CN113240480A - Order processing method and device, electronic terminal and storage medium - Google Patents
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Abstract
The embodiment of the application provides an order processing method and device, an electronic terminal and a storage medium. Wherein, the method comprises the following steps: obtaining a to-be-processed order submitted by a user side; extracting order attribute information from the order to be processed; respectively inputting order attribute information into an order identification model; determining whether the order to be processed is an abnormal order or not according to the recognition result of the order recognition model; the order recognition model is obtained based on order attribute information of a sample order and training of an abnormal state of the sample order. According to the technical scheme, whether the order to be processed submitted by the user side is an abnormal order or not is automatically and timely recognized through the trained order recognition model, so that the abnormal order can be conveniently subsequently prevented from being distributed to the receiving end, and interference of a single person is avoided.
Description
Technical Field
The embodiment of the application relates to the technical field of computer application, in particular to an order processing method, an order processing device, an electronic terminal and a storage medium.
Background
With the development of mobile internet technology, more and more life service software, such as taxi taking software, ordering software, shopping software, express delivery software and the like, emerge. The user installs the life service software on a mobile terminal such as a mobile phone, so that when relevant service requirements exist, the life service software is opened on the mobile terminal, the order is placed by entering an order placing page, and the created order is dispatched to the mobile terminal at the order taker side. In practical applications, some users may add some cheating information such as advertisements to the order, which may interfere with the order taker.
Disclosure of Invention
The embodiment of the application provides an order processing method, an order processing device, an electronic terminal and a storage medium, which are used for automatically and timely identifying abnormal orders.
In a first aspect, an embodiment of the present application provides an order processing method, including:
acquiring a to-be-processed order submitted by a user side;
extracting order attribute information from the order to be processed;
respectively inputting the order attribute information into an order identification model;
determining whether the order to be processed is an abnormal order or not according to the recognition result of the order recognition model; the order recognition model is obtained based on order attribute information of a sample order and training of the abnormal state of the sample order.
In a second aspect, an embodiment of the present application provides an order processing apparatus, including:
the acquisition module is used for acquiring the order to be processed submitted by the user side;
the extraction module is used for extracting order attribute information from the order to be processed;
the identification module is used for respectively inputting the order attribute information into an order identification model;
the determining module is used for determining whether the order to be processed is an abnormal order or not according to the recognition result of the order recognition model; the order recognition model is obtained based on order attribute information of a sample order and training of the abnormal state of the sample order.
In a third aspect, an embodiment of the present application provides an electronic device, including a processing component and a storage component;
the storage component stores one or more computer instructions; the one or more computer instructions to be invoked for execution by the processing component;
the processing component is to:
acquiring a to-be-processed order submitted by a user side;
extracting order attribute information from the order to be processed;
respectively inputting the order attribute information into an order identification model;
determining whether the order to be processed is an abnormal order or not according to the recognition result of the order recognition model; the order recognition model is obtained based on order attribute information of a sample order and training of the abnormal state of the sample order.
In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and the computer program can implement the steps of the above method when executed by a computer.
In the embodiment of the application, the order to be processed submitted by the user side is obtained; extracting order attribute information from the order to be processed; respectively inputting the order attribute information into an order identification model; determining whether the order to be processed is an abnormal order or not according to the recognition result of the order recognition model; the order recognition model is obtained based on order attribute information of a sample order and training of the abnormal state of the sample order. Therefore, whether the order to be processed submitted by the user side is an abnormal order or not is automatically and timely recognized through the trained order recognition model, the abnormal order is conveniently prevented from being distributed to the receiving end subsequently, and interference of a single person is avoided.
These and other aspects of the present application will be more readily apparent from the following description of the embodiments.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 illustrates a block diagram of one embodiment of an order processing system provided in accordance with the present application;
FIG. 2 illustrates a flow diagram according to one embodiment of an order processing method provided herein;
FIG. 3 illustrates an exemplary order information screenshot;
FIG. 4 is a schematic block diagram illustrating one embodiment of an order processing apparatus according to the present application;
fig. 5 is a schematic structural diagram illustrating an embodiment of an order processing apparatus according to the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
In some of the flows described in the specification and claims of this application and in the above-described figures, a number of operations are included that occur in a particular order, but it should be clearly understood that these operations may be performed out of order or in parallel as they appear herein, the number of operations, e.g., 101, 102, etc., merely being used to distinguish between various operations, and the number itself does not represent any order of performance. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor limit the types of "first" and "second" to be different.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making an invasive task, are within the scope of protection of the present application.
