CN109242391B - Cargo identification method and device - Google Patents

Cargo identification method and device Download PDF

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CN109242391B
CN109242391B CN201811096336.7A CN201811096336A CN109242391B CN 109242391 B CN109242391 B CN 109242391B CN 201811096336 A CN201811096336 A CN 201811096336A CN 109242391 B CN109242391 B CN 109242391B
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goods
cargo
data
user
source
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CN109242391A (en
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施文进
施俊
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Wellong Etown International Logistics Co ltd
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Wellong Etown International Logistics Co ltd
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    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/08Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
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    • G06Q10/0838Historical data

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Abstract

The embodiment of the invention provides a cargo identification method and a cargo identification device, wherein the method comprises the following steps: generating a cargo identification model according to the freight historical data; generating a user identification model according to historical data of a source of goods issued by a user; receiving a goods source issuing request of a user, responding to the goods source issuing request, and identifying information carried by the goods source issuing request by using the goods identification model and the user identification model to obtain an identification result of the authenticity of the goods source. The technical scheme provided by the embodiment of the invention solves the safety problem of the goods transportation electronic commerce platform caused by the fact that the goods owner member issues false goods information to a certain extent.

Description

Cargo identification method and device
[ technical field ] A method for producing a semiconductor device
The invention relates to the field of artificial intelligence, in particular to a cargo identification method and a cargo identification device.
[ background of the invention ]
Two major core contents of cargo transportation: one is quick, accurate and economical delivery service; one is safe and reliable cargo transportation and settlement. The initial freight state is still in a rough development stage, and the freight distribution is still stopped in the original driver goods-finding and goods-vehicle-finding stages. However, the driver's goods and vehicles are found, which reduces the efficiency of goods transportation and increases the cost. Therefore, a cargo transportation electronic commerce platform appears later, wherein a vehicle and ship member can use a mobile terminal to issue information of idle transportation capacity of the vehicle and ship member to the platform, a cargo owner member uses the mobile terminal to issue information of cargos to be transported to the platform, and the platform checks the cargos and distributes the vehicles and the ships for transportation after receiving the information. However, in the prior art, some cargo owner members issue false cargo information, which causes the cargo transportation e-commerce platform to have a security problem and lower security.
[ summary of the invention ]
In view of this, embodiments of the present invention provide a cargo identification method and apparatus, which solve the problem of security of a cargo transportation e-commerce platform due to false cargo information issued by a cargo owner member to a certain extent.
In a first aspect, an embodiment of the present invention provides a cargo identification method, including:
generating a cargo identification model according to the freight historical data;
generating a user identification model according to historical data of a source of goods issued by a user;
receiving a goods source issuing request of a user, responding to the goods source issuing request, and identifying information carried by the goods source issuing request by using the goods identification model and the user identification model to obtain an identification result of the authenticity of the goods source.
The above-described aspects and any possible implementations further provide an implementation in which the shipment history data includes a shipment type, a shipment size, a shipment weight, a shipping distance, and a shipping price.
The above-described aspects and any possible implementation further provide an implementation that generates a cargo identification model according to the shipment history data, including:
acquiring a plurality of freight history data, and extracting a cargo type, a cargo size, a cargo weight, a transportation distance and a transportation price from each freight history data to be used as cargo characteristic data;
and training the cargo characteristic data to generate a cargo identification model.
The above-described aspect and any possible implementation further provide an implementation that the user published source history data includes a user identification, a publication time, a published cargo type, a starting place, and a transportation distance.
The above-described aspects and any possible implementation manners further provide an implementation manner, wherein generating a user identification model according to historical data of a user publishing goods source comprises:
acquiring historical data of a plurality of user release sources, and extracting user identification, release time, types of released goods, starting places and transportation distances from the historical data of the user release sources to serve as user characteristic data;
and training the user characteristic data to generate a user identification model.
