Intelligent logistics management method, device, equipment and readable storage medium

Abstract

The present invention provides an intelligent logistics management method, apparatus, device, and readable storage medium, relating to the field of logistics management technology. The method comprises obtaining first and second information about emergency logistics, performing a gray correlation analysis on the first and second information to obtain a correlation between the first and second information; sending the correlation, the first and second information to an optimized logistics model for processing to obtain third information; and calculating the third information according to a heuristic algorithm to obtain an optimized logistics management result. The present invention has the beneficial effects of improving the accuracy of logistics management, greatly enhancing the robustness and reliability of the algorithm, providing certain support for logistics management, and employing a social mechanism of fission and fusion to achieve the goal of finding the optimal solution for the population.

Description

Technical Field

[0001] The present invention relates to the field of logistics management technology, and in particular to intelligent logistics management methods, devices, equipment and readable storage media. Background Art

[0002] At present, the supply chain refers to a complete supply chain formed by integrating multiple logistics around a core logistics enterprise. In the process of supply chain management, due to the large variety of logistics products, high frequency of mobilization, large information differences between logistics products, and scattered distribution of supply areas, conventional supply chain logistics management methods are inefficient.

[0003] In the invention disclosed in CN106408240A, an intelligent warehousing system and warehousing method for logistics management, although the modular management method reduces human errors in data collection, thereby reducing the company's warehousing and logistics costs, it does not fundamentally solve the problem of rapid classification of logistics products and cannot accurately match them to the corresponding logistics collection stations, resulting in problems such as high logistics costs and long logistics time periods. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent logistics management method, device, equipment and readable storage medium to improve the above problems. To achieve the above objectives, the technical solutions adopted by the present invention are as follows:

[0005] In a first aspect, the present application provides for obtaining first and second information of emergency logistics, wherein the first information is the aggregate information of emergency logistics products arriving at logistics collection points, wherein the logistics collection points are a topological map of all the logistics collection points extending in all directions from the location of the emergency logistics products to be transported; the second information is the supply chain information of the emergency logistics products, wherein the supply chain information includes logistics warehousing information, logistics vehicle information, and distribution task characteristic information;

[0006] Performing grey correlation analysis on the first information and the second information to obtain a correlation degree between the first information and the second information;

[0007] Sending the correlation degree, the first information, and the second information to the optimized logistics model for processing to obtain third information;

[0008] The third information is calculated according to a heuristic algorithm to obtain an optimized logistics management result.

[0009] Preferably, performing grey correlation analysis on the first information and the second information to obtain the correlation between the first information and the second information includes:

[0010] Performing sequence analysis on the first information and the second information, wherein the first information is determined as a parent sequence reflecting intelligent logistics management factors, and the second information is determined as a feature sequence reflecting intelligent logistics management factors;

[0011] Performing dimensionless quantization processing on the data after the sequence analysis to obtain a dimensionless quantization processing result;

[0012] Based on the dimensionless quantization processing result, solving the grey correlation coefficient value between the parent sequence and the characteristic sequence;

[0013] The correlation value between each of the parent sequence and the characteristic sequence is calculated according to the grey correlation correlation value, and the correlation values ​​are sorted and analyzed.

[0014] Preferably, obtaining the third information further comprises:

[0015] Processing the third information and obtaining fourth information based on the processed third information, wherein the fourth information is a similarity matrix of a sample space of the third information obtained based on a data set of the third information;

[0016] Obtaining a degree matrix and a Laplace matrix of the third information according to the similarity matrix, and normalizing the obtained results;

[0017] Decomposing the normalized data, and taking the eigenvectors mapped by the first k eigenvalues ​​to form a first eigenvector matrix, wherein the first eigenvector matrix includes the flow direction of the emergency logistics products, the management cost of the logistics collection point, and the logistics efficiency of the emergency logistics products;

[0018] The first eigenvector matrix is ​​clustered based on the k-means clustering algorithm to obtain a classification result.