FIG. 1 is a block diagram illustrating an embodiment of an order processing system according to the present application. Referring to fig. 1, the order processing system includes a user end 1 at the user side, a service end 2, and an order taking end 3 at the order taking person side. The user end 1 interacts with the service end 2, and the service end 2 interacts with the order receiving end 2 on the order receiving personnel side. The user side 1 runs an application program on the user side. A user carries out order placing on a user side 1; the server 2 acquires the order submitted by the user 1 and distributes the order to the receiving end 3; the order receiving end 3 runs an application program at the side of the order receiving personnel, and the order receiving personnel receive orders on the order receiving end 3.
FIG. 2 illustrates a flow diagram according to one embodiment of an order processing method provided herein.
Referring to fig. 2, the order processing method may include the steps of:
201. and acquiring the to-be-processed order submitted by the user side.
For convenience of understanding, taking a taxi taking scene as an example, taxi taking software on a user side is installed at a user side, taxi taking software on an driver side is installed at a single terminal, and the single terminal is the driver terminal.
When the user has a taxi taking demand, a taxi taking order is created through the user side and sent to the server side.
202. Order attribute information is extracted from the order to be processed.
Specifically, the orders have different attributes, each attribute having corresponding order attribute information, wherein the attributes are different depending on the specific scenario. Of all attributes of the order, order attribute information for some attributes is filled in by the user.
In practice, when a user creates an order, cheating information such as advertisements may be filled in the order. Therefore, in order to identify whether some cheating information such as advertisements exists in the to-be-processed order uploaded by the user terminal, order attribute information in the to-be-processed order needs to be extracted. For example, the order attribute information includes, but is not limited to, an order address, order remark information, and the like.
Referring to fig. 3, the order attributes in the order of a certain shipping scenario include city of placing an order, vehicle type of placing an order, cost of placing an order, origin, destination, remark of placing an order, etc.
The first order information is provided with an advertisement at the starting place and the destination, namely (other random connection) generation uploading vehicle paste and plus.
Wherein, the advertisement is filled in the starting place and the destination in the second order information, which are respectively 'one meter tick, express delivery' and 'vehicle sticker uploading'. V: # # # # # ###. ".
And the placing remarks in the third piece of order information fill in the advertisements, and all contents of the placing remarks in the piece of order information are the advertisements.
It can be understood that if the order including the advertisement information is distributed to the driver end, it is likely to interfere with the normal work of the driver, and the driving safety is affected.
203. And respectively inputting the order attribute information into the order identification model.
In order to quickly identify whether the order to be processed uploaded by the user side is an abnormal order with abnormal information, the order identification model is obtained in advance based on the order attribute information of the sample order and the abnormal state training of the sample order.
In practical applications, in order to improve the identification accuracy of the abnormal information, the order identification model may be a neural network model. Optionally, the order recognition model is a BERT (Bidirectional Encoder characterizers of transducers) model in the neural network model. The BERT model is based on a transformer architecture, is a neural network model with bidirectional depth, and adopts a new technology named masking Language model (Masked Language Modeling), which allows bidirectional training in models that were not possible before. There are two phases using the BERT model: pre-training and fine-tuning. During pre-training, the model trains unlabeled data on different pre-training tasks. For fine tuning, the BERT model is first initialized with pre-trained parameters and all parameters are fine tuned using labeled data from downstream tasks. Each downstream task has a separate fine-tuning model even though they are initialized with the same pre-training parameters. One notable feature of the BERT model is its unified architecture across different tasks. The difference between the pre-training architecture and the final downstream architecture is small. During the fine-tuning, all parameters are fine-tuned. For a more detailed description of the BERT model, see the related art.
As one possible implementation, the order recognition model is trained as follows: acquiring a sample order, and respectively extracting order attribute information in the sample order; and (4) taking the order attribute information of the sample order as a model input, taking the abnormal state of the sample order as a model label, and training an order recognition model.