The above aspect and any possible implementation manner further provide an implementation manner, where the identifying information carried by the source issuing request is identified by using the goods identification model and the user identification model, so as to obtain an identification result of the authenticity of the source, including:
identifying the goods source information carried by the goods source issuing request by using the goods identification model to obtain a first identification result, and identifying the goods source information and the user information carried by the goods source issuing request by using the user identification model to obtain a second identification result;
if the first identification result is that the goods source is real, and the second identification result is that the goods source is real, obtaining a real identification result of the goods source; or if the first identification result is that the goods source is not authentic, and/or the second identification result is that the goods source is not authentic, obtaining an identification result that the goods source is not authentic.
In a second aspect, an embodiment of the present invention provides a cargo identification device, including:
the first model generation module is used for generating a cargo identification model according to the freight historical data;
the second model generation module is used for generating a user identification model according to historical data of a source of goods issued by a user;
the receiving module is used for receiving a goods source issuing request of a user;
and the identification module is used for responding to the cargo source issuing request, identifying the information carried by the cargo source issuing request by using the cargo identification model and the user identification model, and obtaining an identification result of the authenticity of the cargo source.
The above-described aspects and any possible implementations further provide an implementation in which the shipment history data includes a shipment type, a shipment size, a shipment weight, a shipping distance, and a shipping price;
the first model generation module is specifically configured to:
acquiring a plurality of freight history data, and extracting a cargo type, a cargo size, a cargo weight, a transportation distance and a transportation price from each freight history data to be used as cargo characteristic data;
and training the cargo characteristic data to generate a cargo identification model.
The above-described aspects and any possible implementations further provide an implementation in which the user published source history data includes a user identification, a publication time, a type of goods published, a starting location, and a transportation distance;
the second model generation module is specifically configured to:
acquiring historical data of a plurality of user release sources, and extracting user identification, release time, types of released goods, starting places and transportation distances from the historical data of the user release sources to serve as user characteristic data;
and training the user characteristic data to generate a user identification model.
The above-described aspect and any possible implementation further provide an implementation, where the identification module is specifically configured to:
identifying the goods source information carried by the goods source issuing request by using the goods identification model to obtain a first identification result, and identifying the goods source information and the user information carried by the goods source issuing request by using the user identification model to obtain a second identification result;
if the first identification result is that the goods source is real, and the second identification result is that the goods source is real, obtaining a real identification result of the goods source; or if the first identification result is that the goods source is not authentic, and/or the second identification result is that the goods source is not authentic, obtaining an identification result that the goods source is not authentic.
The embodiment of the invention has the following beneficial effects:
according to the embodiment of the invention, the corresponding identification model is generated by utilizing the real freight historical data and the historical data of the goods source issued by the user, and then when the user (such as a goods owner member) provides the request for issuing the goods source, the identification model can be utilized to identify the goods source, so that the authenticity of the goods source issued by the user can be identified, the authenticity identification and the control of the goods source issued by the user are realized, the safety problem of the goods transportation electronic commerce platform caused by the fact that the goods owner member issues false goods information in the prior art is avoided, and the safety of the goods transportation electronic commerce platform is improved.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a cargo identification method according to an embodiment of the present invention;
fig. 2 is a functional block diagram of a cargo identification device according to an embodiment of the present invention.
[ detailed description ] embodiments
For better understanding of the technical solutions of the present invention, the following detailed descriptions of the embodiments of the present invention are provided with reference to the accompanying drawings.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be understood that the term "and/or" as used herein is merely one type of association that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
The goods owner member and the vehicle and ship member can respectively use the terminals to log in a client side of a pre-installed goods transportation electronic commerce platform, after the logging is successful, the goods owner member sends a goods issuing request to the goods transportation electronic commerce platform, wherein the goods issuing request carries related information of goods to be delivered, and after the logging is successful, the vehicle and ship member can send related information of idle capacity or idle capacity of the vehicle and ship member to the goods transportation electronic commerce platform. And the cargo transportation e-commerce platform distributes vehicle and ship members to the cargo transportation e-commerce platform according to the related information of the cargo to be delivered. In the scene, the embodiment of the invention provides an idea for identifying the authenticity of the information about the goods to be delivered, which is issued by the cargo owner and member.
