CN117933672A - Park logistics vehicle dispatching management system based on artificial intelligence - Google Patents
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Abstract
The invention belongs to the field of logistics vehicle dispatching, relates to a data analysis technology, and is used for solving the problem that a park logistics vehicle dispatching management system in the prior art cannot carry out omnibearing dispatching analysis by combining processes such as vehicle load analysis, traffic simulation analysis and the like; according to the invention, response analysis can be performed on the demand instruction, logistics vehicle statistics is performed by taking the site position as the center, and then the logistics vehicles are screened from the aspect of loading feasibility, so that the reserved objects can meet the loading requirements of cargo transportation.
Description
Technical Field
The invention belongs to the field of logistics vehicle dispatching, relates to a data analysis technology, and particularly relates to a park logistics vehicle dispatching management system based on artificial intelligence.
Background
With the rapid development of the logistics industry, the traditional vehicle management mode cannot meet the high-efficiency, accurate and real-time logistics requirements of modern parks; therefore, a park logistics vehicle dispatching management system is developed, and the system utilizes advanced information technology and positioning technology to realize comprehensive monitoring and management of the logistics vehicles in the park.
The park logistics vehicle dispatching management system in the prior art can only carry out vehicle dispatching analysis from the vehicle transportation angle, but cannot carry out all-round dispatching analysis in combination with processes such as vehicle load analysis, traffic simulation analysis and the like, so that the actual implementation feasibility of vehicle dispatching management is low, the vehicle full load rate is low, and logistics resource waste is caused.
The application provides a solution to the technical problem.
Disclosure of Invention
The invention aims to provide a park logistics vehicle dispatching management system based on artificial intelligence, which is used for solving the problem that the park logistics vehicle dispatching management system in the prior art cannot be combined with processes such as vehicle load analysis, traffic simulation analysis and the like to carry out omnibearing dispatching analysis;
The technical problems to be solved by the invention are as follows: how to provide a park logistics vehicle dispatching management system based on artificial intelligence, which can carry out omnibearing dispatching analysis by combining processes of vehicle load analysis, traffic simulation analysis and the like.
The aim of the invention can be achieved by the following technical scheme:
The park logistics vehicle dispatching management system based on the artificial intelligence comprises a dispatching management platform, wherein the dispatching management platform is in communication connection with a user side, a response analysis module and a storage module, the response analysis module is also in communication connection with the dispatching management module, the dispatching management module is in communication connection with the user side, and the user side is also in communication connection with a traffic management module;
the user terminal comprises a station terminal, a vehicle terminal and a traffic management terminal, and when the station terminal needs to transport goods, a demand instruction is sent to the dispatching management platform, wherein the demand instruction comprises a station position, a goods weight, a goods volume, a goods type and a target station; the scheduling management platform receives the demand instruction and then sends the demand instruction to the response analysis module;
The response analysis module is used for carrying out response analysis on a demand instruction sent by the site end, marking a reserved object in a response area and sending the reserved object to the scheduling management module;
the dispatching management module is used for carrying out dispatching management analysis on the reserved objects, marking the management objects and sending the management objects to the traffic management end;
and after the traffic management terminal receives the management object, the management object is sent to the traffic management module, and the traffic management module is used for carrying out simulation analysis on the traffic transportation environment of the management object.
As a preferred implementation manner of the invention, the specific process of the response analysis module for carrying out response analysis on the demand instruction sent by the site end comprises the following steps: drawing a circle by taking a site position as a circle center and r1 as a radius, marking the obtained circular area as a response area, marking a logistics vehicle in the response area as a response object, obtaining weight value and volume value of loaded goods of the response object, marking the sum value of the weight and the weight data as a weight bearing value, marking the sum value of the volume and the volume data as a space value, calling the weight bearing threshold and the space threshold of the response object through a storage module, comparing the weight bearing value and the space value of the response object with the weight bearing threshold and the space threshold respectively, and marking the response object as a reserved object or an irrelevant object through a comparison result.
In a preferred embodiment of the present invention, the specific process of comparing the load value and the space value of the response object with the load threshold value and the space threshold value, respectively, includes: if the load value is smaller than or equal to the load threshold value and the space value is smaller than or equal to the space threshold value, judging that the response object meets the transportation requirement, and marking the corresponding response object as a reserved object; otherwise, judging that the response object does not meet the transportation requirement, and marking the corresponding response object as an irrelevant object.
