Logistics carrier digital management method and system

Abstract

The invention relates to a logistics carrier digital management method and system, and the method comprises the steps: obtaining the stock carrier information and the ex-warehouse carrier information of each network point in real time based on the honeycomb Internet of Things technology, judging whether an abnormal ex-warehouse carrier exists or not according to the ex-warehouse carrier information, and further analyzing whether the abnormal ex-warehouse carrier is lost or not; obtaining to-be-processed order information, matching a target website according to the to-be-processed order information, and selecting a target carrier in a balanced and optimized manner according to the actual delivery times of alternative carriers in the target website in the monitoring interval and the to-be-processed order information; and respectively calculating the average utilization rate of the carriers of various models in each network point in the monitoring interval, and judging whether internal supply scheduling is carried out or not according to the average utilization rate. According to the invention, whether the abnormal warehouse-out carrier is lost or not can be found in time, long-term vacancy of the carrier is avoided, optimal scheduling of the carrier is realized, a large amount of repetitive or excess purchase is avoided, and the overall utilization rate is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of digital management technology, and specifically to a digital management method and system for logistics vehicles. Background Art

[0002] With the rapid development of e-commerce and manufacturing, the number and types of logistics vehicles (such as pallets, containers, AGVs, forklifts, etc.) have increased dramatically. At present, the inventory, tracking and control of logistics vehicles rely on manual labor, which is inefficient and low-cost. The lack of effective supervision measures for the entire process and life cycle, lagging management, and difficulty in tracing have led to serious vehicle loss. It is impossible to inventory the number of logistics vehicles in a timely and effective manner, making it difficult to flexibly dispatch needs, resulting in a large number of repeated or over-purchases and serious waste. At the same time, some logistics vehicles have been vacant for a long time and have not been repeatedly put into use. Summary of the invention

[0003] The present invention provides a digital management method and system for logistics vehicles to solve the technical problems mentioned in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions: A digital management method for logistics vehicles, the method comprising: Based on the cellular Internet of Things technology, the inventory vehicle information and outbound vehicle information of each outlet are obtained in real time, and the outbound vehicle information is used to determine whether there is an abnormal outbound vehicle, and further analyze whether the abnormal outbound vehicle is lost; Obtain pending order information, match target outlets based on the pending order information, and balance and optimize the selection of target vehicles based on the actual number of outbound trips of the candidate vehicles in the target outlets within the monitoring interval and the pending order information; The average utilization rate of each type of vehicle in each network point within the monitoring range is calculated separately, and whether to perform internal supply scheduling is determined based on the average utilization rate.

[0005] As a further technical solution of the present invention, the steps of obtaining the inventory vehicle information and outbound vehicle information of each outlet in real time based on the cellular Internet of Things technology, determining whether there is an abnormal outbound vehicle according to the outbound vehicle information, and further analyzing whether the abnormal outbound vehicle is lost include: Based on cellular Internet of Things technology, the inventory vehicle information and outbound vehicle information of each outlet are obtained in real time, wherein the inventory vehicle information includes the inventory vehicle quantity, inventory vehicle model and vehicle idle time, etc., and the outbound vehicle information includes the outbound vehicle quantity, vehicle entry / exit time, etc.; The current circulation time of each outbound vehicle is determined according to the inbound / outbound time of the vehicle; the vehicle circulation time is calculated by the difference between the inbound / outbound time of the vehicle. When the vehicle has only outbound time but no inbound time, the difference between the current time and the outbound time of the vehicle is calculated to obtain the current circulation time of the vehicle; Obtain the current order information corresponding to each outbound vehicle, and determine the predicted flow time of each vehicle based on the current order information corresponding to each outbound vehicle; Determine whether there are abnormal outbound vehicles based on the predicted and current turnover times of each vehicle; When there is an abnormal outbound vehicle, the current position and transportation track of the abnormal outbound vehicle are obtained, and based on the current position of the abnormal outbound vehicle, it is determined whether to generate a lost prompt instruction.