[0019] Preferably, the third information is calculated according to a heuristic algorithm to obtain an optimized logistics management result, which includes:

[0020] Decomposing the third information into a time series using a signal decomposition algorithm to obtain decomposed data;

[0021] Set the parameters and generate the formula:

[0022] s ij＝ s minj +a*(s maxj -s minj )

[0023] Among them, s ij represents the i-th spider monkey in the j-th dimension, s maxj , s minjThey represent the upper and lower boundaries of the spider monkey in the j-th dimension of the search space, and a represents a random number uniformly distributed in [0,1].

[0024] Generate the initial positions of all spider monkeys in the algorithm group according to the formula and sort them based on fitness;

[0025] Through the greedy selection strategy, the local leader with high fitness value is selected as the global leader;

[0026] Based on the decomposed data and the global leader, the optimal solution is selected as the global leader, and recorded as the optimized logistics management result.

[0027] In a second aspect, the present application further provides an intelligent logistics management device, comprising an acquisition module, an analysis module, a processing module, and a calculation module, wherein:

[0028] Acquisition module: used to obtain first information and second information of emergency logistics, the first information is the collection information of emergency logistics products arriving at logistics collection points, and the logistics collection points are a topological map of all logistics collection points extending from the location of the emergency logistics products to be transported; the second information is the supply chain information of the emergency logistics products, and the supply chain information includes logistics warehousing information, logistics vehicle information, and distribution task feature information;

[0029] Analysis module: used for performing grey correlation analysis on the first information and the second information to obtain the correlation degree between the first information and the second information;

[0030] Processing module: used for sending the correlation degree, the first information and the second information to the optimized logistics model for processing to obtain third information;

[0031] Calculation module: used to calculate the third information according to the heuristic algorithm to obtain the optimized logistics management result.

[0032] In a third aspect, the present application further provides an intelligent logistics management device, comprising:

[0033] memory for storing computer programs;

[0034] A processor is used to implement the steps of the intelligent logistics management method when executing the computer program.

[0035] In a fourth aspect, the present application also provides a readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned intelligent logistics management method are implemented.

[0036] The beneficial effects of the present invention are:

[0037] The present invention collects images and analyzes and extracts them, and can observe the appearance of logistics products, making preparations for subsequent classification and clustering in advance, effectively avoiding the loss of logistics products, and improving the timeliness, reliability and efficiency of logistics management of logistics products;

[0038] The present invention uses the grey correlation analysis method to analyze the correlation value. It is an analysis method that determines the correlation degree of factors based on the development trend of logistics changes. Through systematic analysis, the development trend of logistics is clarified. In addition, it does not require too much data, is easy to use, and improves the accuracy of logistics management.

[0039] The present invention uses a serial decomposition method to extract and decompose the time series features of logistics data to remove noise signals, reduce the non-stationarity of the data, avoid error accumulation, maintain the coherence of the time series, reduce training costs, and improve the accuracy of logistics management.

[0040] The present invention greatly improves the robustness and reliability of the algorithm by adjusting the adaptive parameters of the population, provides certain support for logistics management, and adopts the social mechanism of fission and fusion to achieve the purpose of finding the optimal solution of the population.

[0041] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the embodiments of the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 This is a flow chart of the intelligent logistics management method described in an embodiment of the present invention;

[0044] Figure 2 Schematic diagram of the structure of the intelligent logistics management device according to an embodiment of the present invention;

[0045] Figure 3 This is a schematic diagram of the structure of the intelligent logistics management equipment described in an embodiment of the present invention.

[0046] In the figure: 701, acquisition module; 7011, acquisition unit; 7012, detection unit; 7013, third processing unit; 7014, fourth processing unit; 702, analysis module; 7021, analysis unit; 7022, first processing unit; 7023, first solution unit; 7024, calculation unit; 703, processing module; 7031, second solution unit; 7032, second processing unit; 7033, first decomposition unit; 7034, clustering unit; 704, calculation module; 7041, second decomposition unit; 7042, generation unit; 7043, sorting unit; 7044, selection unit; 7045, recording unit; 800, intelligent logistics management equipment; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0048] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are used only to distinguish the description and should not be understood as indicating or implying relative importance.

