Warehouse logistics scheduling system and method based on big data analysis

Abstract

The invention discloses a warehouse logistics scheduling system and method based on big data analysis, relates to the technical field of logistics scheduling, and solves the problems of lack of accuracy of personnel scheduling and serious resource waste of a traditional logistics scheduling system. The system can perform personnel allocation based on the emergency degree of the order, the order quantity and the arrival time of the vehicle; by calculating the arrival time, the order quantity and the emergency order proportion of each vehicle, the system can reasonably arrange workers to carry out unloading operation according to the priorities of different vehicles; the mode based on big data analysis not only can ensure that the emergency order is processed in time, but also can avoid excessive scheduling and reduce the waste of human resources; compared with a traditional mode of arranging workers through experience or simple pre-estimation, the automatic scheduling mode has the advantages that the accuracy of personnel scheduling is remarkably improved, and idle workers or overload work caused by improper scheduling is reduced.

Description

Technical Field

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

[0002] With the rapid development of e-commerce and logistics industries, warehousing and logistics systems have become a key link in enterprise operations and supply chain management. For circulation-type warehousing, traditional warehousing and logistics systems mainly rely on independent warehouse management and manual scheduling. The scheduling process between warehouses is usually carried out through offline communication, simple ERP systems or empirical rules. Although these systems can handle daily warehousing management and scheduling work, their limitations are becoming increasingly prominent when faced with a sharp increase in logistics orders and complex warehousing networks.

[0003] The traditional logistics scheduling system has the following main problems:

[0004] Personnel scheduling lacks precision and wastes resources seriously. In traditional warehousing and logistics scheduling systems, personnel scheduling often relies on the experience of managers or preset shift schedules. This static personnel scheduling method cannot flexibly respond to changes in order priority and arrival time when the number of orders fluctuates greatly or when urgent orders increase suddenly, resulting in a waste of human resources. Problems such as too many workers being idle or insufficient staff during busy hours frequently occur, which not only increases operating costs, but also reduces the overall processing efficiency of warehousing and logistics.

[0005] At the same time, most of the existing warehousing and logistics systems are closed and rigid architectures. Once the business expands or new warehousing is needed, the system usually needs to be transformed or upgraded on a large scale. Due to the lack of flexibility in system design, it is difficult to adapt to complex and changing market demands, which makes companies face high adjustment costs and time costs when expanding their business. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a warehouse logistics scheduling system and method based on big data analysis, which solves the problems in the background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a warehousing logistics scheduling system based on big data analysis, comprising:

[0008] The warehouse link module is used to uniquely identify the circulation warehouses participating in the link and establish a connection relationship for each circulation warehouse participating in the link;

[0009] The station module is used to collect the time when the vehicle goes to the specific secondary warehouse and the number of orders on the vehicle in each circulation warehouse, and package them into data packets. The data packets are named using the corresponding circulation warehouse unique identifier and sent to the cloud platform;

[0010] The cloud platform is used to receive data packets sent from each station module, decompress the data packets according to the naming of the data packets, extract the time of each vehicle going to a specific secondary warehouse and the order quantity information on the vehicle in the data packets, and determine the different time of each vehicle arriving at a specific secondary warehouse in combination with the calling map software function, and integrate the time information and order quantity information respectively in combination with the naming information of the data packets, and send them to the data receiving module in the corresponding specific secondary warehouse;

[0011] The data receiving module is used to receive the integrated information sent by the recognition cloud platform in real time and send it to the personnel scheduling and allocation module;

[0012] The personnel scheduling and allocation module is used to analyze multiple integrated information and allocate workers to the unloading work of each arriving vehicle based on the analysis results.

[0013] Preferably, in the warehouse link module, the specific connection method is:

[0014] Randomly select a circulation warehouse as the target warehouse, and determine the circulation warehouse where the orders of the target warehouse need to arrive based on the actual situation, and record the arriving circulation warehouse as the secondary warehouse of the target warehouse.