Wherein, when obtaining the sample order, the user can select from the stored historical orders. For example, a professional may determine whether the order attribute information of the stored historical order is abnormal, and take the historical order with the abnormal order attribute information as a sample order and add the sample order to the training set. The abnormal order attribute information is, for example, order address information added with cheating information such as advertisements, or order remark information added with cheating information such as advertisements.
And during model training, sequentially taking training samples in a training set as model input data, taking abnormal states as expected model output data, and performing model training to obtain an order recognition model. More details of model training are described in the related art.
204. And determining whether the order to be processed is an abnormal order or not according to the identification result of the order identification model.
Specifically, if the identification result of the order identification model is in an abnormal state, the order to be processed is determined to be an abnormal order. And if the identification result of the order identification model is in a non-abnormal state, determining that the order to be processed is a normal order.
According to the order processing method provided by the embodiment of the application, the order to be processed submitted by the user side is obtained; extracting order attribute information from the order to be processed; respectively inputting order attribute information into an order identification model; determining whether the order to be processed is an abnormal order or not according to the recognition result of the order recognition model; the order recognition model is obtained based on order attribute information of the sample order and abnormal state training of the sample order. Therefore, whether the order to be processed submitted by the user side is an abnormal order or not is automatically and timely recognized through the trained order recognition model, the abnormal order is conveniently prevented from being distributed to the order receiving end subsequently, and interference on the order receiving personnel is avoided.
In some embodiments of the present application, in order to avoid interference with the human interface, after determining whether the order to be processed is an abnormal order, the method may further include: and forbidding to push the order to be processed to the order receiving end, and adding the user at the user end into a blacklist, wherein the user on the blacklist is forbidden to submit a new order through the user end again.
In practical application, a user may not realize that cheating information such as advertisements cannot be added to an order, and in order to keep the user as much as possible, the abnormal number of times of order submission of the user can be restricted. Therefore, in some embodiments of the present application, before prohibiting pushing the pending order to the order taking end and adding the user at the user end to the blacklist, the method may further include: adding one to the order abnormal times of the user side; and if the order abnormity times are larger than the preset time threshold value, the order to be processed is forbidden to be pushed to the order receiving end, and the user of the user end is added into the blacklist. In addition, if the order abnormal times are not larger than the preset times threshold, deleting the abnormal information in the order to be processed, and pushing the order to be processed after the abnormal information is deleted to the order receiving end. Wherein, the preset time threshold is set according to the actual situation.
It can be understood that, for a user who submits an order with a small number of exceptions, the user can submit the order again, but the exception information in the exception order submitted by the user is deleted to leave the user as possible. And directly adding the blacklist to the users who submit orders with abnormal times, and forbidding the users to submit orders again through the user terminals.
In some embodiments of the present application, in order to enhance the awareness that the exception order cannot be submitted, after determining whether the pending order is an exception order, the method may further include: and sending order abnormity prompting information to the user side so that the user side outputs the order abnormity prompting information to the user.
The order abnormity prompting information can prompt that the currently submitted order of the user comprises abnormity information, if the frequent order submission times are too many, the user is added into a blacklist, and the order can not be submitted any more subsequently; or prompting the user that the currently submitted order has violation information which is deleted, if the frequency of submitting the frequent order is too many, the user is added into the blacklist and cannot submit the order any more subsequently, or prompting the user that the frequency of submitting the abnormal order is too many, the user is added into the blacklist and cannot submit the order any more subsequently, and the like.
In practical application, a user is likely to add exception information to order remark information, and therefore, in some embodiments of the present application, the order attribute information includes order remark information, and if the order remark information includes exception information, deleting the exception information in the order to be processed may include: and deleting all the order remark information.
In practical applications, a user is likely to add abnormal information to order address information, and in order to ensure that a receiving end obtains correct geographic location information, the geographic location information in the order address information needs to be retained, and abnormal information unrelated to the geographic location information needs to be deleted, so in some embodiments of the present application, the order attribute information includes the order address information, and if there is abnormal information in the order address information, deleting the abnormal information in the order address information may include: performing word segmentation on the order address information to obtain at least one word segmentation result; matching at least one word segmentation result with a pre-configured geographic location name; and taking the word segmentation result failed in matching as abnormal information, and deleting the abnormal information. And taking the word segmentation result successfully matched as normal geographic position information and keeping the normal geographic position information.
