CN114819791A - Goods transportation management method and system based on Internet of things - Google Patents
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Abstract
The invention discloses a goods transportation management method and system based on the Internet of things, wherein the method comprises the following steps: building a multiple logistics transportation management chain; acquiring basic information of goods to be transported to obtain logistics information to be transported; carrying out statistical management on the receiving address of the goods arrival end to obtain receiving address management data; uploading logistics information to be transported and receiving address management data to a logistics allocation system for intelligent management to obtain a primary logistics allocation management scheme; uploading the primary logistics allocation management scheme to a data optimization model, and optimally screening input information based on an optimization algorithm to obtain an optimal logistics allocation management scheme; and carrying out dynamic tracking management on the optimal logistics allocation management scheme in the logistics transportation process.
Description
Technical Field
The invention relates to the field of data processing, in particular to a goods transportation management method and system based on the Internet of things.
Background
In recent years, the internet of things technology enters a commercial application stage. Along with the internet of things technology constantly combines together with internet technology, has been applied to numerous fields including freight management etc. and has realized carrying out intelligent management to the commodity circulation transportation overall process.
However, the prior art has the technical problems that the logistics transportation can not be carried out with relatively mature all-dimensional transportation allocation, and the logistics is exploded and goods can not be dredged and allocated in busy seasons.
Disclosure of Invention
The invention aims to provide a goods transportation management method and system based on the Internet of things, which are used for solving the technical problems that in the prior art, relatively mature all-around transportation and allocation cannot be carried out on logistics transportation, and warehouse explosion and dredging and allocation of goods cannot be carried out in the busy season of logistics.
In view of the above problems, the invention provides a freight transportation management method and system based on the internet of things.
In a first aspect, the invention provides a freight transportation management method based on the internet of things, which is characterized by comprising the following steps: building a multiple logistics transportation management chain, wherein one end of the multiple logistics transportation management chain is a goods initiating end, and the other end of the multiple logistics transportation management chain is a goods arriving end; based on the goods initiating terminal, basic information acquisition is carried out on the goods to be transported, and logistics information to be transported is obtained; carrying out statistical management on the receiving address of the goods arrival end based on the big data to obtain receiving address management data; uploading the logistics information to be transported and the receiving address management data to a logistics allocation system for intelligent management to obtain a primary logistics allocation management scheme, wherein the logistics allocation system is a central terminal of the multiple logistics transportation management chain; uploading the primary logistics allocation management scheme serving as input information to a data optimization model, and optimally screening the input information based on an optimization algorithm to obtain an optimal logistics allocation management scheme in the input information; and performing dynamic tracking management on the optimal logistics allocation management scheme in the logistics transportation process based on dynamic visual monitoring nodes, wherein the dynamic visual monitoring nodes are set for indefinite points of the multiple logistics transportation management chains.
In another aspect, the present invention further provides an internet of things-based freight transportation management system, configured to execute the internet of things-based freight transportation management method according to the first aspect, where the system includes: the system comprises a first building unit, a second building unit and a third building unit, wherein the first building unit is used for building a multiple logistics transportation management chain, one end of the multiple logistics transportation management chain is a goods starting end, and the other end of the multiple logistics transportation management chain is a goods arrival end; the first acquisition unit is used for acquiring basic information of the goods to be transported based on the goods initiating end to obtain logistics information to be transported; the first statistical unit is used for carrying out statistical management on the receiving address of the goods arrival end based on big data to obtain receiving address management data; the first uploading unit is used for uploading the logistics information to be transported and the receiving address management data to a logistics allocation system for intelligent management to obtain a primary logistics allocation management scheme, wherein the logistics allocation system is a central terminal of the multiple logistics transportation management chain; the second uploading unit is used for uploading the primary logistics allocation management scheme serving as input information to a data optimization model, and optimally screening the input information based on an optimization algorithm to obtain an optimal logistics allocation management scheme in the input information; and the first management unit is used for carrying out dynamic tracking management on the optimal logistics allocation management scheme in the logistics transportation process based on dynamic visual monitoring nodes, wherein the dynamic visual monitoring nodes are set for indefinite points of the multiple logistics transportation management chains.
In a third aspect, the present invention further provides a cargo transportation management system based on the internet of things, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to the first aspect when executing the program.
In a fourth aspect, an electronic device, comprising a processor and a memory;
the memory is used for storing;
the processor is configured to execute the method according to any one of the first aspect above by calling.
In a fifth aspect, a computer program product comprises a computer program and/or instructions which, when executed by a processor, performs the steps of the method of any of the first aspect described above.
