CN111368999A - Logistics scheduling system - Google Patents

Abstract

The invention discloses a logistics scheduling system, which comprises a delivery storage system of a delivery point, a transportation management system, a transfer system of a transfer station and a receiving storage system of a receiving point, wherein the delivery storage system of the delivery point is in data networking with a central scheduling system; the transportation management system comprises a transportation scheduling system and a transportation tool; the transfer system comprises a transfer distribution system and a transfer loading area. Establishing different models from 2 aspects to carry out transportation scheduling on materials, wherein one mode is to take a transportation scheme of the materials as a transportation scheme and establish corresponding operation rules to realize optimization of the scheduling scheme of the materials; and the other is that the materials are effectively tracked according to the dynamic information of the materials when the materials are dynamic, so that the early warning analysis and protection of the transported materials are effectively realized.

Description

Logistics scheduling system

Technical Field

The invention relates to the technical field of logistics transportation, in particular to a high-pressure hydrogenation large-caliber super-wall-thickness seamless three-way pipe fitting.

Background

Along with the development of economic society and the development of electronic commerce, the logistics transportation amount is larger and larger, so the logistics scheduling becomes an important problem of social life of economic development, various materials need to be uniformly arranged and transported, but the materials are complicated and disordered in the actual transportation process, a genetic algorithm is based on the principle of high or low elimination in self science and then is introduced into an optimization algorithm, the genetic algorithm has obvious advantages in processing disordered individual operation, genetic operations in the evolution process comprise coding, selection, crossing, variation and survival selection of suitable persons, function derivation and requirement function continuity are not needed, the phenomena of propagation, crossing, gene mutation and the like in the natural selection and natural genetic process are simulated, a group of candidate solutions are reserved in each iteration, and the better individual is selected from a solution group according to a certain index, the individuals are combined by genetic operators (selection, crossing and variation) to generate a new generation of candidate solution group, the process is repeated until a certain convergence index is met, the fitness function is taken as the basis, and the population individuals are continuously subjected to genetic operation to realize the optimization of the population individuals generation by generation and gradually approach to the optimal solution. The genetic algorithm realizes the gene based on the codes, and then is integrated into a scheme to link the gene segments into chromosomes, and the fitness of each individual to the clustering problem is measured by constructing a fitness function, namely if the codes of a certain individual represent good clustering results, the fitness is high; otherwise, its fitness is low. The fitness function is similar to the action of the environment in the organism evolution process, individuals with high fitness generate more offspring in the reproduction process of one generation and the next generation, and individuals with low fitness gradually die; however, the genetic algorithm has the defects of low calculation efficiency, easy falling into local optimization, difficult convergence and the like, and can cause premature convergence or a large amount of iterative recalculation, in the modern logistics industry, the object flow is more and more large, the method is very consistent with the basic code of the genetic algorithm, the sorting is carried out from an unordered state, how to rapidly and effectively schedule vehicles and schedule vehicles as few as possible, the method is obviously a difficult problem measured among the distance, the delivery time and the economic suitability, and the method cannot be solved by the genetic algorithm.

However, in the transportation process of the materials, after a transportation scheme is established for a single transportation situation of a certain material, the tracking of the certain material also becomes a big problem after a transportation scheme is established for a large amount of materials, for example, the materials are mistaken for a transport vehicle from a delivery station, or the materials need to change a receiving point in the middle of transportation, so that the materials become a special case, but in the special case, if the receiving point is changed again to deliver the materials before the scheduled receiving point, the transportation time is prolonged, and the transportation resources are wasted. Therefore, for material transportation, dynamic tracking needs to be performed on a single material so as to realize early warning, analysis and processing on logistics scheduling transportation.

Disclosure of Invention

The present invention aims to provide a logistics scheduling system for solving the above problems; the system comprises a delivery storage system of a delivery point, a transportation management system, a dynamic logistics analysis and protection system, a transfer system of a transfer station and a receiving storage system of a receiving point, wherein the delivery storage system of the delivery point is in data networking with a central dispatching system; the delivery warehousing system comprises a metering system, a delivery system and a delivery loading area; the transportation management system comprises a transportation scheduling system and a transportation tool; the transfer system comprises a transfer distribution system and a transfer loading area; the receiving warehousing system comprises a receiving feedback end and a receiving bin; the transportation ring in the logistics process is nodularized, so that coding operation and coding retrieval are facilitated;

the metering system measures the weight and the volume of the materials, generates a unique identification code according to the weight and the volume of the materials and the information of a receiving place, and sends the unique identification code to the dispatching system; the goods distribution system identifies the unique identification code of the goods and materials to acquire receiving place information and classifies the goods and materials to be transmitted to a delivery loading area; the delivery loading section stores materials of different destinations;

the unique identification code enables the goods and materials to have identity information, whether goods and materials transportation links are correct or not can be judged and corrected quickly, the experienced links of the goods and materials in the transportation process send retrieval information to the central dispatching system through the unique identification code, the central dispatching system is formed to track the goods and materials effectively, most importantly, the central dispatching system collects basic information of the goods and materials through the unique identification code, such as the weight, the volume, the delivery point, the receiving point and the like of the goods and materials, and a coding basis for logistics scheme operation is provided.

The transportation scheduling system schedules the transportation tool according to the central scheduling system and reports the position of the transportation tool to the central scheduling system;

the transfer cargo distribution system identifies the material unique identification code to acquire receiving place information and classifies the material to be transmitted to a transfer loading area; the transfer loading section stores materials of different receiving points;

the dynamic logistics analysis protection system comprises a data set and a dynamic logistics analysis protection method; the dynamic logistics analysis protection method comprises the following steps:

s11, collecting dynamic logistics data to form a data set;

s12, carrying out region classification on the data set obtained in the step S11;

s13, performing secondary regression through sub-classification conversion on the data set obtained in the step S12;

s14, forming a Wolfe type dual rule of the multi-target score programming, and carrying out data self-repairing protection on the data set obtained in the step S13;

s15, forming a data early warning analysis function, and carrying out early warning analysis on the dynamic logistics data set obtained in the step S14;

the goods receiving feedback end confirms goods receiving of goods through scanning the unique identification code; the goods receiving bin stores goods and materials;

and the central dispatching system records and calculates data uploaded by the delivery storage system, the transportation management system, the dynamic logistics analysis and protection system, the transfer system and the receiving storage system.