[0015] Preferably, the station module includes an order quantity determination unit and a vehicle departure determination unit;

[0016] An order quantity determination unit, used to determine the order quantity on the circulation storage vehicle;

[0017] The specific content of determining the order quantity on the circulation storage vehicle is:

[0018] Determine each sorting area after sorting is completed, count the number of orders in each sorting area, and after the statistics are completed, load the orders in different sorting areas separately;

[0019] The vehicle departure determination unit is used to determine the specific time of departure of each vehicle and the specific secondary warehouse that each vehicle arrives at.

[0020] Preferably, the specific secondary warehouse that each vehicle arrives at is determined as follows: sorting personnel sort the arrived order goods into different areas; the sorting personnel assign the orders to different areas according to the addresses of different logistics orders; after the sorting is completed, the orders in the area are loaded separately, thereby obtaining the specific secondary warehouse that each vehicle arrives at.

[0021] Preferably, in the personnel dispatching and allocation module, the analysis of the plurality of integrated information and the allocation of workers to the unloading work of each arriving vehicle according to the analysis results include:

[0022] S1: Determine the arrival time Ti of each vehicle in all integrated information, where 1≤i≤n, n is the number of integrated information received;

[0023] S2: Sort the vehicles from early to late according to their arrival time;

[0024] S3: Group the arrival time Ti of the vehicles and set each time period t as a time window. If the vehicles arrive in the same time window, the total number of orders in the batch is counted and recorded as Bk; then the number of unloading people N in the batch is determined according to the total number of orders Bk in the batch. k ; Where t is the preset value, 1≤k1≤m, and m is the total number of batches;

[0025] S4: Then, for each vehicle in each batch, according to its order volume and the proportion of urgent orders that need to be processed in the vehicle order, determine its priority index parameter P. j ;

[0026] S5: Then, according to the order in which all vehicles of the batch arrive within the time window t, the number of people N to be unloaded is calculated by the following formula algorithm. k Workers are then assigned to unload each vehicle in the batch:

[0027]

[0028] Among them, W j is the number of workers assigned to the jth vehicle, M j is the total order quantity on the j-th vehicle, 1≤j≤z, and z is the total number of vehicles in the corresponding batch.

[0029] Preferably, in step S3, the number of people unloading is determined by the following formula:

[0030]

[0031] Among them, N k is the number of people unloading this batch, W is the average work efficiency of the workers, and Ts is the preset value, which is the maximum unloading time allowed for this batch.

[0032] Preferably, in step S4, the specific method of determining the priority index is:

[0033] S41: Determine the proportion of urgent orders on the jth vehicle in the batch by calculating R using the following formula j :

[0034]

[0035] Among them, E j is the total number of emergency orders on the jth vehicle;

[0036] S42: Then combine with R j The priority index parameter P of the jth vehicle is determined by the following formula: j :

[0037]

[0038] Among them, α and β are weight coefficients, M max is the maximum order quantity for all vehicles in the batch.

[0039] Preferably, the method for determining the urgent order in step S4 is: when the customer purchases on the shopping platform, according to their own needs, they require the merchant to provide an urgent order shipping mark, and the shipping mark with the urgent order can be identified by the barcode scanner gun in the hands of the sorting personnel, and the number of urgent orders allocated to different areas can be counted.

[0040] A warehouse logistics scheduling method based on big data analysis, the steps are as follows:

[0041] AS1: Uniquely identify the circulation-type warehouses participating in the link and establish a connection relationship for each circulation-type warehouse participating in the link;

[0042] AS2: Collects the time when the vehicle goes to a specific secondary warehouse and the number of orders on the vehicle, packages them into a data package, names the data package using the corresponding circulation warehouse unique identifier, and sends it to the cloud platform;

[0043] AS3: In the cloud platform, it receives data packets sent by each station module, decompresses the data packets according to the names of the data packets, extracts the time when each vehicle goes to a specific secondary warehouse and the order quantity information on the vehicle in the data packets, determines the different times when each vehicle arrives at a specific secondary warehouse, integrates the time information and order quantity information respectively according to the naming information of the data packets, and forms integrated information to be sent to the data receiving module in the corresponding specific secondary warehouse;

[0044] AS4: Circulation-type warehouse receives and identifies the integrated information sent in real time by the cloud platform;

[0045] AS5: Then analyze the multiple integrated information, and according to the analysis results, assign workers to the unloading work of each arriving vehicle.