One or more technical schemes provided by the invention at least have the following technical effects or advantages:
through setting up multiple commodity circulation transportation management chain, goods origin end and goods arrival end based on management chain both ends, and the maincenter commodity circulation allotment system of management chain, treat that the transportation goods carries out omnidirectional multiple allotment, and then utilize the optimization algorithm of computer, optimize elementary commodity circulation management allotment scheme, make and select optimal commodity circulation allotment management scheme, and simultaneously, through setting up dynamic visual monitoring node, carry out dynamic visual monitoring to the transportation overall process, make the problem to appear in the transportation in time discover, solve fast, reached and carried out comparatively ripe transportation allotment to the commodity circulation transportation overall process, improve the technological effect of commodity circulation transportation efficiency.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
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In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can be obtained according to the provided drawings without inventive effort.
Fig. 1 is a schematic flow chart of a cargo transportation management method based on the internet of things according to the invention;
fig. 2 is a schematic flow chart of the dynamic visual monitoring node generated in the cargo transportation management method based on the internet of things according to the present invention;
fig. 3 is a schematic structural diagram of a cargo transportation management system based on the internet of things according to the present invention;
fig. 4 is a schematic structural diagram of an exemplary electronic device of the present invention.
Description of reference numerals:
the system comprises a first building unit 11, a first acquisition unit 12, a first statistic unit 13, a first uploading unit 14, a second uploading unit 15, a first management unit 16, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304 and a bus interface 305.
Detailed Description
The invention provides a goods transportation management method and system based on the Internet of things, and solves the technical problems that in the prior art, relatively mature all-around transportation and allocation cannot be carried out on logistics transportation, and warehouse explosion and dredging and allocation of goods cannot be carried out in a busy season of logistics. Through setting up multiple commodity circulation transportation management chain, goods origin end and goods arrival end based on management chain both ends, and the maincenter commodity circulation allotment system of management chain, treat that the transportation goods carries out omnidirectional multiple allotment, and then utilize the optimization algorithm of computer, optimize elementary commodity circulation management allotment scheme, make and select optimal commodity circulation allotment management scheme, and simultaneously, through setting up dynamic visual monitoring node, carry out dynamic visual monitoring to the transportation overall process, make the problem to appear in the transportation in time discover, solve fast, reached and carried out comparatively ripe transportation allotment to the commodity circulation transportation overall process, improve the technological effect of commodity circulation transportation efficiency.
In the technical scheme of the invention, the data acquisition, storage, use, processing and the like all conform to relevant regulations of national laws and regulations.
In the following, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments of the present invention, and it should be understood that the present invention is not limited by the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention. It should be further noted that, for the convenience of description, only some but not all of the elements associated with the present invention are shown in the drawings.
The invention provides a goods transportation management method based on the Internet of things, which is characterized by comprising the following steps: building a multiple logistics transportation management chain, wherein one end of the multiple logistics transportation management chain is a goods initiating end, and the other end of the multiple logistics transportation management chain is a goods arriving end; based on the goods initiating terminal, basic information acquisition is carried out on the goods to be transported, and logistics information to be transported is obtained; carrying out statistical management on the receiving address of the goods arrival end based on the big data to obtain receiving address management data; uploading the logistics information to be transported and the receiving address management data to a logistics allocation system for intelligent management to obtain a primary logistics allocation management scheme, wherein the logistics allocation system is a central terminal of the multiple logistics transportation management chain; uploading the primary logistics allocation management scheme serving as input information to a data optimization model, and optimally screening the input information based on an optimization algorithm to obtain an optimal logistics allocation management scheme in the input information; and performing dynamic tracking management on the optimal logistics allocation management scheme in the logistics transportation process based on dynamic visual monitoring nodes, wherein the dynamic visual monitoring nodes are set for indefinite points of the multiple logistics transportation management chains.
Having described the general principles of the invention, reference will now be made in detail to various non-limiting embodiments of the invention, examples of which are illustrated in the accompanying drawings.
Example one
Referring to fig. 1, the present invention provides a freight transportation management method based on the internet of things, wherein the method is applied to a freight transportation management system based on the internet of things, and the method specifically includes the following steps:
step S100: building a multiple logistics transportation management chain, wherein one end of the multiple logistics transportation management chain is a goods initiating end, and the other end of the multiple logistics transportation management chain is a goods arriving end;
step S200: based on the goods initiating terminal, basic information acquisition is carried out on the goods to be transported, and logistics information to be transported is obtained;
step S300: carrying out statistical management on the receiving address of the goods arrival end based on the big data to obtain receiving address management data;
in particular, in recent years, the internet of things technology has entered a commercial application stage. Along with the internet of things technology constantly combines together with internet technology, has been applied to numerous fields including freight management etc. and has realized carrying out intelligent management to the commodity circulation transportation overall process.
However, because the logistics transportation in the prior art can not be carried out the all-round transportation allotment of comparatively maturity, when the commodity circulation was in a busy season, the technical problem that the storehouse was exploded, goods can not dredge the allotment appeared.