Aiming at further improvement of the scheme:

further, the method comprises the following steps of; the central dispatching system calculates the uploaded data, and the calculation method comprises a transportation dispatching method based on a genetic algorithm and a neural network algorithm, wherein the transportation dispatching method comprises the following steps:

a1, establishing a data module, wherein the parameters comprise material information, delivery point information, transfer station information, receiving point information and transport means information; the relevant symbols are as follows:

m: material supply; the material comprises Ma material weight; mb material volume; when the goods and materials are dispatched, the weight Ma and the volume Mb of the goods and materials are limited from 2 dimensions, and the carrying capacity of the transport vehicle at one time is limited by the weight or the volume;

n: a delivery point comprising n₁,n₂,n₃… … n }; the delivery point is used as an important gene of the material and is compiled into the gene, the delivery amount of a certain delivery point can be quickly searched, and the delivery amount of the certain delivery point is calculated; thus, the transportation means can be calculated and selected and can be programmed as genes;

q: a transfer station comprising { q₁,q₂,q₃… … q }; in modern logistics, the transfer station plays a crucial role, and the materials of each delivery point are rearranged and sent to each receiving point according to the receiving points, so that the centralized receiving and sending functions of the materials are realized, and a large amount of repeated transportation is avoided;

m: a receiving point comprising { m₁,m₂,m₃… … m }; the receiving points are used as another important gene of the materials, the delivery points and the receiving points form a most basic chromosome (namely a material transportation scheme), the receiving amount of a certain receiving point can be quickly searched, the receiving amount of a certain receiving point is calculated, and accordingly a transport can be calculated and selected, and the transport is coded as the gene;

d: a vehicle; including { D₁,D₂,D₃… … D }; the carrier is loaded into chromosome as a gene fragment, comprising 2 processes, carrier selection from delivery point to transfer station, carrier selection from transfer station to receiving station;

r: a transport path; the transportation route is used as a mode for evaluating the transportation scheme, a delivery point is used as a starting point, a receiving point is used as an end point, and the transportation route passes through a transfer station or does not pass through the transfer station; the transport path has vectorial properties, that is, the transport is in a point-to-point direction, and for a stable logistics system, the delivery point n and the receiving point m are the same set, and a delivery point can be used as a receiving point of another delivery point, even a delivery point can be used as a receiving point of the delivery point.

t: the time is long; the time length is taken as the sum of the single material scheduling transportation time and is taken as an important evaluation parameter for evaluating the actual efficiency of the freight scheme (chromosome);

e: the amount is economic and suitable; the economic applicable quantity is used as an evaluation value which adopts the least transportation means and the shortest transportation path for the unit total cargo quantity, and is used for evaluating R, D, t and evaluating a fitness function; the later iteration stability of the genetic algorithm is realized, and a large amount of iterations are avoided;

a2, generating an initial population G; the starting population comprises N chromosomes; one chromosome is a transportation scheme of materials; the chromosome encodes the material M (Ma and/or Mb) according to any length and features selected as required to generate a material scheduling sequence; the chromosome comprises a plurality of genes, and the gene segments comprise one or more of n, m, q, R and D; and the following conditions are required to be satisfied:

f(D)≥Man_xm_yor f (D) ≧ Mbn_xm_y；x∈n,y∈m；

Initially encoding the weight or volume of the basic material of the chromosome; the same material can also be coded in 2 types based on weight and volume, so that the gene quantity of a population is enlarged, the same material is coded from 2 dimensions, and the material information can be more accurately expressed; the transportation tool can not exceed the rated carrying weight or the rated volume which can be carried by the transportation tool every time of transportation;

a3, constructing a neural network; the metering system of the delivery point is a neural network input end, the delivery feedback end of the delivery point is an output end, and the transfer station is a hidden layer; namely, the neural network is provided with n network input ends, m output ends and q neurons; the mapping relation of the chromosomes is { n }₁,n₂,n₃……n}→{q_xn₁,q_xn₂,q_xn₃……q_xn}→{q_xq_yn₁,q_xq_yn₂,q_xq_yn₃……q_xq_yn}→{m₁,m₂,m₃… … m }; wherein q is more than or equal to x is more than or equal to 1; q is more than or equal to y is more than or equal to 1; the chromosome is input and output through a neural network to form a transportation method of the chromosomeCase;

an artificial neural network is a second way of simulating human thinking; the system is a nonlinear system and is characterized in that distributed storage and parallel cooperative processing of information are realized, although a single neuron has extremely simple structure and limited functions, the behavior realized by a network system formed by a large number of neurons simulates the normal behavior of logistics scheduling, and the neural network can adapt to the environment and summarize the rule and complete certain operation, identification or process control instead of executing operation step by step according to a given program. The neural network is trained through a large amount of data to obtain a mapping relation between input and output, the weight and the threshold value of the network are continuously adjusted through a gradient descent method to enable the error of the network to be minimum, the neural network algorithm is implanted into a genetic algorithm to form large-amplitude subtraction operation of the neural network algorithm on the genetic algorithm, the neural network is based on the gradient descent method, the convergence speed is low, and an error function is easy to fall into a local minimum value. The genetic algorithm searches from the population, does not find the optimal solution from one point, and therefore has good global optimization capability. The neural network is implanted into the genetic algorithm, so that the genetic algorithm is optimized, the operation data of the genetic algorithm is reduced, the iteration times are reduced, and the optimal solution can be obtained more quickly.

A4, starting iteration, and executing the following steps:

s21, carrying out cross operation on chromosomes in the initial population according to the cross probability, and randomly exchanging gene segments in the cross process to form a population G1; the population G1 comprises a coding gene set based on the weight and volume of the material, and the sequence combination of n, m, q and D is carried out by taking Ma and Mb as the starting points of the gene segments; after crossing, the chromosome contains a plurality of genes in uncertain disorder states to form a plurality of scheduling schemes;

s22, evaluating the fitness of the population G1 according to the dispatching targets R, t and e; obtaining a population G2; setting a scheduling target value and fitness evaluation functions of R, t and e, and screening genes in a population G1 to obtain an optimized population G2;

s23, carrying out mutation operation on the population G2 according to the mutation probability; in order to avoid premature convergence or early stabilization of chromosomes in the population, the population G2 is subjected to mutation operation, so that the genes of the chromosomes are mutated, and the types of the chromosomes are enriched;

s24, carrying out cross operation on chromosomes in the population G2 in the step S23 according to cross probability, and randomly exchanging gene segments in the cross process to form a population G3; the gene mutation has uncertain factors, the mutation rate can be set, the mutation form is uncontrollable, the gene of the chromosome is crossed, the mutated gene is better stored, and the first screening after mutation is prevented from being eliminated by a fitness evaluation function;

s25, evaluating the fitness of the population G3 according to the dispatching targets R, t and e; obtaining a population G4; evaluating the fitness of the mutated and cross-recombined chromosome again to reduce the calculation amount; thus, the first iterative operation of the chromosome is completed;

s26, repeating the population iteration to obtain a population Gi, and judging whether a convergence optimization result is achieved; if yes, the iteration is ended, and if not, the iteration is continued. And (5) iterating the population for multiple times, evaluating the chromosome and the target value, and judging whether the expected value of convergence optimization is reached.

Further, the method comprises the following steps of; the economic applicable amount is the unit total cargo amount and adopts the minimum transport means and the evaluation value with the shortest transport path;

λ is an integer and λ ratio

Is greater than 1, Δ D_xIs D_xThe transportation volume of (2);

how many transport vehicles are required for transporting a certain material for a unit transport means, lambda ratio

The integer of the number is larger than 1, namely the vehicle is actually adjusted to meet the transportation of the materials,

the transportation mode of the transportation vehicle is matched from 2 dimensions of the weight or the volume of the material, namely, less transportation vehicles are needed from the perspective of the weight or the volume 2.

Allocating the probability P that the economically applicable amount is selected according to the sequence:

matching the required values according to the selected probability P, wherein P is used as a fitness function of the second level to schedule materials and exert the carrying capacity of a transport tool (vehicle) to the maximum, and the probability P reflects the closest matching value, so that the former P can meet the next selected P, the residual quantity is the minimum, namely the vehicle transportation allowance is the minimum, and the carrying maximum is realized;

setting a weight function (1): u ═ p epsilon₁(ii) a The epsilon₁As a weight parameter, epsilon₁∈[0,1](ii) a And setting a weight function for balancing the convergence of the economic availability function and the probability P function of distributing the selected economic availability in the fitness evaluation transportation scheduling method.