[0046] The present invention provides a warehouse logistics scheduling system and method based on big data analysis. Compared with the prior art, it has the following beneficial effects:

[0047] (1) Through the warehouse link module, the present invention can uniquely identify and associate each circulation warehouse to form a closely connected warehouse network. This connection mode breaks the information island problem in the previous warehouse logistics scheduling, allowing warehouses to share important data such as orders, vehicle information, and arrival time in real time through the cloud platform; transmitted to each secondary warehouse through the cloud platform, so that each link in the logistics system can timely understand the order status and arrange corresponding operations; the final effect is to reduce delays caused by untimely information transmission and improve the overall transportation efficiency of orders, especially in the case of multi-warehouse linkage, the efficiency improvement is particularly significant;

[0048] (2) The present invention uses a personnel scheduling and allocation module, and the system can allocate personnel based on the urgency of the order, the order volume and the arrival time of the vehicle; by calculating the arrival time, order volume and emergency order ratio of each vehicle, the system can reasonably arrange workers to perform unloading operations according to the priority of different vehicles; this method based on big data analysis can not only ensure that emergency orders are processed in a timely manner, but also avoid over-scheduling and reduce the waste of human resources; compared with the traditional method of arranging workers based on experience or simple estimation, this automated scheduling method significantly improves the accuracy of personnel scheduling and reduces the idleness or overload of workers caused by improper scheduling;

[0049] (3) The present invention has strong scalability and flexibility. When the logistics business is expanded, it is only necessary to add a new warehouse to the system. The system will uniquely identify the new warehouse through the warehouse link module and establish a connection relationship with other related warehouses. This connection method is not only limited to the current warehouse network, but can also be dynamically adjusted according to business needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention will be further described below in conjunction with the accompanying drawings.

[0051] Figure 1 This is an overall framework diagram of a warehousing logistics scheduling system based on big data analysis of the present invention;

[0052] Figure 2 This is a framework diagram of the internal modules of a circulation-type warehouse of a warehouse logistics scheduling system based on big data analysis of the present invention;

[0053] Figure 3 This is a flow chart of a warehouse logistics scheduling method based on big data analysis of the present invention. DETAILED DESCRIPTION

[0054] 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Embodiment 1

[0056] See also Figure 1-Figure 2 , the present invention provides a warehousing logistics scheduling system based on big data analysis, including: a warehousing link module, a station module, a cloud platform, a data receiving module and a personnel scheduling and allocation module;

[0057] The warehouse link module is used to uniquely identify the circulation warehouses participating in the link and establish a connection relationship for each circulation warehouse participating in the link;

[0058] It should be noted that the circulation-type warehouses participating in the link can directly or indirectly exchange information and data, thereby breaking the information island problem between the circulation-type warehouses;

[0059] The specific connection method is:

[0060] Randomly select a circulation warehouse as the target warehouse, and determine the circulation warehouse where the orders of the target warehouse need to arrive according to the actual situation, and record the arriving circulation warehouse as the secondary warehouse of the target warehouse;

[0061] Specifically, the transfer of orders between circulation warehouses is generally carried out by transporting orders by vehicles. For the transfer that enables orders to be transferred from one circulation warehouse to another, the two circulation warehouses are connected, and generally a circulation warehouse has a connection relationship with one or more circulation warehouses. For each circulation warehouse, it is taken as the target warehouse, and all the circulation warehouses with which it has a connection relationship are determined, and these circulation warehouses are recorded as secondary warehouses.