[0006] As a further technical solution of the present invention, the step of determining whether to generate a lost prompt instruction based on the current position of the abnormal outbound vehicle includes: Generate a recommended transportation route based on the shipping address and receiving address in the current order information corresponding to the abnormal outbound vehicle and the outbound outlet address; Compare the current position of the abnormal outbound vehicle with the recommended transportation route, and when the current position does not coincide with the recommended transportation route, determine and generate a lost prompt instruction, wherein the lost prompt instruction carries the current position and transportation track of the abnormal outbound vehicle; When the current position coincides with the recommended transportation route, it is determined that an abnormal detention prompt instruction is generated.

[0007] As a further technical solution of the present invention, the steps of obtaining pending order information, matching target outlets according to the pending order information, and selecting target vehicles in a balanced and optimized manner according to the actual number of outbound trips of candidate vehicles in the target outlets within the monitoring interval and the pending order information include: Obtain pending order information, including cargo information, receipt and delivery information, transportation requirements and time information, and order number; Determine the required carrier model and quantity according to the cargo information in the pending order information; Determine a target outlet according to the delivery address in the order information, wherein the target outlet refers to an outlet whose distance from the delivery address meets a preset distance threshold range; Determine the target vehicle model according to the required vehicle model to obtain the alternative vehicle; Count the actual number of times the candidate vehicles in the target outlets leave the warehouse within the monitoring interval, where the monitoring interval refers to a time interval with the current time as the end point and a preset duration as the interval length; Selecting a preset number of target vehicles from the candidate vehicles as a selection plan; Obtain the predicted number of outbound trips of each candidate vehicle within the monitoring interval under each selection scheme; the predicted number of outbound trips of the target vehicle = the actual number of outbound trips + 1, and the predicted number of outbound trips of other candidate vehicles except the target vehicle - the actual number of outbound trips; The variance of the predicted number of outbound shipments is calculated as the eigenvalue of each selection scheme, the selection scheme with the smallest eigenvalue is selected as the final scheme, and the vehicle selected in the final scheme is obtained as the target vehicle.

[0008] As a further technical solution of the present invention, the steps of respectively calculating the average utilization rate of each type of vehicle in each network point within the monitoring interval and determining whether to perform internal supply scheduling according to the average utilization rate include: Calculate the average utilization rate of each type of vehicle in each network point within the monitoring interval; Sort the average usage rates of vehicles of the same model from large to small to obtain a usage rate ranking table; Obtaining the network points corresponding to the maximum and minimum values ​​of the average usage rate in the usage rate ranking table, and determining whether the difference between the maximum and minimum values ​​exceeds a preset difference; When the preset difference is exceeded, the pair of network points is determined as a supply scheduling network point group, so that the network point corresponding to the minimum value in the supply scheduling network point group performs internal supply scheduling to the network point corresponding to the maximum value; When the preset difference is not exceeded, it is determined whether the maximum value of the average utilization rate exceeds the preset utilization rate threshold. When it exceeds the preset utilization rate threshold, a vehicle procurement scheduling prompt instruction is generated. When it does not exceed the preset utilization rate threshold, no replenishment scheduling is performed.

[0009] Another object of the present invention is to provide a digital management system for logistics vehicles, the system comprising: The vehicle loss determination module is used to obtain the inventory vehicle information and outbound vehicle information of each outlet in real time based on the cellular Internet of Things technology, determine whether there is an abnormal outbound vehicle according to the outbound vehicle information, and further analyze whether the abnormal outbound vehicle is lost; The target vehicle selection module is used to obtain pending order information, match the target outlets according to the pending order information, and balance and optimize the selection of the target vehicle according to the actual number of outbound trips of the candidate vehicles in the target outlets within the monitoring interval and the pending order information; The vehicle optimization scheduling module is used to calculate the average utilization rate of various types of vehicles in each network point within the monitoring range, and determine whether to perform internal supply scheduling based on the average utilization rate.