Referring to fig. 1, which is a schematic flow chart of a cargo identification method according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
and S101, generating a cargo identification model according to the freight historical data.
S102, generating a user identification model according to historical data of a source of goods issued by a user.
S103, receiving a goods source issuing request of a user, responding to the goods source issuing request, and identifying information carried by the goods source issuing request by using the goods identification model and the user identification model to obtain an identification result of the authenticity of the goods source.
It should be noted that the execution subject of the recommendation method based on goods transportation provided by the embodiment of the present invention is a goods transportation e-commerce platform.
For the generation of the cargo identification model according to the freight history data involved in step S101, the following feasible implementation schemes are provided in the embodiments of the present invention.
In one possible embodiment, the data of the distribution behavior of each vehicle and vessel member is recorded and stored, and the shipping history data is generated. For example, for each delivery of goods by a member of a vehicle or a ship, time, a type of goods, a size of goods, a weight of goods, a transportation distance of goods, and a transportation price are recorded, and then the recorded data is stored as shipment history data in a database, so that the shipment history data stored in the database includes at least: cargo type, cargo size, cargo weight, shipping distance, and shipping price.
In one possible embodiment, a plurality of shipment history data are obtained, and then the type of goods, the size of goods, the weight of goods, the transportation distance and the transportation price are extracted from each of the shipment history data to serve as the characteristic data of the goods; and finally, training the cargo characteristic data to generate a cargo identification model.
For example, first, the cargo type, the cargo size, the cargo weight, the transportation distance, and the transportation price are extracted from each of the shipment histories according to a character matching algorithm, and then the extracted data is subjected to the normalization processing of the data format. Then, the normalized data is subjected to denoising processing. And finally, performing dimension reduction processing on the data subjected to the denoising processing by using a dimension reduction algorithm to obtain cargo characteristic data.
It can be understood that, because the data formats of the items of data in the collected freight history data are different, in order to facilitate the subsequent denoising processing and dimension reduction processing, it is necessary to firstly perform the normalization processing of the data formats on the items of data. For the cargo type, cargo size, cargo weight, transportation distance and transportation price contained in the freight history data, the standardization processing of data formats needs to be respectively carried out, and the data formats of all data are processed into the same data format. For example, a normalization process of the data format may be implemented using a z-score (z-score) algorithm.
It can be understood that, because more abnormal data may occur due to data collection operation or system abnormality, the generated model may be seriously affected by the abnormal data, so that an error of an output result of the model is increased, and accuracy is reduced.
In a specific implementation process, the dimensionality reduction process may be implemented by using a Principal Component Analysis (PCA) dimensionality reduction algorithm.
For example, first, a corresponding data matrix is generated according to the collected freight history data. The data matrix is then zero-averaged, i.e., the average of the data in the data matrix is calculated, and then the average is subtracted for each data. Then, a covariance matrix is calculated according to the data matrix subjected to the zero-mean processing, and an eigenvalue and an eigenvector of the covariance matrix are calculated. And finally, sorting the eigenvalues in the descending order, selecting the k largest eigenvalues, respectively taking the k eigenvectors corresponding to the k eigenvalues as column vectors, and forming the eigenvectors by using the column vectors.
It should be noted that, in the embodiment of the present invention, the generation of the cargo identification model may be performed periodically, and after each period arrives, the cargo type, the cargo size, the cargo weight, the transportation distance, and the transportation price are acquired according to the latest historical data of the transportation, and then retraining is performed by using these data, so as to update the data and update the model.
For generating the user identification model according to the historical data of the source of the goods issued by the user in step S102, the following feasible implementation schemes are provided in the embodiment of the present invention.
