A logistics vehicle distribution path planning method and control system

Abstract

The present invention provides a method and control system for planning a logistics vehicle delivery path, including an intelligent cloud platform, a control box, a sensor, a memory, a processor and a user terminal; the intelligent cloud platform is used to obtain customer location information and delivery cargo weight information; the sensor is used to collect the load weight of the carriage; the memory includes an information acquisition module, a weight acquisition module and a transmission module II; the processor includes an algorithm module, a judgment module and a transmission module III, and the algorithm module uses an improved hybrid frog leaping algorithm to plan the vehicle driving path; the user terminal includes a wireless sensor and a mobile phone. The present invention takes into account the dynamic carbon emissions of vehicles under delivery orders, and rationally plans the vehicle driving path by improving the hybrid frog leaping algorithm, reduces environmental pollution, and saves delivery costs.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics path planning or the field of logistics distribution, and in particular to a planning method and control system for a logistics vehicle distribution path. Background Art

[0002] With the rapid development of e-commerce, the continuous expansion of the logistics industry, and the growing demand for logistics distribution, the reasonable planning of logistics distribution routes is an important part of the logistics industry. As the number of customers continues to expand and the transportation network becomes increasingly complex, unreasonable route planning will lead to low transportation efficiency, increased transportation costs, and poor service quality. At the same time, with the increase in the amount of distributed goods, unreasonable distribution routes will produce more pollutants and bring pressure to environmental governance. Therefore, scientific and reasonable planning of distribution routes, effective reduction of carbon dioxide emissions, relief of environmental governance pressure, improvement of distribution efficiency, and reduction of distribution costs are crucial research contents. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention provides a logistics vehicle delivery route planning method and control system, which updates the vehicle's load information in real time through an intelligent cloud platform, obtains the dynamic fuel consumption of the vehicle based on the delivery order, and calculates the vehicle's carbon emissions. Then, the algorithm module is used to plan the vehicle route with the lowest total cost including carbon emission cost, vehicle fixed cost, vehicle transportation cost, etc., and transmits it to the user terminal in real time.

[0004] The present invention achieves the above technical objectives through the following technical means.

[0005] A method for planning a logistics vehicle distribution path includes the following steps:

[0006] S1: Determine the objective function, constraint function and initialization algorithm parameters according to the number of customers and the coordinate information of the customers, wherein: the objective function is the minimum value of the sum of carbon emission cost, vehicle fixed cost and vehicle transportation cost, and the objective function is expressed as: f(X m )=min(TC1+TC2+TC3),

[0007] Among them: TC1 represents the fixed cost of the vehicle, and the formula is expressed as

[0008] TC2 represents the vehicle transportation cost, which is expressed as

[0009] TC3 represents the vehicle carbon emission cost, which is expressed as

[0010] In the formula, N represents the set of distribution centers and customer points; H represents the set of fleets; d ij represents the distance traveled by the vehicle from i to j; c1 represents the fixed cost of each vehicle for each delivery; c2 represents the transportation cost of the vehicle; c3 represents the unit carbon emission cost of each vehicle; ξ represents the conversion coefficient between fuel consumption and carbon emissions; ρ 0 represents the fuel consumption rate when no load is applied; ρ * represents the fuel consumption rate when loaded; Q represents the maximum load of the delivery vehicle; f ijh represents the cargo load of the delivery vehicle h traveling from i to j;

[0011] The constraint function is that the cargo load of the vehicle does not exceed the maximum load of the vehicle, and each customer needs to be delivered to and finally returned to the distribution center;

[0012] The algorithm parameters are defined as population size T, number of subgroups t, number of local searches L, number of global iterations G, and number of iterations n;

[0013] S2: Calculate the frog fitness values ​​and sort them in descending order:

[0014] The initial population P is randomly generated by integer coding T =X1,X2,X m …X T , m=1,2,…T, where X m =x1,x2,x3…x N represents the order in which each frog is delivered, x N represents the distribution point number, and calculates the fitness value F(X m ),in,

[0015] The greater the frog's fitness, the closer its path is to the optimal value. T The frogs in the array are arranged in descending order according to their fitness values;