Further, the method comprises the following steps of; the chromosome is a threshold value in a neural network population, and the population of each iteration is a connection weight of the neural network;

g ═ nq + qm + q + m; the connection weight is a gene numerical value of the population, the connection weight of the gene is calculated, and the weight or the threshold of the neural network is continuously adjusted, so that the error of the neural network is minimized, and the identification precision of the network is improved;

the chromosome of the connection weight is endowed to a neural network, and the square sum Q of an expected value y and an output value o of the chromosome is calculated;

wherein i ∈ m;

and endowing the connection weight in the population to a neural network, and calculating the square sum of the output value of the ith receiving point and the error of the output value, wherein the smaller the square sum of the errors is, the smaller the fitness function value is, and the better the chromosome scheduling scheme is.

When the goods and materials sent by a certain delivery point can be delivered to a receiving point without passing through a transfer station, the smaller the Q value is, the more stable the Q value is, even when the delivery point and the receiving point of the goods and materials are the same station, the Q value is 0, namely, the transportation is not needed.

The chromosome variance is therefore: s²Q/f; wherein f is a degree of freedom; and (3) subjecting the error sum of squares to variance, measuring the difference degree of the chromosomes, the degree of variance reaction and center deviation by using a standard deviation, and measuring the fluctuation size of the chromosomes (namely the deviation size of the data from the average), wherein under the condition that the sample connection weight is the same, the larger the variance is, the larger the fluctuation of the chromosomes is, the more unstable the chromosome is, and on the contrary, the smaller the variance is, the smaller the fluctuation of the chromosomes is, the more stable the chromosome is. f is used as a degree of freedom and can be adjusted according to expected values to match iteration of the genetic algorithm, so that premature convergence is avoided.

Setting a weight function (2): u ═ S epsilon₂(ii) a The epsilon₂As a weight parameter, epsilon₂∈[0,1]. The right function is set for balancing convergence of the chromosome error sum-of-squares function and the chromosome variance function in fitness evaluation, and the weight function is set for each fitness evaluation function, so that the convergence and the iteration times of the genetic algorithm in the operation process of the central scheduling system can be guided integrally, and premature convergence or too many iteration times are avoided.

Further, the method comprises the following steps of; genes { n ] of chromosomes in the fitness evaluation step₁,n₂,n₃… … n } no more than 3, i.e., no more than 3 delivery points in a gene at most; avoiding multiple consignments of a transport at a point of delivery, genes { n } in the chromosome₁,n₂,n₃… … n, so that a certain transport vehicle can only go to 3 delivery points at most, thereby improving the receiving efficiency.

The minimum value of chromosome R is:

min(R)＝L₁|n_x-n_x+1|+L₂|n_x+1-n_x+2|+L₃|n_x+2-n_x+3|+L₄|n_x+3-q_y|+L₅|q_y-m_zl, |; wherein L is₁,L₂,L₃,L₄,L₅Is a constant value; i.e. the actual distance from one point to another;

wherein x ∈ n, y ∈ q and z ∈ m, the chromosome expresses a transportation scheme, the path from a certain delivery point to another delivery point is constant, the path from a certain delivery point to a certain transfer station is constant, and the path from a certain transfer station to a certain receiving point is constant, so that the minimum path value of the chromosome can be calculated to measure the minimum transportation distance of the goods and materials.

Set weight function (3): u ═ min (R) ε₃(ii) a The epsilon₃As a weight parameter, epsilon₃∈[0,1]。

Further, the method comprises the following steps of; the minimum value of the chromosome time length t is:

(t) min (r)/v- ρ Δ t; delta t is the loading time of the delivery point, rho is the loading frequency, and rho is less than or equal to 3; since the transport route of the chromosome is constant, the transport time can be calculated assuming that the transport speed is constant, and the gene expression information of the chromosome includes 1 delivery point or a plurality of delivery points, the number ρ of times of loading on the chromosome is also an exact value, and the time from the delivery point to the transfer station on the chromosome can be confirmed assuming that the loading time per delivery point is Δ t. The fitness function is used to evaluate shipping efficiency.

Set weight function (4): u ═ f (t) epsilon₄(ii) a The epsilon₄As a weight parameter, epsilon₄∈[0,1]。

Further, the method comprises the following steps of; genes { m ] of chromosomes in the fitness evaluation step₁,m₂,m₃… … m } no more than 3, i.e., no more than 3 delivery points in a gene at most;

the minimum value of chromosome R is:

min(R)＝L₁|n_j-q_u|+L₂|q_u-m_h|+L₃|m_h-m_h+1|+L₄|m_h+1-m_h+2|+L₅|m_h+2-m_h+3l, |; wherein: l is₁,L₂L₃,L₄,L₅Is a fixed value, i.e. the actual distance from one point to another;

wherein j ∈ n, u ∈ q and h ∈ m, the chromosome expresses a transportation scheme, the path from a certain delivery point to a certain transfer station is also certain, the path from a certain transfer station to a certain receiving point is also certain, and the path from a certain delivery point to another receiving point is also certain, so that the minimum path value of the chromosome can be calculated to measure the minimum transportation path of the material.

Setting a weight function (5): u ═ min (R) ε₅(ii) a The epsilon₅As a weight parameter, epsilon₅∈[0,1]。

Further, the method comprises the following steps of; the minimum value of the chromosome time length t is:

f(t)＝min(R)/v-ρΔt¹；Δt¹loading time for a delivery point, wherein rho is the loading frequency and is less than or equal to 3; since the transport route of the chromosome is constant, the transport time can be calculated assuming a constant transport speed, and the gene expression information of the chromosome includes 1 receiving point or a plurality of receiving points, the number of times ρ of loading on the chromosome is also an exact value, and the loading time per delivery point is assumed to be Δ t¹Therefore, the time from the delivery point to the transfer station in the chromosome can be confirmed. The fitness function is used to evaluate shipping efficiency.

Furthermore, the population after each iteration in the genetic algorithm can be added into the previous population for operation, so that premature convergence and inaccurate data are avoided. For example, the generated population obtained after the G1 operation is added into the G1 again to obtain a population G2.

Setting a weight function (6): u ═ f (t) epsilon₆(ii) a The epsilon₆As a weight parameter, epsilon₆∈[0,1]。

Further, the method comprises the following steps of; the central dispatch system confirms the position of the material during transportation by scanning the unique identification. The unique identification code is provided with logistics information, the progress of each step of operation of the materials is sent to the central dispatching system by retrieving the unique identification code, tracking of the materials is formed, meanwhile, whether errors occur in the logistics transportation process can be timely judged, in the material circulation process, the metering system, the goods distribution system, the delivery loading section, the transportation tool, the transfer goods distribution system, the transfer loading section, the receiving feedback end and the receiving bin are all provided with a quick code scanning device which is used for retrieving the unique identification code of the materials and sending the retrieved information to the central dispatching system, the central dispatching system establishes a database for classification management, calculates related data, and sends the calculation result to the delivery system of the delivery point, the transportation management system, the transfer system of the transfer station and the receiving and warehousing system of the receiving point to complete dispatching.

Further, the method comprises the following steps of; the data set acquired in the step S11 is:

{n_i,yⁱ}，i＝1,2,....,l.

wherein n is_iFor geographical location information of the collected dynamic logistics, yⁱTime information of the collected dynamic logistics; the data set well records the dynamic geographic information of the materials and the acquisition time point, and the data difference matching is carried out by reading the mined data results once and continuously outputting according to the data inflow sequence, so that the materials among different stations form corresponding data tracking.