[0010] As a further technical solution of the present invention, the target vehicle selection module includes: An order information acquisition unit is used to acquire pending order information, including goods information, receipt and delivery information, transportation requirements and time information, and order number, etc.; A carrier model determination unit, used to determine the required carrier model and quantity according to the goods information in the pending order information; A target outlet matching unit, configured to determine a target outlet according to the delivery address in the order information, wherein the target outlet refers to an outlet whose distance from the delivery address meets a preset distance threshold range; An alternative vehicle determination unit, used to determine a target vehicle model according to a required vehicle model, and obtain an alternative vehicle; The actual outbound times statistics unit is used to count the actual outbound times of the candidate vehicles in the target outlet within the monitoring interval, where the monitoring interval refers to a time interval with the current time as the end point and a preset duration as the interval length; A scheme selection setting unit, used for selecting a preset number of target vehicles from the candidate vehicles as a selection scheme; The predicted outbound times statistics unit is used to obtain the predicted outbound times of each candidate vehicle within the monitoring interval under each selection scheme; The target vehicle determination unit is used to calculate the variance of the predicted number of outbound shipments as the characteristic value of each selection scheme, select the selection scheme with the smallest characteristic value as the final scheme, and obtain the vehicle selected in the final scheme as the target vehicle.

[0011] As a further technical solution of the present invention, the vehicle optimization scheduling module includes: A utilization rate calculation unit is used to calculate the average utilization rate of each type of vehicle in each network point within the monitoring interval; A usage rate sorting unit is used to sort the average usage rates of vehicles of the same model from large to small to obtain a usage rate sorting table; A usage comparison unit, used to obtain the network points corresponding to the maximum and minimum values ​​of the average usage in the usage ranking table, and determine whether the difference between the maximum and minimum values ​​exceeds a preset difference; An internal supply scheduling generating unit is used to determine the pair of network points as a supply scheduling network point group when the preset difference is exceeded, so that the network point corresponding to the minimum value in the supply scheduling network point group performs internal supply scheduling to the network point corresponding to the maximum value; The procurement scheduling generation unit is used to determine whether the maximum value of the average utilization rate exceeds a preset utilization rate threshold when the preset difference is not exceeded, and to generate a vehicle procurement scheduling prompt instruction when the preset utilization rate threshold is exceeded; and not to perform replenishment scheduling when the preset utilization rate threshold is not exceeded.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a digital management method and system for logistics vehicles, in which the inventory vehicle information and outbound vehicle information of each outlet can be obtained in real time based on the cellular Internet of Things technology, and whether there are abnormal outbound vehicles are determined according to the outbound vehicle information, and further analysis is performed to timely discover whether the abnormal outbound vehicles are lost. When processing orders, the present invention matches the target outlets according to the pending order information, and balances and optimizes the selection of target vehicles according to the actual outbound times of the alternative vehicles in the target outlets within the monitoring interval and the pending order information, so as to avoid the existence of vehicles being vacant for a long time. On this basis, the present invention determines whether to carry out internal replenishment scheduling according to the average utilization rate of various types of vehicles in each outlet within the monitoring interval, and realizes the optimized scheduling of vehicles again, realizes the repeated and full utilization of vehicles, avoids a large number of duplications or excess purchases, and effectively improves the overall utilization rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The figure is a flowchart of a digital management method for logistics vehicles.

[0014] Figure 2 The present invention is a flowchart of the step of determining whether a vehicle is lost in a digital management method for logistics vehicles.

[0015] Figure 3 The present invention is a flowchart of a step of determining whether to perform replenishment scheduling in a digital management method for logistics vehicles.

[0016] Figure 4 This is a structural block diagram of a digital management system for logistics vehicles. DETAILED DESCRIPTION

