CN109242391A - A kind of cargo recognition methods and device - Google Patents
A kind of cargo recognition methods and device Download PDFInfo
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- CN109242391A CN109242391A CN201811096336.7A CN201811096336A CN109242391A CN 109242391 A CN109242391 A CN 109242391A CN 201811096336 A CN201811096336 A CN 201811096336A CN 109242391 A CN109242391 A CN 109242391A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION 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
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0838—Historical data
Abstract
The embodiment of the invention provides a kind of cargo recognition methods and devices, this method comprises: generating cargo identification model according to freight transport history data；Source of goods historical data is issued according to user, generates user's identification model；The source of goods posting request for receiving user identifies information entrained by the source of goods posting request using the cargo identification model and user's identification model, obtains the recognition result of source of goods authenticity in response to the source of goods posting request.Technical solution provided in an embodiment of the present invention solves owner of cargo member to a certain extent can issue false goods information, lead to safety issue existing for cargo transport e-commerce platform.
Description
[technical field]
The present invention relates to artificial intelligence field more particularly to a kind of cargo recognition methods and devices.
[background technique]
The big core content of the two of cargo transport: one is delivery service that is quick, accurate, saving；One is safe and reliable
Cargo transport and clearing.Initial shipment status is also in extensive developing stage, and goods delivery also rests on original driver
Goods and goods is looked for look for the vehicle stage.However, this driver looks for goods and goods to look for the mode of vehicle, the efficiency of cargo transport, cost ratio are reduced
It is higher.Therefore, occurred a kind of cargo transport e-commerce platform later, car and boat member can be used mobile terminal and transport its free time
The information of power is published to the platform, and goods information to be transported is published to the platform using mobile terminal by owner of cargo member, the platform
After receiving, it is audited and distributes car and boat and is transported.However, in the prior art, some owner of cargo members can issue falseness
Goods information, causes cargo transport e-commerce platform there are safety issue, and safety is relatively low.
[summary of the invention]
In view of this, being solved to a certain extent the embodiment of the invention provides a kind of cargo recognition methods and device
Owner of cargo member can issue false goods information, lead to safety issue existing for cargo transport e-commerce platform.
In a first aspect, the embodiment of the present invention provides a kind of cargo recognition methods, comprising:
According to freight transport history data, cargo identification model is generated；
Source of goods historical data is issued according to user, generates user's identification model；
The source of goods posting request for receiving user, in response to the source of goods posting request, using the cargo identification model and
User's identification model identifies information entrained by the source of goods posting request, obtains the identification knot of source of goods authenticity
Fruit.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the shipping are gone through
History data include cargo type, cargo size, goods weight, transportation range and transport price.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation is gone through according to shipping
History data generate cargo identification model, comprising:
Multiple freight transport history data are obtained, discharging of goods type, cargo size, goods from each freight transport history data
Object weight, transportation range and transport price, using as cargo characteristic；
The cargo characteristic is trained, cargo identification model is generated.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, user's hair
Cloth source of goods historical data includes user identifier, issuing time, the cargo type of publication, starting point and transportation range.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation is sent out according to user
Cloth source of goods historical data generates user's identification model, comprising:
Obtain multiple users and issue source of goods historical datas, from each user issue source of goods historical data extract user identifier,
Issuing time, the cargo type of publication, starting point and transportation range, using as user characteristic data；
The user characteristic data is trained, user's identification model is generated.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation utilizes the goods
Object identification model and user's identification model identify information entrained by the source of goods posting request, and it is true to obtain the source of goods
The recognition result of reality, comprising:
Information of freight source entrained by the source of goods posting request is identified using the cargo identification model, obtains
One recognition result, and, using user's identification model to information of freight source and user entrained by the source of goods posting request
Information is identified, the second recognition result is obtained；
If first recognition result is that the source of goods is true, and it is really, to obtain that second recognition result, which is the source of goods,
To the true recognition result of the source of goods；Alternatively, if first recognition result be the source of goods be false, and/or, it is described second know
Other result be the source of goods be it is false, obtain the false recognition result of the source of goods.
