CN113888096A - Goods management method and system for logistics park - Google Patents
Goods management method and system for logistics park Download PDFInfo
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Abstract
The invention relates to a goods management method of a logistics park, which comprises the steps of obtaining goods information and verifying the goods information; classifying the goods information based on the verification without errors by adopting an intelligent classification algorithm to obtain a primary classification result; optimizing the preliminary classification result by using an intelligent optimization algorithm to obtain a classification result; and transporting the goods to the specified position of the warehouse according to the classification result to finish warehousing. The intelligent classification method is used for preliminarily classifying the cargos based on the cargo information by using the intelligent classification algorithm and optimizing the preliminary classification result of the cargos by using the intelligent optimization algorithm, so that an accurate classification result is obtained, the accuracy of cargo classification can be obviously enhanced, the cargos can be searched more easily, the monitoring of the cargos in the logistics park can be realized, the transparency of warehouse management is improved, the cargo management by a warehouse manager is facilitated, and the management efficiency is greatly improved.
Description
Technical Field
The invention relates to the technical field of intelligent logistics, in particular to a goods management method and system for a logistics park.
Background
The logistics park is a place where logistics participants perform centralized logistics activities, is a place where various logistics facilities and different types of logistics enterprises are arranged in a centralized manner in space, is also a gathering point of the logistics enterprises with a certain scale and various service functions, and mainly comprises four parts, namely a goods source part, a carrier, a fleet and peripheral service providers.
The commodity circulation garden drops into big in earlier stage, and drops into output cycle long, leads to the commodity circulation garden to construct can save the rule and economize, and consequently general commodity circulation garden hardware facilities are generally relatively poor, again because commodity circulation practitioner quality is lower relatively, therefore the commodity circulation garden is mostly dirty, in disorder, poor current situation of operation. Moreover, a plurality of logistics companies exist in a general logistics park, the variety of goods is very various, the goods are difficult to manage in different categories, the difficulty is high when the goods are searched, the management efficiency is limited, the warehouse of the logistics park is lack of transparency, and management is not facilitated. Therefore, it is imperative to find a goods management method and a management system for the logistics park.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to overcome the technical defects in the prior art, and provide a method and a system for managing cargos in a logistics park, which utilize an intelligent classification algorithm to preliminarily classify the cargos based on cargo information, and utilize an intelligent optimization algorithm to optimize the preliminary classification result of the cargos to obtain an accurate classification result, can remarkably enhance the accuracy of cargo classification, and is easier to find the cargos, so that the monitoring of the cargos in the logistics park can be realized, the transparency of warehouse management is improved, the cargo management of a warehouse manager is facilitated, and the management efficiency is greatly improved.
In order to solve the technical problem, the invention provides a goods management method for a logistics park, which comprises the following steps:
acquiring goods information, and verifying the goods information;
classifying the goods information based on the verification without errors by adopting an intelligent classification algorithm to obtain a primary classification result;
optimizing the primary classification result by using an intelligent optimization algorithm to obtain a classification result;
the goods are transported to the specified position of the warehouse according to the classification result, and then are put in storage;
wherein classifying the cargo information based on the verification without errors by using an intelligent classification algorithm comprises:
establishing a neural network classification model, setting an initial smoothing factor of the model, inputting the cargo information into the model, and performing probability operation on the characteristic vector of the cargo information and each class matching relation;
integrating the probabilities, and performing weighted average on the outputs of the network nodes belonging to the same class to obtain the posterior probability of each network node;
and taking the network node with the maximum posterior probability as the output of the model to obtain the preliminary classification result of the goods.
In one embodiment of the present invention, the cargo information includes cargo weight, and the cargo weight is used as an important index for cargo classification.
In one embodiment of the invention, when the probability operation is performed on the feature vector of the cargo information and the matching relation of each category, the input-output relation of the jth node of the ith category is
Where k is the number of data feature vectors.
