CN111461825A - Virtual resource generation method and device, electronic equipment and storage medium - Google Patents
Virtual resource generation method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN111461825A CN111461825A CN202010238199.7A CN202010238199A CN111461825A CN 111461825 A CN111461825 A CN 111461825A CN 202010238199 A CN202010238199 A CN 202010238199A CN 111461825 A CN111461825 A CN 111461825A
- Authority
- CN
- China
- Prior art keywords
- resource data
- data
- resource
- virtual
- generating
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 53
- 238000004458 analytical method Methods 0.000 claims abstract description 47
- 238000012545 processing Methods 0.000 claims abstract description 29
- 238000004891 communication Methods 0.000 claims description 19
- 238000012549 training Methods 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 9
- 238000003062 neural network model Methods 0.000 claims description 7
- 238000004422 calculation algorithm Methods 0.000 description 9
- 230000008569 process Effects 0.000 description 5
- 230000009471 action Effects 0.000 description 3
- 230000002354 daily effect Effects 0.000 description 3
- 238000003066 decision tree Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000014759 maintenance of location Effects 0.000 description 3
- 239000013598 vector Substances 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 241000208125 Nicotiana Species 0.000 description 1
- 235000002637 Nicotiana tabacum Nutrition 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000013145 classification model Methods 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 238000000265 homogenisation Methods 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003058 natural language processing Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
Images
Classifications
-
- 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
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
-
- 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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
- G06Q30/0222—During e-commerce, i.e. online transactions
Abstract
The application relates to a method and a device for generating virtual resources, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring a data processing request, wherein the data processing request carries first resource data; analyzing according to the first resource data through a pre-trained analysis model to obtain target resource data; generating a first virtual resource according to the target resource data; and issuing the first virtual resource to the requester. According to the technical scheme, on one hand, through acquiring the demands of the user, the virtual resources can be more accurately distributed according to the demands of the user, the shopping demands of the user are met, the access amount of the platform is increased while the participation of the user is improved, so that the loss of the user is avoided, on the other hand, through automatically determining the resource data, the artificial judgment factors are added into the wall surface, and meanwhile, the labor cost is also reduced.
Description
Technical Field
The present application relates to the field of data processing, and in particular, to a method and an apparatus for generating virtual resources, an electronic device, and a storage medium.
Background
With the development and maturity of internet technology, more and more users are used to consume and purchase goods through an e-commerce platform. Under the condition that the electronic commerce platforms appear in large quantity and the homogenization is serious, only enough active users can ensure the normal operation of the platforms, so how to improve the retention rate of the users becomes one of the important work of each electronic commerce platform. A common method for increasing the user retention rate is: and information which is interested in the information is pushed to the user, so that the frequency of using the platform by the user is increased, and the retention rate is improved. Wherein, the push information comprises: advertisements, coupons, interactive activities, electronic coupons, and the like.
Because users usually only browse information of interest, and the demands of different users are not completely the same, in order to improve the accuracy of issuing virtual resources, in the prior art, the users are determined to issue virtual resources to the users by acquiring portrait information of the users and according to a trained model for predicting the preference of the users.
However, in the process of implementing the present invention, the inventor finds that the virtual resource issued by the user through the method is not accurate enough because the portrait information can only issue the virtual resource according to one or more kinds of characteristic information of the user, and the actual requirements of the user cannot be accurately located.
Disclosure of Invention
In order to solve the above technical problem or at least partially solve the above technical problem, it is necessary to accurately distribute virtual resources to a user to meet the shopping demand of the user. Therefore, the application provides a method and device for generating virtual resources, an electronic device and a storage medium.
In a first aspect, an embodiment of the present application provides a method for generating a virtual resource, including:
acquiring a data processing request, wherein the data processing request carries first resource data;
analyzing according to the first resource data through a pre-trained analysis model to obtain target resource data;
generating a first virtual resource according to the target resource data;
and issuing the first virtual resource to a requester.