[0016] S3: distribute the frogs to the lotus leaves according to the following rules to generate t subgroups: distribute the entire frog population into t lotus leaves, the first frog is distributed to the first lotus leaf, the second frog is distributed to the second lotus leaf, the tth frog is distributed to the tth lotus leaf, after the first round of distribution, distribute the t+1th to Tth frogs to the 1st to tth lotus leaves in turn, and so on, until all frogs are distributed;

[0017] S4: Use the embedded variable neighborhood search algorithm to perform local search on each subgroup, specifically:

[0018] The frogs with the best and worst solutions in each subgroup are marked as F btWith F wt , the frog with the largest fitness is the frog with the optimal solution, and the frog with the smallest fitness is the frog with the worst solution; the frog with the optimal solution in the population is marked as F g , perform local update operation on each frog on the lotus leaf, and the update formula is:

[0019]

[0020] Among them, r is a random number between 0 and 1; D is the distance the frog moves; D max is the maximum distance the frog is allowed to move;

[0021] If the solution F is obtained after updating t new1 Greater than F wt , then F wt =F t new1 Otherwise, use F g Replace F bt , perform a local position update operation:

[0022]

[0023] If the solution F is obtained after updating t new2 Greater than F wt , then F wt =F t new2 , otherwise a new frog F is randomly generated in the subgroup t new Directly replace the original F wt ; Update F in each subgroup wt After that, the embedded exchange operator and the insertion operator are used to perform variable neighborhood search. wt Randomly select two client nodes x R1 and x R2 Exchange positions and generate a new route; if the solution F t new3 Greater than F wt , then F wt =F t new3 , and repeatedly randomly select two customer nodes to exchange positions to generate a new route;

[0024] If no better solution is found or all exchange combinations have been tried, then the solution F wt Use the insertion operator to add the current solution F wt Randomly select two adjacent customer nodes x from all paths k1 and x k2 , two adjacent customer nodes x k1 and x k2 Insert them to the front of the remaining customer points in order to generate a new route. If the solution F t new4 Greater than F wt , then F wt=F t new4 , and repeatedly randomly select two customer nodes to insert into the front of the remaining customer points to generate a new route; if no better solution is found or all node combinations have been tried, then exit the variable neighborhood search; if F wt Greater than F bt , then F bt =F wt , otherwise continue the iteration operation;

[0025] Repeat the local search for each subgroup L times and update F bt ;

[0026] S5: shuffle the population, reassemble and sort, update F g ;

[0027] Mix all subgroups, re-sort the frogs in the population in descending order according to their fitness values, and record the global optimal solution F g ;

[0028] S6: operation termination judgment;

[0029] If n>G, stop the iteration and output the optimal delivery path; otherwise, n=n+1 and go back to S3.

[0030] Further, the constraint function includes vehicle load constraint and path node constraint;

[0031] The vehicle load constraint is that the cargo load of the vehicle when performing the delivery task cannot exceed the maximum load of the vehicle. At the same time, when the vehicle arrives at the demand point, it must unload the cargo before rushing to the next demand point. The formula is expressed as:

[0032]

[0033] Among them, w 0j Indicates the cargo load of the vehicle during the delivery mission; Q jh represents the load of vehicle h at j; Q ih represents the load of vehicle h at i; q j represents the amount of cargo unloaded by the vehicle at point j;

[0034] The path node constraints are that when the vehicle performs the delivery task, each customer must be served, and the needs of each customer are inseparable and can only be delivered once by the same vehicle; the routing flow of each path node is conserved; the vehicle starts from the distribution center and eventually returns to the distribution center. The formula expression is:

[0035]

[0036] Among them, x 0jh indicates that vehicle h departs from the distribution center; xi0h Indicates that vehicle h finally returns to the distribution center.

[0037] A control system for a logistics vehicle distribution path planning method, comprising an intelligent cloud platform, a control box and a gravity sensor; the intelligent cloud platform transmits vehicle information, cargo information and customer information to the control box through a wireless sensor; the gravity sensor is used to collect cargo weight;

[0038] The control box includes a memory, a processor and a transmission module I;

[0039] The memory includes an information acquisition module, a weight acquisition module and a transmission module II, which are used to collect customer information and cargo weight information. The information acquisition module receives the customer information in the transmission module I; the weight acquisition module receives the cargo information in the transmission module I; the transmission module II is used to transmit the customer and vehicle information in the memory to the processor;

[0040] The processor includes an algorithm module and a judgment module, which are used to plan the best delivery route with the lowest total cost such as carbon emission cost, vehicle fixed cost, and vehicle transportation cost, and monitor the vehicle loading in real time;

[0041] The judgment module is used to judge whether the cargo weight information provided by the intelligent cloud platform and the cargo weight information measured by the gravity sensor are within a preset difference range. The algorithm module is used to plan the delivery route of the delivery vehicle.