Further, the method comprises the following steps of; the region classification formula in the step S12 is:

f(n)＝sng(ω·n+b)

wherein ω is a region weighted additional coefficient, n is the data set, and b is an error term; data can be effectively managed by establishing binary regression, a penalty factor is added with a weighting item by C, the regularity of the data is guaranteed by adding the penalty factor by C, and invalid data in a data set is removed.

Further, the method comprises the following steps of; the formula of the secondary regression of the sub-classification conversion in the step S13 is as follows:

s.t.y_i(ω·x+b)≥1+ξ_i；

wherein, ξ_iThe method comprises the steps of determining the average value of the average values of the lagrangian multipliers α, wherein the average value of the average values of_iAnd α_jThe data set is subjected to double planning, after the double planning is adopted, the dimensionality of the data set supporting the SVM and the input samples is separated, so that the occurrence of a dimensionality disaster is avoided, further, the effective data can be subjected to self-repairing through the Wolfe type dual rule of multi-objective fractional planning, and the integrity of the data is guaranteed.

Further, the method comprises the following steps of; the formula of the Wolfe formula dual rule for forming the multi-objective score programming in the step S14 is as follows:

α therein_iAnd α_jRepresenting the lagrange multiplier.

Further, the method comprises the following steps of; forming a linear function

Aiming at different requirements, linear functions can be selected for early warning analysis or nonlinear functions can be selected for early warning analysis, and when nonlinear function early warning analysis is carried out, a data set { n ] is given_i,yⁱA non-linear function (n)_i,n_j) And nonlinear limitation is carried out, and early warning analysis is carried out in a limited range, so that the analysis effect is more targeted and effective.

Further, the method comprises the following steps of; forming a non-linear function

α therein_iAnd α_jRepresenting the lagrange multiplier. According to the invention, based on SVM dynamic logistics big data early warning analysis and protection algorithm, the logistics intelligentization degree and the informatization level can be effectively improved, the early warning and protection are effectively carried out on the information of the logistics area, the invalid information including boundary isolated point identification and ghost image identification are reduced, and the movement is improvedThe state logistics trend and the regional selection pertinence identify and filter invalid information, and the data management efficiency is improved.

Acquiring geographical position information of materials and acquiring time information of dynamic logistics by acquiring a dynamic logistics data set; the method realizes the acquisition of the information of the materials, forms a new dynamic logistics data set according to different stations reached in the material transportation process, and then carries out data differential comparison to track the individual data as a whole.

Has the advantages that:

1. the invention can identify the materials in each transportation link of a metering system, a distribution system, a delivery loading interval, a transportation tool, a transfer distribution system, a transfer loading interval, a receiving feedback end and a receiving bin by gene coding of the materials, and immediately stop delivering the materials for correction operation if the materials are identified to be wrong.

2. Establishing different models from 2 aspects to carry out transportation scheduling on materials, wherein one mode is to take a transportation scheme of the materials as a transportation scheme and establish corresponding operation rules to realize optimization of the scheduling scheme of the materials; and the other is that the materials are effectively tracked according to the dynamic information of the materials when the materials are dynamic, so that the early warning analysis and protection of the transported materials are effectively realized, and compared with the method that corresponding data are obtained through scanning identification codes one by one, the dynamic logistics analysis protection system can check and track the transported materials more quickly and accurately.

3. The neural network algorithm is added into the genetic algorithm, so that the defects that the calculation efficiency in the genetic algorithm is low, the genetic algorithm is easy to fall into local optimum, the convergence is difficult and the like are avoided, and premature convergence or a large amount of iterative recalculation is avoided.

4. The weight and the threshold value of the network are continuously adjusted through a gradient descent method, so that the network error is minimized, the neural network algorithm is implanted into the genetic algorithm, the large-amplitude subtraction operation of the neural network algorithm on the genetic algorithm is formed, the genetic algorithm is optimized, the operation data of the genetic algorithm is reduced, the iteration times are reduced, and the optimal solution can be obtained more quickly.

5. And evaluating the chromosome through a multi-layer fitness evaluation function, wherein a path function, a sorted and selected probability P function, a square sum Q function of an expected value y and an output value o of the chromosome, a variance function of the chromosome, a time length t, a minimum value function of the chromosome R and an economic suitability e function are used for evaluating the chromosome, and a scheme is rapidly selected from multiple dimensions.

6. The fitness evaluation function evaluates from two opposite angles of the economic applicable amount e and the time length t, the transportation time is long, 2 opposite functions are mutually balanced, and a fast and economic logistics scheduling scheme is realized.

7. The fitness evaluation function is set to be a weight function, the weight coefficient is a number between 0 and 1, the algorithm function of each evaluation function on the transportation scheme is balanced through the adjustment of the weight function, and premature convergence and excessive iteration times are avoided.

8. The setting of the weighting function can be realized by adjusting other objective factors through the weighting coefficient, for example, the weather is bad, and the weighting coefficient with the time length t is increased to adapt to the lengthening of the transportation time in the actual transportation process.

9. Through the transportation scheme that a chromosome expresses a certain material, carry out gene coding with delivery point, transfer station, transport means and receiving point in the transportation for the chromosome has multiple gene, enriched the transportation scheme, the population of logistics scheduling has been enlarged, and simultaneously, delivery point, transfer station, and receiving point have constituted neural network's input, hidden layer and output, through adopting the mode of coding to encode the network connection weight, constantly adjust neural network's weight or threshold value, make neural network error reach the minimum, promote the identification precision of network. And performing error sum-of-squares and variance operations to quickly evaluate the degree of dominance and reaction of the scheduling scheme of the chromosome and the degree of center deviation and measure the fluctuation size of the chromosome.

10. Reading and continuous data output are carried out according to inflow data by establishing a dynamic database of materials, the regularity of the data is ensured by adding a penalty factor C, invalid information including boundary isolated point identification and ghost image identification is reduced, and two Lagrange multipliers α are adopted for the data_iAnd α_jThe data set is subjected to double planning, after the double planning is adopted, the dimensionality of the data set supporting the input samples is separated, so that the occurrence of a dimensionality disaster is avoided, further, the effective data can be subjected to self-repairing through the Wolfe type dual rule of multi-objective fractional planning, and the integrity of the data is guaranteed.

11. Aiming at different requirements, linear functions can be selected for early warning analysis or nonlinear functions can be selected for early warning analysis, and when nonlinear function early warning analysis is carried out, a data set { n ] is given_i,yⁱA non-linear function (n)_i,n_j) And nonlinear limitation is carried out, and early warning analysis is carried out in a limited range, so that the analysis effect is more targeted and effective.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings 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 some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic diagram of a transportation scheduling method and structure according to the present invention.

Fig. 2 is a structural diagram of a fitness function of the transportation scheduling method of the present invention.

Fig. 3 is an iterative structure diagram of embodiment 1 of the present invention.

Fig. 4 is an iterative structure diagram of embodiment 2 of the present invention.

FIG. 5 is a schematic diagram of chromosome crossing operation of the present invention.

FIG. 6 is a schematic flow diagram of the present invention.

Fig. 7 is a schematic flow diagram of the dynamic logistics analysis protection method of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.

The technical solutions of the embodiments of the present invention can be combined, and the technical features of the embodiments can also be combined to form a new technical solution.