Optionally, before the obtaining the data processing request, the method further includes:
determining first resource data according to the received trigger operation and generating corresponding label content;
receiving voice content;
and when the voice information is matched with the label content, generating the data processing request according to the first resource data.
Optionally, before the target resource data is obtained according to the first resource data by analyzing through the pre-trained analysis model, the method further includes:
obtaining a virtual resource obtaining record associated with the requester;
determining the acquisition failure times according to the virtual resource acquisition record;
calculating the acquisition probability according to the acquisition failure times;
and when the acquisition probability is smaller than or equal to the preset threshold value, inputting the first resource data into a pre-trained analysis model.
Optionally, the analyzing the target resource data according to the first resource data by using the pre-trained analysis model includes:
acquiring a pre-trained analysis model;
inputting the first resource data into a pre-trained analysis model, and calculating by the analysis model according to the first resource data to obtain second resource data;
determining attribute information of the requester and third resource data corresponding to the attribute information;
and weighting according to the second resource data and the third resource data to obtain the target resource data.
Optionally, the method further includes:
acquiring sample resource data and annotation content corresponding to the sample resource data, wherein the annotation content comprises: weighting values corresponding to the sample resource data;
and training the preset neural network model by adopting the sample data and the labeled content, and learning the corresponding relation between the sample resource data and the use intention value by the preset neural network model to obtain an analysis model.
Optionally, the generating a first virtual resource according to the target resource data includes:
generating task options according to the target resource data;
determining a target task according to the triggering operation acting on the task option;
acquiring operation data of the target task;
and when the operation data meet a preset condition, generating a first virtual resource according to the target resource data.
Optionally, the method further includes:
receiving a selected operation acting on a plurality of candidate first virtual resources;
determining at least two first virtual resources of the same type based on the selected operation;
calculating according to the resource data of the at least two first virtual resources and a preset probability to obtain fourth resource data;
and generating a second virtual resource according to the fourth resource data, and sending the second virtual resource to the requester.
In a second aspect, the present application provides an apparatus for generating virtual resources, including:
the acquisition module is used for acquiring a data processing request, and the data processing request carries first resource data;
the analysis module is used for analyzing and obtaining target resource data according to the first resource data through a pre-trained analysis model;
the generating module is used for generating a first virtual resource according to the target resource data;
and the sending module is used for sending the first virtual resource to a request party.
In a third aspect, the present application provides an electronic device, comprising: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus;
the memory is used for storing a computer program;
the processor is configured to implement the above method steps when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the above-mentioned method steps.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages: on the one hand, through the demand that acquires the user, can be more accurate according to user's demand granting virtual resource, satisfy user's shopping demand, what improve user's participation also improved the visit volume of platform simultaneously to avoid causing the user to run off, on the other hand, through automatic definite resource data, artificial decision factor is added to the wall, has also reduced the cost of labor simultaneously.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a flowchart of a method for generating virtual resources according to an embodiment of the present application;
fig. 2 is a flowchart of a method for generating virtual resources according to another embodiment of the present application;
fig. 3 is a block diagram of a virtual resource generation apparatus according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, 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 some embodiments of the present application, but 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 application.
The embodiment of the application provides a method and a device for generating virtual resources, electronic equipment and a storage medium. The method provided by the embodiment of the invention can be applied to any required electronic equipment, for example, the electronic equipment can be electronic equipment such as a server and a terminal, and the method is not particularly limited herein, and is hereinafter simply referred to as electronic equipment for convenience in description.
First, a method for generating virtual resources according to an embodiment of the present invention is described below.
Fig. 1 is a flowchart of a method for generating virtual resources according to an embodiment of the present application. As shown in fig. 1, the method comprises the steps of:
step S11, acquiring a data processing request, wherein the data processing request carries first resource data;
step S12, analyzing according to the first resource data through a pre-trained analysis model to obtain target resource data;
step S13, generating a first virtual resource according to the target resource data;
in step S14, the first virtual resource is issued to the requester.