[0062] The station module is used to collect the time when the vehicle goes to the specific secondary warehouse and the number of orders on the vehicle in each circulation warehouse, and package them into data packets. The data packets are named using the corresponding circulation warehouse unique identifier and sent to the cloud platform;

[0063] The station module includes an order quantity determination unit and a vehicle departure determination unit;

[0064] An order quantity determination unit, used to determine the order quantity on the circulation storage vehicle;

[0065] The specific method is: determine each sorting area after sorting is completed, count the number of orders in each sorting area, and after the statistics are completed, load the orders in different sorting areas separately;

[0066] Specifically, the number of orders in each sorting area corresponds to a vehicle that needs to transport the orders in the sorting area to a specific secondary warehouse;

[0067] A vehicle departure determination unit, used to determine the specific time of departure of each vehicle and the specific secondary warehouse that each vehicle arrives at;

[0068] It should be noted that a station module is set up in the circulation warehouses participating in the link. Generally, there are sorting personnel in the circulation warehouse to sort the arriving order goods and sort them to different areas. There are such sorting areas in the circulation warehouse. The sorting personnel are assigned to different areas according to the addresses of different logistics orders. After the sorting is completed, the orders in these areas are loaded separately and driven to the corresponding specific secondary warehouses;

[0069] Through the warehouse link module, the system can uniquely identify and associate each circulation warehouse to form a closely connected warehouse network. This connection mode breaks the information island problem in the previous warehouse logistics scheduling, allowing warehouses to share important data such as orders, vehicle information, and arrival time in real time through the cloud platform; it is transmitted to each secondary warehouse through the cloud platform, so that each link in the logistics system can understand the order status in a timely manner and arrange corresponding operations; the final effect is to reduce delays caused by untimely information transmission and improve the overall transportation efficiency of orders, especially in the case of multi-warehouse linkage, the efficiency improvement is particularly significant;

[0070] The cloud platform is used to receive data packets sent from each station module, decompress the data packets according to the naming of the data packets, extract the time of each vehicle going to a specific secondary warehouse and the order quantity information on the vehicle in the data packets, and determine the different time of each vehicle arriving at a specific secondary warehouse in combination with the calling map software function, and integrate the time information and order quantity information respectively in combination with the naming information of the data packets, and send them to the data receiving module in the corresponding specific secondary warehouse;

[0071] For example: if the information extracted from the data packet contains information about 5 vehicles, namely, vehicle A, vehicle B, vehicle C, vehicle D and vehicle E, which are heading to specific secondary warehouse A, secondary warehouse B, secondary warehouse C, secondary warehouse D and secondary warehouse E respectively, and the number of orders loaded on each vehicle is h1, h2, h3, h4 and h5 respectively, and the departure time of each vehicle is 6:00, 6:20, 6:25, 6:30 and 6:40 respectively, by calling map software such as Amap or Baidu Map, the location of the target warehouse is first determined, and then the specific location information of the destination secondary warehouse A, secondary warehouse B, secondary warehouse C, secondary warehouse D and secondary warehouse E is determined in turn, and the specific driving time c1, c2, c3, c4 and c5 of each vehicle is calculated, and then the different times when each vehicle arrives at the specific secondary warehouse are determined. The time intervals are 6:00+c1, 6:20+c2, 6:25+c3, 6:30+c4, and 6:40+c5, then the time information 6:00+c1 is combined with the naming sub-information of the data packet and the order quantity h1 and sent to the data receiving module of the secondary warehouse A, the time information 6:20+c2 is combined with the naming sub-information of the data packet and the order quantity h2 and sent to the data receiving module of the secondary warehouse B, the time information 6:25+c3 is combined with the naming sub-information of the data packet and the order quantity h3 and sent to the data receiving module of the secondary warehouse C, the time information 6:30+c4 is combined with the naming sub-information of the data packet and the order quantity h3 and sent to the data receiving module of the secondary warehouse C, the time information 6:40+c5 is combined with the naming sub-information of the data packet and the order quantity h5 and sent to the data receiving module of the secondary warehouse E;