[0017] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] like Figure 1 As shown, an embodiment of the present invention provides a digital management method for logistics vehicles, the method comprising: Step S100, based on the cellular Internet of Things technology, the inventory vehicle information and outbound vehicle information of each network point are obtained in real time, and whether there is an abnormal outbound vehicle is determined according to the outbound vehicle information, and further analysis is performed on whether the abnormal outbound vehicle is lost; wherein, the vehicle may be a container / iron box, a pallet, etc., and specifically refers to an iron box in the embodiment, and the network point may be a warehouse, a distribution center, a logistics site, etc., and specifically refers to a warehouse for storing iron boxes in the present embodiment, and the inventory vehicle information includes the inventory vehicle quantity, the inventory vehicle model, and the vehicle idle time, etc., and the outbound vehicle information includes the outbound vehicle quantity, the vehicle entry / exit time, etc.; See also Figure 2 , specifically including the following steps: Step S101: Based on the cellular Internet of Things technology, the inventory vehicle information and outbound vehicle information of each outlet are obtained in real time, wherein the inventory vehicle information includes the inventory vehicle quantity, inventory vehicle model and vehicle idle time, etc., and the outbound vehicle information includes the outbound vehicle quantity, vehicle entry / exit time, etc.; Step S102: determine the current circulation time of each outgoing vehicle according to the vehicle in / out time; the vehicle circulation time is calculated by the difference between the vehicle in / out time. When the vehicle only has an outgoing time but no incoming time, the difference between the current time and the vehicle outgoing time is calculated to obtain the current circulation time of the vehicle; Step S103, obtaining the current order information corresponding to each outbound vehicle, and determining the predicted flow time of each vehicle according to the current order information corresponding to each outbound vehicle; specifically, historical data may be collected, the historical data including historical order information and the flow time of each order, a prediction model is established using the historical data, the current order information is input into the prediction model, and the flow time of each vehicle is predicted. The current order usually refers to the order being executed, and the current order information includes cargo information, receipt and delivery information, transportation requirements and time information, and order number, etc. The cargo information includes cargo name, cargo weight and volume, and cargo type; the receipt and delivery information includes the shipping address and the receiving address, and the consignee information; Step S104: determine whether there is an abnormal outbound vehicle according to the predicted circulation time and the current circulation time of each vehicle; calculate the time difference between the current circulation time and the predicted circulation time, and determine whether the time difference exceeds a preset time threshold. When the time difference exceeds the preset time threshold, determine that there is an abnormal outbound vehicle; when the time difference does not exceed the preset time threshold, determine that there is no abnormal outbound vehicle; Step S105: When there is an abnormal outbound vehicle, the current position and transportation track of the abnormal outbound vehicle are obtained, and based on the current position of the abnormal outbound vehicle, it is determined whether to generate a lost prompt instruction.

[0019] The step of determining whether to generate a lost prompt instruction based on the current position of the abnormal outbound vehicle in this embodiment includes: A recommended transportation route is generated based on the shipping address and receiving address in the current order information corresponding to the abnormal outbound vehicle and the address of the outbound outlet; the current position of the abnormal outbound vehicle is compared with the recommended transportation route, and when the current position does not coincide with the recommended transportation route (that is, the recommended transportation route does not include the current position), it is determined to generate a lost prompt instruction, and the lost prompt instruction carries the current position and transportation trajectory of the abnormal outbound vehicle; when the current position coincides with the recommended transportation route, it is determined to generate an abnormal detention prompt instruction.

[0020] Step S200: Obtain pending order information, match target outlets according to the pending order information, and select target vehicles in a balanced and optimized manner according to the actual number of shipments of candidate vehicles in the target outlets within the monitoring interval and the pending order information; The specific steps include: Step S201, obtaining pending order information, the order information includes cargo information, receiving and shipping information, transportation requirements and time information, and order number, etc. The cargo information includes cargo name, cargo weight and volume, cargo type (such as ordinary cargo, dangerous goods, refrigerated goods, etc.); the receiving and shipping information includes the shipping address and the receiving address, and the consignee information (including the consignee's name, contact information and address, etc.); Step S202, determine the required carrier model and quantity according to the cargo information in the pending order information; determine the appropriate carrier type / model according to the cargo information: Ordinary cargo: standard trucks or containers can be used. Dangerous goods: special dangerous goods transport vehicles that comply with safety regulations are required. Refrigerated goods: Refrigerated trucks or refrigerated containers equipped with temperature control equipment are required. After determining the carrier type / model, calculate the required number of carriers based on the total weight and total volume of the cargo: Carrying capacity of a single carrier: check the specifications of the carrier to understand its maximum carrying weight and volume. Total weight and total volume of cargo: add the weight and volume of all cargo in the order. Calculate the required quantity: divide the total weight and total volume of the cargo by the carrying capacity and space of a single carrier, and round up to get the required number of carriers.