According to the method provided by the embodiment, on one hand, the actual requirements of the user can be positioned according to the received processing request, and on the other hand, more accurate virtual resources can be generated for the user by adopting the analysis model according to the resource data carried in the processing request, and on the other hand, the addition of artificial judgment factors is avoided by automatically determining the resource data, and meanwhile, the labor cost is also reduced.
In this embodiment, before acquiring the data processing request, the method further includes: acquiring a trigger operation acting on a registration interface, determining first resource data according to the trigger operation, generating corresponding label content, receiving voice content, and generating a data processing request according to the first resource data when the voice information is matched with the label content.
As an example, when a user logs into an electronic mall and enters a pickup interface, the area interface includes: the method comprises the following steps of receiving triggering operation of a user based on virtual resource options, determining first resource data according to the triggering operation, and generating label content after the first resource data is determined, wherein the triggering operation comprises the following steps: and voice text or numbers and the like, then receiving the voice content, analyzing the voice content, and generating a data processing request according to the first resource data when the voice content is matched with the tag content.
It can be understood that, when the voice content matches the tag content in the present embodiment, the following steps are included: the voice content is the same as the tag content, for example, both the tag content and the voice content are: 987987. or the voice content and the tag content conform to a preset relationship, for example: the label content is: the sunshine incense burner produces purple tobacco, and the voice content is as follows: the waterfall hanging front river is seen remotely.
In this embodiment, before the target resource data is obtained according to the first resource data by the pre-trained analysis model, the method further includes: the method comprises the steps of obtaining a virtual resource obtaining record associated with a requester, determining obtaining failure times according to the virtual resource obtaining record, calculating obtaining probability according to the obtaining failure times, and inputting first resource data into a pre-trained analysis model when the obtaining probability is smaller than or equal to a preset threshold value.
As an example, a user ID of a user may be first obtained, a virtual resource pickup record associated with the user ID is obtained, it is determined that the number of times of acquisition failure is x, and the acquisition probability is: and x/(1+ x), and when the acquisition probability is less than or equal to a preset threshold value, inputting the first resource data into a pre-trained analysis model.
In this embodiment, analyzing the target resource data according to the first resource data by using a pre-trained analysis model includes: the method comprises the steps of obtaining a pre-trained analysis model, inputting first resource data into the pre-trained analysis model, obtaining second resource data through calculation according to the first resource data through the analysis model, determining attribute information of a user associated with a requester and third resource data corresponding to the attribute information, and weighting according to the second resource data and the third resource data to obtain target resource data. The attribute information of the user may be member information, consumption level, browsing duration, etc.
In the prior art, functions of each cost deduction value and each corresponding transaction probability are fitted, a user group is divided into a plurality of classes based on the fitted functions and a plurality of preset parameters, and a pushed virtual resource corresponding to each class of users is determined. However, the granularity of the model is large, and prediction and virtual resource distribution cannot be really achieved for each user, so that the accuracy of generating virtual resources is low. In addition, the cost of pushing the virtual resource also includes the charge deduction value of the use of the virtual resource. However, in the prior art, the cost caused by the actual use of the virtual resource is not considered, and the cost for pushing the virtual resource is easily too high.
Therefore, in the embodiment, the target resource data is obtained by weighting according to the second resource data and the third resource data, and the resource data which is expected by the user can be calculated more accurately.
The training process of the analysis model in this embodiment is as follows: acquiring sample resource data and annotation content corresponding to the sample resource data, wherein the annotation content comprises: a weighted value corresponding to the sample resource data, wherein the weighted value can be a use intention value; and training the preset neural network model by adopting the sample data and the labeled content, and learning the corresponding relation between the sample resource data and the weighted value by the preset neural network model to obtain an analysis model.
