CN113850500A - Logistics risk early warning method and device based on DE-BP neural network and electronic equipment - Google Patents
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Abstract
The invention relates to a logistics risk early warning method, a logistics risk early warning device, electronic equipment and a computer readable storage medium based on a DE-BP neural network, wherein the method comprises the following steps: acquiring logistics risk data, and generating a data set according to the logistics risk data; training the DE-BP neural network according to the data set to obtain the DE-BP neural network after complete training; and acquiring real-time data of logistics, and acquiring a logistics risk early warning result according to the real-time data and the DE-BP neural network after the training is complete. The logistics risk early warning method based on the DE-BP neural network improves the efficiency and the fault tolerance rate of the logistics risk early warning process.
Description
Technical Field
The invention relates to the field of production logistics, in particular to a logistics risk early warning method and device based on a DE-BP neural network, electronic equipment and a computer readable storage medium.
Background
The enterprise production logistics is logistics activity generated in the enterprise to ensure production needs, and the improvement of the production logistics operation plays an important role in ensuring the production of the enterprise, reducing the logistics cost and ensuring the delivery period. The logistics risk early warning method is a method for reducing risk occurrence by identifying risk in advance, evaluating and controlling possible harm and consequence in advance and adopting effective means.
The conventional logistics risk early warning method adopts a conventional neural network to carry out risk early warning, so that the early warning accuracy can be improved to a certain extent, but because the initial weight and the threshold value acquisition method of the conventional neural network are randomly generated, a better weight can be obtained only by continuously adjusting the neural network in the training process, the efficiency is low, and a larger error is easy to generate.
Disclosure of Invention
In view of the above, it is necessary to provide a logistics risk early warning method, a logistics risk early warning device, an electronic device and a computer-readable storage medium based on a DE-BP neural network, so as to solve the problems of low fault tolerance and low efficiency in the logistics risk early warning process of a manufacturing enterprise.
In order to solve the problems, the invention provides a logistics risk early warning method based on a DE-BP neural network, which comprises the following steps:
acquiring logistics risk data, and generating a data set according to the logistics risk data;
training the DE-BP neural network according to the data set to obtain the DE-BP neural network after complete training;
and acquiring real-time data of logistics, and acquiring a logistics risk early warning result according to the real-time data and the DE-BP neural network after the training is complete.
Further, generating a data set according to the logistics risk data, comprising:
and performing dimensionality reduction on the logistics risk data to obtain logistics risk data subjected to dimensionality reduction, performing normalization processing on the logistics risk data subjected to dimensionality reduction to obtain processed logistics risk data, and generating a data set according to the processed logistics risk data.
Further, performing dimensionality reduction processing on the logistics risk data to obtain logistics risk data subjected to dimensionality reduction, and the method comprises the following steps:
and acquiring the contribution rate of the logistics risk data by using a principal component algorithm, and when the cumulative sum of the contribution rate reaches a preset value, obtaining the corresponding logistics risk data after dimensionality reduction.
Further, before training the DE-BP neural network according to the data set, the method further includes:
and forming a BP neural network by using an input layer, a hidden layer and an output layer, and optimizing the initial weight and the threshold of the BP neural network by using a differential evolution algorithm to construct the DE-BP neural network.
Further, forming a BP neural network using the input layer, the hidden layer, and the output layer, comprising:
acquiring the number of nodes of an input layer and the number of nodes of an output layer, and determining the number of nodes of a hidden layer according to the number of nodes of the input layer and the number of nodes of the output layer, wherein the formula is
Wherein d is the number of nodes of the hidden layer, x is the number of nodes of the input layer, b is the number of nodes of the output layer, and epsilon is a constant between 1 and 10;
and forming a BP neural network by using the input layer, the hidden layer and the output layer determined by the node number.
Further, optimizing the initial weight and the threshold of the BP neural network by using a differential evolution algorithm to construct the DE-BP neural network, wherein the method comprises the following steps:
randomly generating an initial population by using the data set, training a BP neural network by using the initial population to obtain a training error of the BP neural network, and if the training error meets a termination condition, acquiring an optimal initial weight and a threshold of the BP neural network;
if the training error does not meet the termination condition, performing variation and cross operation to obtain an intermediate population, selecting the intermediate population and the initial population to obtain a new population, and acquiring the training error again by using the new population;
and inputting the obtained optimal initial weight and threshold of the BP neural network into the BP neural network to construct the DE-BP neural network.