Second aspect, the embodiment of the present invention provide a kind of stock keeping unit, comprising:
First model generation module, for generating cargo identification model according to freight transport history data；
Second model generation module generates user's identification model for issuing source of goods historical data according to user；
Receiving module, for receiving the source of goods posting request of user；
Identification module, for being known using the cargo identification model and the user in response to the source of goods posting request
Other model identifies information entrained by the source of goods posting request, obtains the recognition result of source of goods authenticity.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the shipping are gone through
History data include cargo type, cargo size, goods weight, transportation range and transport price；
The first model generation module, is specifically used for:
Multiple freight transport history data are obtained, discharging of goods type, cargo size, goods from each freight transport history data
Object weight, transportation range and transport price, using as cargo characteristic；
The cargo characteristic is trained, cargo identification model is generated.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, user's hair
Cloth source of goods historical data includes user identifier, issuing time, the cargo type of publication, starting point and transportation range；
The second model generation module, is specifically used for:
Obtain multiple users and issue source of goods historical datas, from each user issue source of goods historical data extract user identifier,
Issuing time, the cargo type of publication, starting point and transportation range, using as user characteristic data；
The user characteristic data is trained, user's identification model is generated.
The aspect and any possible implementation manners as described above, it is further provided a kind of implementation, the identification mould
Block is specifically used for:
Information of freight source entrained by the source of goods posting request is identified using the cargo identification model, obtains
One recognition result, and, using user's identification model to information of freight source and user entrained by the source of goods posting request
Information is identified, the second recognition result is obtained；
If first recognition result is that the source of goods is true, and it is really, to obtain that second recognition result, which is the source of goods,
To the true recognition result of the source of goods；Alternatively, if first recognition result be the source of goods be false, and/or, it is described second know
Other result be the source of goods be it is false, obtain the false recognition result of the source of goods.
The embodiment of the present invention has the advantages that
The embodiment of the present invention, which passes through, issues the generation of source of goods historical data accordingly using true freight transport history data and user
Identification model, then when have user (such as owner of cargo member) provide the source of goods posting request when, can use identification model to its into
Row identification realizes the true and false identification and control that the source of goods is issued to user, to keep away so as to identify the authenticity of the source of goods
False goods information can be issued by having exempted from owner of cargo member in the prior art, lead to safety existing for cargo transport e-commerce platform
Problem improves the safety of cargo transport e-commerce platform.
[Detailed description of the invention]
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached
Figure is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for this field
For those of ordinary skill, without creative efforts, it can also be obtained according to these attached drawings other attached drawings.
Fig. 1 is the flow diagram of cargo recognition methods provided by the embodiment of the present invention；
Fig. 2 is the functional block diagram of stock keeping unit provided by the embodiment of the present invention.
[specific embodiment]
For a better understanding of the technical solution of the present invention, being retouched in detail to the embodiment of the present invention with reference to the accompanying drawing
It states.
It will be appreciated that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Base
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts it is all its
Its embodiment, shall fall within the protection scope of the present invention.
The term used in embodiments of the present invention is only to be not intended to be limiting merely for for the purpose of describing particular embodiments
The present invention.In the embodiment of the present invention and the "an" of singular used in the attached claims, " described " and "the"
It is also intended to including most forms, unless the context clearly indicates other meaning.
It should be appreciated that term "and/or" used herein is only a kind of incidence relation for describing affiliated partner, indicate
There may be three kinds of relationships, for example, A and/or B, can indicate: individualism A, exist simultaneously A and B, individualism B these three
Situation.In addition, character "/" herein, typicallys represent the relationship that forward-backward correlation object is a kind of "or".
Depending on context, word as used in this " if " can be construed to " ... when " or " when ...