[0049] Example 1:

[0050] This embodiment provides an intelligent logistics management method.

[0051] See also Figure 1 , the figure shows that the method includes step S100, step S200, step S300 and step S400.

[0052] S100. Obtain the first information and the second information of emergency logistics. The first information is the collective information of emergency logistics products arriving at the logistics collection points. The logistics collection points are a topological map of all the logistics collection points extending from the location of the emergency logistics products to be transported to the surrounding areas. The second information is the supply chain information of the emergency logistics products. The supply chain information includes logistics warehousing information, logistics vehicle information and distribution task feature information.

[0053] It should be understood that all the logistics collection points extending from the location of the emergency logistics product to be transported are network nodes, and the paths between different logistics collection points are represented by connecting lines as network edges. The set formula is as follows:

[0054] R(A)={γ a |1≤a≤n};

[0055] Q(A)={q ab |q ab = <r a , r b >, 1≤a≤n, 1≤b≤n};

[0056] Where R(A) is the set of logistics collection points, Q(A) is the edge set of network nodes, A is the topological graph of logistics collection nodes; n is the total number of all logistics collection nodes in the topological graph of logistics collection nodes; r a Indicates the location of the logistics product, r b Indicates the node of the logistics collection point; q ab Indicates (r a , r b ) edge.

[0057] It is understandable that the process of acquiring logistics warehousing information in step S100 includes S101, S102, S103 and S104, wherein:

[0058] S101: Capture a first image using an electronic device, where the first image is image information of the emergency logistics product at the logistics collection point;

[0059] S102: normalize the first image to obtain a logistics warehousing grayscale image, perform gradient detection on the logistics warehousing grayscale image to obtain a gradient image, where the gradient image includes logistics warehousing edge pixels and logistics warehousing non-edge pixels;

[0060] Specifically, a histogram analysis is performed on the normalized image, and a dynamic range threshold is determined based on whether the image details are clear. The first image is adjusted according to the determined dynamic range threshold to obtain an optimized grayscale image.

[0061] S103, processing the gradient image using a Gabor filter to obtain a second image, and performing a dot multiplication of the first image and the second image based on a dot multiplication algorithm to obtain a third image;

[0062] It is understandable that in order to eliminate the noise mixed in when the image is digitized and eliminate the global image intensity non-uniformity, this embodiment uses a Gabor filter to smooth the gradient image and further eliminate the non-uniform illumination intensity in the second image. The transfer function of the Gabor filter is calculated to obtain a secondary spatial domain image, and the formula is as follows:

[0063]

[0064] Where: g(x, y, λ, θ, α, δ, γ) is the distance from the pixel (x, y) to the origin; x is the horizontal coordinate of the pixel; y is the vertical coordinate of the pixel; x' is the x parameter obtained by the coordinate system rotation transformation formula; y' is the y parameter obtained by the coordinate system rotation transformation formula; λ is the wavelength of the sine wave; θ is the fringe direction in the Gabor function; α is the phase and is set to zero; δ represents the bandwidth, i.e., the variance of the Gaussian function; γ is the spatial aspect ratio and is set to zero; H(x, y) is the transfer function operator; and d0 is the distance from the cutoff frequency to the origin. The primary spatial domain image is then right-multiplied by the transfer function operator to further enhance the pixels in the edge area, resulting in a secondary spatial domain image.

[0065] S104 . Perform convolution smoothing processing on the third image based on a mean shift algorithm to obtain a logistics warehousing information image.

[0066] This step determines the images of different emergency logistics products in the third image by performing row convolution smoothing on the third image, thereby identifying the pixel key points of the emergency logistics products. The pixels of each grayscale value are clustered, and the center point of the cluster is determined to identify the key products in the emergency logistics products. This method of collecting images and analyzing and extracting them can also observe the appearance of logistics products, preparing for subsequent classification and clustering, effectively preventing the loss of logistics products and improving the timeliness, reliability, and efficiency of logistics product management.

