Cloud digital logistics management method and system

Abstract

The invention discloses a cloud digital logistics management method and system, and the method comprises the steps: obtaining commodity order information packaged by a merchant, determining a consignment code and distributing a consignment vehicle for the commodity order information, and enabling the consignment code and the consignment vehicle of each commodity to have a unique corresponding binding relation; based on a logistics network, the departure time of the consignment vehicle is obtained, and the consignment vehicle is tracked in real time by adopting a dual-combination positioning mode of monitoring and cooperative monitoring; after it is tracked that the consignment vehicle arrives at the destination, goods receiving time and place information of the goods are pushed to the Hakka; after the Hakka determines the receiving information, consignment is completed, consignment data information is recorded, and a transportation volume distribution scheme is optimized according to the consignment data information. According to the invention, the consignment vehicle is tracked in real time by adopting a dual-combination positioning mode of monitoring and cooperative monitoring, the performance of the consignment vehicle positioning system is greatly improved, the consignment vehicle can be ensured to be continuously positioned in a complex environment, the position of the consignment vehicle can be known in time, and flexible management of logistics is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and in particular to a cloud-based digital logistics management method and system. Background Art

[0002] With the rapid development of e-commerce, online shopping has become a trend. With existing technology, large shipments are currently handled by a dispatching system (dispatcher) that pre-books a vehicle. Once the driver picks up the goods and delivers them, the warehousing system creates a vehicle based on the dispatch order. During the delivery process, if the positioning signal is weak or lost, the vehicle's location cannot be determined in a timely manner. Improving the efficiency of transportation resource utilization, reducing waste, and lowering logistics costs are also pressing challenges. Summary of the Invention

[0003] The purpose of the present invention is to provide a cloud-based digital logistics management method and system to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions: A cloud-based digital logistics management method, comprising: Step 1: Obtain the merchant's packaged product order information, determine the shipping code and assign a shipping vehicle for the product order information. Each product's shipping code and shipping vehicle have a unique corresponding binding relationship; Step 2: Based on the logistics network, obtain the departure time of the consignment vehicle and use a dual-combination positioning method of monitoring and collaborative monitoring to track the consignment vehicle in real time; Step 3: After tracking the consignment vehicle to its destination, the delivery time and location information of the goods will be pushed to the customer; Step 4: After confirming the delivery information, the customer completes the shipment and records the shipment data information, and optimizes the transportation volume allocation plan based on the shipment data information.

[0005] Furthermore, in step 1, the commodity order information includes product information and / or dealer information. When the consignment vehicle breaks down and becomes a faulty vehicle, the consignment code corresponding to the faulty vehicle is released, and the consignment code is re-bound to the new consignment vehicle. At the same time, the faulty vehicle exchanges its location information with the new consignment vehicle.

[0006] Furthermore, in step 2, the real-time tracking of the consignment vehicle using a dual combination positioning method of monitoring and collaborative monitoring includes: The consignment vehicle is equipped with three positioning modules, and the control terminal is located in the center of the logistics network, interacting with logistics nodes. During the process of moving the consignment vehicle from its current position to the next position, the two positioning modules are alternately turned on and off, and the acquired position information is reported to the control end and communicates with the control end uninterruptedly. The remaining positioning module is in long-term sleep. When the control end communication is disconnected, the long-sleeping positioning module is awakened to obtain the current position information and report it to the control end.

[0007] Furthermore, when the positioning module does not respond to the communication signal with the control terminal within a predetermined time, the positioning module reports the current position information to the control terminal using a recursive algorithm.

[0008] Furthermore, in step 3, after pushing the delivery time and location information of the goods to the Hakka customer, a response time is set. Within the response time, if the Hakka customer agrees with the delivery time and location of the goods, the goods will be delivered on schedule. If the Hakka customer disagrees, communication confirmation will be carried out and the communication confirmation result will be recorded.

[0009] Furthermore, in step 3, when the current location information obtained by the positioning module is consistent with the destination of the product order information, the shipping vehicle has arrived at the destination.

[0010] Furthermore, in step 4, an optimization model is used to optimize the recorded shipping data information to obtain a transportation volume allocation plan.