When " or " in response to determination " or " in response to detection ".Similarly, depend on context, phrase " if it is determined that " or " if detection
(condition or event of statement) " can be construed to " when determining " or " in response to determination " or " when the detection (condition of statement
Or event) when " or " in response to detection (condition or event of statement) ".
Owner of cargo member and car and boat member can respectively put down in the preassembled cargo transport e-commerce of using terminal login
The client of platform, after logining successfully, owner of cargo member sends cargo posting request and gives cargo transport e-commerce platform, wherein taking
Relevant information with cargo to be dispensed, car and boat member, can be by the phases of its unloaded transport power or idle transport power after logining successfully
It closes information and is sent to cargo transport e-commerce platform.Cargo transport e-commerce platform is believed according to the correlation of cargo to be dispensed
Breath distributes car and boat member for it.The embodiment of the present invention under the scene, provide solve how to the owner of cargo member publication wait match
The relevant information of delivery object carries out the thinking of authenticity identification.
Referring to FIG. 1, its flow diagram for cargo recognition methods provided by the embodiment of the present invention, as shown in Figure 1,
Method includes the following steps:
S101, according to freight transport history data, generate cargo identification model.
S102, source of goods historical data is issued according to user, generates user's identification model.
S103, the source of goods posting request for receiving user identify mould using the cargo in response to the source of goods posting request
Type and user's identification model identify information entrained by the source of goods posting request, obtain the knowledge of source of goods authenticity
Other result.
It should be noted that the executing subject of the recommended method based on cargo transport provided by the embodiment of the present invention is goods
Object transport electrons business platform.
For according to freight transport history data, cargo identification model is generated involved in step S101, the embodiment of the present invention is mentioned
Following feasible embodiment is supplied.
In a kind of feasible embodiment, the data of the distribution activity of each car and boat member are recorded and deposited
Storage generates freight transport history data.For example, being directed to each goods delivery of car and boat member, time, cargo type, goods are all recorded
Then object size, goods weight, the transportation range of cargo and transport price are stored the data of record as freight transport history data
Into database, therefore, the freight transport history data that database is stored are included at least: cargo type, cargo size, cargo weight
Amount, transportation range and transport price.
In a kind of feasible embodiment, multiple freight transport history data are obtained first, are then gone through from each shipping
Discharging of goods type, cargo size, goods weight, transportation range and transport price in history data, using as cargo characteristic；
Finally the cargo characteristic is trained, generates cargo identification model.
For example, firstly, according to character match algorithm, discharging of goods type, cargo from every freight transport history record
Then size, goods weight, transportation range and transport price carry out the standardization of data format to the data of extraction.So
Afterwards, denoising is carried out to the data Jing Guo standardization.Finally, using dimension-reduction algorithm, to the data Jing Guo denoising
Dimension-reduction treatment is carried out, cargo characteristic is obtained.
It is understood that the data format due to all data in collected freight transport history data is different, it is
Facilitate subsequent denoising and dimension-reduction treatment, needs first to carry out all data the standardization of data format.For
Cargo type, cargo size, goods weight, transportation range and the transport price that freight transport history data include need to carry out respectively
The data format of all data is all processed into identical data format by the standardization of data format.It is, for example, possible to use
The standardization of z-score (z-score) algorithm realization data format.
It is understood that the acquisition operation or system exception due to data are likely to lead to occur more exception
Data, abnormal data can seriously affect the model of generation, so that the output resultant error of model increases, accuracy rate be caused to reduce,
Therefore, it in the embodiment of the present invention, needs to carry out denoising to the freight transport history data of acquisition, so as to remove the goods of acquisition
Transport the isolated data and/or abnormal data in historical data.
During a concrete implementation, principal component analysis (Principal Component can use
Analysis, PCA) dimension-reduction algorithm realization dimension-reduction treatment.