[0067] S200: Perform grey correlation analysis on the first information and the second information to obtain a correlation degree between the first information and the second information.

[0068] It can be understood that step S200 includes S201, S202, S203 and S204, wherein:

[0069] S201. Perform sequence analysis on the first information and the second information, wherein the first information is determined as a parent sequence reflecting intelligent logistics management factors, and the second information is determined as a feature sequence reflecting intelligent logistics management factors;

[0070] It should be noted that the parent sequence reflecting the intelligent logistics management factors and the characteristic sequence reflecting the intelligent logistics management factors are subjected to sequence analysis. Specifically, the logistics distribution vehicle of the logistics vehicle information in the second information includes data on vehicle type, vehicle registration ID and vehicle load capacity. The license plate information of the vehicle, the service vehicle responsible for logistics distribution and the distribution route when the vehicle provides distribution services are matched, and the logistics distribution vehicle and the driver responsible for logistics distribution are associated.

[0071] S202, performing dimensionless quantization processing on the data after the sequence analysis to obtain a dimensionless quantization processing result;

[0072] It can be understood that the dimensionless quantification in the present invention is to organize the first information and the second information, and create an Excel table, and then perform positive, standardized and normalized processing on the data in the table, and calculate the mean of the processed data, wherein the formula for the mean calculation is as follows:

[0073]

[0074] Among them, x ij The jth data in the i-th row, n is the total number of data.

[0075] S203, based on the dimensionless quantization processing result, solving the grey correlation coefficient value between the parent sequence and the characteristic sequence;

[0076] In this embodiment, the processing method includes but is not limited to normalization and initialization, and the mean method is used for dimensionless quantization processing.

[0077] S204: Calculate the correlation value between each of the parent sequence and the characteristic sequence according to the grey correlation value, and sort and analyze the correlation values.

[0078] It should be noted that a larger value indicates a stronger correlation with the parent sequence. It can be understood that the calculation formula for the correlation value in the above steps is as follows:

[0079] Where: ε t is the correlation degree corresponding to the independent variable t; t is the data type of the parent sequence; h is the data type of the feature sequence; m is the total number of samples of the feature sequence data; γ f (h) is the grey correlation coefficient of the characteristic sequence data f relative to the dependent variable h.

[0080] The present invention compares the first and second information of emergency logistics during the process based on the changing trends of the two factors. A high degree of synchronization indicates a high degree of correlation; conversely, a low degree of correlation indicates a low degree of correlation. This method can determine the relationship between the time a product arrives at a logistics station and the supply chain information of the emergency logistics product. This analytical method determines the degree of correlation based on the changing trends of logistics. Through systematic analysis, logistics trends can be clearly identified. This method also requires minimal data, is easy to use, and improves the accuracy of logistics management.

[0081] S300: Send the correlation degree, the first information, and the second information to an optimized logistics model for processing to obtain third information.

[0082] It can be understood that step S300 includes S301, S302, S303 and S304, wherein:

[0083] S301: Process the third information and obtain fourth information based on the processed third information, where the fourth information is a similarity matrix of a sample space of the third information obtained based on a data set of the third information;

[0084] It should be noted that spectral clustering is not limited to specific spatial characteristics when clustering. It only needs to solve the eigenvectors of a general matrix to complete the task. It compresses high-dimensional data and reduces it to lower-dimensional data before performing the calculation. This reduces the workload and improves the accuracy of the clustering results by measuring the similarity between data points. In this step, the Gaussian kernel function is used to calculate the similarity of the spectral clustering algorithm and calculate the similarity matrix.