[0072] The data receiving module is used to receive the integrated information sent by the recognition cloud platform in real time and send it to the personnel scheduling and allocation module;

[0073] It should be noted that a data receiving module is set in each circulation warehouse participating in the link. The data receiving module receives one or more integrated information sent by the cloud platform in a certain period of time. These integrated information come from different circulation warehouses. For each integrated information, the order sent by the vehicle from a specific circulation warehouse is determined through the data packet naming information, the specific arrival time of the vehicle is determined through the time information, and the specific order quantity transported by the vehicle is determined through the order quantity information.

[0074] The personnel dispatching and allocation module is used to analyze multiple integrated information and allocate workers to the unloading work of each arriving vehicle based on the analysis results;

[0075] The specific analysis method is:

[0076] S1: Determine the arrival time Ti of each vehicle in all integrated information, where 1≤i≤n, n is the number of integrated information received;

[0077] S2: Sort the vehicles from early to late according to their arrival time;

[0078] S3: Group the arrival time Ti of the vehicles, set each t time period as a time window, and if the vehicles arrive in the same time window, count the total number of orders in the batch, record it as Bk; then determine the number of unloading people in the batch according to the total number of orders in the batch Bk; where t is a preset value, and the specific parameters are set by the management personnel, 1≤k1≤m, and m is the total number of batches;

[0079] S31: The number of people to be unloaded is determined by the following formula:

[0080]

[0081] Among them, N k is the number of people unloading in this batch, if N k If it is not an integer, it will be rounded up. W is the average work efficiency of the workers. Ts is the maximum unloading time allowed for the batch. The specific parameters of Ts are set by the management personnel according to the actual situation.

[0082] S4: Then, for each vehicle in each batch, determine its priority index parameter according to its order volume and the proportion of urgent orders that need to be processed in the vehicle orders;

[0083] The specific method to determine its priority indicators is:

[0084] S41: Determine the proportion of urgent orders on the jth vehicle in the batch by calculating R using the following formula j :

[0085]

[0086] Among them, M j is the total order quantity on the jth vehicle, M j is the total number of emergency orders on the jth vehicle, 1≤j≤z, z is the total number of vehicles in the corresponding batch;

[0087] S42: Then combine with R j The priority index parameter of the jth vehicle is determined by the following formula:

[0088]

[0089] Among them, α and β are weight coefficients, representing the emphasis on the proportion of urgent orders and the total order volume, respectively. Managers adjust these two parameters according to specific circumstances to balance the emphasis on urgent orders and order volume. max The maximum order quantity of all vehicles in the batch, used to normalize the order quantity of each vehicle;

[0090] S5: Then, according to the order in which all vehicles of the batch arrive within the time window t, the number of people N to be unloaded is calculated by the following formula algorithm. k Workers are then assigned to unload each vehicle in the batch:

[0091]

[0092] Among them, W j is the number of workers assigned to the jth vehicle;

[0093] Personnel allocation is performed based on the urgency of the order, the order volume and the arrival time of the vehicle. By calculating the arrival time, order volume and emergency order ratio of each vehicle, the system can reasonably arrange workers for unloading operations according to the priority of different vehicles. This method based on big data analysis can not only ensure that emergency orders are processed in a timely manner, but also avoid over-scheduling and reduce the waste of human resources. Compared with the traditional method of arranging workers based on experience or simple estimates, this automated scheduling method significantly improves the accuracy of personnel scheduling and reduces the idleness or overload of workers caused by improper scheduling.