For example, firstly, generating corresponding data matrix according to the freight transport history data of acquisition.Then to data matrix
Zero averaging processing, the i.e. average value of data described in calculating data matrix are carried out, then each data are subtracted average
Value.Then, according to the data matrix by zero averaging processing, covariance matrix is calculated, and calculates the spy of covariance matrix
Value indicative and feature vector.Finally, being ranked up according to descending sequence to characteristic value, k feature of maximum is selected
Value, using the corresponding k feature vector of k characteristic value as column vector, using Column vector groups at feature vector.
It should be noted that in the embodiment of the present invention generation of cargo identification model can be carried out periodically, in each week
After phase reaches, according to newest freight transport history data, cargo type, cargo size, goods weight, transportation range and transport are obtained
Then price carries out re -training using these data, to realize the update of data and the update of model.
Source of goods historical data is issued according to user for involved in step S102, generates user's identification model, the present invention
Embodiment provides following feasible embodiment.
In a kind of feasible embodiment, the source of goods data of each owner of cargo member publication are recorded and deposited
Storage generates user and issues source of goods historical data.For example, for the owner of cargo member publication source of goods data, all record user identifier,
Then time, issuing time, the cargo type of publication, starting point and transportation range issue goods for the data of record as user
Source history data store is into database, and therefore, the user that database is stored issues source of goods historical data and includes at least: user
Mark, issuing time, the cargo type of publication, starting point and transportation range.
In a kind of feasible embodiment, multiple users are obtained first and issue source of goods historical data, are sent out from each user
Cloth source of goods historical data extracts user identifier, issuing time, the cargo type of publication, starting point and transportation range, using as with
Family characteristic；Finally the user characteristic data is trained, generates user's identification model.
For example, firstly, being issued from every user according to character match algorithm and extracting user's mark in source of goods historical data
Then knowledge, issuing time, the cargo type of publication, starting point and transportation range carry out the mark of data format to the data of extraction
Quasi-ization processing.Then, denoising is carried out to the data Jing Guo standardization.Finally, using dimension-reduction algorithm, to by denoising
The data of processing carry out dimension-reduction treatment, obtain user characteristic data.
It is understood that since collected user issues the data format of all data in source of goods historical data not
Together, therefore in order to facilitate subsequent denoising and dimension-reduction treatment, need first to carry out all data the standardization of data format
Processing.For user issue source of goods historical data include user identifier, issuing time, the cargo type of publication, starting point and
Transportation range needs to carry out the standardization of data format respectively, the data format of all data is all processed into identical
Data format.It is, for example, possible to use the standardizations that z-score (z-score) algorithm realizes data format.
It is understood that the acquisition operation or system exception due to data are likely to lead to occur more exception
Data, abnormal data can seriously affect the model of generation, so that the output resultant error of model increases, accuracy rate be caused to reduce,
Therefore, it in the embodiment of the present invention, needs to issue the user of acquisition the progress denoising of source of goods historical data, so as to remove
The user of acquisition issues isolated data and/or abnormal data in source of goods historical data.
During a concrete implementation, principal component analysis (Principal Component can use
Analysis, PCA) dimension-reduction algorithm realization dimension-reduction treatment.
For example, firstly, issuing source of goods historical data according to the user of acquisition generates corresponding data matrix.Then right
Data matrix carries out zero averaging processing, the i.e. average value of data described in calculating data matrix, then for each data
Subtract average value.Then, according to the data matrix by zero averaging processing, covariance matrix is calculated, and calculates covariance
The characteristic value and feature vector of matrix.Finally, being ranked up according to descending sequence to characteristic value, selection is wherein maximum
K characteristic value, using the corresponding k feature vector of k characteristic value as column vector, using column vector composition characteristic to
Amount.
It should be noted that in the embodiment of the present invention generation of user's identification model can be carried out periodically, in each week
Phase reach after, according to newest user issue source of goods historical data, obtain user identifier, issuing time, publication cargo type,
Then starting point and transportation range carry out re -training using these data, to realize the update of data and the update of model.