[0042] Furthermore, the processor also includes a transmission module III, and the processor transmits the distribution plan to the transmission module III and then transmits it to the customer terminal through the wireless sensor.

[0043] Furthermore, if the judgment module determines that the cargo weight information provided by the intelligent cloud platform and the cargo weight information measured by the gravity sensor exceed a preset difference range, an alarm is issued, the cargo weight information in the intelligent cloud platform is updated, and the delivery route is adjusted.

[0044] The beneficial effects of the present invention are:

[0045] The logistics vehicle delivery route planning method and control system described in the present invention can plan the delivery vehicle's route with the lowest cost as the goal based on the customer's coordinate information and cargo demand by improving the hybrid frog leaping algorithm, thereby reducing logistics transportation costs; at the same time, the present invention takes into account the real-time dynamic carbon emissions of vehicles under delivery orders, and through effective route planning, reduces the vehicle's fuel consumption, reduces carbon emission costs, and realizes green travel for delivery vehicles; the device monitors the cargo weight information in real time, adjusts the delivery route in time, and transmits the delivery route to the mobile phone terminal through wireless sensing technology, thereby ensuring the normal progress of the delivery process. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 Schematic diagram of the planning and control system for the logistics vehicle delivery route described in the present invention.

[0048] Figure 2 This is an installation diagram of the control box and gravity sensor described in the present invention.

[0049] Figure 3 This is a schematic diagram of the principle of planning the driving path of the delivery vehicle according to the present invention.

[0050] Figure 4 This is a flow chart of the method for planning the logistics vehicle distribution route described in the present invention.

[0051] In the figure:

[0052] 1-intelligent cloud platform; 2-wireless sensor; 3-mobile terminal; 4-wireless communication link; 5-memory; 6-processor; 7-control box; 8-gravity sensor; 9-carriage; 301-information acquisition module; 302-weight acquisition module; 303-algorithm module; 304-judgment module; 305-transmission module I; 306-transmission module II; 307-transmission module III. DETAILED DESCRIPTION

[0053] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments, but the protection scope of the present invention is not limited thereto.

[0054] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0055] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "axial", "radial", "vertical", "horizontal", "inner", "outer" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0056] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0057] like Figure 1 As shown, the planning and control system of the logistics vehicle distribution path described in the present invention includes an intelligent cloud platform 1, a control box 7, a gravity sensor 8 and a mobile phone terminal 3; the intelligent cloud platform 1 transmits vehicle information, cargo information and customer information to the control box 7 through a wireless sensor 2; wherein the control box 7 includes a memory 5, a processor 6 and a transmission module I305; the memory 5 is installed on the side of the control box 7, and the memory 5 includes an information acquisition module 301, a weight acquisition module 302 and a transmission module II 306; the processor 6 is installed at the bottom of the control box 7, and the processor 6 includes an algorithm module 303, a judgment module 304 and a transmission module III 307; as shown Figure 2 As shown, the control box 7 is loaded with a control console of the vehicle; the gravity sensor 8 is installed on the frame just below the carriage 9 to monitor the weight of the cargo in real time.

[0058] like Figure 3As shown, the intelligent cloud platform 1 transmits vehicle information, cargo information and customer information to the transmission module I 305 through the wireless sensor 3; the gravity sensor 8 transmits the cargo weight information to the transmission module I 305; the information acquisition module 301 receives the customer information in the transmission module I 305; the weight acquisition module 302 receives the cargo information in the transmission module I 305; the memory 5 transmits the vehicle and customer information to the processor 6 through the transmission module II 306; wherein, the algorithm module 303 is used to plan the delivery route of the delivery vehicle, and the judgment module 304 is used to judge whether the cargo weight information provided by the intelligent cloud platform 1 and the cargo weight information measured by the gravity sensor 8 are within a preset difference range; the processor 6 transmits the distribution plan to the transmission module III 307 and then transmits it to the mobile terminal 3 through the wireless sensor 2, so as to achieve efficient logistics distribution.