In one embodiment of the invention, the posterior probability of the network node is calculated by the formula
In an embodiment of the present invention, a method for optimizing the preliminary classification result by using an intelligent optimization algorithm includes:
setting parameters of an intelligent optimization algorithm, randomly generating N smoothing factors as initial factor groups in a parameter interval, calculating goods classification results corresponding to the smoothing factors, and selecting the smoothing factor with the most accurate classification result as an optimal smoothing factor;
updating the state of each smoothing factor to obtain a new smoothing factor, calculating a goods classification result corresponding to the new smoothing factor, selecting the new smoothing factor with the most accurate classification result as a new optimal smoothing factor, and when the new optimal smoothing factor is greater than the optimal smoothing factor, using the new optimal smoothing factor as a further optimal smoothing factor,
judging whether the goods classification result corresponding to the further optimal smoothing factor reaches a satisfactory error interval, if so, taking the further optimal smoothing factor as a final optimal smoothing factor, and if not, continuing to update the smoothing factor until the final optimal smoothing factor is found;
and training the final optimal smoothing factor to obtain an optimal goods classification result.
In one embodiment of the present invention, the parameters of the intelligent optimization algorithm include the number N of smoothing factors, the maximum number of iterations T, the sensing range V, the maximum moving step S, and the crowdedness factor D.
In one embodiment of the invention, the update of the smoothing factor requires three rules to be followed: the number of smoothing factors in the perception range V does not exceed the crowdedness factor D at most; the updating directions of adjacent smoothing factors in the sensing range V are consistent; updating towards the center position of the smoothing factor within the perception range V.
In addition, the present invention also provides a cargo management system for a logistics park, comprising:
the information acquisition module is used for acquiring cargo information and verifying the cargo information;
the cargo classification module is used for classifying the cargo information without errors by adopting an intelligent classification algorithm to obtain a primary classification result;
the classification optimization module is used for optimizing the primary classification result by using an intelligent optimization algorithm to obtain a classification result;
the goods warehousing module is used for transporting goods to a position appointed by the warehouse according to the classification result to complete warehousing;
wherein classifying the cargo information based on the verification without errors by using an intelligent classification algorithm comprises:
establishing a neural network classification model, setting an initial smoothing factor of the model, inputting the cargo information into the model, and performing probability operation on the characteristic vector of the cargo information and each class matching relation;
integrating the probabilities, and performing weighted average on the outputs of the network nodes belonging to the same class to obtain the posterior probability of each network node;
and taking the network node with the maximum posterior probability as the output of the model to obtain the preliminary classification result of the goods.
In one embodiment of the invention, when the probability operation is performed on the feature vector of the cargo information and the matching relation of each category, the input-output relation of the jth node of the ith category is
Wherein k is the number of the data feature vectors; integrating the probabilities, and performing weighted average on the outputs of the network nodes belonging to the same class to obtain the posterior probability of each network node, wherein the computing formula of the posterior probability of the network nodes is
In one embodiment of the invention, the classification optimization module comprises a classification optimization unit that performs the following operations:
setting parameters of an intelligent optimization algorithm, randomly generating N smoothing factors as initial factor groups in a parameter interval, calculating goods classification results corresponding to the smoothing factors, and selecting the smoothing factor with the most accurate classification result as an optimal smoothing factor;
updating the state of each smoothing factor to obtain a new smoothing factor, calculating a goods classification result corresponding to the new smoothing factor, selecting the new smoothing factor with the most accurate classification result as a new optimal smoothing factor, and when the new optimal smoothing factor is greater than the optimal smoothing factor, using the new optimal smoothing factor as a further optimal smoothing factor,
judging whether the goods classification result corresponding to the further optimal smoothing factor reaches a satisfactory error interval, if so, taking the further optimal smoothing factor as a final optimal smoothing factor, and if not, continuing to update the smoothing factor until the final optimal smoothing factor is found;
and training the final optimal smoothing factor to obtain an optimal goods classification result.
Compared with the prior art, the technical scheme of the invention has the following advantages:
the invention provides a goods management method and a management system for a logistics park, which are used for preliminarily classifying goods by using an intelligent classification algorithm based on goods information and optimizing the preliminary classification result of the goods by using an intelligent optimization algorithm, so that an accurate classification result is obtained, the accuracy of goods classification can be obviously enhanced, the goods are easier to search, the monitoring of the goods in the logistics park can be realized, the transparency of warehouse management is improved, the goods management by a warehouse manager is facilitated, and the management efficiency is greatly improved.