As an example, in the training phase, the sample resource data is usage data of a preset user, the sample resource data of the preset user is used as input content of the analysis model, and weight values of various virtual resources of the preset user are used as labeling information, so that supervised training of the analysis model is achieved. When the sample resource data of the preset user is input into the analysis model, vector conversion can be performed on the sample resource data of the sample user to obtain a multi-dimensional vector, and each dimension corresponds to one type of specific information. It should be noted that any algorithm capable of converting text into vectors can be applied to the embodiments of the present invention, for example: word2vec algorithm. Here, word2vec is a natural language processing algorithm, and is characterized in that all words are vectorized, so that the relationship between words can be quantitatively measured, and the relationship between words can be mined.
In addition, the model may be trained by adding auxiliary information, where the auxiliary information may include user characteristics, for example, browsing duration of each user every day, the internet surfing durations of different users are personalized, and the values are relatively cluttered, for example, 1 hour, 5 minutes, 45 minutes, 3 hours, 30 minutes, and the like, and further, it is assumed that one node in a certain decision tree obtained by model training is: if the daily internet surfing time exceeds 1 hour, the user characteristic is converted from the daily user internet surfing time into the user characteristic, if the daily user internet surfing time exceeds 1 hour, the characteristic value is 1, otherwise, the characteristic value is 0, and the time value of the continuously changed internet surfing time is converted into two characteristic values of 0 and 1.
In the embodiment of the present invention, the weight values of the user to be predicted on various sample resource data, which are predicted by using the analysis model implemented by the decision tree model, may be a number between 0 and 1.
Of course, in a specific application, the analysis model is not limited to the decision tree model in the machine learning algorithm model, and may also include other algorithm models in the machine learning algorithm model. For example: clustering algorithm models, bayesian classification models, support vector machine models, and the like. In addition, a deep learning algorithm model may also be adopted as the analysis model in the embodiment of the present invention. Here, the deep learning algorithm model may include: convolutional neural networks, cyclic neural networks, and multi-layered perceptrons, among others.
In practical application, a plurality of analysis models can be trained and stored, and each prediction model corresponds to one issuing scene. Here, the release scenarios differ mainly in that: the number of users to be predicted is different, and/or the number of virtual resources is different. Therefore, when the release scene is determined, the user to be predicted can be determined, and the analysis model corresponding to the application scene can be correspondingly determined.
In this embodiment, generating the first virtual resource according to the target resource data includes: generating a task option according to the target resource data, determining a target task according to a trigger operation acting on the task option, acquiring operation data of the target task, and generating a first virtual resource according to the target resource data when the operation data meets a preset condition.
As one example, task options include; send link, voice password, etc., such as: and when the triggering operation acted on the task option is performed, determining that the target task is a sending link, acquiring a propagation path after the sending of the sending link is completed, acquiring operation data based on the propagation path, wherein the operation data comprises the browsing times of the link, and when the browsing times are greater than or equal to the preset times, generating the first virtual resource according to the target resource share data.
In this embodiment, after the first virtual resource is obtained, the user batch associated with the virtual resource needs to be queried to determine the batch number. After determining the batch number, adding a logical lock to the batch number, wherein adding the logical lock comprises: and judging whether the target user batch corresponding to the batch number is occupied by other virtual resource issuing tasks or not. If the virtual resource issuing task is not occupied by other virtual resource issuing tasks, the batch number is locked, so that other virtual resource issuing tasks cannot use the target user batch, namely, the locking is successful. However, if occupied by other virtual resource issuance tasks, a wait is made until the other virtual resource issuance tasks release the occupation with the target user batch, i.e., the locking is unsuccessful.
In particular, Redis can be employed as a sourced, high-performance key-value storage system, with all key values of Redis stored in memory, having very high stand-alone read-write performance
Fig. 2 is a flowchart of a method for generating virtual resources according to another embodiment of the present application. As shown in fig. 2, the method further comprises the steps of:
step S21, receiving a selected operation acting on a plurality of candidate first virtual resources;
step S22, determining at least two first virtual resources of the same type based on the selected operation;
step S23, calculating according to the resource data of at least two first virtual resources and a preset probability to obtain fourth resource data;
step S24, generating a second virtual resource according to the fourth resource data, and sending the second virtual resource to the requester.