As a preferred embodiment of the present invention, the specific process of the scheduling management module for performing scheduling management analysis on the reserved object includes: acquiring straight distance data ZJ, weight difference data ZC and volume difference data TC of a reserved object, wherein the straight distance data ZJ is a straight line distance value between the current position of the reserved object and the site position, the weight difference data ZC is a difference value between a load threshold value and a load value of the reserved object, and the volume difference data TC is a difference value between a space threshold value and a space value of the reserved object; obtaining a scheduling coefficient DD of a reserved object by carrying out numerical calculation on the straight-distance data ZJ, the weight difference data ZC and the volume difference data TC; and marking the L1 reserved objects with the smallest dispatching coefficient DD values as management objects.
As a preferred embodiment of the invention, the specific process of the traffic management module for carrying out simulation analysis on the traffic and transportation environment of the management object comprises the following steps: acquiring an occupancy representation value ZB and an occupancy set value ZJ of a management object, and performing numerical value calculation to obtain a management coefficient GL of the management object; and marking the management object with the minimum management coefficient GL value as a matching object, and sending a scheduling signal to a vehicle end of the matching object, wherein the scheduling signal comprises a demand instruction, a goods taking route and a goods delivering route.
As a preferred embodiment of the present invention, the process for obtaining the occupancy representation value ZB and the occupancy set value ZJ of the management object includes: generating corresponding route data for the management object, wherein the route data comprises a picking route and a delivery route, generating a logistics period of the management object for executing the logistics task through the running speed of the management object and the picking route and the delivery route, dividing the logistics period into a plurality of logistics periods, and obtaining road section occupation data of the logistics periods: marking a road section where the management object is located in the logistics period as an analysis road section, acquiring the maximum value of the number of simultaneous load vehicles of the analysis road section in the logistics period and marking the road section occupation data as the logistics period, summing the road section occupation data of the management object in all the logistics period, taking an average value to obtain an occupation representation value ZB, and performing variance calculation on the road section occupation data of the management object in all the logistics period to obtain an occupation set value ZJ.
As a preferred embodiment of the invention, the working method of the park logistics vehicle scheduling management system based on the artificial intelligence comprises the following steps:
step one: when the site end needs to transport goods, a demand instruction is sent to a dispatching management platform, and the dispatching management platform sends the demand instruction to a response analysis module after receiving the demand instruction;
Step two: responding and analyzing a demand instruction sent by a site end: drawing a circle by taking the site position as the circle center and r1 as the radius, marking the obtained circular area as a response area, and marking the logistics vehicles meeting the transportation requirement in the response area as reserved objects;
step three: and carrying out scheduling management analysis on the reserved object: obtaining straight distance data ZJ, weight difference data ZC and volume difference data TC of a reserved object, performing numerical value calculation to obtain a scheduling coefficient DD of the reserved object, and marking a management object through the scheduling coefficient DD;
Step four: simulation analysis is carried out on the traffic and transportation environment of the management object: generating corresponding route data for the management object, dividing the logistics period into a plurality of logistics periods, carrying out numerical calculation on the road section occupation data of the logistics periods to obtain a management coefficient GL, marking the matching object through the management coefficient GL, and sending a scheduling signal to the vehicle end of the matching object.
The invention has the following beneficial effects:
1. The response analysis module can perform response analysis on the demand instruction, the logistics vehicle statistics is performed by taking the site position as the center, and then the logistics vehicle is screened from the aspect of loading feasibility, so that the reserved object can meet the loading requirement of cargo transportation;
2. The scheduling management module can perform scheduling management analysis on the reserved objects, and calculates the scheduling coefficient of the reserved objects from the goods taking distance, the space full load degree and the weight full load degree of the reserved objects, so that the marked management objects have the advantages of being close in goods taking distance, high in space occupancy and close to rated load in weight, improving the vehicle utilization rate, reducing the probability that the vehicle is marked as the reserved objects again, and reducing the calculation force of the system in subsequent scheduling analysis;
3. The traffic management module can simulate and analyze the traffic and transportation environment of the management objects, the generated logistics period is divided, then the number of vehicles running at the same time of the road section where the management objects of each logistics period are located is simulated, then a plurality of parameters are calculated to obtain a management coefficient, the final matched objects are screened according to the management coefficient, the macroscopic traffic network and the microscopic vehicle state are comprehensively scheduled and analyzed, and the feasibility and the actual scheduling efficiency of the scheduling scheme are ensured.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is an overall system block diagram of the present invention;
FIG. 2 is a system block diagram of a first embodiment of the present invention;
FIG. 3 is a system block diagram of a second embodiment of the present invention;
fig. 4 is a flowchart of a method according to a third embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in FIG. 1, the system for dispatching and managing the campus logistics vehicles based on the artificial intelligence comprises a dispatching and managing platform, wherein the dispatching and managing platform is in communication connection with a user side, a response analysis module and a storage module, the response analysis module is also in communication connection with the dispatching and managing module, the dispatching and managing module is in communication connection with the user side, and the user side is also in communication connection with a traffic management module.