Drawings
In order that the present disclosure may be more readily and clearly understood, reference is now made to the following detailed description of the embodiments of the present disclosure taken in conjunction with the accompanying drawings, in which
Fig. 1 is a schematic flow chart of a cargo management method for a logistics park according to the present invention.
Fig. 2 is a schematic structural diagram of a cargo management system of a logistics park disclosed in the present invention.
The reference numerals are explained below: 10. an information acquisition module; 20. a cargo classification module; 30. a classification optimization module; 40. and a goods warehousing module.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
Example one
Referring to fig. 1, the present embodiment provides a cargo management method for a logistics park, including the following steps:
s100: acquiring goods information, and verifying the goods information;
s200: classifying the goods information based on the verification without errors by adopting an intelligent classification algorithm to obtain a primary classification result;
s300: optimizing the primary classification result by using an intelligent optimization algorithm to obtain a classification result;
s400: and transporting the goods to the specified position of the warehouse according to the classification result to finish warehousing.
The cargo information described in the present disclosure may be, but is not limited to, cargo weight information, and other key information.
Wherein, the cargo weight information described above is used as an important index for cargo classification.
Wherein, the above-mentioned goods weight information can be weighed by a weighing machine.
The other key information described above may be, but is not limited to, the name of the goods, whether the goods are fragile, the quantity, the warehousing time, the transportation destination of the goods, the personal information of the shipper, the carrier of the goods, and the personal information of the consignee.
The personal information of the owner and the receiver at least comprises a name, a mobile phone number and an address.
The invention discloses a goods management method for a logistics park, which is characterized in that goods are preliminarily classified by using an intelligent classification algorithm based on goods information, and the preliminary classification result of the goods is optimized by using an intelligent optimization algorithm, so that an accurate classification result is obtained, the accuracy of goods classification can be obviously enhanced, the goods are easier to search, the monitoring of the goods in the logistics park can be realized, the transparency of warehouse management is improved, a warehouse manager can conveniently manage the goods, and the management efficiency is greatly improved.
For the cargo management method of the logistics park according to the above embodiment, in step S100, the method for verifying the cargo information includes: and checking the actual cargo information with the cargo information on the bill, checking the conditions of cargo omission and loss, recording the information and sending the information to a cargo owner if the cargo is defective, suspending the cargo to enter a warehouse, and waiting for the confirmation of the cargo owner.
For the cargo management method of the logistics park according to the above embodiment, in step S200, the method for classifying the cargo information based on the verification of no error by using the intelligent classification algorithm includes:
establishing a neural network classification model, setting an initial smoothing factor of the model, inputting the cargo information into the model, and performing probability operation on a characteristic vector of the cargo information and each class matching relation, wherein the input-output relation of the jth node of the ith class is
Wherein k is the number of the data feature vectors;
integrating the probabilities, and performing weighted average on the outputs of the network nodes belonging to the same class to obtain the posterior probability of each network node, wherein the computing formula of the posterior probability of the network nodes is
And taking the network node with the maximum posterior probability as the output of the model to obtain the preliminary classification result of the goods.
In the above disclosed step S200, the output of the neural network classification model is the probability.
For the method for managing goods in a logistics park according to the above embodiment, in step S300, the method for optimizing the preliminary classification result by using the intelligent optimization algorithm includes:
setting parameters of an intelligent optimization algorithm, randomly generating N smoothing factors as initial factor groups in a parameter interval, calculating goods classification results corresponding to the smoothing factors, and selecting the smoothing factor with the most accurate classification result as an optimal smoothing factor;
updating the state of each smoothing factor to obtain a new smoothing factor, calculating a goods classification result corresponding to the new smoothing factor, selecting the new smoothing factor with the most accurate classification result as a new optimal smoothing factor, and when the new optimal smoothing factor is greater than the optimal smoothing factor, using the new optimal smoothing factor as a further optimal smoothing factor,
judging whether the goods classification result corresponding to the further optimal smoothing factor reaches a satisfactory error interval, if so, taking the further optimal smoothing factor as a final optimal smoothing factor, and if not, continuing to update the smoothing factor until the final optimal smoothing factor is found;
and training the final optimal smoothing factor to obtain an optimal goods classification result.