[0094] Embodiment 2

[0095] In the specific implementation process of this embodiment, based on the first embodiment, this embodiment further illustrates the method of determining the emergency order in the personnel scheduling and allocation module analysis step S4 in the first embodiment:

[0096] The method of determining urgent orders is that when customers purchase on the shopping platform, they can ask the merchant to give an urgent order shipping mark according to their own needs. The shipping mark of the urgent order can be identified by the barcode scanner in the hands of the sorting staff, and the number of urgent orders allocated to different areas can be counted;

[0097] Embodiment 3

[0098] In the specific implementation process, this embodiment includes all the implementation processes of the above two groups of embodiments. The present invention also provides a technical solution: a warehouse logistics scheduling method based on big data analysis, which is implemented using the above warehouse logistics scheduling system based on big data analysis, see Figure 3 , the steps are as follows:

[0099] AS1: Uniquely identify the circulation-type warehouses participating in the link and establish a connection relationship for each circulation-type warehouse participating in the link;

[0100] AS2: Collects the time when the vehicle goes to a specific secondary warehouse and the number of orders on the vehicle, packages them into a data package, names the data package using the corresponding circulation warehouse unique identifier, and sends it to the cloud platform;

[0101] AS3: In the cloud platform, it receives data packets sent by each station module, decompresses the data packets according to the names of the data packets, extracts the time when each vehicle goes to a specific secondary warehouse and the order quantity information on the vehicle in the data packets, determines the different times when each vehicle arrives at a specific secondary warehouse, integrates the time information and order quantity information respectively according to the naming information of the data packets, and forms integrated information to be sent to the data receiving module in the corresponding specific secondary warehouse;

[0102] AS4: Circulation-type warehouse receives and identifies the integrated information sent in real time by the cloud platform;

[0103] AS5: Then analyze the multiple integrated information, and according to the analysis results, assign workers to the unloading work of each arriving vehicle.

[0104] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0105] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A warehouse logistics scheduling system based on big data analysis, characterized in that: include: The warehouse link module is used to uniquely identify the circulation warehouses participating in the link and establish a connection relationship for each circulation warehouse participating in the link; The station module is used to collect the time when the vehicle goes to the specific secondary warehouse and the number of orders on the vehicle in each circulation warehouse, and package them into data packets. The data packets are named using the corresponding circulation warehouse unique identifier and sent to the cloud platform; The cloud platform is used to receive data packets sent from each station module, decompress the data packets according to the naming of the data packets, extract the time of each vehicle going to a specific secondary warehouse and the order quantity information on the vehicle in the data packets, and determine the different time of each vehicle arriving at a specific secondary warehouse in combination with the calling map software function, and integrate the time information and order quantity information respectively in combination with the naming information of the data packets, and send them to the data receiving module in the corresponding specific secondary warehouse; The data receiving module is used to receive the integrated information sent by the recognition cloud platform in real time and send it to the personnel scheduling and allocation module; The personnel scheduling and allocation module is used to analyze multiple integrated information and allocate workers to the unloading work of each arriving vehicle based on the analysis results.

2. According to the warehousing logistics scheduling system based on big data analysis according to claim 1, it is characterized in that: In the warehouse link module, the specific connection method is: Randomly select a circulation warehouse as the target warehouse, and determine the circulation warehouse where the orders of the target warehouse need to arrive based on the actual situation, and record the arriving circulation warehouse as the secondary warehouse of the target warehouse.

3. According to the warehousing logistics scheduling system based on big data analysis according to claim 1, it is characterized in that: The station module includes an order quantity determination unit and a vehicle departure determination unit; An order quantity determination unit, used to determine the order quantity on the circulation storage vehicle; The specific content of determining the order quantity on the circulation storage vehicle is: Determine each sorting area after sorting is completed, count the number of orders in each sorting area, and after the statistics are completed, load the orders in different sorting areas separately; The vehicle departure determination unit is used to determine the specific time of departure of each vehicle and the specific secondary warehouse that each vehicle arrives at.