Example 1
As shown in fig. 2, the user side includes a station side, a vehicle side and a traffic management side, and when the station side needs to transport goods, the user side sends a demand instruction to the dispatching management platform, wherein the demand instruction includes a station position, a goods weight, a goods volume, a goods type and a target station; and the scheduling management platform sends the demand instruction to the response analysis module after receiving the demand instruction.
The response analysis module is used for carrying out response analysis on demand instructions sent by the site end: drawing a circle by taking a site position as a circle center and r1 as a radius, marking the obtained circular area as a response area, marking a logistics vehicle in the response area as a response object, obtaining weight value and volume value of loaded cargoes of the response object, marking the sum value of the cargoes weight and the weight data as weight values, marking the sum value of the cargoes volume and the volume data as space values, calling the weight threshold and the space threshold of the response object through a storage module, and comparing the weight value and the space value of the response object with the weight threshold and the space threshold respectively: if the load value is smaller than or equal to the load threshold value and the space value is smaller than or equal to the space threshold value, judging that the response object meets the transportation requirement, and marking the corresponding response object as a reserved object; otherwise, judging that the response object does not meet the transportation requirement, and marking the corresponding response object as an irrelevant object; transmitting the reserved object to a scheduling management module; and carrying out response analysis on the demand instruction, carrying out logistics vehicle statistics by taking the site position as the center, and then screening the logistics vehicles from the angle of loading feasibility, so that the reserved objects can meet the loading requirements of cargo transportation.
The scheduling management module is used for performing scheduling management analysis on the reserved objects: acquiring straight distance data ZJ, weight difference data ZC and volume difference data TC of a reserved object, wherein the straight distance data ZJ is a straight line distance value between the current position of the reserved object and the site position, the weight difference data ZC is a difference value between a load threshold value and a load value of the reserved object, and the volume difference data TC is a difference value between a space threshold value and a space value of the reserved object; obtaining a scheduling coefficient DD of a reserved object through a formula DD=c1×ZJ+c2×ZC+c3×TC, wherein c1, c2 and c3 are proportionality coefficients, and c1 > c2 > c3 > 1; marking L1 reserved objects with minimum dispatching coefficient DD values as management objects, and sending the management objects to a traffic management end; the method comprises the steps of carrying out scheduling management analysis on the reserved objects, calculating scheduling coefficients of the reserved objects from the goods taking distance, the space full load degree and the weight full load degree of the reserved objects, enabling the marked management objects to have a close goods taking distance, a high space occupancy and a weight close to a rated load, improving the vehicle utilization rate, reducing the probability that the vehicle is marked as the reserved objects again, and reducing the calculation force of the system in subsequent scheduling analysis.
Example two
As shown in fig. 3, after the traffic management terminal receives the management object, the traffic management terminal sends the management object to the traffic management module, and the traffic management module is used for performing simulation analysis on the traffic and transportation environment of the management object: generating corresponding route data for the management object, wherein the route data comprises a picking route and a delivery route, generating a logistics period of the management object for executing the logistics task through the running speed of the management object and the picking route and the delivery route, dividing the logistics period into a plurality of logistics periods, and obtaining road section occupation data of the logistics periods: marking a road section where a management object is located in a logistics period as an analysis road section, acquiring the road section occupation data of the maximum value of the number of simultaneous load vehicles of the analysis road section in the logistics period and marking the road section occupation data as the logistics period, summing the road section occupation data of the management object in all the logistics period to obtain an occupation representation value ZB, performing variance calculation on the road section occupation data of the management object in all the logistics period to obtain an occupation set value ZJ, and obtaining a management coefficient GL of the management object through a formula GL=k1 x ZB-k2 x ZJ, wherein k1 and k2 are proportionality coefficients, and k1 is more than k2 is more than 1; marking a management object with the minimum management coefficient GL value as a matching object, and sending a scheduling signal to a vehicle end of the matching object, wherein the scheduling signal comprises a demand instruction, a goods taking route and a goods delivering route; the method comprises the steps of carrying out simulation analysis on the traffic and transportation environment of a management object, dividing a generated logistics period, simulating the number of running vehicles at the same time of a road section where the management object of each logistics period is located, carrying out numerical calculation on a plurality of parameters to obtain a management coefficient, screening a final matched object according to the management coefficient, carrying out scheduling analysis on a comprehensive macroscopic traffic network and microscopic vehicle state, and ensuring the feasibility and the actual scheduling efficiency of a scheduling scheme.