In the above-disclosed step S300, the parameters of the intelligent optimization algorithm include the number N of smoothing factors, the maximum number of iterations T, the sensing range V, the maximum moving step S, and the congestion factor D.
In step S300 disclosed above, the updating of the smoothing factor requires following three rules: the number of smoothing factors in the perception range V does not exceed the crowdedness factor D at most; the updating directions of adjacent smoothing factors in the sensing range V are consistent; updating towards the center position of the smoothing factor within the perception range V.
The cargo type described in the present disclosure may be, but is not limited to, bulk cargo, liquid cargo, piece goods, bulk cargo, dry cargo, wet cargo, packaged cargo, open cargo, bulk cargo, heavy cargo, light cargo, and extra-long cargo.
For the cargo management method of the logistics park in the above embodiment, in step S400, the method for transporting the cargo to the designated location of the warehouse according to the classification result and completing warehousing includes: goods can be transported to the designated position of the warehouse through a forklift, namely the goods are transferred to the wheel-mounted goods shelf, the goods state is updated to be in the warehouse, and after the goods are put in the warehouse, the warehousing information is sent to the goods owner and the goods receiver, so that warehousing is completed.
The wheel-mounted shelf disclosed and described above follows the principle of first-in first-out, fully utilizes the storage space through the wheel-mounted structure, and solves the problem of inconvenient goods management of the traditional shelf.
The invention provides a goods management method and a management system for a logistics park, which are used for preliminarily classifying goods by using an intelligent classification algorithm based on goods information and optimizing the preliminary classification result of the goods by using an intelligent optimization algorithm, so that an accurate classification result is obtained, the accuracy of goods classification can be obviously enhanced, the goods are easier to search, the monitoring of the goods in the logistics park can be realized, the transparency of warehouse management is improved, the goods management by a warehouse manager is facilitated, and the management efficiency is greatly improved.
Example two
In the following, a cargo management system of a logistics park disclosed in the second embodiment of the present invention is introduced, and a cargo management system of a logistics park described below and a cargo management method of a logistics park described above may be referred to each other.
Referring to fig. 2, an embodiment of the present invention discloses a cargo management system for a logistics park, including:
the information acquisition module 10, the information acquisition module 10 is used for acquiring the cargo information and verifying the cargo information;
the cargo classification module 20, the cargo classification module 20 is configured to classify the cargo information based on the verification without error by using an intelligent classification algorithm to obtain a preliminary classification result;
a classification optimization module 30, wherein the classification optimization module 30 is configured to optimize the preliminary classification result by using an intelligent optimization algorithm to obtain a classification result;
and the goods warehousing module 40 is used for transporting goods to the specified position of the warehouse according to the classification result and completing warehousing.
For the cargo management system of the logistics park according to the above embodiment, in the cargo classification module 20, the classifying the cargo information based on the verification without error by using the intelligent classification algorithm includes:
establishing a neural network classification model, setting an initial smoothing factor of the model, inputting the cargo information into the model, and performing probability operation on a characteristic vector of the cargo information and each class matching relation, wherein the input-output relation of the jth node of the ith class is
Wherein k is the number of the data feature vectors;
integrating the probabilities, and performing weighted average on the outputs of the network nodes belonging to the same class to obtain the posterior probability of each network node, wherein the computing formula of the posterior probability of the network nodes is
And taking the network node with the maximum posterior probability as the output of the model to obtain the preliminary classification result of the goods.
In the cargo classification module 20 disclosed above, the output of the neural network classification model is the probability.
