in the same period is calculated by the formula β=(a1×B1+a2×B2+a3×B3) / (B1+B2+B3); where a1, a2, and a3 are different state influencing factors, 0<a1<a2<a3<1, a1+a2+a3=1, and the proportional coefficient in the formula can be set by technicians in this field according to actual conditions, or obtained by simulating historical big data through simulation software; When classifying the overall saturation of logistics warehouses in the same period according to saturation, if the saturation is not greater than the saturation normal threshold, a saturation normal label is generated; If the saturation is greater than the saturation normal threshold and not greater than the saturation abnormal threshold, a saturation mild abnormal label is generated; the saturation normal threshold and the saturation abnormal threshold are determined according to the design parameters of the logistics warehouse in the early stage; If the saturation is greater than the saturation anomaly threshold, a saturation severe anomaly label is generated; The saturated normal label, saturated mild abnormal label or saturated severe abnormal label constitutes the saturation analysis data and is uploaded to the management platform in real time. According to the saturated mild abnormal label or saturated severe abnormal label in the saturation analysis data, the abnormal overall saturation situation of the logistics warehouse is warned with the corresponding degree of abnormality.

[0024] Different from the existing technical solutions, which cannot monitor and analyze the overall saturation situation of the logistics warehouse and issue real-time alarm prompts, the logistics warehouse cannot take timely and efficient measures to respond, which in turn affects the overall turnover efficiency and processing efficiency of the logistics warehouse; in the embodiment of the present invention, by expanding the utilization of the periodic local storage status supervision results of different storage locations in the early stage, the overall saturation situation of the logistics warehouse can be analyzed and classified and real-time alarm prompts can be issued, which realizes data calculation and analysis from the overall storage status and dynamic alarm prompts, thereby improving the supervision effect of the overall saturation situation of logistics warehousing and the risk estimation prompt effect.

[0025] In addition, the formulas involved in the above are all dimensionless and numerical calculations. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and simulating it with simulation software.

[0026] In the several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the above-described embodiments of the invention are only illustrative, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0027] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0028] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0029] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the basic features of the present invention.

[0030] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, ordinary operation and maintenance personnel in the field should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A big data logistics warehousing management system, characterized in that: It includes a local monitoring and analysis module for logistics in and out of the warehouse, which is used to implement periodic data monitoring and statistics on the in and out of the logistics warehouse and implement data analysis and dynamic prompts on the throughput change status of the corresponding period; including: Monitor the incoming objects in the logistics warehouse within the preset evaluation period. When collecting data on the scanned incoming objects, number and mark the incoming objects in the same batch in the order of scanning time. The scanning technology is used to obtain the cargo number, cargo time, cargo location and corresponding cargo volume of different incoming objects. The cargo number, cargo number, cargo time, cargo location and corresponding cargo volume of all incoming objects in the same batch constitute the incoming monitoring data and are uploaded to the management platform in real time. After all incoming objects of the batch have been scanned, the total number of incoming objects of the batch is counted, and the total number of incoming objects is compared and analyzed with the total number of transported objects recorded during the transportation of the batch; If the total number of incoming goods is equal to the total number of goods transported, a normal incoming goods label is generated and the data statistics of the next process are started; otherwise, an abnormal incoming goods label is generated and the corresponding batch incoming goods are marked as abnormal batch incoming goods, and an alarm is issued to prompt the staff to intervene for review; When monitoring the storage status of different storage locations in the logistics warehouse in the same period according to the normal labels of incoming warehouses, the storage status of different storage locations in the logistics warehouse is calculated according to the real-time incoming objects and real-time outgoing objects to obtain the filling degree μ corresponding to different storage locations; the filling degree μ corresponding to different storage locations is calculated by the formula μ=(Vc+Vin-Vout) / VB; in the formula, Vc is the total volume of objects stored in the storage location, Vin is the volume of incoming objects to be stored in the storage location, Vout is the volume of outgoing objects to be out of the storage location, and VB is the total storage capacity of objects that can be stored in the storage location; Compare and analyze the filling degree with the filling warning value to obtain the storage of normal labels and normal warehouses, the storage of slightly abnormal labels and slightly abnormal warehouses, and the storage of severely abnormal labels and severely abnormal warehouses; The overall monitoring and analysis module for storage location filling is used to evaluate the overall filling stability of storage locations based on the filling conditions of different storage locations in each batch, and dynamically adjust the subsequent filling of objects in different storage locations based on the evaluation results.