[0085] S302: Obtain a degree matrix and a Laplace matrix of the third information according to the similarity matrix, and normalize the obtained results;

[0086] It should be noted that the Laplace matrix L is composed of the difference between the degree matrix D and the weight matrix W. The result is normalized and the formula is as follows:

[0087] L=D -1 / 2 LD -1 / 2

[0088] S303: Decompose the normalized data and take the eigenvectors mapped by the first k eigenvalues ​​to form a first eigenvector matrix, where the first eigenvector matrix includes the flow direction of emergency logistics products, the management cost of the logistics collection point, and the logistics efficiency of emergency logistics products;

[0089] It should be noted that K eigenvalues ​​are obtained and eigenvectors are obtained. The K eigenvectors are formed into an N*K eigenmatrix, denoted as Q. In this step, the flow of emergency logistics products includes but is not limited to the transportation destination and the quantity transported. The management costs of the logistics collection point include the expenses incurred by management personnel and system usage. The logistics efficiency of emergency logistics products refers to the time spent in the entire process.

[0090] S304 : Clustering the first eigenvector matrix based on a k-means clustering algorithm to obtain a classification result.

[0091] It should be noted that the steps for clustering Q using the k-means clustering algorithm to obtain an N-dimensional vector C as a result and obtain the classification result are as follows:

[0092] x=|x i |∈R n , i=1,2,…n

[0093] 400. Calculate the third information according to a heuristic algorithm to obtain an optimized logistics management result, including:

[0094] It can be understood that step S400 includes S401, S402, S403, S404 and S405, wherein:

[0095] S401, performing time series decomposition on the third information using a signal decomposition algorithm to obtain decomposed data;

[0096] It is understandable that the logistics data is subjected to time series feature extraction and decomposition, including but not limited to, through the serial decomposition method of variational mode VMD-empirical wavelet transform EWT, so as to strip off noise signals, reduce the non-stationarity of the data, avoid error accumulation, continue the coherence of the time series, and reduce training costs.

[0097] S402. Set parameters and generate formula:

[0098] s ij＝ s minj +a*(s maxj -s minj )

[0099] Among them, s ij represents the i-th spider monkey in the j-th dimension, s maxj , s minj They represent the upper and lower boundaries of the spider monkey in the j-th dimension of the search space, and a represents a random number uniformly distributed in [0,1].

[0100] S403, generating the initial positions of all spider monkeys in the algorithm group according to the formula, and sorting them based on fitness;

[0101] S404, using a greedy selection strategy, selecting a local leader with a high fitness value as the global leader;

[0102] This stage identifies whether there is a global leader in the entire population. If so, the identified spider monkey is used as the intermediate global leader, and checks whether the global leader has updated its position to a certain threshold before taking the next action.

[0103] S405. Based on the decomposed data and the global leader, an optimal solution is selected as the global leader, and recorded as the optimized logistics management result.

[0104] It should be noted that if the global leader reaches a fixed threshold (the so-called global leader limit), and the current number of groups reaches the maximum number, the global optimal leader will perform a fission operation on the group, redividing it into smaller groups and outputting the optimal solution as the optimized logistics management result. By adjusting the adaptive parameters of the population, the robustness and reliability of the algorithm are greatly improved, providing certain support for logistics management. During the solution process, the position of each spider monkey in the algorithm represents a set of feasible solutions to the problem, the population size represents the number of selected solutions, the dimensionality of the search space represents the number of candidate cities, and the position of the global leader represents the selected distribution center.

[0105] Example 2:

[0106] like Figure 2 As shown, this embodiment provides an intelligent logistics management device, see Figure 2 The device includes an intelligent logistics management device, including an acquisition module 701, an analysis module 702, a processing module 703 and a calculation module 704, wherein:

[0107] Acquisition module 701 is used to acquire first information and second information of emergency logistics, wherein the first information is the aggregate information of emergency logistics products arriving at logistics collection points, which is a topological map of all logistics collection points extending from the location of the emergency logistics products to be transported; the second information is the supply chain information of the emergency logistics products, which includes logistics warehousing information, logistics vehicle information, and distribution task feature information;

[0108] Analysis module 702: configured to perform grey correlation analysis on the first information and the second information to obtain a correlation degree between the first information and the second information;

[0109] Processing module 703: used to send the correlation degree, the first information and the second information to the optimized logistics model for processing to obtain third information;

[0110] Calculation module 704: used to calculate the third information according to the heuristic algorithm to obtain an optimized logistics management result.