For the cargo management system of the logistics park in the above embodiment, in the classification optimization module 30, the method for optimizing the preliminary classification result by using the intelligent optimization algorithm includes:
setting parameters of an intelligent optimization algorithm, randomly generating N smoothing factors as initial factor groups in a parameter interval, calculating goods classification results corresponding to the smoothing factors, and selecting the smoothing factor with the most accurate classification result as an optimal smoothing factor;
updating the state of each smoothing factor to obtain a new smoothing factor, calculating a goods classification result corresponding to the new smoothing factor, selecting the new smoothing factor with the most accurate classification result as a new optimal smoothing factor, and when the new optimal smoothing factor is greater than the optimal smoothing factor, using the new optimal smoothing factor as a further optimal smoothing factor,
judging whether the goods classification result corresponding to the further optimal smoothing factor reaches a satisfactory error interval, if so, taking the further optimal smoothing factor as a final optimal smoothing factor, and if not, continuing to update the smoothing factor until the final optimal smoothing factor is found;
and training the final optimal smoothing factor to obtain an optimal goods classification result.
In the classification optimization module 30 disclosed above, the parameters of the intelligent optimization algorithm include the number N of smoothing factors, the maximum number of iterations T, the sensing range V, the maximum moving step S, and the congestion factor D.
In the classification optimization module 30 disclosed above, the updating of the smoothing factor requires following three rules: the number of smoothing factors in the perception range V does not exceed the crowdedness factor D at most; the updating directions of adjacent smoothing factors in the sensing range V are consistent; updating towards the center position of the smoothing factor within the perception range V.
For the cargo management system of the logistics park in the above embodiment, in the cargo warehousing module, the step of transporting the cargo to the designated position of the warehouse according to the classification result to complete warehousing comprises: goods can be transported to the designated position of the warehouse through a forklift, namely the goods are transferred to the wheel-mounted goods shelf, the goods state is updated to be in the warehouse, and after the goods are put in the warehouse, the warehousing information is sent to the goods owner and the goods receiver, so that warehousing is completed.
The wheel-mounted shelf disclosed and described above follows the principle of first-in first-out, fully utilizes the storage space through the wheel-mounted structure, and solves the problem of inconvenient goods management of the traditional shelf.
The invention provides a goods management system of a logistics park, which is used for primarily classifying goods by using an intelligent classification algorithm based on goods information and optimizing the primarily classified results of the goods by using an intelligent optimization algorithm, so that accurate classification results are obtained, the accuracy of goods classification can be obviously enhanced, the goods are easier to search, the monitoring of the goods in the logistics park can be realized, the transparency of warehouse management is improved, a warehouse manager can conveniently manage the goods, and the management efficiency is greatly improved.
The goods management system of the logistics park of the embodiment is used for implementing the goods management method of the logistics park, so the specific implementation of the system can be seen from the above embodiment part of the goods management method of the logistics park, so the specific implementation thereof can refer to the description of the corresponding partial embodiments, and will not be further described herein.
In addition, since the cargo management system of the logistics park of this embodiment is used for implementing the cargo management method of the logistics park, the role thereof corresponds to that of the above method, and details are not described here.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of the invention may be made without departing from the spirit or scope of the invention.
Claims (10)
1. A method for managing goods in a logistics park, comprising:
acquiring goods information, and verifying the goods information;
classifying the goods information based on the verification without errors by adopting an intelligent classification algorithm to obtain a primary classification result;
optimizing the primary classification result by using an intelligent optimization algorithm to obtain a classification result;
the goods are transported to the specified position of the warehouse according to the classification result, and then are put in storage;
wherein classifying the cargo information based on the verification without errors by using an intelligent classification algorithm comprises:
establishing a neural network classification model, setting an initial smoothing factor of the model, inputting the cargo information into the model, and performing probability operation on the characteristic vector of the cargo information and each class matching relation;
integrating the probabilities, and performing weighted average on the outputs of the network nodes belonging to the same class to obtain the posterior probability of each network node;
and taking the network node with the maximum posterior probability as the output of the model to obtain the preliminary classification result of the goods.
2. The method for managing goods in a logistics park of claim 1, wherein: the cargo information includes cargo weight, which is used as an important index for cargo classification.
3. The method for managing goods in a logistics park of claim 1, wherein: when probability operation is carried out on the characteristic vector of the cargo information and each class matching relation, the input-output relation of the jth node of the ith class is
Where k is the number of data feature vectors.