In the method provided by this embodiment, the selected candidate first virtual resources of the user are fused, and the virtual resources more meeting the user's expectations are calculated according to the resource data of the candidate first virtual resources.
It is understood that the same type in this embodiment refers to virtual resources that are all full minus types or virtual resources that are all discount types. Calculating according to the resource data of the at least two first virtual resources and the preset probability to obtain fourth resource data, wherein the calculation mode is as follows:
D＝H*EU+(1-H)*ES
and D is fourth resource data, H is a preset factor, EU is first preferential data, and ES is target preferential data.
In addition, in the embodiment of the present application, when a virtual resource is issued, a data item of the virtual resource to be issued is determined, where the data item includes remark information, an issue time or a recharge record, and then a corresponding virtual resource issue record is derived according to the data item, for example: the data item is remark information of the virtual resource issuing record, such as a mobile phone number, and the derived virtual resource issuing record is the virtual resource issuing record including the mobile phone number.
In this embodiment, when the virtual resource is queried, the corresponding virtual resource issuing record may be derived according to the user-defined data item, and finally, a report is generated according to the derived virtual resource issuing record, so that the derived virtual resource issuing record is displayed in a form, and flexibility of viewing the virtual resource issuing record is improved.
Fig. 3 is a block diagram of an apparatus for generating a virtual resource according to an embodiment of the present application, where the apparatus may be implemented as part or all of an electronic device through software, hardware, or a combination of the two. As shown in fig. 3, the apparatus includes:
an obtaining module 31, configured to obtain a data processing request, where the data processing request carries first resource data;
the analysis module 32 is used for analyzing the pre-trained analysis model according to the first resource data to obtain target resource data;
a generating module 33, configured to generate a first virtual resource according to the target resource data;
the sending module 34 is configured to issue the first virtual resource to a requester corresponding to the data processing request.
The obtaining module in the embodiment of the application is specifically configured to obtain a trigger operation applied to a registration interface, determine first resource data according to the trigger operation, generate corresponding tag content, receive voice content, and generate a data processing request according to the first resource data when voice information is matched with the tag content.
The device in the embodiment of the application further comprises a processing module, wherein the processing module is used for acquiring the virtual resource acquisition record associated with the requester; determining the number of acquisition failures according to the virtual resource acquisition record; calculating the acquisition probability according to the acquisition failure times; and when the acquisition probability is smaller than or equal to a preset threshold value, inputting the first resource data into a pre-trained analysis model.
The analysis module in the embodiment of the application is specifically used for acquiring a pre-trained analysis model; inputting the first resource data into a pre-trained analysis model, and calculating by the analysis model according to the first resource data to obtain second resource data; determining attribute information of a requester and third resource data corresponding to the attribute information; and weighting according to the second resource data and the third resource data to obtain target resource data.
The device in the embodiment of the application further comprises a training module, wherein the training module is used for acquiring the sample data and the labeled content corresponding to the sample data; and training the preset neural network model by adopting the sample data and the labeled content to obtain an analysis model.
The analysis module in the embodiment of the application is specifically configured to generate task options according to the target resource data; determining a target task according to the triggering operation acting on the task option; acquiring operation data of a target task; and when the operation data meet the preset conditions, generating a first virtual resource according to the target resource data.
Optionally, the apparatus in this embodiment of the present application further includes a composition module, configured to receive a selected operation that acts on the plurality of candidate first virtual resources; determining at least two first virtual resources of the same type based on the selected operation; calculating according to the resource data of the at least two first virtual resources and the preset probability to obtain fourth resource data; and generating a second virtual resource according to the fourth resource data, and sending the second virtual resource to the requester.
An embodiment of the present application further provides an electronic device, as shown in fig. 4, the electronic device may include: the system comprises a processor 1501, a communication interface 1502, a memory 1503 and a communication bus 1504, wherein the processor 1501, the communication interface 1502 and the memory 1503 complete communication with each other through the communication bus 1504.