[0021] Step S203: determining a target outlet according to the delivery address in the order information, wherein the target outlet refers to an outlet whose distance from the delivery address meets a preset distance threshold range; Step S204: determine the target vehicle model according to the required vehicle model to obtain an alternative vehicle; Step S205: Count the actual number of times the candidate vehicles in the target outlet leave the warehouse within the monitoring interval, where the monitoring interval refers to a time interval with the current time as the end point and a preset duration as the interval length; Step S206, selecting a preset number (the preset number here refers to the required number of vehicles) of target vehicles from the candidate vehicles as a selection scheme; Step S207, obtaining the predicted number of times each candidate vehicle leaves the warehouse within the monitoring interval under each selection scheme; the predicted number of times the target vehicle leaves the warehouse = the actual number of times the vehicle leaves the warehouse + 1, the predicted number of times the candidate vehicles other than the target vehicle leave the warehouse - the actual number of times the vehicle leaves the warehouse; Step S208, respectively calculate the variance of the predicted number of outbound shipments as the characteristic value of each selection scheme, select the selection scheme with the smallest characteristic value as the final scheme, and obtain the vehicle selected in the final scheme as the target vehicle.

[0022] Step S300, respectively calculating the average utilization rate of each type of vehicle in each network point within the monitoring interval, and determining whether to perform internal supply scheduling according to the average utilization rate; See also Figure 3 , specifically including the following steps: Step S301, respectively calculate the average utilization rate of each type of carrier in each outlet within the monitoring interval, the average utilization rate of a certain type of carrier in the outlet = the sum of the actual number of times a certain type of carrier is shipped out within the monitoring interval / the preset duration of the monitoring interval (i.e., the interval length); Step S302, sorting the average utilization rates of vehicles of the same model from large to small to obtain a utilization rate sorting table; Step S303, obtaining the network points corresponding to the maximum and minimum values ​​of the average usage rate in the usage rate ranking table, and determining whether the difference between the maximum and minimum values ​​exceeds a preset difference; Step S304: when the preset difference is exceeded, the pair of network points is determined as a supply scheduling network point group, so that the network point corresponding to the minimum value in the supply scheduling network point group performs internal supply scheduling to the network point corresponding to the maximum value. Scheduling refers to scheduling the same type of vehicle. The specific scheduling quantity is determined according to a preset scheduling rule table. The preset scheduling rule table includes the average usage rate intervals set in pairs and their corresponding scheduling quantities. Step S305: When the preset difference is not exceeded, determine whether the maximum value of the average utilization rate exceeds the preset utilization rate threshold. When it exceeds the preset utilization rate threshold, generate a vehicle procurement scheduling prompt instruction. When it does not exceed the preset utilization rate threshold, do not perform replenishment scheduling.

[0023] In this embodiment, the inventory vehicle information and outbound vehicle information of each outlet can be obtained in real time based on the cellular Internet of Things technology, and whether there are abnormal outbound vehicles can be determined based on the outbound vehicle information, and further analysis can be performed to promptly discover whether the abnormal outbound vehicle is lost. When processing an order, the present invention matches the target outlet according to the pending order information, and balances and optimizes the selection of the target vehicle based on the actual outbound times of the alternative vehicles in the target outlet within the monitoring interval and the pending order information, to avoid long-term vacancy of the vehicle. On this basis, the present invention determines whether to perform internal replenishment scheduling based on the average utilization rate of various types of vehicles in each outlet within the monitoring interval, and again realizes the optimized scheduling of the vehicle, realizes the repeated and full utilization of the vehicle, avoids a large number of duplications or excess purchases, and effectively improves the overall utilization rate.