Example 1: as shown in fig. 6, the logistics scheduling system of this embodiment includes a shipping warehouse system of a shipping point, a transportation management system, a dynamic logistics analysis protection system, a transfer system of a transfer station, and a receiving warehouse system of a receiving point, which are in data networking with the central scheduling system; the delivery warehousing system comprises a metering system, a delivery system and a delivery loading area; the transportation management system comprises a transportation scheduling system and a transportation tool; the transfer system comprises a transfer distribution system and a transfer loading area; the receiving warehousing system comprises a receiving feedback end and a receiving bin; the transportation ring in the logistics process is nodularized, so that coding operation and coding retrieval are facilitated;

the metering system measures the weight and the volume of the material, generates a unique identification code according to the weight and the volume of the material and the information of a receiving place, and sends the unique identification code to the dispatching system; the goods distribution system identifies the unique identification code of the goods and materials to acquire receiving place information and classifies the goods and materials to be transmitted to a delivery loading area; the delivery loading section stores materials of different destinations;

the unique identification code enables the goods and materials to have identity information, whether goods and materials transportation links are correct or not can be judged and corrected quickly, the experienced links of the goods and materials in the transportation process send retrieval information to the central dispatching system through the unique identification code, the central dispatching system is formed to track the goods and materials effectively, most importantly, the central dispatching system collects basic information of the goods and materials through the unique identification code, such as the weight, the volume, the delivery point, the receiving point and the like of the goods and materials, and a coding basis for logistics scheme operation is provided.

The dynamic logistics analysis protection system comprises a data set and a dynamic logistics analysis protection method; the dynamic logistics analysis protection method comprises the following steps:

s11, collecting dynamic logistics data to form data set, { n_i,yⁱ}x_i∈Rⁿ，i＝1,2,....,l.

Wherein x is_iFor geographical location information of the collected dynamic logistics, yⁱTime information of the collected dynamic logistics;

s12, carrying out region classification on the data set obtained in the step S11; the region classification formula is:

f(n)＝sng(ω·n+b)

wherein ω is a region weighted additional coefficient, n is the data set, and b is an error term;

s13, performing secondary regression through sub-classification conversion on the data set obtained in the step S12; the formula of the secondary regression of the sub-classification transformation is as follows:

s.t.y_i(ω·x+b)≥1+ξ_i；

wherein, ξ_iMore than or equal to 0, i ═ 1,2,.. gtoreq.l; c represents a penalty factor with a weight term, and the larger the empirical error value of C is, the larger the penalty is;

s14, forming a Wolfe type dual rule of the multi-target score programming, and carrying out data self-repairing protection on the data set obtained in the step S13; the formula of the Wolfe formula dual rule of the multi-objective fractional programming is as follows:

α therein_iAnd α_jRepresents a lagrange multiplier;

s15, forming a data early warning analysis function, and carrying out early warning analysis on the dynamic logistics data set obtained in the step S14;

forming a linear function

α therein_iAnd α_jRepresenting the lagrange multiplier.

The transportation scheduling system schedules the transportation tool according to the central scheduling system and reports the position of the transportation tool to the central scheduling system;

as shown in fig. 1-3; further, the method comprises the following steps of; the transportation scheduling system calculates according to the material information collected by the delivery point to form a material scheduling scheme, and the calculation method comprises the following steps:

a1, setting initialization parameters, establishing a data module according to a transportation scheduling management system, and taking a transportation scheme of materials as a chromosome, wherein the parameters comprise delivery point information, transfer station information, receiving point information, transportation tool information and material information as genes of the chromosome; the relevant symbols are as follows:

n: a delivery point comprising { n1, n2, n3 … … n };

m: a receiving point comprising { m1, m2, m3 … … m };

in the actual logistics scheduling, a delivery point and a receiving point are integrated, namely the delivery point is also used as the receiving point, the receiving point is also used as the delivery point, and n is equal to m; due to the material liquidity, 2 kinds of labels are provided for each site, namely a delivery point and a receiving point;

q: the transfer station comprises { q1, q2, q3 … … q }; the transfer station rearranges the materials of each delivery point according to the receiving points and sends the materials to each receiving point; the transfer station rearranges the materials of each delivery point according to the receiving points and sends the materials to each receiving point;

m: material supply; the material comprises Ma material weight; mb material volume; when the goods and materials are dispatched, the weight and the volume of the goods and materials are limited from 2 dimensions, and the carrying capacity of the transport vehicle at one time is limited by the weight or the volume;

d: a vehicle; including { D₁,D₂,D₃… … D }; the transport vehicle has a direct relationship with the material, i.e., the transport vehicle has a nominal load bearing or nominal space;

r: a transport path; the transport route is one way to evaluate the transport solution.

t: the time is long; the time length is taken as the sum of the single material scheduling transportation time and is taken as an important evaluation parameter for evaluating the actual efficiency of the freight scheme (chromosome);

e: the amount is economic and suitable; the economic applicable quantity is used as an evaluation value which adopts the least transportation means and the shortest transportation path for the unit total cargo quantity, and is used for evaluating R, D, t and evaluating a fitness function; the later iteration stability of the genetic algorithm is realized, and a large amount of iterations are avoided;

a2, generating an initial population G; the starting population comprises N chromosomes; the chromosome encodes the material M (Ma and/or Mb) according to any length and features selected as required to generate a material scheduling sequence; the chromosome comprises a plurality of genes, and the gene segments comprise one or more of n, m, q, R and D; and the following conditions are required to be satisfied:

f(D)≥Ma n_xm_yor f (D) ≧ Mbn_xm_yX ∈ n, y ∈ m, wherein the chromosome has material information and information of a delivery point and a receiving point, namely, a complete transportation scheme is formed, wherein { n1, n2, n3 … … n } - { m1, m2, m3 … … m }, namely, the delivery point and the receiving point can be the same station, and a chromosome (the material transportation scheme) is also formed;

further, the method comprises the following steps of; chromosome containing basic gene Ma n_xm_yOr Mbn n_xm_yAnd also comprises one or more of q, R and D genes;

thus, the genes in the chromosome are the sum of the { Ma, n, q, R, D, m } array set, and the chromosome sequence is the permutation and combination of the { Ma, n, q, R, D, m } array set; such as: ma n₂m₃，Mb n₅m₇，Ma n₂m₃n₁m₈，Mb n₁n₄D₃m₇R₉Wherein any one of n, q, R and D can appear for many times and then can be combined randomly; if n, a plurality of the genes can be contained in the same chromosome;

a3, constructing a neural network; the delivery point is a measuring system and is an input end of a neural network, the receiving point is an output end, and the transfer station is a hidden layer; namely n network input ends, m output ends and q neurons of the neural network; the mapping relation of the chromosomes is { n }₁,n₂,n₃……n}→{q_xn₁,q_xn₂,q_xn₃……q_xn}→{q_xq_yn₁,q_xq_yn₂,q_xq_yn₃……q_xq_yn}→{m₁,m₂,m₃… … m }; wherein q is more than or equal to x is more than or equal to 1; q is more than or equal to y is more than or equal to 1; inputting and outputting the chromosome through a neural network to form a transportation scheme of the chromosome;

the neural network adapts to the environment by itself, summarizes the rules, and completes certain operations, identification or process control. The neural network is trained through a large amount of data to obtain a mapping relation between input and output, the weight and the threshold value of the network are continuously adjusted through a gradient descent method to enable the network error to be minimum, the neural network algorithm is implanted into the genetic algorithm, and the large-amplitude subtraction operation of the neural network algorithm on the genetic algorithm is formed.