It should be noted that the execution sequence of step S101 and step S102 can be to first carry out in the embodiment of the present invention
S101 executes S102 afterwards, alternatively, being also possible to first carry out S102, executes S101 afterwards, alternatively, it is same to be also possible to S101 and S102
Shi Zhihang, the sequence in attached drawing by way of example only, are not used to limit and execute sequence.
For the source of goods posting request for receiving user involved in step S103, in response to the source of goods posting request, benefit
Information entrained by the source of goods posting request is identified with the cargo identification model and user's identification model, is obtained
To the recognition result of source of goods authenticity, the embodiment of the invention provides following feasible embodiments.
It is asked specifically, cargo transport e-commerce platform can receive owner of cargo member by the publication of terminal shipment
It asks, which can carry the information of cargo to be dispensed, to request cargo transport e-commerce platform for its point
With car and boat member, to realize its cargo transport to designated place.
Further, cargo transport e-commerce platform is sent out in response to the cargo posting request received according to the cargo
The entrained information of cloth request, and source of goods authenticity is carried out using pre-generated cargo identification model and user's identification model
Identification, obtains recognition result.Since the cargo identification model of generation is to be trained to obtain using true freight transport history record
, therefore, the authenticity of the source of goods can be identified, judge goods according to the relevant information carried in cargo posting request
Whether source meets universal law, similarly, since user's identification model of generation is to issue source of goods history number using true user
It, therefore, can be according to the relevant information carried in cargo posting request, to the user of the publication source of goods according to what is be trained
(such as owner of cargo member) identifies, judges whether the publication behavior of owner of cargo member meets universal law, and will be to owner of cargo member's
Recognition result of the recognition result as the source of goods.
Information of freight source entrained by the source of goods posting request is carried out specifically, can use the cargo identification model
Identification, obtains the first recognition result, and, using user's identification model to the source of goods entrained by the source of goods posting request
Information and user information are identified, the second recognition result is obtained.If first recognition result is that the source of goods is true, and institute
It is really, to obtain the true recognition result of the source of goods that the second recognition result, which is stated, as the source of goods；Alternatively, if first recognition result is
The source of goods is false, and/or, second recognition result be the source of goods be it is false, obtain the false identification of the source of goods and tie
Fruit.That is, when there are at least one recognition result be that the source of goods is false, then it is assumed that final recognition result be the source of goods not
Really.
It further, can be further the car and boat member of cargo to be dispensed distribution if it is true for recognizing the source of goods.
Alternatively, if recognize the source of goods be it is untrue, need the terminal used to owner of cargo member return source of goods recognition result be it is untrue
Prompt information, and provide again issue cargo entrance, so that owner of cargo member issues the transport request of cargo again.
The embodiment of the invention also provides a kind of stock keeping units, referring to FIG. 2, it is provided by the embodiment of the present invention
Stock keeping unit functional block diagram, as shown in Fig. 2, the device includes:
First model generation module 20, for generating cargo identification model according to freight transport history data；
Second model generation module 21 generates user's identification model for issuing source of goods historical data according to user；
Receiving module 22, for receiving the source of goods posting request of user；
Identification module 23, for utilizing the cargo identification model and the user in response to the source of goods posting request
Identification model identifies information entrained by the source of goods posting request, obtains the recognition result of source of goods authenticity.
In a kind of specific embodiment, the freight transport history data include cargo type, cargo size, cargo weight
Amount, transportation range and transport price；
The first model generation module 20, is specifically used for:
Multiple freight transport history data are obtained, discharging of goods type, cargo size, goods from each freight transport history data
Object weight, transportation range and transport price, using as cargo characteristic；
The cargo characteristic is trained, cargo identification model is generated.
In a kind of specific embodiment, the user issue source of goods historical data include user identifier, issuing time,
Cargo type, starting point and the transportation range of publication；
The second model generation module 21, is specifically used for:
Obtain multiple users and issue source of goods historical datas, from each user issue source of goods historical data extract user identifier,
Issuing time, the cargo type of publication, starting point and transportation range, using as user characteristic data；
The user characteristic data is trained, user's identification model is generated.