[0024] See also Figure 4 Another object of the present invention is to provide a digital management system for logistics vehicles, the system comprising: The vehicle loss determination module 100 is used to obtain the inventory vehicle information and outbound vehicle information of each outlet in real time based on the cellular Internet of Things technology, determine whether there is an abnormal outbound vehicle according to the outbound vehicle information, and further analyze whether the abnormal outbound vehicle is lost; wherein, the vehicle inventory management based on the cellular Internet of Things technology usually includes a system architecture of a perception layer, a network layer and an application layer. Perception layer: IoT sensors and RFID tags deployed in various outlets are used to collect inventory vehicle information in real time, such as quantity, model, etc. At the same time, these devices can also record the in / out information of the vehicle, including quantity and time, etc. Network layer: Through the cellular Internet of Things technology, the data collected by the perception layer is transmitted to the cloud server or data center. This layer mainly uses mobile communication networks (such as 4G / 5G) to realize wireless transmission of data to ensure the real-time and accuracy of the data. Application layer: Deploy inventory management applications on cloud servers or data centers to process, analyze and display the received data. Through this layer, managers can view the inventory vehicle information and vehicle in / out information of each outlet in real time, and make corresponding decisions and management; The target vehicle selection module 200 is used to obtain the pending order information, match the target outlets according to the pending order information, and select the target vehicle in a balanced and optimized manner according to the actual number of outbound trips of the candidate vehicles in the target outlets within the monitoring interval and the pending order information; The vehicle optimization scheduling module 300 is used to calculate the average utilization rate of each type of vehicle in each network point within the monitoring interval, and determine whether to perform internal supply scheduling based on the average utilization rate.

[0025] As a preferred embodiment of the present invention, the target vehicle selection module 200 includes: An order information acquisition unit is used to acquire pending order information, including goods information, receipt and delivery information, transportation requirements and time information, and order number, etc.; A carrier model determination unit, used to determine the required carrier model and quantity according to the goods information in the pending order information; A target outlet matching unit, configured to determine a target outlet according to the delivery address in the order information, wherein the target outlet refers to an outlet whose distance from the delivery address meets a preset distance threshold range; An alternative vehicle determination unit, used to determine a target vehicle model according to a required vehicle model, and obtain an alternative vehicle; The actual outbound times statistics unit is used to count the actual outbound times of the candidate vehicles in the target outlet within the monitoring interval, where the monitoring interval refers to a time interval with the current time as the end point and a preset duration as the interval length; A scheme selection setting unit, used for selecting a preset number of target vehicles from the candidate vehicles as a selection scheme; The predicted outbound times statistics unit is used to obtain the predicted outbound times of each candidate vehicle in the monitoring interval under each selection scheme; the predicted outbound times of the target vehicle = the actual outbound times + 1, and the predicted outbound times of other candidate vehicles except the target vehicle - the actual outbound times; The target vehicle determination unit is used to calculate the variance of the predicted number of outbound shipments as the characteristic value of each selection scheme, select the selection scheme with the smallest characteristic value as the final scheme, and obtain the vehicle selected in the final scheme as the target vehicle.

[0026] As a preferred embodiment of the present invention, the vehicle optimization scheduling module 300 includes: The utilization rate calculation unit is used to calculate the average utilization rate of each type of vehicle in each outlet within the monitoring interval. The average utilization rate of a certain type of vehicle in the outlet = the sum of the actual number of times a certain type of vehicle is shipped out of the warehouse within the monitoring interval / the preset duration of the monitoring interval (i.e., the interval length); A usage rate sorting unit is used to sort the average usage rates of vehicles of the same model from large to small to obtain a usage rate sorting table; A usage comparison unit, used to obtain the network points corresponding to the maximum and minimum values ​​of the average usage in the usage ranking table, and determine whether the difference between the maximum and minimum values ​​exceeds a preset difference; An internal supply scheduling generating unit is used to determine the pair of network points as a supply scheduling network point group when the preset difference is exceeded, so that the network point corresponding to the minimum value in the supply scheduling network point group performs internal supply scheduling to the network point corresponding to the maximum value; The procurement scheduling generation unit is used to determine whether the maximum value of the average utilization rate exceeds a preset utilization rate threshold when the preset difference is not exceeded, and to generate a vehicle procurement scheduling prompt instruction when the preset utilization rate threshold is exceeded; and not to perform replenishment scheduling when the preset utilization rate threshold is not exceeded.