4. The method for managing goods in a logistics park of claim 3, wherein: the posterior probability of the network node is calculated by the formula
5. The method for managing goods in a logistics park of claim 1, wherein: the method for optimizing the primary classification result by using an intelligent optimization algorithm comprises the following steps:
setting parameters of an intelligent optimization algorithm, randomly generating N smoothing factors as initial factor groups in a parameter interval, calculating goods classification results corresponding to the smoothing factors, and selecting the smoothing factor with the most accurate classification result as an optimal smoothing factor;
updating the state of each smoothing factor to obtain a new smoothing factor, calculating a goods classification result corresponding to the new smoothing factor, selecting the new smoothing factor with the most accurate classification result as a new optimal smoothing factor, and when the new optimal smoothing factor is greater than the optimal smoothing factor, using the new optimal smoothing factor as a further optimal smoothing factor,
judging whether the goods classification result corresponding to the further optimal smoothing factor reaches a satisfactory error interval, if so, taking the further optimal smoothing factor as a final optimal smoothing factor, and if not, continuing to update the smoothing factor until the final optimal smoothing factor is found;
and training the final optimal smoothing factor to obtain an optimal goods classification result.
6. The method for managing goods in a logistics park of claim 5, wherein: the parameters of the intelligent optimization algorithm comprise the number N of smoothing factors, the maximum iteration number T, a sensing range V, the maximum moving step S and a crowding factor D.
7. The method for managing goods in a logistics park of claim 5, wherein: updating the smoothing factor requires following three rules: the number of smoothing factors in the perception range V does not exceed the crowdedness factor D at most; the updating directions of adjacent smoothing factors in the sensing range V are consistent; updating towards the center position of the smoothing factor within the perception range V.
8. A cargo management system for a logistics park, comprising:
the information acquisition module is used for acquiring cargo information and verifying the cargo information;
the cargo classification module is used for classifying the cargo information without errors by adopting an intelligent classification algorithm to obtain a primary classification result;
the classification optimization module is used for optimizing the primary classification result by using an intelligent optimization algorithm to obtain a classification result;
the goods warehousing module is used for transporting goods to a position appointed by the warehouse according to the classification result to complete warehousing;
wherein classifying the cargo information based on the verification without errors by using an intelligent classification algorithm comprises:
establishing a neural network classification model, setting an initial smoothing factor of the model, inputting the cargo information into the model, and performing probability operation on the characteristic vector of the cargo information and each class matching relation;
integrating the probabilities, and performing weighted average on the outputs of the network nodes belonging to the same class to obtain the posterior probability of each network node;
and taking the network node with the maximum posterior probability as the output of the model to obtain the preliminary classification result of the goods.
9. The system for managing cargo in a logistics park of claim 8 wherein: when probability operation is carried out on the characteristic vector of the cargo information and each class matching relation, the input-output relation of the jth node of the ith class is
Wherein k is the number of the data feature vectors; integrating the probabilities to belong toThe output of the network nodes of the same class is weighted and averaged to obtain the posterior probability of each network node, and the posterior probability of each network node is calculated according to the formula
10. The system for managing cargo in a logistics park of claim 8 wherein: the classification optimization module comprises a classification optimization unit which executes the following operations:
setting parameters of an intelligent optimization algorithm, randomly generating N smoothing factors as initial factor groups in a parameter interval, calculating goods classification results corresponding to the smoothing factors, and selecting the smoothing factor with the most accurate classification result as an optimal smoothing factor;
updating the state of each smoothing factor to obtain a new smoothing factor, calculating a goods classification result corresponding to the new smoothing factor, selecting the new smoothing factor with the most accurate classification result as a new optimal smoothing factor, and when the new optimal smoothing factor is greater than the optimal smoothing factor, using the new optimal smoothing factor as a further optimal smoothing factor,
judging whether the goods classification result corresponding to the further optimal smoothing factor reaches a satisfactory error interval, if so, taking the further optimal smoothing factor as a final optimal smoothing factor, and if not, continuing to update the smoothing factor until the final optimal smoothing factor is found;
and training the final optimal smoothing factor to obtain an optimal goods classification result.
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