In a kind of specific embodiment, the identification module 23 is specifically used for:
Information of freight source entrained by the source of goods posting request is identified using the cargo identification model, obtains
One recognition result, and, using user's identification model to information of freight source and user entrained by the source of goods posting request
Information is identified, the second recognition result is obtained；
If first recognition result is that the source of goods is true, and it is really, to obtain that second recognition result, which is the source of goods,
To the true recognition result of the source of goods；Alternatively, if first recognition result be the source of goods be false, and/or, it is described second know
Other result be the source of goods be it is false, obtain the false recognition result of the source of goods.
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description,
The specific work process of device and module, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
In several embodiments provided by the present invention, it should be understood that disclosed system, device and method can be with
It realizes by another way.For example, the apparatus embodiments described above are merely exemplary, for example, the module
It divides, only a kind of logical function partition, there may be another division manner in actual implementation, for example, multiple modules or group
Part can be combined or can be integrated into another system, or some features can be ignored or not executed.Another point, it is shown
Or the mutual coupling, direct-coupling or communication connection discussed can be through some interfaces, device or module it is indirect
Coupling or communication connection can be electrical property, mechanical or other forms.
The module as illustrated by the separation member may or may not be physically separated, aobvious as module
The component shown may or may not be physical module, it can and it is in one place, or may be distributed over multiple
In network unit.Some or all of the modules therein can be selected to realize the mesh of this embodiment scheme according to the actual needs
's.
It, can also be in addition, each functional module in each embodiment of the present invention can integrate in one processing unit
It is that modules physically exist alone, can also be integrated in one unit with two or more modules.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of hardware adds SFU software functional unit.
The above-mentioned integrated unit being realized in the form of SFU software functional unit can store and computer-readable deposit at one
In storage media.Above-mentioned SFU software functional unit is stored in a storage medium, including some instructions are used so that a computer
It is each that device (can be personal computer, server or network equipment etc.) or processor (Processor) execute the present invention
The part steps of embodiment the method.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (Read-
Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic or disk etc. it is various
It can store the medium of program code.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Within mind and principle, any modification, equivalent substitution, improvement and etc. done be should be included within the scope of the present invention.
Claims (10)
1. a kind of cargo recognition methods, which is characterized in that the described method includes:
According to freight transport history data, cargo identification model is generated；
Source of goods historical data is issued according to user, generates user's identification model；
The source of goods posting request for receiving user utilizes the cargo identification model and described in response to the source of goods posting request
User's identification model identifies information entrained by the source of goods posting request, obtains the recognition result of source of goods authenticity.
2. the method according to claim 1, wherein the freight transport history data include cargo type, cargo ruler
Very little, goods weight, transportation range and transport price.
3. according to the method described in claim 2, it is characterized in that, generating cargo identification model, packet according to freight transport history data
It includes:
Multiple freight transport history data are obtained, discharging of goods type, cargo size, cargo weight from each freight transport history data
Amount, transportation range and transport price, using as cargo characteristic；
The cargo characteristic is trained, cargo identification model is generated.
4. the method according to claim 1, wherein it includes user's mark that the user, which issues source of goods historical data,
Knowledge, issuing time, the cargo type of publication, starting point and transportation range.
5. according to the method described in claim 4, generation user knows it is characterized in that, issuing source of goods historical data according to user
Other model, comprising:
It obtains multiple users and issues source of goods historical data, issue source of goods historical data from each user and extract user identifier, publication
Time, the cargo type of publication, starting point and transportation range, using as user characteristic data；
The user characteristic data is trained, user's identification model is generated.