[0027] It should be noted that, in this article, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0028] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A digital management method for logistics vehicles, characterized in that: The method comprises: Based on the cellular Internet of Things technology, the inventory vehicle information and outbound vehicle information of each outlet are obtained in real time, and the outbound vehicle information is used to determine whether there is an abnormal outbound vehicle, and further analyze whether the abnormal outbound vehicle is lost; Obtain pending order information, match target outlets based on the pending order information, and balance and optimize the selection of target vehicles based on the actual number of outbound trips of the candidate vehicles in the target outlets within the monitoring interval and the pending order information; The average utilization rate of each type of vehicle in each network point within the monitoring range is calculated separately, and whether to perform internal supply scheduling is determined based on the average utilization rate.

2. A digital management method for logistics vehicles according to claim 1, characterized in that: The steps of acquiring the inventory vehicle information and outbound vehicle information of each outlet in real time based on the cellular Internet of Things technology, determining whether there is an abnormal outbound vehicle according to the outbound vehicle information, and further analyzing whether the abnormal outbound vehicle is lost include: Based on cellular Internet of Things technology, the inventory vehicle information and outbound vehicle information of each outlet are obtained in real time, wherein the inventory vehicle information includes the inventory vehicle quantity, inventory vehicle model and vehicle idle time, and the outbound vehicle information includes the outbound vehicle quantity and vehicle entry / exit time; Determine the current circulation time of each outbound vehicle according to the inbound / outbound time of the vehicle; Obtain the current order information corresponding to each outbound vehicle, and determine the predicted flow time of each vehicle based on the current order information corresponding to each outbound vehicle; Determine whether there are abnormal outbound vehicles based on the predicted and current turnover times of each vehicle; When there is an abnormal outbound vehicle, the current position and transportation track of the abnormal outbound vehicle are obtained, and based on the current position of the abnormal outbound vehicle, it is determined whether to generate a lost prompt instruction.

3. A digital management method for logistics vehicles according to claim 2, characterized in that: The step of determining whether to generate a lost prompt instruction based on the current position of the abnormal outbound vehicle comprises: Generate a recommended transportation route based on the shipping address and receiving address in the current order information corresponding to the abnormal outbound vehicle and the outbound outlet address; Compare the current position of the abnormal outbound vehicle with the recommended transportation route, and when the current position does not coincide with the recommended transportation route, determine and generate a lost prompt instruction, wherein the lost prompt instruction carries the current position and transportation track of the abnormal outbound vehicle; When the current position coincides with the recommended transportation route, it is determined that an abnormal detention prompt instruction is generated.

4. A method for digital management of logistics vehicles according to claim 1, characterized in that: The steps of obtaining pending order information, matching target outlets according to the pending order information, and selecting target vehicles in a balanced and optimized manner according to the actual number of outbound trips of candidate vehicles in the target outlets within the monitoring interval and the pending order information include: Obtain pending order information, including cargo information, receipt and delivery information, transportation requirements and time information, and order number; Determine the required carrier model and quantity according to the cargo information in the pending order information; Determine a target outlet according to the delivery address in the order information, wherein the target outlet refers to an outlet whose distance from the delivery address meets a preset distance threshold range; Determine the target vehicle model according to the required vehicle model to obtain the alternative vehicle; Count the actual number of times the candidate vehicles in the target outlets leave the warehouse within the monitoring interval, where the monitoring interval refers to a time interval with the current time as the end point and a preset duration as the interval length; Selecting a preset number of target vehicles from the candidate vehicles as a selection plan; Obtain the predicted number of times each candidate vehicle will leave the warehouse within the monitoring interval under each selection scheme; The variance of the predicted number of outbound shipments is calculated as the eigenvalue of each selection scheme, the selection scheme with the smallest eigenvalue is selected as the final scheme, and the vehicle selected in the final scheme is obtained as the target vehicle.