[0059] The algorithm module 303 plans the delivery path through the logistics vehicle delivery path planning method of the present invention, and the specific steps are as follows:

[0060] S1: Determine the objective function, constraint function and initialization algorithm parameters according to the number of customers and the coordinate information of the customers, wherein: the objective function is the minimum value of the sum of carbon emission cost, vehicle fixed cost and vehicle transportation cost, and the objective function is expressed as: f(X m )=min(TC1+TC2+TC3),

[0061] Among them: TC1 represents the fixed cost of the vehicle, and the formula is expressed as

[0062] TC2 represents the vehicle transportation cost, which is expressed as

[0063] TC3 represents the vehicle carbon emission cost, which is expressed as

[0064] In the formula, N represents the set of distribution centers and customer points; H represents the set of fleets; d ij represents the distance traveled by the vehicle from i to j; c1 represents the fixed cost of each vehicle for each delivery; c2 represents the transportation cost of the vehicle; c3 represents the unit carbon emission cost of each vehicle; ξ represents the conversion coefficient between fuel consumption and carbon emissions; ρ 0 represents the fuel consumption rate when no load is applied; ρ * represents the fuel consumption rate when loaded; Q represents the maximum load of the delivery vehicle; f ijh represents the cargo load of the delivery vehicle h traveling from i to j;

[0065] The constraint function is that the cargo load of the vehicle does not exceed the maximum load of the vehicle, and each customer needs to be delivered to and finally returned to the distribution center;

[0066] The algorithm parameters are defined as population size T, number of subgroups t, number of local searches L, number of global iterations G, and number of iterations n;

[0067] S2: Calculate the frog fitness values ​​and sort them in descending order:

[0068] The initial population P is randomly generated by integer coding T =X1,X2,X m …X T , m=1,2,…T, where X m =x1,x2,x3…x N represents the order in which each frog is delivered, x N represents the distribution point number, and calculates the fitness value F(X m ),in,

[0069] The greater the frog's fitness, the closer its path is to the optimal value. T The frogs in the array are arranged in descending order according to their fitness values;

[0070] S3: distribute the frogs to the lotus leaves according to the following rules to generate t subgroups: distribute the entire frog population into t lotus leaves, the first frog is distributed to the first lotus leaf, the second frog is distributed to the second lotus leaf, the tth frog is distributed to the tth lotus leaf, after the first round of distribution, distribute the t+1th to Tth frogs to the 1st to tth lotus leaves in turn, and so on, until all frogs are distributed;

[0071] S4: Use the embedded variable neighborhood search algorithm to perform local search on each subgroup, specifically:

[0072] The frogs with the best and worst solutions in each subgroup are marked as F bt With F wt , the frog with the largest fitness is the frog with the optimal solution, and the frog with the smallest fitness is the frog with the worst solution; the frog with the optimal solution in the population is marked as F g , perform local update operation on each frog on the lotus leaf, and the update formula is:

[0073]

[0074] Among them, r is a random number between 0 and 1; D is the distance the frog moves; D max is the maximum distance the frog is allowed to move;

[0075] If the solution F is obtained after updating t new1 Greater than F wt , then F wt =F t new1 Otherwise, use F g Replace F bt , perform a local position update operation:

[0076]

[0077] If the solution F is obtained after updating t new2 Greater than F wt , then F wt =F t new2 , otherwise a new frog F is randomly generated in the subgroup t new Directly replace the original F wt ; Update F in each subgroup wt After that, the embedded exchange operator and the insertion operator are used to perform variable neighborhood search. wt Randomly select two client nodes x R1 and x R2 Exchange positions and generate a new route; if the solution F t new3 Greater than F wt , then F wt =F t new3 , and repeatedly randomly select two customer nodes to exchange positions to generate a new route;

[0078] If no better solution is found or all exchange combinations have been tried, then the solution F wt Use the insertion operator to add the current solution F wt Randomly select two adjacent customer nodes x from all paths k1 and x k2 , two adjacent customer nodes x k1 and x k2 Insert them to the front of the remaining customer points in order to generate a new route. If the solution F t new4 Greater than F wt , then F wt =F t new4 , and repeatedly randomly select two customer nodes to insert into the front of the remaining customer points to generate a new route; if no better solution is found or all node combinations have been tried, then exit the variable neighborhood search; if F wt Greater than F bt , then F bt =F wt , otherwise continue the iteration operation;

[0079] Repeat the local search for each subgroup L times and update F bt ;

[0080] S5: shuffle the population, reassemble and sort, update F g ;

[0081] Mix all subgroups, re-sort the frogs in the population in descending order according to their fitness values, and record the global optimal solution F g ;

[0082] S6: operation termination judgment;

[0083] If n>G, stop the iteration and output the optimal delivery path; otherwise, n=n+1 and go back to S3.