[0011] To achieve the above object, the present invention further provides the following technical solutions: A cloud-based digital logistics management system, comprising: The binding unit is used to obtain the merchant's packaged product order information, determine the shipping code for the product order information, and allocate a shipping vehicle. There is a unique corresponding binding relationship between the shipping code and the shipping vehicle for each product; The tracking unit is used to obtain the departure time of the consignment vehicle based on the logistics network and track the consignment vehicle in real time using a dual-combination positioning method of monitoring and collaborative monitoring; The push unit is used to track the delivery vehicle's arrival at the destination and push the product's delivery time and location information to the customer; The optimization unit is used to complete the consignment and record the consignment data information after the customer confirms the receipt information, and optimize the transportation volume allocation plan based on the consignment data information.

[0012] Compared with the prior art, the present invention has the following beneficial effects: This invention utilizes a dual-positioning method of monitoring and collaborative monitoring to track consigned vehicles in real time. This significantly improves the performance of the consigned vehicle positioning system, ensuring continuous positioning of consigned vehicles in complex environments and timely understanding of their current location. Using an optimization model, goods from different supply / production locations can be dispatched to demand locations based on market demand and transportation costs, minimizing overall transportation costs. Specifically, the overall transportation cost of packaged goods from merchants to Hakka delivery addresses is minimized, enabling flexible logistics management. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 Schematic diagram of the process of the present invention.

[0014] Figure 2 This is an operation diagram for the consignment vehicle tracking mode.

[0015] Figure 3 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0017] See also Figures 1 to 3 , the present invention provides a technical solution: A cloud-based digital logistics management method, comprising: Step 1: Obtain the merchant's packaged product order information, determine the shipping code and assign a shipping vehicle for the product order information. Each product's shipping code and shipping vehicle have a unique corresponding binding relationship; Step 2: Based on the logistics network, obtain the departure time of the consignment vehicle and use a dual-combination positioning method of monitoring and collaborative monitoring to track the consignment vehicle in real time; Step 3: After tracking the consignment vehicle to its destination, the delivery time and location information of the goods will be pushed to the customer (or consignee); Step 4: After confirming the delivery information, the customer completes the shipment and records the shipment data information, and optimizes the transportation volume allocation plan based on the shipment data information.

[0018] Specifically, in step 1, the commodity order information includes product information and / or dealer information or merchant related information such as address, contact information and required notes. When the consignment vehicle breaks down and becomes a faulty vehicle, the consignment code corresponding to the faulty vehicle is released, and the consignment code is re-bound to the new consignment vehicle. At the same time, the faulty vehicle exchanges its location information with the new consignment vehicle.

[0019] Specifically, in step 2, the real-time tracking of the consignment vehicle using a dual combination positioning method of monitoring and collaborative monitoring includes: The consignment vehicle is equipped with three positioning modules, and the control terminal is located in the center of the network, interacting with logistics nodes; During the process of moving the consignment vehicle from its current position to the next position, the two positioning modules are alternately turned on and off, and the acquired position information is reported to the control end and communicates with the control end uninterruptedly. The remaining positioning module is in long-term sleep. When the control end communication is disconnected, the long-sleeping positioning module is awakened to obtain the current position information and report it to the control end.

[0020] Specifically, when the positioning module does not respond to the communication signal with the control terminal within a predetermined time, the positioning module reports the current position information to the control terminal using a recursive algorithm.

[0021] Specifically, in step 3, the delivery time and location information of the goods are pushed to the Hakka customer and a response time is set. Within the response time, if the Hakka customer agrees with the delivery time and location of the goods, the goods will be delivered on schedule. If the Hakka customer disagrees, communication confirmation will be carried out and the communication confirmation result will be recorded.

[0022] Specifically, in step 3, when the current location information obtained by the positioning module is consistent with the destination of the product order information, the shipping vehicle has arrived at the destination.

[0023] Specifically, in step 4, an optimization model is used to optimize the recorded shipping data information to obtain a transportation volume allocation plan.

[0024] A cloud-based digital logistics management system, comprising: The binding unit is used to obtain the merchant's packaged product order information, determine the shipping code for the product order information, and allocate a shipping vehicle. There is a unique corresponding binding relationship between the shipping code and the shipping vehicle for each product; The tracking unit is used to obtain the departure time of the consignment vehicle based on the logistics network and track the consignment vehicle in real time using a dual-combination positioning method of monitoring and collaborative monitoring; The push unit is used to track the delivery vehicle's arrival at the destination and push the product's delivery time and location information to the customer; The optimization unit is used to complete the consignment and record the consignment data information after the customer confirms the receipt information, and optimize the transportation volume allocation plan based on the consignment data information.