5. A digital management method for logistics vehicles according to claim 1, characterized in that: The steps of respectively calculating the average utilization rate of each type of vehicle in each network point within the monitoring interval and determining whether to perform internal supply scheduling according to the average utilization rate include: Calculate the average utilization rate of each type of vehicle in each network point within the monitoring interval; Sort the average usage rates of vehicles of the same model from large to small to obtain a usage rate ranking table; Obtaining the network points corresponding to the maximum and minimum values ​​of the average usage rate in the usage rate ranking table, and determining whether the difference between the maximum and minimum values ​​exceeds a preset difference; When the preset difference is exceeded, the pair of network points is determined as a supply scheduling network point group, so that the network point corresponding to the minimum value in the supply scheduling network point group performs internal supply scheduling to the network point corresponding to the maximum value; When the preset difference is not exceeded, it is determined whether the maximum value of the average utilization rate exceeds the preset utilization rate threshold. When it exceeds the preset utilization rate threshold, a vehicle procurement scheduling prompt instruction is generated. When it does not exceed the preset utilization rate threshold, no replenishment scheduling is performed.

6. A digital management system for logistics vehicles, characterized in that: The system comprises: The vehicle loss determination module is used to obtain the inventory vehicle information and outbound vehicle information of each outlet in real time based on the cellular Internet of Things technology, determine whether there is an abnormal outbound vehicle according to the outbound vehicle information, and further analyze whether the abnormal outbound vehicle is lost; The target vehicle selection module is used to obtain pending order information, match the target outlets according to the pending order information, and balance and optimize the selection of the target vehicle according to the actual number of outbound trips of the candidate vehicles in the target outlets within the monitoring interval and the pending order information; The vehicle optimization scheduling module is used to calculate the average utilization rate of various types of vehicles in each network point within the monitoring range, and determine whether to perform internal supply scheduling based on the average utilization rate.

7. A digital management system for logistics vehicles according to claim 6, characterized in that: The target vehicle selection module includes: An order information acquisition unit, used to acquire information of pending orders; A carrier model determination unit, used to determine the required carrier model and quantity according to the goods information in the pending order information; A target outlet matching unit, configured to determine a target outlet according to the delivery address in the order information, wherein the target outlet refers to an outlet whose distance from the delivery address meets a preset distance threshold range; An alternative vehicle determination unit, used to determine a target vehicle model according to a required vehicle model, and obtain an alternative vehicle; The actual outbound times statistics unit is used to count the actual outbound times of the candidate vehicles in the target outlet within the monitoring interval, where the monitoring interval refers to a time interval with the current time as the end point and a preset duration as the interval length; A scheme selection setting unit, used for selecting a preset number of target vehicles from the candidate vehicles as a selection scheme; The predicted outbound times statistics unit is used to obtain the predicted outbound times of each candidate vehicle within the monitoring interval under each selection scheme; The target vehicle determination unit is used to calculate the variance of the predicted number of outbound shipments as the characteristic value of each selection scheme, select the selection scheme with the smallest characteristic value as the final scheme, and obtain the vehicle selected in the final scheme as the target vehicle.

8. A digital management system for logistics vehicles according to claim 6, characterized in that: The vehicle optimization scheduling module includes: A utilization rate calculation unit is used to calculate the average utilization rate of each type of vehicle in each network point within the monitoring interval; A usage rate sorting unit is used to sort the average usage rates of vehicles of the same model from large to small to obtain a usage rate sorting table; A usage comparison unit, used to obtain the network points corresponding to the maximum and minimum values ​​of the average usage in the usage ranking table, and determine whether the difference between the maximum and minimum values ​​exceeds a preset difference; An internal supply scheduling generating unit, used for determining the pair of network points as a supply scheduling network point group when the preset difference is exceeded, so that the network point corresponding to the minimum value in the supply scheduling network point group performs internal supply scheduling to the network point corresponding to the maximum value; The procurement scheduling generation unit is used to determine whether the maximum value of the average utilization rate exceeds a preset utilization rate threshold when the preset difference is not exceeded, and to generate a vehicle procurement scheduling prompt instruction when the preset utilization rate threshold is exceeded; and not to perform replenishment scheduling when the preset utilization rate threshold is not exceeded.

Images