In one possible implementation, the source data issued by each owner member is recorded and stored, and user issued source historical data is generated. For example, for the source data published by the owner member, the user identification, time, publication time, type of the published goods, origin and transportation distance are recorded, and then the recorded data is stored as the user published source history data in the database, so that the user published source history data stored in the database at least includes: user identification, release time, type of goods released, origin and distance of transport.
In a possible implementation scheme, a plurality of user release source historical data are obtained, and a user identifier, release time, a released cargo type, a starting place and a transportation distance are extracted from each user release source historical data to serve as user characteristic data; and finally, training the user characteristic data to generate a user identification model.
For example, first, according to a character matching algorithm, a user identifier, a release time, a released cargo type, a starting place and a transportation distance are extracted from historical data of each user release source, and then the extracted data is subjected to data format standardization processing. Then, the normalized data is subjected to denoising processing. And finally, performing dimension reduction processing on the data subjected to the denoising processing by using a dimension reduction algorithm to obtain user characteristic data.
It can be understood that, because the data formats of the data items in the collected historical data of the user release source are different, in order to facilitate the subsequent denoising processing and dimension reduction processing, the data formats of the data items need to be standardized. For the user identification, the release time, the released cargo type, the starting place and the transportation distance contained in the historical data of the user release cargo source, the data formats are required to be standardized respectively, and the data formats of all data are processed into the same data format. For example, a normalization process of the data format may be implemented using a z-score (z-score) algorithm.
It can be understood that, because more abnormal data may occur due to data collection operation or system abnormality, the generated model may be seriously affected by the abnormal data, so that an error of an output result of the model is increased, and accuracy is reduced.
In a specific implementation process, the dimensionality reduction process may be implemented by using a Principal Component Analysis (PCA) dimensionality reduction algorithm.
For example, first, a corresponding data matrix is generated according to collected historical data of a user release source. The data matrix is then zero-averaged, i.e., the average of the data in the data matrix is calculated, and then the average is subtracted for each data. Then, a covariance matrix is calculated according to the data matrix subjected to the zero-mean processing, and an eigenvalue and an eigenvector of the covariance matrix are calculated. And finally, sorting the eigenvalues in the descending order, selecting the k largest eigenvalues, respectively taking the k eigenvectors corresponding to the k eigenvalues as column vectors, and forming the eigenvectors by using the column vectors.
It should be noted that, in the embodiment of the present invention, a user identification model may be periodically generated, and after each period is reached, according to the latest historical data of the user distribution source, the user identifier, the distribution time, the distribution cargo type, the origin and the transportation distance are acquired, and then retraining is performed by using these data, so as to update the data and update the model.
It should be noted that in the embodiment of the present invention, the execution sequence of step S101 and step S102 may be that S101 is executed first and then S102 is executed, or S102 is executed first and then S101 is executed, or S101 and S102 are executed simultaneously, and the sequence in the drawing is only for illustration and is not limited to the execution sequence.
Aiming at the goods source issuing request of the receiving user related to the step S103, in response to the goods source issuing request, the goods identification model and the user identification model are utilized to identify the information carried by the goods source issuing request, and the identification result of the authenticity of the goods source is obtained.
Specifically, the cargo transportation e-commerce platform can receive a cargo release request sent by a cargo owner member through a terminal, wherein the cargo release request can carry information of cargos to be delivered, and is used for requesting the cargo transportation e-commerce platform to distribute vehicle and ship members for the cargo transportation e-commerce platform so as to realize the purpose of transporting the cargos to a specified place.