Further, acquiring a logistics risk early warning result according to the real-time data and the DE-BP neural network after the training is complete, wherein the method comprises the following steps:
and inputting the real-time data into the DE-BP neural network after the training is complete to obtain a risk value, acquiring a logistics risk grade according to the risk value, and taking the logistics risk grade as a logistics risk early warning result.
The invention also provides a logistics risk early warning device based on the DE-BP neural network, which comprises a data acquisition module, a network training module and a risk early warning module;
the data acquisition module is used for acquiring logistics risk data and generating a data set according to the logistics risk data;
the network training module is used for training the DE-BP neural network according to the data set to obtain the DE-BP neural network after complete training;
and the risk early warning module is used for acquiring real-time data of logistics and acquiring a logistics risk early warning result according to the real-time data and the DE-BP neural network after the training is complete.
The invention also provides an electronic device, which comprises a processor and a memory, wherein the memory is stored with a computer program, and when the computer program is executed by the processor, the logistics risk early warning method based on the DE-BP neural network is realized.
The invention also provides a computer readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the logistics risk early warning method based on the DE-BP neural network according to any one of the above technical solutions is implemented.
The beneficial effects of adopting the above embodiment are: according to the logistics risk early warning method based on the DE-BP neural network, provided by the invention, the logistics risk data are obtained, the data are preprocessed and then put into the DE-BP neural network, the initial weight and the threshold of the neural network are optimized by using a differential evolution algorithm, the logistics risk early warning result is obtained, and the fault tolerance rate of the neural network and the logistics risk early warning efficiency are improved.
Drawings
Fig. 1 is a schematic diagram of an application scenario of a logistics risk early warning device based on a DE-BP neural network provided by the invention;
FIG. 2 is a schematic flow chart of an embodiment of a logistics risk early warning method based on a DE-BP neural network provided by the invention;
fig. 3 is a schematic flow chart of constructing a logistics risk early warning index system provided in the embodiment of the present invention;
fig. 4 is a schematic diagram of a logistics risk early warning index system provided in an embodiment of the invention;
fig. 5 is a schematic structural diagram of a BP neural network provided in an embodiment of the present invention;
FIG. 6 is a schematic flow chart of a DE-BP neural network provided in an embodiment of the present invention;
FIG. 7 is a block diagram of an embodiment of a logistics risk early warning device based on a DE-BP neural network provided by the invention;
fig. 8 is a block diagram of an embodiment of an electronic device provided in the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
The invention provides a logistics risk early warning method and device based on a DE-BP neural network, electronic equipment and a computer readable storage medium, which are respectively explained in detail below.
Fig. 1 is a schematic diagram of an application scenario of the logistics risk early warning device based on the DE-BP neural network provided by the present invention, and the system may include a server 100, where the logistics risk early warning device based on the DE-BP neural network is integrated in the server 100, such as the server in fig. 1.
The server 100 in the embodiment of the present invention is mainly used for:
acquiring logistics risk data, and generating a data set according to the logistics risk data;
training the DE-BP neural network according to the data set to obtain the DE-BP neural network after complete training;
and acquiring real-time data of logistics, and acquiring a logistics risk early warning result according to the real-time data and the DE-BP neural network after the training is complete.
In this embodiment of the present invention, the server 100 may be an independent server, or may be a server network or a server cluster composed of servers, for example, the server 100 described in this embodiment of the present invention includes, but is not limited to, a computer, a network host, a single network server, a plurality of network server sets, or a cloud server composed of a plurality of servers. Among them, the Cloud server is constituted by a large number of computers or web servers based on Cloud Computing (Cloud Computing).
It is to be understood that the terminal 200 used in the embodiments of the present invention may be a device that includes both receiving and transmitting hardware, i.e., a device having receiving and transmitting hardware capable of performing two-way communication over a two-way communication link. Such a device may include: a cellular or other communication device having a single line display or a multi-line display or a cellular or other communication device without a multi-line display. The specific terminal 200 may be a desktop, a laptop, a web server, a Personal Digital Assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, an embedded device, and the like, and the type of the terminal 200 is not limited in this embodiment.