Example III
As shown in fig. 4, a park logistics vehicle scheduling management method based on artificial intelligence includes the following steps:
step one: when the site end needs to transport goods, a demand instruction is sent to a dispatching management platform, and the dispatching management platform sends the demand instruction to a response analysis module after receiving the demand instruction;
Step two: responding and analyzing a demand instruction sent by a site end: drawing a circle by taking the site position as the circle center and r1 as the radius, marking the obtained circular area as a response area, and marking the logistics vehicles meeting the transportation requirement in the response area as reserved objects;
step three: and carrying out scheduling management analysis on the reserved object: obtaining straight distance data ZJ, weight difference data ZC and volume difference data TC of a reserved object, performing numerical value calculation to obtain a scheduling coefficient DD of the reserved object, and marking a management object through the scheduling coefficient DD;
Step four: simulation analysis is carried out on the traffic and transportation environment of the management object: generating corresponding route data for the management object, dividing the logistics period into a plurality of logistics periods, carrying out numerical calculation on the road section occupation data of the logistics periods to obtain a management coefficient GL, marking the matching object through the management coefficient GL, and sending a scheduling signal to the vehicle end of the matching object.
The park logistics vehicle dispatching management system based on the artificial intelligence sends a demand instruction to a dispatching management platform when a site end needs to transport goods, and the dispatching management platform sends the demand instruction to a response analysis module after receiving the demand instruction; drawing a circle by taking the site position as the circle center and r1 as the radius, marking the obtained circular area as a response area, and marking the logistics vehicles meeting the transportation requirement in the response area as reserved objects; obtaining straight distance data ZJ, weight difference data ZC and volume difference data TC of a reserved object, performing numerical value calculation to obtain a scheduling coefficient DD of the reserved object, and marking a management object through the scheduling coefficient DD; generating corresponding route data for the management object, dividing the logistics period into a plurality of logistics periods, carrying out numerical calculation on the road section occupation data of the logistics periods to obtain a management coefficient GL, marking the matching object through the management coefficient GL, and sending a scheduling signal to the vehicle end of the matching object.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions; such as: the formula dd=c1 zj+c2 zc+c3 TC; collecting a plurality of groups of sample data by a person skilled in the art and setting a corresponding scheduling coefficient for each group of sample data; substituting the set scheduling coefficient and the acquired sample data into a formula, forming a ternary one-time equation set by any three formulas, screening the calculated coefficient, and taking an average value to obtain values of c1, c2 and c3 of 2.85, 2.03 and 1.76 respectively;
The size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the corresponding harmful coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected, for example, the scheduling coefficient is proportional to the value of the scheduling coefficient.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (5)
1. The park logistics vehicle dispatching management system based on the artificial intelligence is characterized by comprising a dispatching management platform, wherein the dispatching management platform is in communication connection with a user side, a response analysis module and a storage module, the response analysis module is also in communication connection with the dispatching management module, the dispatching management module is in communication connection with the user side, and the user side is also in communication connection with a traffic management module;
the user terminal comprises a station terminal, a vehicle terminal and a traffic management terminal, and when the station terminal needs to transport goods, a demand instruction is sent to the dispatching management platform, wherein the demand instruction comprises a station position, a goods weight, a goods volume, a goods type and a target station; the scheduling management platform receives the demand instruction and then sends the demand instruction to the response analysis module;
The response analysis module is used for carrying out response analysis on a demand instruction sent by the site end, marking a reserved object in a response area and sending the reserved object to the scheduling management module;
the dispatching management module is used for carrying out dispatching management analysis on the reserved objects, marking the management objects and sending the management objects to the traffic management end;
The traffic management module is used for carrying out simulation analysis on the traffic transportation environment of the management object;
The specific process of the response analysis module for carrying out response analysis on the demand instruction sent by the site end comprises the following steps: drawing a circle by taking a site position as a circle center and r1 as a radius, marking the obtained circular area as a response area, marking a logistics vehicle in the response area as a response object, obtaining weight value and volume value of loaded cargoes of the response object, marking the sum value of the cargoes weight and the weight data as weight values, marking the sum value of the cargoes volume and the volume data as space values, calling the weight threshold and the space threshold of the response object through a storage module, comparing the weight value and the space value of the response object with the weight threshold and the space threshold respectively, and marking the response object as a reserved object or an irrelevant object through a comparison result;
The specific process for comparing the load value and the space value of the response object with the load threshold value and the space threshold value respectively comprises the following steps: if the load value is smaller than or equal to the load threshold value and the space value is smaller than or equal to the space threshold value, judging that the response object meets the transportation requirement, and marking the corresponding response object as a reserved object; otherwise, judging that the response object does not meet the transportation requirement, and marking the corresponding response object as an irrelevant object.