[0084] The judgment module 304 sets the preset difference range between the cargo weight information provided by the intelligent cloud platform 1 and the cargo weight information measured by the gravity sensor 8 to ±2kg; if it exceeds this range, an alarm is issued, the cargo weight information in the intelligent cloud platform 1 is updated, and the delivery path is adjusted; if it does not exceed this range, delivery is carried out according to the optimal delivery path.

[0085] It should be understood that although this specification is described according to various embodiments, not every embodiment contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0086] The series of detailed descriptions listed above are only specific descriptions of feasible embodiments of the present invention. They are not intended to limit the scope of protection of the present invention. All equivalent embodiments or changes that do not deviate from the technical spirit of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for planning a logistics vehicle distribution route, characterized in that: The steps include: S1: Determine the objective function, constraint function and initialization algorithm parameters according to the number of customers and the coordinate information of the customers, wherein: the objective function is the minimum value of the sum of carbon emission cost, vehicle fixed cost and vehicle transportation cost, and the objective function f(X m ) is expressed as: f(X m )=min(TC1+TC2+TC3), Among them: TC1 represents the fixed cost of the vehicle, and the formula is expressed as TC2 represents the vehicle transportation cost, which is expressed as TC3 represents the vehicle carbon emission cost, which is expressed as In the formula, N represents the set of distribution centers and customer points; H represents the set of fleets; d ij represents the distance traveled by the vehicle from i to j; c1 represents the fixed cost of each vehicle for each delivery; c2 represents the transportation cost of the vehicle; c3 represents the unit carbon emission cost of each vehicle; ξ represents the conversion coefficient between fuel consumption and carbon emissions; ρ 0 represents the fuel consumption rate when there is no load; ρ * represents the fuel consumption rate when loaded; Q represents the maximum load of the delivery vehicle; f ijh represents the cargo load of the delivery vehicle h traveling from i to j; The constraint function is that the cargo load of the vehicle does not exceed the maximum load of the vehicle, and each customer needs to be delivered to and finally returned to the distribution center; The algorithm parameters are defined as population size T, number of subgroups t, number of local searches L, number of global iterations G, and number of iterations n; S2: Calculate the frog fitness values ​​and sort them in descending order: The initial population P is randomly generated by integer coding T =X1,X2,X m …X T , m=1,2,…T, where X m =x1,x2,x3…x N represents the order in which each frog is delivered, x N represents the distribution point number, and calculates the fitness value F(X m ),in, The greater the frog's fitness, the closer its path is to the optimal value. T The frogs in the array are arranged in descending order according to their fitness values; S3: distribute the frogs to the lotus leaves according to the following rules to generate t subgroups: distribute the entire frog population into t lotus leaves, the first frog is distributed to the first lotus leaf, the second frog is distributed to the second lotus leaf, the tth frog is distributed to the tth lotus leaf, after the first round of distribution, distribute the t+1th to Tth frogs to the 1st to tth lotus leaves in turn, and so on, until all frogs are distributed; S4: Use the embedded variable neighborhood search algorithm to perform local search on each subgroup, specifically: The frogs with the best and worst solutions in each subgroup are marked as F bt With F wt , the frog with the largest fitness is the frog with the optimal solution, and the frog with the smallest fitness is the frog with the worst solution; the frog with the optimal solution in the population is marked as F g , perform local update operation on each frog on the lotus leaf, and the update formula is: Among them, r is a random number between 0 and 1; D is the distance the frog moves; D max is the maximum distance the frog is allowed to move; If the solution F is obtained after updating tnew1 Greater than F wt , then F wt =F tnew1 Otherwise, use F g Replace F bt , perform a local position update operation: If the solution F is obtained after updating tnew2 Greater than F wt , then F wt =F tnew2 , otherwise a new frog F is randomly generated in the subgroup tnew Directly replace the original F wt ; Update F in each subgroup wt After that, the embedded exchange operator and the insertion operator are used to perform variable neighborhood search. wt Randomly select two client nodes x R1 and x R2 Exchange positions and generate a new route; if the solution F tnew3 Greater than F wt , then F wt =F tnew3 , and repeatedly randomly select two customer nodes to exchange positions to generate a new route; If no better solution is found or all exchange combinations have been tried, then the solution F wt Use the insertion operator to add the current solution F wt Randomly select two adjacent customer nodes x from all paths k1 and x k2 , two adjacent customer nodes x k1 and x k2 Insert them to the front of the remaining customer points in order to generate a new route. If the solution F tnew4 Greater than F wt , then F wt =F tnew4 , and repeatedly randomly select two customer nodes to insert into the front of the remaining customer points to generate a new route; if no better solution is found or all node combinations have been tried, then exit the variable neighborhood search; if F wt Greater than F bt , then F bt =F wt , otherwise continue the iteration operation; Repeat the local search for each subgroup L times and update F bt ; S5: shuffle the population, reassemble and sort, update F g ; Mix all subgroups, re-sort the frogs in the population in descending order according to their fitness values, and record the global optimal solution F g ; S6: operation termination judgment; If n>G, stop the iteration and output the optimal delivery path; otherwise, n=n+1 and go back to S3.