Further, the cargo transportation e-commerce platform responds to the received cargo release request, and identifies the authenticity of the cargo source by utilizing a pre-generated cargo identification model and a pre-generated user identification model according to information carried by the cargo release request to obtain an identification result. The generated goods identification model is obtained by training by using a real freight history record, so that the authenticity of a goods source can be identified according to related information carried in a goods release request, whether the goods source accords with a general rule or not is judged, and similarly, the generated user identification model is obtained by training by using real user release goods source history data, so that a user (such as a goods owner member) for releasing the goods source can be identified according to the related information carried in the goods release request, whether the release behavior of the goods owner member accords with the general rule or not is judged, and the identification result of the goods owner member is taken as the identification result of the goods source.
Specifically, the cargo identification model may be used to identify the cargo source information carried by the cargo source issuing request to obtain a first identification result, and the user identification model may be used to identify the cargo source information and the user information carried by the cargo source issuing request to obtain a second identification result. If the first identification result is that the goods source is real, and the second identification result is that the goods source is real, obtaining a real identification result of the goods source; or if the first identification result is that the goods source is not authentic, and/or the second identification result is that the goods source is not authentic, obtaining an identification result that the goods source is not authentic. That is, when there is at least one recognition result that the source is not authentic, the final recognition result is considered that the source is not authentic.
Further, if the source of the goods is identified as real, the member of the vehicle and the ship to be distributed the goods can be further distributed. Or, if the source is identified to be unreal, prompt information that the source identification result is unreal needs to be returned to the terminal used by the owner member, and an entrance for reissuing the goods is provided, so that the owner member reissues the transportation request of the goods.
An embodiment of the present invention further provides a cargo identification device, please refer to fig. 2, which is a functional block diagram of the cargo identification device according to the embodiment of the present invention, as shown in fig. 2, the cargo identification device includes:
the first model generation module 20 is used for generating a cargo identification model according to the freight historical data;
the second model generation module 21 is used for generating a user identification model according to historical data of a source of goods issued by a user;
a receiving module 22, configured to receive a source issuing request of a user;
and the identification module 23 is configured to respond to the source issuing request, and identify information carried by the source issuing request by using the cargo identification model and the user identification model to obtain an identification result of the authenticity of the source.
In a particular embodiment, the shipment history data includes a cargo type, a cargo size, a cargo weight, a shipping distance, and a shipping price;
the first model generation module 20 is specifically configured to:
acquiring a plurality of freight history data, and extracting a cargo type, a cargo size, a cargo weight, a transportation distance and a transportation price from each freight history data to be used as cargo characteristic data;
and training the cargo characteristic data to generate a cargo identification model.
In a specific embodiment, the user published source history data comprises user identification, publication time, published cargo type, origin and transportation distance;
the second model generation module 21 is specifically configured to:
acquiring historical data of a plurality of user release sources, and extracting user identification, release time, types of released goods, starting places and transportation distances from the historical data of the user release sources to serve as user characteristic data;
and training the user characteristic data to generate a user identification model.
In a specific embodiment, the identification module 23 is specifically configured to:
identifying the goods source information carried by the goods source issuing request by using the goods identification model to obtain a first identification result, and identifying the goods source information and the user information carried by the goods source issuing request by using the user identification model to obtain a second identification result;
if the first identification result is that the goods source is real, and the second identification result is that the goods source is real, obtaining a real identification result of the goods source; or if the first identification result is that the goods source is not authentic, and/or the second identification result is that the goods source is not authentic, obtaining an identification result that the goods source is not authentic.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the apparatus and the module described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and other divisions may be realized in practice, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each module may exist alone physically, or two or more modules are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) or a Processor (Processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (2)

1. A cargo identification method, the method comprising:
generating a cargo identification model according to the freight historical data; wherein the shipment history data includes a cargo type, a cargo size, a cargo weight, a transportation distance, and a transportation price; generating a cargo identification model according to the freight historical data, comprising: acquiring a plurality of freight history data, and extracting a cargo type, a cargo size, a cargo weight, a transportation distance and a transportation price from each freight history data to be used as cargo characteristic data; firstly, generating a corresponding data matrix according to collected freight historical data; calculating the average of the data in the data matrix and then subtracting the average for each data; then, according to the data matrix subjected to zero-mean processing, a covariance matrix is calculated, and an eigenvalue and an eigenvector of the covariance matrix are calculated; sorting the eigenvalues in the descending order, selecting the k largest eigenvalues, respectively taking the k eigenvectors corresponding to the k eigenvalues as column vectors, and forming the eigenvectors by using the column vectors; training the cargo characteristic data to generate a cargo identification model; wherein, still include: processing the data formats of all the data into the same data format;
generating a user identification model according to historical data of a source of goods issued by a user; the historical data of the source of the goods released by the user comprises user identification, release time, the type of the goods released, a starting place and a transportation distance; generating a user identification model according to historical data of a source of goods issued by a user, wherein the user identification model comprises the following steps: acquiring historical data of a plurality of user release sources, and extracting user identification, release time, types of released goods, starting places and transportation distances from the historical data of the user release sources to serve as user characteristic data; training the user characteristic data to generate a user identification model;
receiving a goods source release request of a user, responding to the goods source release request, and identifying information carried by the goods source release request by using the goods identification model and the user identification model; identifying the information carried by the goods source issuing request by using the goods identification model and the user identification model to obtain an identification result of the authenticity of the goods source, wherein the identification result comprises the following steps: identifying the goods source information carried by the goods source issuing request by using the goods identification model to obtain a first identification result, and identifying the goods source information and the user information carried by the goods source issuing request by using the user identification model to obtain a second identification result;
if the first identification result is that the goods source is real, and the second identification result is that the goods source is real, obtaining a real identification result of the goods source; or if the first identification result is that the goods source is not authentic, and/or the second identification result is that the goods source is not authentic, obtaining an identification result that the goods source is not authentic.
2. A cargo identification device, the device comprising:
the first model generation module is used for acquiring a plurality of freight historical data, and extracting the type of goods, the size of the goods, the weight of the goods, the transportation distance and the transportation price from each freight historical data to be used as characteristic data of the goods; the freight history data comprises the type of goods, the size of the goods, the weight of the goods, the transportation distance and the transportation price; generating a cargo identification model according to the freight historical data, comprising: acquiring a plurality of freight history data, and extracting a cargo type, a cargo size, a cargo weight, a transportation distance and a transportation price from each freight history data to be used as cargo characteristic data; firstly, generating a corresponding data matrix according to collected freight historical data; calculating the average of the data in the data matrix and then subtracting the average for each data; then, according to the data matrix subjected to zero-mean processing, a covariance matrix is calculated, and an eigenvalue and an eigenvector of the covariance matrix are calculated; sorting the eigenvalues in the descending order, selecting the k largest eigenvalues, respectively taking the k eigenvectors corresponding to the k eigenvalues as column vectors, and forming the eigenvectors by using the column vectors; training the cargo characteristic data to generate a cargo identification model; wherein, still include: processing the data formats of all the data into the same data format;
a second model generation module, specifically configured to: acquiring historical data of a plurality of user release sources, and extracting user identification, release time, types of released goods, starting places and transportation distances from the historical data of the user release sources to serve as user characteristic data; training the user characteristic data to generate a user identification model; the historical data of the source of the goods released by the user comprises user identification, release time, the type of the goods released, a starting place and a transportation distance;
the receiving module is used for receiving a goods source issuing request of a user;
the identification module is used for responding to the cargo source issuing request, identifying information carried by the cargo source issuing request by using the cargo identification model and the user identification model, identifying cargo source information carried by the cargo source issuing request by using the cargo identification model to obtain a first identification result, and identifying cargo source information carried by the cargo source issuing request and user information by using the user identification model to obtain a second identification result; if the first identification result is that the goods source is real, and the second identification result is that the goods source is real, obtaining a real identification result of the goods source; or if the first identification result is that the goods source is not authentic, and/or the second identification result is that the goods source is not authentic, obtaining an identification result that the goods source is not authentic.
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