Those skilled in the art can understand that the application environment shown in fig. 1 is only one application scenario of the present invention, and does not constitute a limitation on the application scenario of the present invention, and that other application environments may further include more or fewer terminals than those shown in fig. 1, for example, only 2 terminals are shown in fig. 1, and it can be understood that the logistics risk early warning apparatus based on the DE-BP neural network may further include one or more other terminals, which is not limited herein.
In addition, as shown in fig. 1, the logistics risk early warning device based on the DE-BP neural network may further include a memory 200 for storing data, such as logistics risk data.
It should be noted that the scene schematic diagram of the logistics risk early warning device based on the DE-BP neural network shown in fig. 1 is only an example, the logistics risk early warning device based on the DE-BP neural network and the scene described in the embodiment of the present invention are for more clearly illustrating the technical solution of the embodiment of the present invention, and do not form a limitation on the technical solution provided by the embodiment of the present invention, and it can be known by those skilled in the art that the technical solution provided in the embodiment of the present invention is also applicable to similar technical problems along with the evolution of the logistics risk early warning device based on the DE-BP neural network and the occurrence of a new service scene.
The embodiment of the invention provides a logistics risk early warning method based on a DE-BP neural network, which has a flow schematic diagram, as shown in FIG. 2, and comprises the following steps:
step S201, acquiring logistics risk data, and generating a data set according to the logistics risk data;
step S202, training the DE-BP neural network according to the data set to obtain the DE-BP neural network after complete training;
and S203, collecting real-time data of logistics, and acquiring a logistics risk early warning result according to the real-time data and the DE-BP neural network after the training is complete.
In a specific embodiment, a flow diagram of a logistics risk early warning index system is constructed, as shown in fig. 3, risk data generated in a logistics process of a production enterprise is acquired as a basic data set to construct an evaluation index set, the evaluation index set is evaluated according to an evaluation standard set by an expert, an evaluation model is constructed to obtain an evaluation result, that is, the logistics risk early warning index system, and as shown in fig. 4, logistics risk data is acquired according to the logistics risk early warning index system.
As a preferred embodiment, generating a data set according to the logistics risk data comprises:
and performing dimensionality reduction on the logistics risk data to obtain logistics risk data subjected to dimensionality reduction, performing normalization processing on the logistics risk data subjected to dimensionality reduction to obtain processed logistics risk data, and generating a data set according to the processed logistics risk data.
It should be noted that, the dimension reduction processing and normalization processing are performed on the logistics risk data, so that the data can be simplified, and the neural network training efficiency can be improved.
As a preferred embodiment, performing dimension reduction processing on the logistics risk data to obtain dimension-reduced logistics risk data includes:
and acquiring the contribution rate of the logistics risk data by using a principal component algorithm, and when the cumulative sum of the contribution rate reaches a preset value, obtaining the corresponding logistics risk data after dimensionality reduction.
In a specific embodiment, a principal component algorithm is used for performing principal component analysis on the logistics risk data to obtain a characteristic index, a characteristic value and a contribution rate corresponding to the logistics risk data, and a comprehensive index and a contribution rate of the logistics risk data, as shown in table 1 below, when the cumulative contribution rate reaches 85%, the logistics risk data corresponding to the serial numbers 1-6 are output.
TABLE 1 comprehensive index of logistics risk data and its contribution rate
As a preferred embodiment, before training the DE-BP neural network according to the data set, the method further comprises:
and forming a BP neural network by using an input layer, a hidden layer and an output layer, and optimizing the initial weight and the threshold of the BP neural network by using a differential evolution algorithm to construct the DE-BP neural network.
It should be noted that, the initial weight and the threshold of the BP neural network are optimized by using a differential evolution algorithm, so that the training process of the neural network is simplified, and the fault tolerance and the training efficiency of the neural network are improved.
As a preferred embodiment, a BP neural network is formed by using an input layer, a hidden layer and an output layer, and comprises:
acquiring the number of nodes of an input layer and the number of nodes of an output layer, and determining the number of nodes of a hidden layer according to the number of nodes of the input layer and the number of nodes of the output layer, wherein the formula is
Wherein d is the number of nodes of the hidden layer, x is the number of nodes of the input layer, b is the number of nodes of the output layer, and epsilon is a constant between 1 and 10;
and forming a BP neural network by using the input layer, the hidden layer and the output layer determined by the node number.
In a specific embodiment, as shown in fig. 5, a schematic structural diagram of the BP neural network determines that the number of nodes of the input layer is 14 according to a logistics risk early warning index system, and determines that the number of nodes of the output layer is 4 according to a logistics risk level.