In order to solve the problems in the prior art, the application provides a goods transportation management method based on the Internet of things, through building a multiple logistics transportation management chain, based on a goods initiating end and a goods arriving end at two ends of the management chain, and a central logistics allocation system of the management chain, all-around multiple allocation is carried out on goods to be transported, further, an optimization algorithm of a computer is utilized, a primary logistics management allocation scheme is optimized, an optimal logistics allocation management scheme is selected, meanwhile, dynamic visual monitoring is carried out on the whole transportation process through setting dynamic visual monitoring nodes, problems occurring in the transportation process are timely found and rapidly solved, the technical effects of carrying out relatively mature transportation allocation on the whole logistics transportation process and improving logistics transportation efficiency are achieved.
Specifically, the multiple logistics transportation management chain comprises a goods initiating end, a goods arriving end and a logistics allocation central system, wherein the multiple logistics transportation management chain is used for intelligently and uniformly allocating goods to be transported, multiple goods are represented that multiple allocation schemes exist in the goods to be transported, the goods initiating end is the place where the goods are sent, the goods arriving end is the place where the goods arrive, and the logistics allocation central system is used for integrating the goods initiating end and the goods arriving end to perform intelligent allocation on the goods to be transported. The logistics information to be transported comprises basic information of the goods to be transported and vehicle information of the transported goods, and the receiving address management data is a data set for managing the receiving address of the goods to be transported.
Step S400: uploading the logistics information to be transported and the receiving address management data to a logistics allocation system for intelligent management to obtain a primary logistics allocation management scheme, wherein the logistics allocation system is a central terminal of the multiple logistics transportation management chain;
further, step S400 includes:
step S410: extracting key features of the logistics information to be transported to obtain article type information;
step S420: extracting key features of the goods receiving address management data to obtain a goods assembly sequence;
step S430: and based on the logistics deployment system, carrying out dual management on the article type information and the cargo assembly sequence to obtain the primary logistics deployment management scheme.
Specifically, after the logistics information to be transported and the receiving address management data are obtained, the logistics information to be transported can be uploaded to a logistics allocation system for intelligent management, specifically, key features of the logistics information to be transported can be extracted, and article type information is obtained, wherein the article type information comprises types of goods to be transported, the types of the goods are different, transportation conditions are adjusted accordingly, and exemplarily, if the transported goods are melons, fruits and vegetables, freshness keeping conditions and delivery and arrival time in the transportation process need to be ensured. Meanwhile, key features of the receiving address management data can be extracted to obtain a cargo assembly sequence, generally, cargo arriving closer can be assembled last, cargo arriving farther away can be assembled preferentially, and the cargo assembly sequence is the example.
After the key features are extracted, the article type information and the cargo assembly sequence can be managed doubly based on the logistics deployment system, that is, the article type information and the cargo assembly sequence are subjected to double comprehensive reference, so that a management result is obtained, the primary logistics deployment management scheme is a result obtained by primary deployment, and in order to improve the deployment scheme efficiency, subsequent scheme upgrading and optimization are required.
Step S500: uploading the primary logistics allocation management scheme serving as input information to a data optimization model, and optimally screening the input information based on an optimization algorithm to obtain an optimal logistics allocation management scheme in the input information;
further, step S500 includes:
step S510: the data optimization model is embedded with an NSGA-II genetic algorithm;
step S520: performing data preprocessing on the primary logistics allocation management scheme, and defining the processed data as standard sample data;
step S530: setting a constraint condition for the optimization algorithm according to the standard sample data;
step S540: and optimizing the standard sample data based on the NSGA-II genetic algorithm and the constraint conditions, and optimally dividing an optimization result to obtain the optimal logistics allocation management scheme.
Further, step S530 includes:
step S531: defining a decision variable X to be optimized based on the standard sample data;
step S532: defining an objective function vector based on the decision variable X to be optimized;
step S533: and setting a constraint condition for the optimization algorithm according to the decision variable X to be optimized and the objective function vector.
Specifically, when the primary logistics deployment management scheme is optimized, specifically, data optimization may be performed based on an NSGA-II genetic algorithm embedded in the data optimization model, and the input information is optimally screened based on an optimization algorithm until an optimal logistics deployment management scheme in the input information is obtained. NSGA-II is one of the most popular multi-target genetic algorithms at present, reduces the complexity of the non-inferior ranking genetic algorithm, has the advantages of high running speed and good convergence of solution sets, and becomes the basis of the performance of other multi-target optimization algorithms.