[0025] In the present invention, the control terminal is located in the network center area and is responsible for calculating, storing, and communicating the information transmitted by the logistics nodes. Figure 2As shown in the figure, a shipping vehicle, acting as a tracking target, moves randomly within the logistics network. The logistics network contains multiple wireless sensor units, which are connected to the logistics network as logistics nodes. Logistics nodes self-organize to form an interconnected network. Logistics nodes are capable of environmental perception, data storage, transmission, and processing. The shipping vehicle's movement progresses from (3,4,8) to (3,4,5) to (2,3,5) to (2,5,6). The tracking method is as follows: logistics nodes are divided into 1, 2, 3, and so on. As the detected shipping vehicle moves, neighboring nodes take over the tracking of the target until it leaves the node's coverage area. When the target reaches position (3,4,8), node 4 first detects the target. As the target moves toward position (3,4,5), node 4 notifies nodes 3 and 8 to collaborate on tracking the target and feeds the tracking data back to the control end. When the target reaches position (3,4,5), node 3 takes over tracking the target, and node 4 enters a dormant state until the next target arrives. At node 4, the three positioning modules are named A, B, and C. A is in the power-on state, achieving real-time positioning and communicating with the control end, B is in the dormant state, and C is in the long-dormant state. When entering node 3, A is in the dormant state, achieving real-time positioning, B is in the power-on state, achieving real-time positioning and communicating with the control end, and C is still in the long-dormant state. When entering the next node from node 3, the states of A and B alternate. Once a fault occurs and A and B fail to communicate with the control end, the node where the consignment vehicle is located will report to the control end to start the long-dormant C for position positioning and simultaneously initiate an early warning.

[0026] The present invention, as Figure 2 As shown, m The nodes can be plane P Divided into multiple sub-areas s i . Each s i in k The data points are s i1 to s ik , the following relationship exists (1): max ( d ( q , s i1 ), d ( q , s i2 ),…, d ( q , s ik )) ≤ d ( d ,s ij )(1).

[0027] flat P Located in the sub-region s i Any point in q , this point q Distance data points s ij The distance is less than q Distance plane division s ij The distance to other data nodes, j =1, 2, ..., k .

[0028] Each sub-area s i Both correspond k data point, and name the detection range of the data point as its “jurisdiction range”. According to formula (1), when a node begins to detect that a consignment vehicle enters its own “jurisdiction range”, the node can start tracking and detecting the consignment vehicle.

[0029] Since the consignment vehicle may encounter different geographical environments in the driving map, such as entering mountainous areas, tunnels, and urban high-rise buildings, which can be regarded as obstacles, the mutual shielding of these obstacles will generally cause poor satellite signal reception, resulting in the loss of GPS signals, making it impossible to track and match logistics monitoring information. In order to improve the real-time tracking of consignment vehicles, it is necessary to improve the positioning accuracy to obtain more accurate positioning information. The location information of the consignment vehicle obtained can also provide data reference for the selection of transportation routes. In this embodiment, the positioning module uses a recursive algorithm to report the current position information to the control end. The positioning module integrates an inertial sensor and a control system that communicates with the positioning module and the inertial sensor.

[0030] The present invention, based on the Kalman filter, uses a recursive algorithm to analyze and process the specific values ​​of the current state and the specific values ​​of the previous state to predict the next state. The algorithm is as follows: J(ψ)=QJ(ψ-1)+BT(ψ)+Z(ψ) (2); The measurements of the linear system are as follows: Y(ψ)=VT (ψ)+L(ψ) (3); where J(ψ) is the state of the (control) system at time ψ, T(ψ) is the controlled variable of the system at time ψ, Q and B are system parameters, Y(ψ) is the measurement value obtained at time ψ, V is a parameter, Z(ψ) is the process noise, and L(ψ) is the measurement noise.

[0031] The Kalman filter analyzes the current state (system model) and predicts the next state (system model). The current state is represented by the time ψ. The system can predict the current system state at the time ψ-1, as follows: J(ψ|ψ-1)=QJ(ψ-1|ψ-1)+BT (ψ) (4); Where J(ψ|ψ-1) is the predicted value of the system at the previous moment, and J(ψ-1|ψ-1) is the optimal value of the system at the previous moment. After updating the system results, the covariance of J(ψ|ψ-1) has not yet been displayed. The covariance matrix of the current state is specifically: R(ψ|ψ-1)= QR(ψ|ψ-1) Q T +u (5); Where R(ψ|ψ-1) is the covariance matrix corresponding to the state J(ψ|ψ-1), and similarly R(ψ-1|ψ-1) is the covariance matrix corresponding to J(ψ-1|ψ-1), Q T represents the transposed matrix of Q, and u represents the covariance matrix. The prediction of the system state can be obtained by formulas (4) and (5).