[0111] Specifically, the process of acquiring logistics warehousing information in the acquisition module 701 includes a collection unit 7011, a detection unit 7012, a third processing unit 7013, and a fourth processing unit 7014, wherein:

[0112] Acquisition unit 7011: configured to use an electronic device to acquire a first image, where the first image is image information of the emergency logistics product at the logistics collection point;

[0113] Detection unit 7012: configured to perform normalization processing on the first image to obtain a logistics warehousing grayscale image, perform gradient detection on the logistics warehousing grayscale image to obtain a gradient image, wherein the gradient image includes logistics warehousing edge pixels and logistics warehousing non-edge pixels;

[0114] The third processing unit 7013 is configured to process the gradient image using a Gabor filter to obtain a second image, and perform a dot multiplication of the first image and the second image based on a dot multiplication algorithm to obtain a third image;

[0115] The fourth processing unit 7014 is configured to perform convolution smoothing processing on the third image based on a mean shift algorithm to obtain a logistics warehousing information image.

[0116] Specifically, the analysis module 702 includes an analysis unit 7021, a first processing unit 7022, a first solution unit 7023, and a calculation unit 7024, wherein:

[0117] Analysis unit 7021: configured to perform sequence analysis on the first information and the second information, wherein the first information is determined as a parent sequence reflecting intelligent logistics management factors, and the second information is determined as a feature sequence reflecting intelligent logistics management factors;

[0118] The first processing unit 7022 is used to perform dimensionless quantization processing on the data after the sequence analysis to obtain a dimensionless quantization processing result;

[0119] The first solving unit 7023 is used to solve the grey correlation coefficient value between the mother sequence and the characteristic sequence based on the dimensionless quantization processing result;

[0120] The calculation unit 7024 is configured to calculate the correlation value between each of the parent sequence and the characteristic sequence according to the grey correlation coefficient value, and sort and analyze the correlation values.

[0121] Specifically, the processing module 703 then includes a second solving unit 7031, a second processing unit 7032, a first decomposition unit 7033 and a clustering unit 7034, wherein:

[0122] The second solving unit 7031 is configured to process the third information and solve fourth information based on the processed third information, wherein the fourth information is a similarity matrix of a sample space of the third information obtained based on a data set of the third information;

[0123] The second processing unit 7032 is configured to obtain the degree matrix and the Laplacian matrix of the third information according to the similarity matrix, and perform normalization processing on the obtained results;

[0124] A first decomposition unit 7033 is configured to decompose the normalized data and take the eigenvectors mapped by the first k eigenvalues ​​to form a first eigenvector matrix, where the first eigenvector matrix includes the flow direction of emergency logistics products, the management cost of the logistics collection point, and the logistics efficiency of emergency logistics products.

[0125] Clustering unit 7034: used to cluster the first eigenvector matrix based on the k-means clustering algorithm to obtain a classification result.

[0126] Specifically, the calculation module 704 includes a second decomposition unit 7041, a generation unit 7042, a sorting unit 7043, a selection unit 7044, and a recording unit 7045, wherein:

[0127] The second decomposition unit 7041 is configured to perform time series decomposition on the third information using a signal decomposition algorithm to obtain decomposed data;

[0128] Generation unit 7042: used to set parameters and generate formulas:

[0129] s ij＝ s minj +a*(s maxj -s minj )

[0130] Among them, s ij represents the i-th spider monkey in the j-th dimension, s maxj , s minj They represent the upper and lower boundaries of the spider monkey in the j-th dimension of the search space, and a represents a random number uniformly distributed in [0,1].