Specifically, the primary logistics allocation management scheme is subjected to data preprocessing, specifically including preprocessing of a missing value, an abnormal value, and a special value, wherein the missing value: the missing value is the data absence of some characteristic variables in the sample, if the data is randomly missing, the sample can be deleted, or interpolation is carried out according to a missing mechanism; if data is missing non-randomly, this feature needs to be preserved and samples containing non-random missing values can be divided into the same bin. Abnormal value: the abnormal value means that the occurrence frequency of a certain class value in a class type variable is too small, or some values of interval type variables are too large. The presence of outliers can interfere with the calculation and evaluation of the model coefficients, thereby reducing the stability of the model. It is therefore necessary to separately bin samples containing outliers. Special values: if there is a particular value in the sample to be marked, the particular value needs to be extracted for individual binning, according to the research problem.
After the missing, outlier, special values are binned separately, the remaining samples are pre-binned. The pre-binning may use a simple partitioning point finding strategy, such as equidistant binning, where m partitioning points are found to divide the sample into n-m +1 bins, where the divided bins are defined as (— infinity, s) 1 ),[s 1 ,s 2 ),...,[s m ,∞)。
Furthermore, defining a decision variable X according to the n divided pre-bins, wherein X comprises a lower triangular matrix with the size of n, and X ij ∈{0，1}，
At the beginning of the matrixThe values on the diagonal are all 1, which means that all pre-binning was initially selected. The decision variable X must satisfy the following constraint: (1) each column must contain a 1, a condition that ensures that each bin in the pre-sorted bin is present and can be merged through adjacent bins, but cannot be deleted. (2) Each row in the decision variable triangular matrix takes a monotonous value and the condition ensures that the starting endpoint of the next box must be after the end of the previous box. (3) The last box must be of the form [ s ] k Infinity), k is less than or equal to n, and X is required to be satisfied nn 1. (4) Only consecutive pre-bins can be merged and non-adjacent bins cannot be merged.
And then, defining a target function vector based on the decision variable X to be optimized, wherein the target function is a two-dimensional vector consisting of negative numbers of IV and HHI, and the larger the value of each dimension of the target vector is, the better the variable binning effect is. And finally, setting a constraint condition for the optimization algorithm according to the decision variable X to be optimized and the objective function vector.
And finally, optimizing the standard sample data based on the NSGA-II genetic algorithm and the constraint conditions, and optimally dividing an optimization result to obtain the optimal logistics allocation management scheme. Specifically, in a feasible domain of an optimization problem, some decision variables X are initialized randomly as an initial population, a target vector to be optimized is used as fitness, and the fitness of the initial population is calculated; carrying out non-dominant sorting on the initial population to divide the sample into a plurality of non-dominant layers; and obtaining a first generation filial generation population through three basic operations of selection, crossing and mutation of a genetic algorithm. And from the second generation, merging the parent population and the child population, performing rapid non-dominant sorting, simultaneously performing crowding degree calculation on the individuals in each non-dominant layer, and selecting proper individuals according to the non-dominant relationship and the crowding degree of the individuals to form a new parent population. Generating a new filial generation population through basic operation of a genetic algorithm; and combining the new parent population and the new child population, and so on until the iteration times set by the genetic algorithm are reached. And performing multi-objective optimization solution according to the NSGA-II genetic algorithm to obtain a plurality of optimal solutions, selecting a final decision variable X according to the first dimension (namely IV maximization) of the objective function vector, and finally reducing the X into a corresponding division point. And the division result of the corresponding division point is the optimal logistics allocation management scheme.
Step S600: and performing dynamic tracking management on the optimal logistics allocation management scheme in the logistics transportation process based on dynamic visual monitoring nodes, wherein the dynamic visual monitoring nodes are set for indefinite points of the multiple logistics transportation management chains.
Further, as shown in fig. 2, step S600 includes:
step S610: presetting an initial monitoring node distribution chain based on the multiple logistics transportation management chain;
step S620: according to the primary logistics allocation management scheme, primary transit point distribution and primary arrival point distribution of logistics information are obtained, the primary transit point distribution is used as father monitoring node distribution, and the primary arrival point distribution is used as son monitoring node distribution;
step S630: inputting the father monitoring node distribution and the son monitoring node distribution into the initial monitoring node distribution chain for node filling, and generating a filled monitoring node distribution chain;
step S640: traversing and analyzing the optimal logistics allocation management scheme to obtain optimal transit point distribution and optimal arrival point distribution of the logistics information;
step S650: inputting the optimal transit point distribution and the optimal arrival point distribution into a logistics transportation evaluation model for training to obtain a logistics transportation efficiency evaluation result of the optimal logistics allocation management scheme;
step S660: judging whether the logistics transportation efficiency evaluation result meets the expected logistics transportation efficiency;
step S670: and if the logistics transportation efficiency evaluation result meets the expected logistics transportation efficiency, optimizing the distribution chain of the filled monitoring nodes based on the optimal transit point distribution and the optimal arrival point distribution to generate the dynamic visual monitoring nodes.