A4, starting iteration, and executing the following steps:

s21, as shown in figure 5, carrying out cross operation on chromosomes in the population according to the cross probability of the initial population, and randomly exchanging gene segments in the cross process to form a population G1; the population G1 comprises a coding gene set based on the weight and volume of the material, and the sequence combination of n, m, q and D is carried out by taking Ma and Mb as the starting points of the gene segments; after crossing (gene recombination, the random exchange recombination of genes in the chromosome), the chromosome contains a plurality of genes in uncertain disordered states to form a plurality of scheduling schemes;

s22, evaluating the fitness of the population G1 according to the dispatching targets R, t and e; obtaining a population G2; setting a scheduling target value and fitness evaluation functions of R, t and e, and screening genes in a population G1 to obtain an optimized population G2;

s23, as shown in FIG. 5, carrying out mutation operation on the population G2 according to the mutation probability; in order to avoid premature convergence or early stabilization of chromosomes in the population, the population G2 is subjected to mutation operation, so that the genes of the chromosomes are mutated, and the types of the chromosomes are enriched;

s24, carrying out cross operation on chromosomes in the population according to the cross probability by the population G2 in the step 3, and randomly exchanging gene segments in the cross process to form a population G3; the gene mutation has uncertain factors, the mutation rate can be set, the mutation form is uncontrollable, the gene of the chromosome is crossed, the mutated gene is better stored, and the first screening after mutation is prevented from being eliminated by a fitness evaluation function;

s25, evaluating the fitness of the population G3 according to the dispatching targets R, t and e; obtaining a population G4; evaluating the fitness of the mutated and cross-recombined chromosome again to reduce the calculation amount; thus, the first iterative operation of the chromosome is completed;

s26, repeating the population iteration to obtain a population Gi, and judging whether a convergence optimization result is achieved; if yes, the iteration is ended, and if not, the iteration is continued. And (5) iterating the population for multiple times, evaluating the chromosome and the target value, and judging whether the expected value of convergence optimization is reached.

Further, the method comprises the following steps of; the economic applicable amount is the unit total cargo amount and adopts the minimum transport means and the evaluation value with the shortest transport path;

λ is an integer and λ ratio

Is greater than 1, Δ D_xIs D_xThe transportation volume of (2);

how many transport vehicles are required for transporting a certain material for a unit transport means, lambda ratio

The integer of the number is larger than 1, namely the vehicle is actually adjusted to meet the transportation of the materials,

the transportation mode of the transportation vehicle is matched from 2 dimensions of the weight or the volume of the material, namely, less transportation vehicles are needed from the perspective of the weight or the volume 2.

Such as

If the value is 1.8, the lambda is 2, namely 2 vehicles are used for transporting materials, wherein 1 vehicle has the rest of 0.2 rated carrying weight or rated carrying space;

in this way, the goods and materials to be transported at each station can determine how many vehicles are needed, and the surplus of the goods and materials at each station after the transportation tool is matched can be determined;

the weight and volume of the material are used as basic information of the material, and can be recorded into the operation code when entering a delivery point.

The margin of each station is used as a carrying resource and cannot be wasted, so the probability P that the economic applicable quantity is selected is distributed according to the sequence:

i.e. the margin is used to match the closest demand value, if the margin f (e) is 0.8, the margin of 1-f (e) is matched, i.e. the margin 1-f (e) is 0.2, the actual demand value is 0.8, and the margin value matches the closest demand value.

In the actual logistics scheduling, perfect matching is few, so the probability P is used for matching the margin requirement relationship of the transport vehicles, the optimal solution is that 1 vehicle has a margin after being loaded at a certain delivery point, and the margin can also load materials from another delivery point nearby to the same transfer station.

The required values are matched according to the selected probability P, the P serves as a fitness function of the second level to schedule materials, the carrying capacity of the transport means (vehicles) is exerted to the maximum, the probability P reflects the closest matching value, therefore, the former P can meet the next selected P, the residual quantity is the minimum, namely the vehicle transport allowance is the minimum, and the carrying maximum is realized.

Further, the method comprises the following steps of; setting a weight function (1): u ═ p epsilon₁(ii) a The epsilon₁As a weight parameter, epsilon₁∈[0,1](ii) a And setting a weight function for balancing the convergence of the economic availability function and the probability P function of distributing the selected economic availability in the fitness evaluation transportation scheduling method.

Further, the method comprises the following steps of; the chromosome is a threshold value in a neural network population, and the population of each iteration is a connection weight (gene) of the neural network;

G＝nq+qm+q+m。

the connection weight is a gene numerical value of the population, the connection weight of the gene is calculated, the weight or the threshold of the neural network is continuously adjusted, the error of the neural network is minimized, and the identification precision of the network is improved.

Further, the method comprises the following steps of; the chromosome of the connection weight is endowed to a neural network, and the square sum Q of an expected value y and an output value o of the chromosome is calculated;

wherein i ∈ m;

the smaller the sum of squared errors of the chromosomes, the smaller the fitness function value, indicating the better the scheduling scheme of the chromosomes.

For example: desired value y_iAnd the output value o_iThe same, namely the material receiving point of the delivery point i is the delivery point, the Q value is 0, and the chromosome transportation scheme is optimal; the chromosome transport scheme also has a small Q value when the material is delivered to the point of delivery without passing through a transfer station.

Further, the method comprises the following steps of; the chromosome variance is: s²Q/f; wherein f is a degree of freedom. The sum of squared errors is squared, standard deviation is used to measure the difference degree of chromosome, and the variance response and the central deviation degree are used as balanceThe fluctuation size of the chromosome (i.e. the size of the data deviation from the average) is measured, and under the condition that the sample connection weight is the same, the larger the variance is, the larger the fluctuation of the chromosome is, the more unstable the chromosome is, and on the contrary, the smaller the variance is, the smaller the fluctuation of the chromosome is, the more stable the chromosome is. f is used as a degree of freedom and can be adjusted according to expected values to match iteration of the genetic algorithm, so that premature convergence is avoided.

Further, the method comprises the following steps of; setting a weight function (2): u ═ S epsilon₂(ii) a The epsilon₂As a weight parameter, epsilon₂∈[0,1]. The right function is set for balancing convergence of the chromosome error sum-of-squares function and the chromosome variance function in fitness evaluation, and the weight function is set for each fitness evaluation function, so that the convergence and the iteration times of the genetic algorithm in the operation process of the central scheduling system can be guided integrally, and premature convergence or too many iteration times are avoided.

Further, the method comprises the following steps of; genes { n ] of chromosomes in the fitness evaluation step₁,n₂,n₃… … n, i.e., no more than 3 delivery points in a gene at the most. Avoiding multiple consignments of a transport at a point of delivery, genes { n } in the chromosome₁,n₂,n₃… … n, so that a certain transport vehicle can only go to 3 delivery points at most, thereby improving the receiving efficiency.

Further, the method comprises the following steps of; the minimum value of chromosome R is:

min(R)＝L₁|n_x-n_x+1|+L₂|n_x+1-n_x+2|+L₃|n_x+2-n_x+3|+L₄|n_x+3-q_y|+L₅|q_y-m_zl, |; wherein L is₁,L₂,L₃,L₄,L₅Is a constant value; l is₁|n_x-n_x+1I is n_xTo n_x+1The actual distance of (d); the positions of the screen dots are fixed, so the distance between the screen dots is also fixed.