[0032] The optimal estimate of the current system state is affected by both the predicted value and the measured value, represented by ψ. A single increase or decrease will affect the value of ψ, specifically: J(ψ|ψ)= J(ψ|ψ-1)+Kg(ψ)( Y(ψ)- VT (ψ|ψ-1)) (6); Where Kg(ψ) is the Kalman gain, specifically: Kg(ψ)= R(ψ|ψ-1) V T (V (ψ|ψ-1) V T +Π) -1 (7); where J(ψ|ψ) is the optimal estimate of the system at time ψ, and Π is the specific manifestation in the system. Due to the influence of linear discrete systems, the system state at time ψ needs to be constantly updated to maintain the filter's termination condition.

[0033] The covariance matrix is: R(ψ|ψ)=(1- Kg(ψ) V) R(ψ|ψ-1) (8); The calculation method adopted during the end of the operation is autoregressive calculation.

[0034] In the present invention, when the positioning module is turned on, the positioning module receives the GPS signal. When the GPS signal is strong, the system reads the GPS module data and sends the information to the control end by executing the data sending task. When the GPS signal is weak or even lost, the system cannot obtain the current position and speed information of the consigned vehicle through the GPS positioning module. At this time, it is necessary to call the inertial positioning task, and use the initial values ​​of the consigned vehicle's current latitude and longitude, heading angle, speed, etc. obtained by the GPS module before the signal is lost. The consigned vehicle is positioned by substituting the three-axis (X, Y, Z) acceleration and rotation angle data obtained by the inertial sensor into the filtering algorithm.

[0035] The present invention uses an optimization model to optimize the recorded shipping data information to obtain a transportation volume allocation plan, which specifically includes: allocating goods from different supply locations (goods packaged by merchants) to the demand location (Hakka delivery address) according to market demand and transportation costs to minimize the comprehensive transportation cost G.

[0036] The optimization model is expressed as follows:

[0037] in, Supply Transport to the required place the number of Supply Transport to The unit transportation cost at the demand location is: , Is a natural number, indicating the indivual.

[0038] With the lowest transportation cost as the function objective, the cost allocation matrix, constraints and total cost calculation are solved (using the SUMPRODUCT function and the planning and solving add-in) to obtain the optimal solution for the transportation allocation of the merchant's packaged goods.

[0039] In the present invention, during the transportation process, the arrival time of the consignment vehicle needs to be predicted, which requires predicting the speed / speed risk of the consignment vehicle. The specific process is as follows: Using LSTM (Long Short-Term Memory Network) in deep learning to predict speed risk, assuming that t The speed data input at any moment is , the output is the speed prediction value at the future moment ;LSMT model formula:

[0040]

[0041]

[0042]

[0043]

[0044] in For the Gate of Forgetfulness, is the input gate, is the cell state, is the output gate, is hidden state, is the sigmoid activation function, is the forget gate weight, is the input gate weight, is the weight matrix of the memory unit, is the weight of the output gate, 、 、 、 They represent the bias conditions of the forget gate, input gate, memory unit, and output gate respectively. is the hidden state at the previous moment, The function represents the weight coefficient matrix of the forget gate, input gate, output gate and memory unit.

[0045] Predicting Speed ​​Risk: ; Adaptive adjustment strategy: adjustment strategy = f (( )+current speed status); the predicted speed risk judgment conditions are as follows: if If the speed is lower than the safety threshold, you need to increase the speed; if Above the safety threshold: you need to reduce the speed; when it is predicted If the speed is within the safe range, no adjustment is required. The safety threshold can be flexibly set according to actual conditions, such as 60-80 km / h for provincial roads and 80-120 km / h for highways.

[0046] Speeding too fast affects safety, while speeding too slow affects arrival time. Under normal road conditions, the LSMT model can be used to predict the speed of the consignment vehicle in real time, triggering an early warning mechanism and providing feedback to the driver for adjustment.