2. The system for dispatching management of campus logistics vehicles based on artificial intelligence according to claim 1, wherein the specific process of dispatching management analysis of the reserved objects by the dispatching management module comprises: acquiring straight distance data ZJ, weight difference data ZC and volume difference data TC of a reserved object, wherein the straight distance data ZJ is a straight line distance value between the current position of the reserved object and the site position, the weight difference data ZC is a difference value between a load threshold value and a load value of the reserved object, and the volume difference data TC is a difference value between a space threshold value and a space value of the reserved object; obtaining a scheduling coefficient DD of a reserved object by carrying out numerical calculation on the straight-distance data ZJ, the weight difference data ZC and the volume difference data TC; marking L1 reserved objects with minimum dispatching coefficient DD values as management objects;
The calculation formula of the scheduling coefficient DD of the reserved object is as follows: dd=c1 zj+c2 zc+c3 TC, where c1, c2 and c3 are scaling factors and c1 > c2 > c3 > 1.
3. The system for managing the dispatching of campus logistics vehicles based on artificial intelligence according to claim 2, wherein the specific process of the traffic management module performing the simulation analysis on the traffic environment of the management object comprises: acquiring an occupancy representation value ZB and an occupancy set value ZJ of a management object, and performing numerical value calculation to obtain a management coefficient GL of the management object; marking a management object with the minimum management coefficient GL value as a matching object, and sending a scheduling signal to a vehicle end of the matching object, wherein the scheduling signal comprises a demand instruction, a goods taking route and a goods delivering route;
the calculation formula of the management coefficient GL of the management object is: gl=k1×zb-k2×zj, where k1 and k2 are scaling factors, and k1 > k2 > 1.
4. The system for managing the dispatching of campus logistics vehicles based on artificial intelligence according to claim 3, wherein the process for obtaining the occupancy representation value ZB and the occupancy set value ZJ of the management object comprises: generating corresponding route data for the management object, wherein the route data comprises a picking route and a delivery route, generating a logistics period of the management object for executing the logistics task through the running speed of the management object and the picking route and the delivery route, dividing the logistics period into a plurality of logistics periods, and obtaining road section occupation data of the logistics periods: marking a road section where the management object is located in the logistics period as an analysis road section, acquiring the maximum value of the number of simultaneous load vehicles of the analysis road section in the logistics period and marking the road section occupation data as the logistics period, summing the road section occupation data of the management object in all the logistics period, taking an average value to obtain an occupation representation value ZB, and performing variance calculation on the road section occupation data of the management object in all the logistics period to obtain an occupation set value ZJ.
5. An artificial intelligence based campus logistics vehicle dispatch management system in accordance with any one of claims 1 to 4, wherein the method of operation of the artificial intelligence based campus logistics vehicle dispatch management system comprises the steps of:
step one: when the site end needs to transport goods, a demand instruction is sent to a dispatching management platform, and the dispatching management platform sends the demand instruction to a response analysis module after receiving the demand instruction;
Step two: responding and analyzing a demand instruction sent by a site end: drawing a circle by taking the site position as the circle center and r1 as the radius, marking the obtained circular area as a response area, and marking the logistics vehicles meeting the transportation requirement in the response area as reserved objects;
step three: and carrying out scheduling management analysis on the reserved object: obtaining straight distance data ZJ, weight difference data ZC and volume difference data TC of a reserved object, performing numerical value calculation to obtain a scheduling coefficient DD of the reserved object, and marking a management object through the scheduling coefficient DD;
Step four: simulation analysis is carried out on the traffic and transportation environment of the management object: generating corresponding route data for the management object, dividing the logistics period into a plurality of logistics periods, carrying out numerical calculation on the road section occupation data of the logistics periods to obtain a management coefficient GL, marking the matching object through the management coefficient GL, and sending a scheduling signal to the vehicle end of the matching object.
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