Wherein, x ∈ n, y ∈ q and z ∈ m, because the chromosome expresses a transportation scheme, the path from a certain delivery point to another delivery point is constant, the path from a certain delivery point to a certain transfer station is also constant, and the path from a certain transfer station to a certain receiving point is also constant, therefore, the minimum path value of the chromosome can be calculated for evaluating the minimum transportation distance of the goods and materials.

When the delivery point is the receiving point, min (R) is also 0, when the materials do not need to pass through the transfer station, min (R) is that the delivery is realized among 2 stations or 3 stations, and the value of min (R) is the distance among the network points,

further, the method comprises the following steps of; set weight function (3): u ═ min (R) ε₃(ii) a The epsilon₃As a weight parameter, epsilon₃∈[0,1]。

Further, the method comprises the following steps of; the minimum value of the chromosome time length t is:

(t) min (r)/v- ρ Δ t; Δ t is a loading time of the delivery point, ρ is a number of times of loading, and ρ is 3 or less. Since the transport route of the chromosome is constant, the transport time can be calculated assuming that the transport speed is constant, and the gene expression information of the chromosome includes 1 delivery point or a plurality of delivery points, the number ρ of times of loading on the chromosome is also an exact value, and the time from the delivery point to the transfer station on the chromosome can be confirmed assuming that the loading time per delivery point is Δ t. The fitness function is used to evaluate shipping efficiency.

Further, the method comprises the following steps of; set weight function (4): u ═ f (t) epsilon₄(ii) a The epsilon₄As a weight parameter, epsilon₄∈[0,1]。

Further, the method comprises the following steps of; genes { m ] of chromosomes in the fitness evaluation step₁,m₂,m₃… … m, no more than 3, i.e., no more than 3, delivery points in a gene at the most. Avoiding multiple deliveries of the vehicle at the point of receipt, genes { m } in the chromosome₁,m₂,m₃… … m, so that a certain transport vehicle can only go to 3 receiving points at most, thereby improving the delivery efficiency.

Further, the method comprises the following steps of; the minimum value of chromosome R is:

min(R)＝L₁|n_j-q_u|+L₂|q_u-m_h|+L₃|m_h-m_h+1|+L₄|m_h+1-m_h+2|+L₅|m_h+2-m_h+3l, |; wherein: l is₁,L₂L₃,L₄,L₅Is a constant value, L₄|m_h+1-m_h+2I is m_h+1To m_h+2The actual distance of (c).

Wherein j ∈ n, u ∈ q and h ∈ m, the chromosome expresses a transportation scheme, the path from a certain delivery point to a certain transfer station is also certain, the path from a certain transfer station to a certain receiving point is also certain, and the path from a certain delivery point to another receiving point is also certain, so that the minimum path value of the chromosome can be calculated to measure the minimum transportation path of the goods and materials.

Further, the method comprises the following steps of; setting a weight function (5): u ═ min (R) ε₅(ii) a The epsilon₅As a weight parameter, epsilon₅∈[0,1]。

Further, the method comprises the following steps of; the minimum value of the chromosome time length t is:

f(t)＝min(R)/v-ρΔt¹；Δt¹ρ is the number of times of loading for the shipping point loading time, and ρ is 3 or less. Since the transport route of the chromosome is constant, the transport time can be calculated assuming a constant transport speed, and the gene expression information of the chromosome includes 1 receiving point or a plurality of receiving points, the number of times ρ of loading on the chromosome is also an exact value, and the loading time per delivery point is assumed to be Δ t¹Therefore, the time from the delivery point to the transfer station in the chromosome can be confirmed. The fitness function is used to evaluate shipping efficiency.

Further, the method comprises the following steps of; setting a weight function (6): u ═ f (t) epsilon₆(ii) a The epsilon₆As a weight parameter, epsilon₆∈[0,1]。

During transportation, other factors such as weather and traffic jam occur, and the weighting function is used for defining the weighting ratio of each evaluation function during evaluation, for example, in the case of bad weather, the transportation time is increased, and the time length f (t) is used as the weighting coefficient epsilon₅And ε₆Increase the transportation scheme phase with long transportation time in the operation processShould be easy to retain.

Example 2: this embodiment is substantially the same as embodiment 1, and as shown in fig. 4, the difference is that the population after each iteration in the genetic algorithm is added to the previous population for operation, so as to avoid premature convergence and inaccurate data. For example, the generated population obtained after the G1 operation is added into the G1 again to obtain a population G2, and iteration is performed accordingly.

Step S15 is multi-shaped to form a non-linear function

The invention is based on that when the goods and materials are sent to a delivery point, the goods and materials are weighed and subjected to volume accounting, and are coded according to the method, in the actual operation process, the goods and materials are scheduled to be t equal to 0h, 20 logistics network points in a certain area are selected for testing, and the testing is respectively carried out by using an embodiment 1, an embodiment 2, an independent genetic algorithm and an independent neural network algorithm:

	long average scheduling time	Long operation time	Required vehicle	Economic and applicable dosage
					Example 1	41.75h	1235s	36	0.72
Example 2	41.43h	1894s	35	0.70
					Genetic algorithm	51.32h	2864s	39	0.97
Neural network algorithm	18.69h	2563s	38	0.95

As described above, the operation time of embodiment 1 is shorter than that of embodiment 2, and the scheduling time of embodiment 1 is longer than that of embodiment 2. And the method is obviously superior to a single genetic algorithm and a neural network algorithm, and has obvious advantages in solving a large number of logistics scheduling problems.

The technical solutions of the embodiments of the present invention can be combined, and the technical features of the embodiments can also be combined to form a new technical solution.

The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

Claims (10)

1. A logistics scheduling system, characterized by; the system comprises a delivery storage system of a delivery point, a transportation management system, a transfer system of a transfer station, a receiving storage system of a receiving point and a dynamic logistics analysis protection system which are in data networking with a central dispatching system; the delivery warehousing system comprises a metering system, a delivery system and a delivery loading area; the transportation management system comprises a transportation scheduling system and a transportation tool; the transfer system comprises a transfer distribution system and a transfer loading area; the receiving warehousing system comprises a receiving feedback end and a receiving bin;

the metering system measures the weight and the volume of the materials, generates a unique identification code according to the weight and the volume of the materials and the information of a receiving place, and sends the unique identification code to the dispatching system; the goods distribution system identifies the unique identification code of the goods and materials to acquire receiving place information and classifies the goods and materials to be transmitted to a delivery loading area; the delivery loading section stores materials of different destinations;

the transportation scheduling system schedules the transportation tool according to the central scheduling system and reports the position of the transportation tool to the central scheduling system;

the transfer cargo distribution system identifies the material unique identification code to acquire receiving place information and classifies the material to be transmitted to a transfer loading area; the transfer loading section stores materials of different receiving points;

the goods receiving feedback end confirms goods receiving of goods through scanning the unique identification code; the goods receiving bin stores goods and materials;

the dynamic logistics analysis protection system comprises a data set and a dynamic logistics analysis protection method; the dynamic logistics analysis protection method comprises the following steps:

s11, collecting dynamic logistics data to form a data set;

s12, carrying out region classification on the data set obtained in the step S11;

s13, performing secondary regression through sub-classification conversion on the data set obtained in the step S12;

s14, forming a Wolfe type dual rule of the multi-target score programming, and carrying out data self-repairing protection on the data set obtained in the step S13;

s15, forming a data early warning analysis function, and carrying out early warning analysis on the dynamic logistics data set obtained in the step S14;

and the central dispatching system records and calculates data uploaded by the delivery storage system, the transportation management system, the dynamic logistics analysis and protection system, the transfer system and the receiving storage system.