[0047] This invention uses a dual-combination positioning method of monitoring and collaborative monitoring to track consigned vehicles in real time. This significantly improves the performance of the consigned vehicle positioning system, ensuring continuous positioning of consigned vehicles in complex environments and timely understanding of their current locations. Using an optimization model, goods from different supply / production locations can be allocated to demand locations based on market demand and transportation costs, minimizing overall transportation costs. Specifically, the overall transportation cost of goods packaged by merchants to Hakka delivery addresses is minimized. This in turn compensates for the lack of efficient use of transportation resources and reduces waste, enabling flexible logistics management.

[0048] The present invention, the undescribed part is the prior art.

[0049] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A cloud-based digital logistics management method, characterized in that: include: Step 1: Obtain the order information of the goods packaged by the merchant, determine the shipping code and assign the shipping vehicle for the order information of the goods, and there is a unique corresponding binding relationship between the shipping code and the shipping vehicle of each product; Step 2: Based on the logistics network, obtain the departure time of the consignment vehicle, and use a dual combination positioning method of monitoring and collaborative monitoring to track the consignment vehicle in real time; Step 3: After tracking the consignment vehicle to arrive at the destination, push the product delivery time and location information to the customer; Step 4: After the customer confirms the delivery information, the consignment is completed and the consignment data is recorded, and the transportation volume allocation plan is optimized according to the consignment data; In step 2, the real-time tracking of the consignment vehicle is carried out by using a dual combination positioning method of monitoring and collaborative monitoring, including: The consignment vehicle is equipped with three positioning modules, and the control end is located in the center of the logistics network, interacting with the logistics node information; During the process of moving the consignment vehicle from the current position to the next position, the two positioning modules are alternately turned on and off, and the acquired position information is reported to the control end and the control end is communicated with uninterruptedly. The remaining positioning module is in long-term sleep. When the control end communication is disconnected, the long-sleeping positioning module is awakened to obtain the current position information and report it to the control end.

2. A cloud-based digital logistics management method as claimed in claim 1, characterized in that: In step 1, the commodity order information includes product information and / or dealer information. When a consignment vehicle breaks down and becomes a faulty vehicle, the consignment code corresponding to the faulty vehicle is released, and the consignment code is re-bound to the new consignment vehicle. At the same time, the faulty vehicle exchanges its location information with the new consignment vehicle.

3. A cloud-based digital logistics management method as claimed in claim 1, characterized in that: When the communication signal between the positioning module and the control end does not respond within a predetermined time, the positioning module reports the current position information to the control end using a recursive algorithm.

4. A cloud-based digital logistics management method as claimed in claim 1, characterized in that: In step 3, the delivery time and location information of the goods are pushed to the Hakka customer and a response time is set. Within the response time, if the Hakka customer agrees with the delivery time and location of the goods, the goods will be delivered on schedule. If the Hakka customer disagrees, communication confirmation will be conducted and the communication confirmation result will be recorded.

5. A cloud-based digital logistics management method as claimed in claim 1, characterized in that: In step 3, when the current location information obtained by the positioning module is consistent with the destination of the commodity order information, the shipping vehicle has arrived at the destination.

6. A cloud-based digital logistics management method as claimed in claim 1, characterized in that: In step 4, an optimization model is used to optimize the recorded shipping data information to obtain a transportation volume allocation plan.

7. A cloud-based digital logistics management system, characterized in that: include: The binding unit is used to obtain the commodity order information packaged by the merchant, determine the consignment code and allocate the consignment vehicle for the commodity order information, and there is a unique corresponding binding relationship between the consignment code and the consignment vehicle of each commodity; Tracking unit, used to obtain the departure time of the consignment vehicle based on the logistics network, and to track the consignment vehicle in real time using a dual combination positioning method of monitoring and collaborative monitoring; The push unit is used to push the delivery time and location information of the goods to the customer after tracking the consignment vehicle arriving at the destination; The optimization unit is used to complete the consignment and record the consignment data information after the customer confirms the receipt information, and optimize the transportation volume allocation plan based on the consignment data information; The real-time tracking of consignment vehicles using a dual combination of monitoring and collaborative monitoring includes: The consignment vehicle is equipped with three positioning modules, and the control end is located in the center of the logistics network, interacting with the logistics node information; During the process of moving the consignment vehicle from the current position to the next position, the two positioning modules are alternately turned on and off, and the acquired position information is reported to the control end and the control end is communicated with uninterruptedly. The remaining positioning module is in long-term sleep. When the control end communication is disconnected, the long-sleeping positioning module is awakened to obtain the current position information and report it to the control end.

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