Wherein the preconfigured geographical location name may be obtained from a database of electronic maps, such as a grand map or a Baidu map.
The order address information may be subjected to word segmentation and Matching between a word segmentation result and a pre-configured geographic location name by using a Forward Maximum Matching method (FMM), a Backward Maximum Matching method (BMM) or a bidirectional Maximum Matching method in a chinese word segmentation algorithm.
Fig. 4 is a schematic structural diagram illustrating an embodiment of an order processing apparatus according to the present application. The device is an execution main body of the verification method of the graphic verification code, is composed of hardware and/or software, and can be integrated in mobile phones, tablet computers, vehicle-mounted computers and wearable equipment.
Referring to fig. 4, the order processing apparatus may include:
an obtaining module 401, configured to obtain an order to be processed submitted by a user;
an extracting module 402, configured to extract order attribute information from an order to be processed;
an identification module 403, configured to input the order attribute information into the order identification models respectively;
a determining module 404, configured to determine whether the order to be processed is an abnormal order according to the recognition result of the order recognition model; the order recognition model is obtained based on order attribute information of the sample order and abnormal state training of the sample order.
In some embodiments of the present application, the order recognition model is trained as follows:
acquiring a sample order, and respectively extracting order attribute information in the sample order;
and (4) taking the order attribute information of the sample order as a model input, taking the abnormal state of the sample order as a model label, and training an order recognition model.
In some embodiments of the present application, the apparatus further includes a processing module, configured to prohibit pushing the to-be-processed order to the order receiving end after determining whether the to-be-processed order is an abnormal order, and add the user at the user end to a blacklist, where the user on the blacklist prohibits submitting a new order through the user end again.
In some embodiments of the present application, before prohibiting pushing of the pending order to the order taker and adding the user at the user end to the blacklist, the processing module is further configured to:
adding one to the order abnormal times of the user side;
and if the order abnormity times are larger than the preset time threshold value, the order to be processed is forbidden to be pushed to the order receiving end, and the user at the user end is added into the blacklist.
In some embodiments of the present application, the processing module is further configured to: and if the order abnormity frequency is not more than the preset frequency threshold value, deleting the abnormity information in the order to be processed, and pushing the order to be processed after the abnormity information is deleted to the order receiving end.
In some embodiments of the present application, the order attribute information includes order remark information, and if the order remark information includes abnormal information, the deleting, by the processing module, the abnormal information in the to-be-processed order is specifically: and deleting all the order remark information.
In some embodiments of the present application, the order attribute information includes order address information, and if there is abnormal information in the order address information, the deleting, by the processing module, the abnormal information in the order address information specifically is: performing word segmentation on the order address information to obtain at least one word segmentation result; matching at least one word segmentation result with a pre-configured geographic location name; and taking the word segmentation result failed in matching as abnormal information, and deleting the abnormal information.
In some embodiments of the present application, the processing module is further configured to: and sending order abnormity prompt information to the user side so that the user side outputs the order abnormity prompt information to the user.
The order processing apparatus in fig. 4 may execute the order processing method in the embodiment shown in fig. 2, and the implementation principle and the technical effect are not described again. The specific manner in which each module and unit of the order processing apparatus in the above embodiments perform operations has been described in detail in the embodiments related to the method, and will not be described in detail herein.
In one possible design, the order processing apparatus of the embodiment shown in fig. 4 may be implemented as an electronic device, which may include a storage component 501 and a processing component 502 as shown in fig. 5;
the storage component 501 stores one or more computer instructions, wherein the one or more computer instructions are for execution by the processing component call.
The processing component 502 is configured to:
acquiring a to-be-processed order submitted by a user side;
extracting order attribute information from the order to be processed;
respectively inputting order attribute information into an order identification model;
determining whether the order to be processed is an abnormal order or not according to the recognition result of the order recognition model;
the order recognition model is obtained based on order attribute information of the sample order and abnormal state training of the sample order.
The processing component 502 may include one or more processors executing computer instructions to perform all or part of the steps of the method described above. Of course, the processing elements may also be implemented as one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components configured to perform the above-described methods.