2. A big data logistics warehousing management system according to claim 1, characterized in that: When monitoring the incoming objects in the logistics warehouse within the same assessment period, the outgoing objects are numbered and marked in the order of scanning time; and the cargo number, outgoing time, outgoing location and corresponding outgoing object volume of different outgoing objects are obtained through scanning technology. The outgoing number, cargo number, outgoing time, outgoing location and corresponding outgoing object volume of all outgoing objects in the same batch constitute the outgoing monitoring data and are uploaded to the management platform in real time.

3. A big data logistics warehousing management system according to claim 2, characterized in that: The warehouse location corresponding to the filling degree not greater than the filling warning value is marked as a normal warehouse and a storage normal label is generated; The warehouse location corresponding to the filling degree greater than the filling warning value but not greater than the filling warning value M is marked as a slightly abnormal warehouse and a storage slightly abnormal label is generated; the value range of M is (1, 2); The warehouse location corresponding to the filling degree greater than the filling warning value M is marked as a severely abnormal warehouse and a storage severely abnormal label is generated.

4. A big data logistics warehousing management system according to claim 1, characterized in that: The working steps of the overall monitoring and analysis module for filling storage locations include: Different storage locations in the logistics warehouse are numbered and marked as j, j=1, 2, 3, ..., m; m is a positive integer; all normal storage tags, slightly abnormal storage tags and severely abnormal storage tags in different storage locations are counted to obtain the total number of normal tags Zzj, the total number of slightly abnormal tags Qyj and the total number of severely abnormal tags Zyj; The first filling support φ1 and the second filling support φ2 corresponding to different storage locations are calculated by the formula φ1=Zzj / (Zzj+Zzj+Zyj) and φ2=Zyj / (Zzj+Zzj+Zyj).

5. A big data logistics warehousing management system according to claim 4, characterized in that: According to the values ​​of all the first filling support and the second filling support, all the storage locations are arranged in descending order to obtain the first filling sorted set and the second filling sorted set, and the top K storage locations in the first filling sorted set and the second filling sorted set are marked as the first selected warehouse and the second selected warehouse respectively, and alarm prompts for dynamic adjustment of logistics filling are implemented for several first selected warehouses and the second selected warehouses.

6. A big data logistics warehousing management system according to claim 1, characterized in that: The overall monitoring and analysis module for logistics in and out of the warehouse is used to analyze the overall saturation situation of the logistics warehouse and issue dynamic alarm prompts based on the storage status of different storage locations in the logistics warehouse during the same period.

7. A big data logistics warehousing management system according to claim 6, characterized in that: The total number of all normal storage tags B1, the total number of slightly abnormal storage tags B2, and the total number of severely abnormal storage tags B3 that appear in real time in different storage locations in the logistics warehouse are counted, the values ​​of the marked data are extracted, and the saturation β of the logistics warehouse in the same period is calculated by the formula β=(a1×B1+a2×B2+a3×B3) / (B1+B2+B3); where a1, a2, and a3 are different state influencing factors, 0<a1<a2<a3<1, a1+a2+a3=1; The overall saturation of logistics warehouses in the same period is classified according to the saturation level.

8. A big data logistics warehousing management system according to claim 7, characterized in that: If the saturation is not greater than the saturation normal threshold, a saturation normal label is generated; If the saturation is greater than the saturation normal threshold and not greater than the saturation abnormal threshold, a saturation mild abnormal label is generated; If the saturation is greater than the saturation anomaly threshold, a saturation severe anomaly label is generated; The saturated normal label, saturated mild abnormal label or saturated severe abnormal label constitutes the saturation analysis data and is uploaded to the management platform in real time. According to the saturated mild abnormal label or saturated severe abnormal label in the saturation analysis data, the abnormal overall saturation situation of the logistics warehouse is warned with the corresponding degree of abnormality.

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