[0131] Sorting unit 7043: used to generate the initial positions of all spider monkeys in the algorithm group according to the formula and sort them based on fitness;

[0132] Selection unit 7044: used to select a local leader with a high fitness value as the global leader through a greedy selection strategy;

[0133] Recording unit 7045 is used to select the optimal solution as the global leader based on the decomposed data and the global leader, and record it as the optimized logistics management result.

[0134] It should be noted that, regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated on here.

[0135] Example 3:

[0136] Corresponding to the above method embodiment, this embodiment further provides an intelligent logistics management device. The intelligent logistics management device described below and the intelligent logistics management method described above can refer to each other.

[0137] Figure 3 FIG. 8 is a block diagram of an intelligent logistics management device 800 according to an exemplary embodiment. Figure 3 As shown, the intelligent logistics management device 800 may include: a processor 801 and a memory 802. The intelligent logistics management device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0138] The processor 801 is used to control the overall operation of the intelligent logistics management device 800 to complete all or part of the steps in the intelligent logistics management method described above. The memory 802 is used to store various types of data to support the operation of the intelligent logistics management device 800. For example, this data may include instructions for any application or method operating on the intelligent logistics management device 800, as well as application-related data, such as contact information, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 802 or sent via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the intelligent logistics management device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 may include: a Wi-Fi module, a Bluetooth module, an NFC module.

[0139] In an exemplary embodiment, the intelligent logistics management device 800 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned intelligent logistics management method.

[0140] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, the program instructions implement the steps of the aforementioned intelligent logistics management method. For example, the computer-readable storage medium may be the aforementioned memory 802 including the program instructions. The program instructions may be executed by the processor 801 of the intelligent logistics management device 800 to implement the aforementioned intelligent logistics management method.

[0141] Example 4:

[0142] Corresponding to the above method embodiment, this embodiment further provides a readable storage medium. The readable storage medium described below and the intelligent logistics management method described above can refer to each other.

[0143] A readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the intelligent logistics management method of the above-mentioned method embodiment.

[0144] The readable storage medium may specifically be any readable storage medium that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0145] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

[0146] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. An intelligent logistics management method, characterized in that: include: Obtaining first information and second information of emergency logistics, wherein the first information is the aggregate information of emergency logistics products arriving at logistics collection points, and the logistics collection points are a topological map of all the logistics collection points extending from the location of the emergency logistics products to be transported; The second information is supply chain information of emergency logistics products, and the supply chain information includes logistics warehousing information, logistics vehicle information and delivery task feature information; Performing grey correlation analysis on the first information and the second information to obtain a correlation degree between the first information and the second information; The method includes: performing sequence analysis on the first information and the second information, wherein the first information is determined as a parent sequence reflecting intelligent logistics management factors, and the second information is determined as a characteristic sequence reflecting intelligent logistics management factors; performing dimensionless quantization processing on the sequence-analyzed data to obtain dimensionless quantization processing results; solving the grey correlation coefficient value between the parent sequence and the characteristic sequence based on the dimensionless quantization processing results; calculating the correlation degree value between each parent sequence and the characteristic sequence based on the grey correlation coefficient value, and sorting and analyzing the correlation degree values; Sending the correlation degree, the first information, and the second information to the optimized logistics model for processing to obtain third information; The third information is calculated according to a heuristic algorithm to obtain an optimized logistics management result; this includes: using a signal decomposition algorithm to perform time series decomposition on the third information to obtain decomposed data; setting parameters, and generating a formula: s ij＝ s minj +a*(s maxj -s minj ) Among them, s ij represents the i-th spider monkey in the j-th dimension, s maxj , s minj They represent the upper and lower boundaries of the spider monkey in the j-th dimension of the search space, and a represents a random number uniformly distributed in [0,1]. The initial positions of all spider monkeys in the algorithm group are generated according to the formula and sorted based on the fitness value. The local leader with the highest fitness value is selected as the global leader through the greedy selection strategy. Based on the decomposed data and the global leader, the optimal solution is selected as the global leader, which is recorded as the optimized logistics management result.