Specifically, in the logistics transportation process, any node in the transportation process can be dynamically monitored, namely, the dynamic visual monitoring node is realized based on the dynamic visual monitoring node, the dynamic visual monitoring node is not fixedly arranged but is arranged in an indefinite point, and can be correspondingly adjusted according to the transportation condition in the cargo transportation process, illustratively, when uncontrollable epidemic infectious diseases occur, the monitoring nodes in the logistics transportation process can be properly reduced, so that the transported cargoes are prevented from staying for too long time as much as possible; on the contrary, if the transported goods are perishable food, monitoring nodes in the transportation process need to be increased properly so as to avoid timely removing the deteriorated food and avoid influencing the normal storage environment of other food, and meanwhile, the monitoring nodes are utilized to optimize and improve the storage environment of the food, so that the safe and non-deteriorated transported goods can be guaranteed to arrive at the destination.
Specifically, the initial monitoring node distribution chain is the distribution of the initial monitoring nodes, which may be understood as the distribution of monitoring nodes initially set by the system, and the primary transit point distribution and the primary arrival point distribution of the logistics information are obtained through the primary logistics deployment management scheme, where the primary transit point distribution may be understood as the distribution of transit points in the logistics transportation process, which is generally a provincial city of a transit city, the primary arrival point distribution is the distribution of route sites in the cargo transportation process, and the primary transit point distribution may be used as the distribution of parent monitoring nodes and the primary arrival point distribution as the distribution of child monitoring nodes.
And inputting the father monitoring node distribution and the son monitoring node distribution into the initial monitoring node distribution chain for node filling to generate a filled monitoring node distribution chain, wherein the filled monitoring node distribution chain is a result of filling the actual cargo transportation node and the initial monitoring node, and meanwhile, in order to further optimize the filled monitoring node distribution chain, the optimized monitoring node distribution chain can be optimized and upgraded based on an optimal logistics allocation management scheme.
Specifically, the optimal logistics allocation management scheme can be traversed and analyzed to obtain the optimal transit point distribution and the optimal arrival point distribution of the logistics information, wherein the optimal transit point distribution is the freight transportation transit point distribution analyzed from the optimal logistics allocation management scheme, the optimal distribution of the arrival points is the distribution of the freight transportation route points analyzed from the optimal logistics allocation management scheme, and then inputting the optimal transit point distribution and the optimal arrival point distribution into a logistics transportation evaluation model for training to obtain a logistics transportation efficiency evaluation result of the optimal logistics deployment management scheme, wherein the logistics transportation evaluation model can carry out appropriate evaluation on the nodes in the input goods transportation process, the cargo transportation efficiency under the node distribution can be evaluated, and specifically, the efficiency can be evaluated through the cargo arrival time and the cargo arrival state.
And then, whether the logistics transportation efficiency evaluation result meets the expected logistics transportation efficiency is judged, the expected logistics transportation efficiency is that the preset goods arrival time is earlier than the planned arrival time, the goods arrival state is normal, no damage and other signs exist, if the logistics transportation efficiency evaluation result meets the expected logistics transportation efficiency, the optimized goods transportation monitoring nodes meet the expected setting, the distribution chain of the filled monitoring nodes can be optimized based on the optimal transit point distribution and the optimal arrival point distribution, and the dynamic visual monitoring nodes are generated.
Further, step S650 includes:
step S651: inputting the optimal transit point distribution and the optimal arrival point distribution as input information into a logistics transportation evaluation model, and performing identification training on the input information based on an expected transportation time period and an expected transportation safety degree;
step S652: and training the input information to a convergence state based on the logistics transportation evaluation model to obtain a logistics transportation efficiency evaluation result.
Specifically, when the optimal transit point distribution and the optimal arrival point distribution are input into a logistics transportation evaluation model for training, input information can be subjected to identification training based on an expected transportation time period and an expected transportation safety degree, wherein the expected transportation time period can be understood as a time earlier than a planned arrival time, and the expected transportation safety degree can be understood as a setting that no damage is caused in a cargo transportation process and transportation quality is high.