4. A warehousing logistics scheduling system based on big data analysis according to claim 3, characterized in that: The method to determine the specific secondary warehouse that each vehicle arrives at is: sorting personnel sort the arrived order goods and sort them into different areas. Sorting personnel assign them to different areas according to the addresses of different logistics orders. After the sorting is completed, the orders in the area are loaded separately, and then the specific secondary warehouse that each vehicle arrives at is obtained.

5. The warehousing logistics scheduling system based on big data analysis according to claim 1 is characterized in that: In the personnel dispatching and allocation module, the analysis of the multiple integrated information and the allocation of workers to the unloading work of each arriving vehicle based on the analysis results include: S1: Determine the arrival time Ti of each vehicle in all integrated information, where 1≤i≤n, n is the number of integrated information received; S2: Sort the vehicles from early to late according to their arrival time; S3: Group the arrival time Ti of the vehicles and set each time period t as a time window. If the vehicles arrive in the same time window, the total number of orders in the batch is counted and recorded as Bk. Then, the number of unloading people N in the batch is determined according to the total number of orders in the batch Bk. k ; Where t is the preset value, 1≤k1≤m, and m is the total number of batches; S4: Then, for each vehicle in each batch, according to its order volume and the proportion of urgent orders that need to be processed in the vehicle order, determine its priority index parameter P. j ; S5: Then, according to the order in which all vehicles of the batch arrive within the time window t, the number of people N to be unloaded is calculated by the following formula algorithm. k Workers are then assigned to unload each vehicle in the batch: Among them, W j is the number of workers assigned to the jth vehicle, M j is the total order quantity on the j-th vehicle, 1≤j≤z, and z is the total number of vehicles in the corresponding batch.

6. A warehousing logistics scheduling system based on big data analysis according to claim 5, characterized in that: In step S3, the number of people unloading is determined by the following formula: Among them, N k is the number of people unloading this batch, W is the average work efficiency of the workers, and Ts is the preset value, which is the maximum unloading time allowed for this batch.

7. The warehousing logistics scheduling system based on big data analysis according to claim 5 is characterized in that: In step S4, the specific method of determining the priority index is: S41: The proportion of urgent orders on the jth vehicle in the batch is calculated by the following formula R j : Among them, E j is the total number of emergency orders on the jth vehicle; S42: Then combine with R j The priority index parameter P of the jth vehicle is determined by the following formula: j : Among them, α and β are weight coefficients, M max is the maximum order quantity for all vehicles in the batch.

8. The warehousing logistics scheduling system based on big data analysis according to claim 5 is characterized in that: The method for determining the urgent order in step S4 is: when the customer purchases on the shopping platform, he or she can ask the merchant to give an urgent order shipping mark according to his or her own needs. The urgent order shipping mark can be identified by the barcode scanner in the hands of the sorting personnel, and the number of urgent orders allocated to different areas can be counted.

9. A warehouse logistics scheduling method based on big data analysis, characterized in that: The method is implemented by a warehousing logistics scheduling system based on big data analysis as described in any one of claims 1 to 8, and the steps are as follows: AS1: Uniquely identify the circulation-type warehouses participating in the link and establish a connection relationship for each circulation-type warehouse participating in the link; AS2: Collects the time when the vehicle goes to a specific secondary warehouse and the number of orders on the vehicle, packages them into a data package, names the data package using the corresponding circulation warehouse unique identifier, and sends it to the cloud platform; AS3: In the cloud platform, it receives data packets sent by each station module, decompresses the data packets according to the names of the data packets, extracts the time when each vehicle goes to a specific secondary warehouse and the order quantity information on the vehicle in the data packets, determines the different times when each vehicle arrives at a specific secondary warehouse, integrates the time information and order quantity information respectively according to the naming information of the data packets, and forms integrated information to be sent to the data receiving module in the corresponding specific secondary warehouse; AS4: Circulation-type warehouse receives and identifies the integrated information sent in real time by the cloud platform; AS5: Then analyze the multiple integrated information, and according to the analysis results, assign workers to the unloading work of each arriving vehicle.

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