2. The method for planning a logistics vehicle delivery route according to claim 1, characterized in that: The constraint function includes vehicle load constraint and path node constraint; The vehicle load constraint is that the cargo load of the vehicle when performing the delivery task cannot exceed the maximum load of the vehicle. At the same time, when the vehicle arrives at the demand point, it must unload the cargo before rushing to the next demand point. The formula is expressed as: Among them, w 0j Indicates the cargo load of the vehicle during the delivery mission; Q jh represents the load of vehicle h at j; Q ih represents the load of vehicle h at i; q j represents the amount of cargo unloaded by the vehicle at point j; The path node constraints are that when the vehicle performs the delivery task, each customer must be served, and the needs of each customer are inseparable and can only be delivered once by the same vehicle; the routing flow of each path node is conserved; the vehicle starts from the distribution center and eventually returns to the distribution center. The formula expression is: Among them, x 0jh indicates that vehicle h departs from the distribution center; x i0h Indicates that vehicle h finally returns to the distribution center.

3. A control system for the logistics vehicle distribution path planning method according to claim 1, characterized in that: It comprises an intelligent cloud platform (1), a control box (7) and a gravity sensor (8); the intelligent cloud platform (1) transmits vehicle information, cargo information and customer information to the control box (7) via a wireless sensor (2); the gravity sensor (8) is used to collect cargo weight; The control box (7) comprises a memory (5), a processor (6) and a transmission module I (305); The memory (5) comprises an information acquisition module (301), a weight acquisition module (302) and a transmission module II (306), which are used to collect customer information and cargo weight information; the information acquisition module (301) receives the customer information in the transmission module I (305); the weight acquisition module (302) receives the cargo information in the transmission module I (305); the transmission module II (306) is used to transmit the customer and vehicle information in the memory (5) to the processor (6); The processor (6) includes an algorithm module (303) and a judgment module (304), which are used to plan the best delivery route with the goal of minimizing the sum of carbon emission cost, vehicle fixed cost, and vehicle transportation cost, and to monitor the vehicle loading in real time; The judgment module (304) is used to judge whether the cargo weight information provided by the intelligent cloud platform (1) and the cargo weight information measured by the gravity sensor (8) are within a preset difference range; the algorithm module (303) is used to plan the delivery route of the delivery vehicle.

4. The control system of the logistics vehicle distribution path planning method according to claim 3 is characterized in that: The processor (6) further comprises a transmission module III (307). The processor (6) transmits the distribution plan to the transmission module III (307) and then transmits the distribution plan to the client terminal via the wireless sensor (2).

5. The control system of the logistics vehicle distribution path planning method according to claim 3 is characterized in that: When the judgment module (304) judges that the cargo weight information provided by the intelligent cloud platform (1) and the cargo weight information measured by the gravity sensor (8) exceed a preset difference range, an alarm is issued, the cargo weight information in the intelligent cloud platform (1) is updated, and the delivery route is adjusted.

Images