As a preferred embodiment, optimizing the initial weight and the threshold of the BP neural network by using a differential evolution algorithm to construct a DE-BP neural network, including:
randomly generating an initial population by using the data set, training a BP neural network by using the initial population to obtain a training error of the BP neural network, and if the training error meets a termination condition, acquiring an optimal initial weight and a threshold of the BP neural network;
if the training error does not meet the termination condition, performing variation and cross operation to obtain an intermediate population, selecting the intermediate population and the initial population to obtain a new population, and acquiring the training error again by using the new population;
and inputting the obtained optimal initial weight and threshold of the BP neural network into the BP neural network to construct the DE-BP neural network.
In a specific embodiment, a flow diagram of the DE-BP neural network is shown in FIG. 6.
As a preferred embodiment, obtaining a logistics risk early warning result according to the real-time data and the DE-BP neural network after the training is completed includes:
and inputting the real-time data into the DE-BP neural network after the training is complete to obtain a risk value, acquiring a logistics risk grade according to the risk value, and taking the logistics risk grade as a logistics risk early warning result.
In one specific example, the criteria for classifying the risk levels of logistics are shown in table 2 below.
TABLE 2 criteria for classifying the risk level of logistics
The embodiment of the invention provides a logistics risk early warning device based on a DE-BP neural network, which has a structural block diagram, as shown in FIG. 7, and comprises a data acquisition module 701, a network training module 702 and a risk early warning module 703;
the data acquisition module 701 is configured to acquire logistics risk data and generate a data set according to the logistics risk data;
the network training module 702 is configured to train the DE-BP neural network according to the data set to obtain a completely trained DE-BP neural network;
the risk early warning module 703 is configured to acquire real-time data of logistics, and obtain a logistics risk early warning result according to the real-time data and the DE-BP neural network after the training is complete.
As shown in fig. 8, in the above logistics risk early warning method based on the DE-BP neural network, an embodiment of the present invention further provides an electronic device, where the electronic device may be a mobile terminal, a desktop computer, a notebook, a palm computer, a server, or other computing devices. The electronic device comprises a processor 10, a memory 20 and a display 30.
The storage 20 may in some embodiments be an internal storage unit of the computer device, such as a hard disk or a memory of the computer device. The memory 20 may also be an external storage device of the computer device in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the computer device. Further, the memory 20 may also include both an internal storage unit and an external storage device of the computer device. The memory 20 is used for storing application software installed in the computer device and various data, such as program codes installed in the computer device. The memory 20 may also be used to temporarily store data that has been output or is to be output. In an embodiment, the memory 20 stores a logistics risk early warning method program 40 based on a DE-BP neural network, and the logistics risk early warning method program 40 based on the DE-BP neural network can be executed by the processor 10, so as to implement the logistics risk early warning method based on the DE-BP neural network according to the embodiments of the present invention.
The processor 10 may be a Central Processing Unit (CPU), a microprocessor or other data Processing chip in some embodiments, and is used to execute program codes stored in the memory 20 or process data, such as executing a logistics risk warning method program based on a DE-BP neural network.
The display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch panel, or the like in some embodiments. The display 30 is used for displaying information at the computer device and for displaying a visual user interface. The components 10-30 of the computer device communicate with each other via a system bus.
In one embodiment, when the processor 10 executes the logistics risk early warning method program 40 based on the DE-BP neural network in the memory 20, the following steps are implemented:
acquiring logistics risk data, and generating a data set according to the logistics risk data;
training the DE-BP neural network according to the data set to obtain the DE-BP neural network after complete training;
and acquiring real-time data of logistics, and acquiring a logistics risk early warning result according to the real-time data and the DE-BP neural network after the training is complete.
The embodiment also provides a computer readable storage medium, on which a logistics risk early warning method program based on the DE-BP neural network is stored, and when being executed by a processor, the logistics risk early warning method program based on the DE-BP neural network realizes the following steps:
acquiring logistics risk data, and generating a data set according to the logistics risk data;
training the DE-BP neural network according to the data set to obtain the DE-BP neural network after complete training;
and acquiring real-time data of logistics, and acquiring a logistics risk early warning result according to the real-time data and the DE-BP neural network after the training is complete.