2. The logistics scheduling system of claim 1, wherein: the central dispatching system calculates the uploaded data, and the calculation method comprises a transportation dispatching method based on a genetic algorithm and a neural network algorithm, wherein the transportation dispatching method comprises the following steps:

a1, establishing a data module, wherein the parameters comprise material information, delivery point information, transfer station information, receiving point information and transport means information; the relevant symbols are as follows:

m: material supply; the material comprises Ma material weight; mb material volume;

n: a delivery point comprising n₁,n₂,n₃……n}；

q: a transfer station comprising { q₁,q₂,q₃……q}；

m: a receiving point comprising { m₁,m₂,m₃……m}；

D: a vehicle; including { D₁,D₂,D₃……D}；

R: a transport path;

t: the time is long;

e: the amount is economic and suitable;

a2, generating an initial population G; the starting population comprises N chromosomes; one chromosome is a transportation scheme of materials; the chromosome encodes the material M (Ma and/or Mb) according to any length and features selected as required to generate a material scheduling sequence; the chromosome comprises a plurality of genes, and the gene segments comprise one or more of n, m, q, R and D; and the following conditions are required to be satisfied:

f(D)≥Man_xm_yor f (D) ≧ Mbn_xm_y；x∈n,y∈m；

A3, constructing a neural network; the metering system of the delivery point is a neural network input end, the delivery feedback end of the delivery point is an output end, and the transfer station is a hidden layer; namely, the neural network is provided with n network input ends, m output ends and q neurons; the mapping relation of the chromosomes is { n }₁,n₂,n₃……n}→{q_xn₁,q_xn₂,q_xn₃……q_xn}→{q_xq_yn₁,q_xq_yn₂,q_xq_yn₃……q_xq_yn}→{m₁,m₂,m₃… … m }; wherein q is more than or equal to x is more than or equal to 1; q is more than or equal to y is more than or equal to 1; inputting and outputting the chromosome through a neural network to form a transportation scheme of the chromosome;

a4, starting iteration, and executing the following steps:

s21, carrying out cross operation on chromosomes in the initial population according to the cross probability, and randomly exchanging gene segments in the cross process to form a population G1;

s22, evaluating the fitness of the population G1 according to the dispatching targets R, t and e; obtaining a population G2;

s23, carrying out mutation operation on the population G2 according to the mutation probability;

s24, carrying out cross operation on chromosomes in the population G2 in the step S23 according to cross probability, and randomly exchanging gene segments in the cross process to form a population G3;

s25, evaluating the fitness of the population G3 according to the dispatching targets R, t and e; obtaining a population G4;

s26, repeating the population iteration to obtain a population Gi, and judging whether a convergence optimization result is achieved; if yes, the iteration is ended, and if not, the iteration is continued.

3. The logistics scheduling system of claim 2, wherein: the economic applicable amount is the unit total cargo amount and adopts the minimum transport means and the evaluation value with the shortest transport path;

λ is an integer and λ ratio

Is greater than 1, Δ D_xIs D_xThe transportation volume of (2);

allocating the probability P that the economically applicable amount is selected according to the sequence:

setting a weight function (1): u ═ p epsilon₁(ii) a The epsilon₁As a weight parameter, epsilon₁∈[0,1]。

4. The logistics scheduling system of claim 2, wherein: the chromosome is a threshold value in a neural network population, and the population of each iteration is a connection weight of the neural network;

G＝nq+qm+q+m；

the chromosome of the connection weight is endowed to a neural network, and the square sum Q of an expected value y and an output value o of the chromosome is calculated;

wherein i ∈ m;

the chromosome variance is therefore: s²Q/f; wherein f is a degree of freedom;

setting a weight function (2): u ═ S epsilon₂(ii) a The epsilon₂As a weight parameter, epsilon₂∈[0,1]。

5. The logistics scheduling system of claim 2 wherein; genes { n ] of chromosomes in the fitness evaluation step₁,n₂,n₃… … n } no more than 3, i.e., no more than 3 delivery points in a gene at most;

the minimum value of chromosome R is:

min(R)＝L₁|n_x-n_x+1|+L₂|n_x+1-n_x+2|+L₃|n_x+2-n_x+3|+L₄|n_x+3-q_y|+L₅|q_y-m_zl, |; wherein L is₁,L₂,L₃,L₄,L₅Is a constant value; i.e. the actual distance from one point to another;

wherein, x ∈ n, y ∈ q, z ∈ m;

set weight function (3): u ═ min (R) ε₃(ii) a The epsilon₃As a weight parameter, epsilon₃∈[0,1]。

6. The logistics scheduling system of claim 5, wherein: the minimum value of the chromosome time length t is:

(t) min (r)/v- ρ Δ t; delta t is the loading time of the delivery point, rho is the loading frequency, and rho is less than or equal to 3;

set weight function (4): u ═ f (t) epsilon₄(ii) a The epsilon₄As a weight parameter, epsilon₄∈[0,1]。

7. The logistics scheduling system of claim 2 wherein; genes { m ] of chromosomes in the fitness evaluation step₁,m₂,m₃… … m } no more than 3, i.e., no more than 3 delivery points in a gene at most;

the minimum value of chromosome R is:

min(R)＝L₁|n_j-q_u|+L₂|q_u-m_h|+L₃|m_h-m_h+1|+L₄|m_h+1-m_h+2|+L₅|m_h+2-m_h+3l, |; wherein: l is₁,L₂L₃,L₄,L₅Is a fixed value, i.e. the actual distance from one point to another;

wherein, j ∈ n, u ∈ q, h ∈ m;

setting a weight function (5): u ═ min (R) ε₅(ii) a The epsilon₅As a weight parameter, epsilon₅∈[0,1]。

8. The logistics scheduling system of claim 7, wherein: the minimum value of the chromosome time length t is:

f(t)＝min(R)/v-ρΔt¹；Δt¹loading time for a delivery point, wherein rho is the loading frequency and is less than or equal to 3;

setting a weight function (6): u ═ f (t) epsilon₆(ii) a The epsilon₆As a weight parameter, epsilon₆∈[0,1]。

9. The logistics scheduling system of claim 2, wherein:

the data set acquired in the step S11 is:

{n_i,yⁱ}，i＝1,2,....,l.

wherein n is_iFor geographical location information of the collected dynamic logistics, yⁱTime information of the collected dynamic logistics;

the region classification formula in the step S12 is:

f(n)＝sng(ω·n+b)

wherein ω is a region weighted additional coefficient, n is the data set, and b is an error term;

the formula of the secondary regression of the sub-classification conversion in the step S13 is as follows:

s.t.y_i(ω·x+b)≥1+ξ_i；

wherein, ξ_iMore than or equal to 0, i ═ 1,2,.. gtoreq.l; c represents a penalty factor with a weight term, and the larger the empirical error value of C is, the larger the penalty is;

the formula of the Wolfe formula dual rule for forming the multi-objective score programming in the step S14 is as follows:

α therein_iAnd α_jRepresentational pullThe glanray multiplier.

10. The logistics scheduling system of claim 9, wherein:

forming a linear function

Forming a non-linear function

α therein_iAnd α_jRepresenting the lagrange multiplier.

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