A memory 1503 for storing a computer program;
the processor 1501 is configured to implement the steps of the above embodiments when executing the computer program stored in the memory 1503.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the following steps.
A computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including AN object oriented programming language such as Java, C + +, or the like, as well as conventional procedural programming languages, such as the "C" language or similar programming languages.
It should be noted that, for the above-mentioned apparatus, electronic device and computer-readable storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiments.
It is further noted that, herein, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for generating virtual resources, comprising:
acquiring a data processing request, wherein the data processing request carries first resource data;
analyzing according to the first resource data through a pre-trained analysis model to obtain target resource data;
generating a first virtual resource according to the target resource data;
and issuing the first virtual resource to a requester.
2. The method of claim 1, wherein prior to said obtaining a data processing request, the method further comprises:
determining first resource data according to the received trigger operation and generating corresponding label content;
receiving voice content based on the registration interface;
and when the voice information is matched with the label content, generating the data processing request according to the first resource data.
3. The method of claim 1, wherein prior to analyzing target resource data from the first resource data via a pre-trained analytical model, the method further comprises:
obtaining a virtual resource obtaining record associated with the requester;
determining the acquisition failure times according to the virtual resource acquisition record;
calculating the acquisition probability according to the acquisition failure times;
and when the acquisition probability is smaller than or equal to the preset threshold value, inputting the first resource data into a pre-trained analysis model.
4. The method of claim 1, wherein analyzing target resource data from the first resource data through a pre-trained analytical model comprises:
acquiring a pre-trained analysis model;
inputting the first resource data into a pre-trained analysis model, and calculating by the analysis model according to the first resource data to obtain second resource data;
determining attribute information of the requester and third resource data corresponding to the attribute information;
and weighting according to the second resource data and the third resource data to obtain the target resource data.
5. The method of claim 3, further comprising:
acquiring sample resource data and annotation content corresponding to the sample resource data, wherein the annotation content comprises: weighting values corresponding to the sample resource data;
and training the preset neural network model by adopting the sample data and the labeled content, and learning the corresponding relation between the sample resource data and the use intention value by the preset neural network model to obtain an analysis model.
6. The method of claim 1, wherein generating the first virtual resource from the target resource data comprises:
generating task options according to the target resource data;
determining a target task according to the triggering operation acting on the task option;
acquiring operation data of the target task;
and when the operation data meet a preset condition, generating a first virtual resource according to the target resource data.
7. The method of claim 1, further comprising:
receiving a selected operation acting on a plurality of candidate first virtual resources;
determining at least two first virtual resources of the same type based on the selected operation;
calculating according to the resource data of the at least two first virtual resources and a preset probability to obtain fourth resource data;
and generating a second virtual resource according to the fourth resource data, and sending the second virtual resource to the requester.
8. A virtual resource generator apparatus, comprising:
the acquisition module is used for acquiring a data processing request, and the data processing request carries first resource data;
the analysis module is used for analyzing and obtaining target resource data according to the first resource data through a pre-trained analysis model;
the generating module is used for generating a first virtual resource according to the target resource data;
and the sending module is used for sending the first virtual resource to a request party.