The invention discloses a logistics risk early warning method, a logistics risk early warning device, electronic equipment and a computer readable storage medium based on a DE-BP neural network.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A logistics risk early warning method based on a DE-BP neural network is characterized by comprising the following steps:
acquiring logistics risk data, and generating a data set according to the logistics risk data;
training the DE-BP neural network according to the data set to obtain the DE-BP neural network after complete training;
and acquiring real-time data of logistics, and acquiring a logistics risk early warning result according to the real-time data and the DE-BP neural network after the training is complete.
2. The DE-BP neural network-based logistics risk early warning method of claim 1, wherein generating a data set according to the logistics risk data comprises:
and performing dimensionality reduction on the logistics risk data to obtain logistics risk data subjected to dimensionality reduction, performing normalization processing on the logistics risk data subjected to dimensionality reduction to obtain processed logistics risk data, and generating a data set according to the processed logistics risk data.
3. The logistics risk early warning method based on the DE-BP neural network as claimed in claim 2, wherein the logistics risk data is subjected to dimensionality reduction processing to obtain the logistics risk data after dimensionality reduction, comprising:
and acquiring the contribution rate of the logistics risk data by using a principal component algorithm, and when the cumulative sum of the contribution rate reaches a preset value, obtaining the corresponding logistics risk data after dimensionality reduction.
4. The method for logistics risk early warning based on DE-BP neural network as claimed in claim 1, wherein before training the DE-BP neural network according to the data set, further comprising:
and forming a BP neural network by using an input layer, a hidden layer and an output layer, and optimizing the initial weight and the threshold of the BP neural network by using a differential evolution algorithm to construct the DE-BP neural network.
5. The logistics risk early warning method based on the DE-BP neural network as claimed in claim 4, wherein the BP neural network is formed by using the input layer, the hidden layer and the output layer, and comprises:
acquiring the number of nodes of an input layer and the number of nodes of an output layer, and determining the number of nodes of a hidden layer according to the number of nodes of the input layer and the number of nodes of the output layer, wherein the formula is
Wherein d is the number of nodes of the hidden layer, x is the number of nodes of the input layer, b is the number of nodes of the output layer, and epsilon is a constant between 1 and 10;
and forming a BP neural network by using the input layer, the hidden layer and the output layer determined by the node number.
6. The logistics risk early warning method based on the DE-BP neural network as claimed in claim 4, wherein the initial weight and the threshold of the BP neural network are optimized by using a differential evolution algorithm to construct the DE-BP neural network, comprising:
randomly generating an initial population by using the data set, training a BP neural network by using the initial population to obtain a training error of the BP neural network, and if the training error meets a termination condition, acquiring an optimal initial weight and a threshold of the BP neural network;
if the training error does not meet the termination condition, performing variation and cross operation to obtain an intermediate population, selecting the intermediate population and the initial population to obtain a new population, and acquiring the training error again by using the new population;
and inputting the obtained optimal initial weight and threshold of the BP neural network into the BP neural network to construct the DE-BP neural network.
7. The logistics risk early warning method based on the DE-BP neural network as claimed in claim 1, wherein obtaining a logistics risk early warning result according to the real-time data and the DE-BP neural network after the training is complete comprises:
and inputting the real-time data into the DE-BP neural network after the training is complete to obtain a risk value, acquiring a logistics risk grade according to the risk value, and taking the logistics risk grade as a logistics risk early warning result.
8. A logistics risk early warning device based on a DE-BP neural network is characterized by comprising a data acquisition module, a network training module and a risk early warning module;
the data acquisition module is used for acquiring logistics risk data and generating a data set according to the logistics risk data;
the network training module is used for training the DE-BP neural network according to the data set to obtain the DE-BP neural network after complete training;
and the risk early warning module is used for acquiring real-time data of logistics and acquiring a logistics risk early warning result according to the real-time data and the DE-BP neural network after the training is complete.
9. An electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to implement the method for logistics risk pre-warning based on the DE-BP neural network according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method for logistics risk pre-warning based on the DE-BP neural network according to any one of claims 1 to 7.
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| CN114387767A (en) * | 2021-12-03 | 2022-04-22 | 中国铁道科学研究院集团有限公司标准计量研究所 | Railway dangerous goods in-transit state warning method and device based on 5G |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114387767A (en) * | 2021-12-03 | 2022-04-22 | 中国铁道科学研究院集团有限公司标准计量研究所 | Railway dangerous goods in-transit state warning method and device based on 5G |
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