6. the method according to claim 1, wherein identifying mould using the cargo identification model and the user
Type identifies information entrained by the source of goods posting request, obtains the recognition result of source of goods authenticity, comprising:
Information of freight source entrained by the source of goods posting request is identified using the cargo identification model, obtains the first knowledge
Not as a result, and, using user's identification model to information of freight source and user information entrained by the source of goods posting request
It is identified, obtains the second recognition result；
If first recognition result is that the source of goods is true, and it is really, to obtain arrival that second recognition result, which is the source of goods,
The true recognition result in source；Alternatively, if first recognition result be the source of goods be false, and/or, it is described second identification knot
Fruit be the source of goods be it is false, obtain the false recognition result of the source of goods.
7. a kind of stock keeping unit, which is characterized in that described device includes:
First model generation module, for generating cargo identification model according to freight transport history data；
Second model generation module generates user's identification model for issuing source of goods historical data according to user；
Receiving module, for receiving the source of goods posting request of user；
Identification module, for identifying mould using the cargo identification model and the user in response to the source of goods posting request
Type identifies information entrained by the source of goods posting request, obtains the recognition result of source of goods authenticity.
8. device according to claim 7, which is characterized in that the freight transport history data include cargo type, cargo ruler
Very little, goods weight, transportation range and transport price；
The first model generation module, is specifically used for:
Multiple freight transport history data are obtained, discharging of goods type, cargo size, cargo weight from each freight transport history data
Amount, transportation range and transport price, using as cargo characteristic；
The cargo characteristic is trained, cargo identification model is generated.
9. device according to claim 7, which is characterized in that it includes user's mark that the user, which issues source of goods historical data,
Knowledge, issuing time, the cargo type of publication, starting point and transportation range；
The second model generation module, is specifically used for:
It obtains multiple users and issues source of goods historical data, issue source of goods historical data from each user and extract user identifier, publication
Time, the cargo type of publication, starting point and transportation range, using as user characteristic data；
The user characteristic data is trained, user's identification model is generated.
10. device according to claim 7, which is characterized in that the identification module is specifically used for:
Information of freight source entrained by the source of goods posting request is identified using the cargo identification model, obtains the first knowledge
Not as a result, and, using user's identification model to information of freight source and user information entrained by the source of goods posting request
It is identified, obtains the second recognition result；
If first recognition result is that the source of goods is true, and it is really, to obtain arrival that second recognition result, which is the source of goods,
The true recognition result in source；Alternatively, if first recognition result be the source of goods be false, and/or, it is described second identification knot
Fruit be the source of goods be it is false, obtain the false recognition result of the source of goods.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811096336.7A CN109242391B (en) | 2018-09-19 | 2018-09-19 | Cargo identification method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811096336.7A CN109242391B (en) | 2018-09-19 | 2018-09-19 | Cargo identification method and device |
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| CN110717717A (en) * | 2019-10-11 | 2020-01-21 | 惠龙易通国际物流股份有限公司 | Model generation method and system, and vehicle distribution method and device |
| CN110765226A (en) * | 2019-11-01 | 2020-02-07 | 江苏满运软件科技有限公司 | Goods owner matching method, device, equipment and medium |
| CN112396372A (en) * | 2020-11-16 | 2021-02-23 | 上海燕汐软件信息科技有限公司 | Goods delivery information determining method, device and system |
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| CN106600188A (en) * | 2016-12-07 | 2017-04-26 | 传化物流集团有限公司 | Verification method, apparatus and server for the authenticity of goods in transportation |
| CN107437147A (en) * | 2017-08-02 | 2017-12-05 | 辽宁友邦网络科技有限公司 | Reduce the vehicle travel risk dynamic assessment method and its system of freight logistics scene |
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| CN106600188A (en) * | 2016-12-07 | 2017-04-26 | 传化物流集团有限公司 | Verification method, apparatus and server for the authenticity of goods in transportation |
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| CN110765226A (en) * | 2019-11-01 | 2020-02-07 | 江苏满运软件科技有限公司 | Goods owner matching method, device, equipment and medium |
| CN110765226B (en) * | 2019-11-01 | 2022-10-14 | 江苏满运软件科技有限公司 | Goods owner matching method, device, equipment and medium |
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| CN109242391B (en) | 2022-02-22 |
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