The storage component 501 is configured to store various types of data to support operations at the terminal. The storage components may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
The electronic device may further include a display component 503, and the display component 503 may be an Electroluminescent (EL) element, a liquid crystal display or a micro-display having a similar structure, or a retina-directly-displayable or similar laser scanning type display.
Of course, the electronic device may of course also comprise other components, such as input/output interfaces, communication components, etc.
The input/output interface provides an interface between the processing components and peripheral interface modules, which may be output devices, input devices, etc.
The communication component is configured to facilitate wired or wireless communication between the electronic device and other devices, and the like.
As used herein, an "electronic device" may be a remote web server, a computer networking device, a chipset, a desktop computer, a notebook computer, a workstation, or any other processing device or equipment.
The electronic device may be a physical device or an elastic computing host provided by a cloud computing platform, and the electronic device may be a cloud server, and the processing component, the storage component, and the like may be basic server resources rented or purchased from the cloud computing platform.
An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a computer, the order processing method in the embodiment shown in fig. 2 may be implemented.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, 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. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and of course, can also be implemented by hardware. With this understanding in mind, the above technical solutions may be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute the method according to the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present application.
Claims (11)
1. An order processing method, comprising:
acquiring a to-be-processed order submitted by a user side;
extracting order attribute information from the order to be processed;
respectively inputting the order attribute information into an order identification model;
determining whether the order to be processed is an abnormal order or not according to the recognition result of the order recognition model;
the order recognition model is obtained based on order attribute information of a sample order and training of the abnormal state of the sample order.
2. The method of claim 1, wherein the order recognition model is trained as follows:
acquiring a sample order, and respectively extracting order attribute information in the sample order;
and taking the order attribute information of the sample order as a model input, taking the abnormal state of the sample order as a model label, and training the order recognition model.
3. The method of claim 1, after determining whether the pending order is an exception order, further comprising:
and forbidding to push the order to be processed to a receiving end, and adding the user of the user end into a blacklist, wherein the user of the blacklist forbids to submit a new order through the user end again.
4. The method of claim 3, further comprising, before prohibiting pushing of the pending order to a customer terminal and blacklisting a user at the customer terminal:
adding one to the order abnormal times of the user side;
and if the order abnormity times are larger than a preset time threshold value, the order to be processed is forbidden to be pushed to a receiving terminal, and the user of the user terminal is added into a blacklist.
5. The method of claim 4, further comprising:
and if the order abnormity times are not more than a preset time threshold value, deleting the abnormity information in the order to be processed, and pushing the order to be processed after the abnormity information is deleted to the order taking end.
6. The method according to claim 5, wherein the order attribute information includes order remark information, and if the order remark information includes exception information, the deleting the exception information in the to-be-processed order includes:
and deleting all the order remark information.
7. The method according to claim 5, wherein the order attribute information includes order address information, and if there is abnormal information in the order address information, the deleting the abnormal information in the order address information includes:
performing word segmentation on the order address information to obtain at least one word segmentation result;
matching at least one word segmentation result with a pre-configured geographic location name;
and taking the word segmentation result failed in matching as abnormal information, and deleting the abnormal information.
8. The method of claim 1, wherein after determining whether the pending order is an exception order, further comprising:
and sending order abnormity prompt information to the user side so that the user side outputs the order abnormity prompt information to the user.
9. An order processing apparatus, comprising:
the acquisition module is used for acquiring the order to be processed submitted by the user side;
the extraction module is used for extracting order attribute information from the order to be processed;
the identification module is used for respectively inputting the order attribute information into an order identification model;
the determining module is used for determining whether the order to be processed is an abnormal order or not according to the identification result of the order identification model; the order recognition model is obtained based on order attribute information of a sample order and training of the abnormal state of the sample order.
10. An electronic device comprising a processing component and a storage component;
the storage component stores one or more computer instructions; the one or more computer instructions to be invoked for execution by the processing component;
the processing component is to:
acquiring a to-be-processed order submitted by a user side;
extracting order attribute information from the order to be processed;
respectively inputting the order attribute information into an order identification model;
determining whether the order to be processed is an abnormal order or not according to the recognition result of the order recognition model;
the order recognition model is obtained based on order attribute information of a sample order and training of the abnormal state of the sample order.
11. A computer-readable storage medium, in which a computer program is stored which, when being executed by a computer, carries out the steps of the above-mentioned method.
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