In summary, the goods transportation management method based on the internet of things provided by the invention has the following technical effects:
1. building a multiple logistics transportation management chain; acquiring basic information of goods to be transported to obtain logistics information to be transported; carrying out statistical management on the receiving address of the goods arrival end to obtain receiving address management data; uploading logistics information to be transported and receiving address management data to a logistics allocation system for intelligent management to obtain a primary logistics allocation management scheme; uploading the primary logistics allocation management scheme to a data optimization model, and optimally screening input information based on an optimization algorithm to obtain an optimal logistics allocation management scheme; and carrying out dynamic tracking management on the optimal logistics allocation management scheme in the logistics transportation process. Through setting up multiple commodity circulation transportation management chain, goods origin end and goods arrival end based on management chain both ends, and the maincenter commodity circulation allotment system of management chain, treat that the transportation goods carries out omnidirectional multiple allotment, and then utilize the optimization algorithm of computer, optimize elementary commodity circulation management allotment scheme, make and select optimal commodity circulation allotment management scheme, and simultaneously, through setting up dynamic visual monitoring node, carry out dynamic visual monitoring to the transportation overall process, make the problem to appear in the transportation in time discover, solve fast, reached and carried out comparatively ripe transportation allotment to the commodity circulation transportation overall process, improve the technological effect of commodity circulation transportation efficiency.
2. Through the actual transportation node of goods, fill the initialization monitoring node of system, can obtain the actual monitoring node distribution of commodity circulation transportation, simultaneously, optimize the actual monitoring node distribution based on optimal commodity circulation transportation node, can generate dynamic change's commodity circulation transportation monitoring node, realized the dynamic monitoring to the freight transportation in-process, ensure that the transportation is accurate high-efficient.
Example two
Based on the cargo transportation management method based on the internet of things in the foregoing embodiments, the invention also provides a cargo transportation management system based on the internet of things, referring to fig. 3, where the system includes:
the system comprises a first building unit 11, wherein the first building unit 11 is used for building a multiple logistics transportation management chain, one end of the multiple logistics transportation management chain is a goods initiating end, and the other end of the multiple logistics transportation management chain is a goods arriving end;
the first acquisition unit 12 is configured to acquire basic information of the cargo to be transported based on the cargo initiating end, and obtain logistics information to be transported;
the first statistical unit 13 is configured to perform statistical management on the receiving address of the cargo arrival end based on big data to obtain receiving address management data;
a first uploading unit 14, where the first uploading unit 14 is configured to upload the logistics information to be transported and the receiving address management data to a logistics deployment system for intelligent management, so as to obtain a primary logistics deployment management scheme, where the logistics deployment system is a hub of the multiple logistics transportation management chain;
the second uploading unit 15 is configured to upload the primary logistics allocation management scheme as input information to a data optimization model, and optimally filter the input information based on an optimization algorithm to obtain an optimal logistics allocation management scheme in the input information;
a first management unit 16, where the first management unit 16 is configured to perform dynamic tracking management on the optimal logistics allocation management scheme in a logistics transportation process based on a dynamic visual monitoring node, where the dynamic visual monitoring node is set for an indefinite point of the multiple logistics transportation management chain.
Further, the system further comprises:
the first preset unit is used for presetting an initial monitoring node distribution chain based on the multiple logistics transportation management chain;
a first obtaining unit, configured to obtain a primary transit point distribution and a primary arrival point distribution of logistics information according to the primary logistics allocation management scheme, use the primary transit point distribution as a parent monitoring node distribution, and use the primary arrival point distribution as a child monitoring node distribution;
and the first input unit is used for inputting the father monitoring node distribution and the son monitoring node distribution into the initial monitoring node distribution chain for node filling, and generating a filled monitoring node distribution chain.
Further, the system further comprises:
the first analysis unit is used for performing traversal analysis on the optimal logistics allocation management scheme to obtain optimal transit point distribution and optimal arrival point distribution of the logistics information;
the second input unit is used for inputting the optimal transit point distribution and the optimal arrival point distribution into a logistics transportation evaluation model for training to obtain a logistics transportation efficiency evaluation result of the optimal logistics allocation management scheme;
the first judging unit is used for judging whether the logistics transportation efficiency evaluation result meets the expected logistics transportation efficiency;
and the first optimization unit is used for optimizing the distribution chain of the filled monitoring nodes based on the optimal transit point distribution and the optimal arrival point distribution if the evaluation result of the logistics transportation efficiency meets the expected logistics transportation efficiency, so as to generate the dynamic visual monitoring nodes.
Further, the system further comprises:
a third input unit, configured to input the optimal transit point distribution and the optimal arrival point distribution as input information into a logistics transportation evaluation model, and perform identification training on the input information based on an expected transportation time period and an expected transportation safety degree;
and the second obtaining unit is used for training the input information to a convergence state based on the logistics transportation evaluation model to obtain the logistics transportation efficiency evaluation result.
Further, the system further comprises:
the first extraction unit is used for extracting key features of the logistics information to be transported to obtain article type information;
the second extraction unit is used for extracting key features of the receiving address management data to obtain a cargo assembly sequence;
and the second management unit is used for carrying out dual management on the article type information and the cargo assembly sequence based on the logistics deployment system to obtain the primary logistics deployment management scheme.