2. The intelligent logistics management method according to claim 1 is characterized in that , the obtaining of the third information then includes: Processing the third information and obtaining fourth information based on the processed third information, wherein the fourth information is a similarity matrix of a sample space of the third information obtained based on a data set of the third information; Obtaining a degree matrix and a Laplace matrix of the third information according to the similarity matrix, and normalizing the obtained results; Decomposing the normalized data, and taking the eigenvectors mapped by the first k eigenvalues ​​to form a first eigenvector matrix, wherein the first eigenvector matrix includes the flow direction of the emergency logistics products, the management cost of the logistics collection point, and the logistics efficiency of the emergency logistics products; The first eigenvector matrix is ​​clustered based on the k-means clustering algorithm to obtain a classification result.

3. An intelligent logistics management device, characterized in that: include: Acquisition module: used to acquire first information and second information of emergency logistics, wherein the first information is the aggregate information of emergency logistics products arriving at logistics collection points, and the logistics collection points are a topological map of all the logistics collection points extending from the location of the emergency logistics products to be transported; The second information is supply chain information of emergency logistics products, and the supply chain information includes logistics warehousing information, logistics vehicle information and delivery task feature information; Analysis module: used for performing grey correlation analysis on the first information and the second information to obtain the correlation degree between the first information and the second information; Wherein, the analysis module includes: An analyzing unit configured to perform sequence analysis on the first information and the second information, wherein the first information is determined as a parent sequence reflecting intelligent logistics management factors, and the second information is determined as a feature sequence reflecting intelligent logistics management factors; The first processing unit is used to perform dimensionless quantization processing on the data after the sequence analysis to obtain a dimensionless quantization processing result; A first solving unit is configured to solve the grey correlation coefficient value between the mother sequence and the characteristic sequence based on the dimensionless quantization processing result; A calculation unit is configured to calculate the correlation value between each of the parent sequence and the characteristic sequence according to the grey correlation value, and to sort and analyze the correlation values; Processing module: used for sending the correlation degree, the first information and the second information to the optimized logistics model for processing to obtain third information; Calculation module: used to calculate the third information according to the heuristic algorithm to obtain an optimized logistics management result; Wherein, the calculation module includes: A second decomposition unit is configured to perform time series decomposition on the third information using a signal decomposition algorithm to obtain decomposed data; Generate unit: used to set parameters and generate formulas: s ij＝ s minj +a*(s maxj -s minj ) Among them, s ij represents the i-th spider monkey in the j-th dimension, s maxj , s minj They represent the upper and lower boundaries of the spider monkey in the j-th dimension of the search space, and a represents a random number uniformly distributed in [0,1]. Sorting unit: used to generate the initial positions of all spider monkeys in the algorithm group according to the formula and sort them based on fitness; Selection unit: used to select the local leader with high fitness value as the global leader through greedy selection strategy; Recording unit: used to select the optimal solution as the global leader based on the decomposed data and the global leader, and record it as the optimized logistics management result.

4. The intelligent logistics management device according to claim 3, characterized in that: The processing module then includes: A second solving unit is configured to process the third information and solve fourth information based on the processed third information, wherein the fourth information is a similarity matrix of a sample space of the third information obtained based on a data set of the third information; A second processing unit is configured to obtain a degree matrix and a Laplace matrix of the third information according to the similarity matrix, and perform normalization processing on the obtained results; A first decomposition unit is configured to decompose the normalized data and take the eigenvectors mapped by the first k eigenvalues ​​to form a first eigenvector matrix, wherein the first eigenvector matrix includes the flow direction of the emergency logistics products, the management cost of the logistics collection point, and the logistics efficiency of the emergency logistics products; Clustering unit: used to cluster the first eigenvector matrix based on the k-means clustering algorithm to obtain a classification result.

5. An intelligent logistics management device, characterized in that: include: memory for storing computer programs; A processor, configured to implement the steps of the intelligent logistics management method according to any one of claims 1 to 2 when executing the computer program.

6. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the intelligent logistics management method according to any one of claims 1 to 2.

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