Further, the system further comprises:
a first embedding unit, wherein the first embedding unit is used for embedding the NSGA-II genetic algorithm into the data optimization model;
the first processing unit is used for carrying out data preprocessing on the primary logistics allocation management scheme and defining the processed data as standard sample data;
a first setting unit, configured to set a constraint condition for the optimization algorithm according to the standard sample data;
and the second optimization unit is used for optimizing the standard sample data based on the NSGA-II genetic algorithm and the constraint conditions, and optimally dividing an optimization result to obtain the optimal logistics allocation management scheme.
Further, the system further comprises:
a first defining unit for defining a decision variable X to be optimized based on the standard sample data;
a second defining unit, configured to define an objective function vector based on the decision variable X to be optimized;
and the second setting unit is used for setting a constraint condition for the optimization algorithm according to the decision variable X to be optimized and the objective function vector.
In the present description, each embodiment is described in a progressive manner, and the focus of the description of each embodiment is on the difference from other embodiments, and the foregoing cargo transportation management method based on the internet of things in the first embodiment of fig. 1 and the specific example are also applicable to the cargo transportation management system based on the internet of things of this embodiment. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Exemplary electronic device
The electronic device of the present invention is described below with reference to fig. 4.
Fig. 4 illustrates a schematic structural diagram of an electronic device according to the present invention.
Based on the inventive concept of the cargo transportation management method based on the internet of things in the foregoing embodiments, the invention further provides a cargo transportation management system based on the internet of things, on which a computer program is stored, and the program, when executed by a processor, implements the steps of any one of the foregoing cargo transportation management methods based on the internet of things.
Where in fig. 4 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
The invention provides a goods transportation management method based on the Internet of things, which is characterized by comprising the following steps: building a multiple logistics transportation management chain, wherein one end of the multiple logistics transportation management chain is a goods initiating end, and the other end of the multiple logistics transportation management chain is a goods arriving end; based on the goods initiating terminal, basic information acquisition is carried out on the goods to be transported, and logistics information to be transported is obtained; carrying out statistical management on the receiving address of the goods arrival end based on the big data to obtain receiving address management data; uploading the logistics information to be transported and the receiving address management data to a logistics allocation system for intelligent management to obtain a primary logistics allocation management scheme, wherein the logistics allocation system is a central terminal of the multiple logistics transportation management chain; uploading the primary logistics allocation management scheme serving as input information to a data optimization model, and optimally screening the input information based on an optimization algorithm to obtain an optimal logistics allocation management scheme in the input information; and performing dynamic tracking management on the optimal logistics allocation management scheme in the logistics transportation process based on dynamic visual monitoring nodes, wherein the dynamic visual monitoring nodes are set for indefinite points of the multiple logistics transportation management chains. The problem of prior art exist can't carry out comparatively ripe all-round transportation allotment to the commodity circulation transportation for when the commodity circulation was in a busy season, the storehouse appears exploding, the goods can't dredge the technical problem of allotment. Through setting up multiple commodity circulation transportation management chain, goods origin end and goods arrival end based on management chain both ends, and the maincenter commodity circulation allotment system of management chain, treat that the transportation goods carries out omnidirectional multiple allotment, and then utilize the optimization algorithm of computer, optimize elementary commodity circulation management allotment scheme, make and select optimal commodity circulation allotment management scheme, and simultaneously, through setting up dynamic visual monitoring node, carry out dynamic visual monitoring to the transportation overall process, make the problem to appear in the transportation in time discover, solve fast, reached and carried out comparatively ripe transportation allotment to the commodity circulation transportation overall process, improve the technological effect of commodity circulation transportation efficiency.
The invention also provides an electronic device, which comprises a processor and a memory;
the memory is used for storing;
the processor is configured to execute the method according to any one of the first embodiment through calling.
The invention also provides a computer program product comprising a computer program and/or instructions which, when executed by a processor, performs the steps of the method of any of the above embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely software embodiment, an entirely hardware embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention is in the form of a computer program product that may be embodied on one or more computer-usable storage media having computer-usable program code embodied therewith. And such computer-usable storage media include, but are not limited to: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk Memory, a Compact Disc Read-Only Memory (CD-ROM), and an optical Memory.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the present invention and its equivalent technology, it is intended that the present invention also include such modifications and variations.
Claims (10)
1. A cargo transportation management method based on the Internet of things is characterized by comprising the following steps:
building a multiple logistics transportation management chain, wherein one end of the multiple logistics transportation management chain is a goods initiating end, and the other end of the multiple logistics transportation management chain is a goods arriving end;
based on the goods initiating terminal, basic information acquisition is carried out on the goods to be transported, and logistics information to be transported is obtained;
carrying out statistical management on the receiving address of the goods arrival end based on the big data to obtain receiving address management data;
uploading the logistics information to be transported and the receiving address management data to a logistics allocation system for intelligent management to obtain a primary logistics allocation management scheme, wherein the logistics allocation system is a central terminal of the multiple logistics transportation management chain;
uploading the primary logistics allocation management scheme serving as input information to a data optimization model, and optimally screening the input information based on an optimization algorithm to obtain an optimal logistics allocation management scheme in the input information;
and performing dynamic tracking management on the optimal logistics allocation management scheme in the logistics transportation process based on dynamic visual monitoring nodes, wherein the dynamic visual monitoring nodes are set for indefinite points of the multiple logistics transportation management chains.
2. The method of claim 1, wherein the method comprises:
presetting an initial monitoring node distribution chain based on the multiple logistics transportation management chain;
according to the primary logistics allocation management scheme, primary transit point distribution and primary arrival point distribution of logistics information are obtained, the primary transit point distribution is used as father monitoring node distribution, and the primary arrival point distribution is used as son monitoring node distribution;
and inputting the father monitoring node distribution and the son monitoring node distribution into the initial monitoring node distribution chain for node filling, and generating a filled monitoring node distribution chain.
3. The method of claim 2, wherein the method comprises:
traversing and analyzing the optimal logistics allocation management scheme to obtain optimal transit point distribution and optimal arrival point distribution of the logistics information;
inputting the optimal transit point distribution and the optimal arrival point distribution into a logistics transportation evaluation model for training to obtain a logistics transportation efficiency evaluation result of the optimal logistics allocation management scheme;
judging whether the logistics transportation efficiency evaluation result meets the expected logistics transportation efficiency;
and if the logistics transportation efficiency evaluation result meets the expected logistics transportation efficiency, optimizing the distribution chain of the filled monitoring nodes based on the optimal transit point distribution and the optimal arrival point distribution to generate the dynamic visual monitoring nodes.
4. The method of claim 3, wherein the method comprises:
inputting the optimal transit point distribution and the optimal arrival point distribution as input information into a logistics transportation evaluation model, and performing identification training on the input information based on an expected transportation time period and an expected transportation safety degree;
and training the input information to a convergence state based on the logistics transportation evaluation model to obtain a logistics transportation efficiency evaluation result.
5. The method of claim 4, wherein the method comprises:
extracting key features of the logistics information to be transported to obtain article type information;
extracting key features of the goods receiving address management data to obtain a goods assembly sequence;
and based on the logistics deployment system, carrying out dual management on the article type information and the cargo assembly sequence to obtain the primary logistics deployment management scheme.
6. The method of claim 5, wherein the method comprises:
the data optimization model is embedded with an NSGA-II genetic algorithm;
performing data preprocessing on the primary logistics allocation management scheme, and defining the processed data as standard sample data;
setting a constraint condition for the optimization algorithm according to the standard sample data;
and optimizing the standard sample data based on the NSGA-II genetic algorithm and the constraint conditions, and optimally dividing an optimization result to obtain the optimal logistics allocation management scheme.
7. The method of claim 6, wherein the method comprises:
defining a decision variable X to be optimized based on the standard sample data;
defining an objective function vector based on the decision variable X to be optimized;
and setting a constraint condition for the optimization algorithm according to the decision variable X to be optimized and the objective function vector.
8. A cargo transportation management system based on the Internet of things, the system comprising:
the system comprises a first building unit, a second building unit and a third building unit, wherein the first building unit is used for building a multiple logistics transportation management chain, one end of the multiple logistics transportation management chain is a goods starting end, and the other end of the multiple logistics transportation management chain is a goods arrival end;
the first acquisition unit is used for acquiring basic information of the goods to be transported based on the goods initiating end to obtain logistics information to be transported;
the first statistical unit is used for carrying out statistical management on the receiving address of the goods arrival end based on big data to obtain receiving address management data;
the first uploading unit is used for uploading the logistics information to be transported and the receiving address management data to a logistics allocation system for intelligent management to obtain a primary logistics allocation management scheme, wherein the logistics allocation system is a central terminal of the multiple logistics transportation management chain;
the second uploading unit is used for uploading the primary logistics allocation management scheme serving as input information to a data optimization model, and optimally screening the input information based on an optimization algorithm to obtain an optimal logistics allocation management scheme in the input information;
and the first management unit is used for carrying out dynamic tracking management on the optimal logistics allocation management scheme in the logistics transportation process based on dynamic visual monitoring nodes, wherein the dynamic visual monitoring nodes are set for indefinite points of the multiple logistics transportation management chains.
9. An electronic device comprising a processor and a memory;
the memory is used for storing;
the processor is used for executing the method of any one of claims 1-7 through calling.
10. A computer program product comprising a computer program and/or instructions, characterized in that the computer program and/or instructions, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.
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