9. An electronic device, comprising: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus;
the memory is used for storing a computer program;
the processor, when executing the computer program, implementing the method steps of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method steps of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010238199.7A CN111461825B (en) | 2020-03-30 | 2020-03-30 | Virtual resource generation method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010238199.7A CN111461825B (en) | 2020-03-30 | 2020-03-30 | Virtual resource generation method and device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111461825A true CN111461825A (en) | 2020-07-28 |
| CN111461825B CN111461825B (en) | 2024-04-09 |
Family
ID=71683428
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010238199.7A Active CN111461825B (en) | 2020-03-30 | 2020-03-30 | Virtual resource generation method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111461825B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110185421A1 (en) * | 2010-01-26 | 2011-07-28 | Silver Tail Systems, Inc. | System and method for network security including detection of man-in-the-browser attacks |
| CN106095846A (en) * | 2016-06-03 | 2016-11-09 | 财付通支付科技有限公司 | The processing method of a kind of virtual resource and server |
| CN109783822A (en) * | 2019-01-24 | 2019-05-21 | 中国—东盟信息港股份有限公司 | A kind of data sample identifying system and its method based on identifying code |
| US20190238488A1 (en) * | 2016-08-04 | 2019-08-01 | Tencent Technology (Shenzhen) Company Limited | Information processing method, apparatus, device and storage medium |
| CN110109750A (en) * | 2019-04-03 | 2019-08-09 | 平安科技（深圳）有限公司 | Virtual resource acquisition methods, device, computer equipment and storage medium |
| CN110209922A (en) * | 2018-06-12 | 2019-09-06 | 中国科学院自动化研究所 | Object recommendation method, apparatus, storage medium and computer equipment |
| US20200074294A1 (en) * | 2018-08-30 | 2020-03-05 | Qualtrics, Llc | Machine-learning-based digital survey creation and management |
-
2020
- 2020-03-30 CN CN202010238199.7A patent/CN111461825B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110185421A1 (en) * | 2010-01-26 | 2011-07-28 | Silver Tail Systems, Inc. | System and method for network security including detection of man-in-the-browser attacks |
| CN106095846A (en) * | 2016-06-03 | 2016-11-09 | 财付通支付科技有限公司 | The processing method of a kind of virtual resource and server |
| US20190238488A1 (en) * | 2016-08-04 | 2019-08-01 | Tencent Technology (Shenzhen) Company Limited | Information processing method, apparatus, device and storage medium |
| CN110209922A (en) * | 2018-06-12 | 2019-09-06 | 中国科学院自动化研究所 | Object recommendation method, apparatus, storage medium and computer equipment |
| US20200074294A1 (en) * | 2018-08-30 | 2020-03-05 | Qualtrics, Llc | Machine-learning-based digital survey creation and management |
| CN109783822A (en) * | 2019-01-24 | 2019-05-21 | 中国—东盟信息港股份有限公司 | A kind of data sample identifying system and its method based on identifying code |
| CN110109750A (en) * | 2019-04-03 | 2019-08-09 | 平安科技（深圳）有限公司 | Virtual resource acquisition methods, device, computer equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 傅小康: "云环境下的集散型物流服务协同模型与优化" * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111461825B (en) | 2024-04-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110874440B (en) | Information pushing method and device, model training method and device, and electronic equipment | |
| Yadav et al. | LARAVEL: a PHP framework for e-commerce website | |
| CN114265979B (en) | Method for determining fusion parameters, information recommendation method and model training method | |
| CN106897905B (en) | Method and device for pushing information and electronic equipment | |
| CN105101122A (en) | Verification code inputting method and device | |
| CN111008335A (en) | Information processing method, device, equipment and storage medium | |
| CN107291774B (en) | Error sample identification method and device | |
| US20230259959A1 (en) | Multi-target prediction method and apparatus, device, storage medium and program product | |
| CN112598472A (en) | Product recommendation method, device, system, medium and program product | |
| CN116629937A (en) | Marketing strategy recommendation method and device | |
| CN113297287B (en) | Automatic user policy deployment method and device and electronic equipment | |
| CN113205189B (en) | Method for training prediction model, prediction method and device | |
| CN103425767A (en) | Method and system for determining prompt data | |
| CN106817296B (en) | Information recommendation test method and device and electronic equipment | |
| CN111275071A (en) | Prediction model training method, prediction device and electronic equipment | |
| CN116109374A (en) | Resource bit display method, device, electronic equipment and computer readable medium | |
| CN111461825A (en) | Virtual resource generation method and device, electronic equipment and storage medium | |
| CN111507471B (en) | Model training method, device, equipment and storage medium | |
| CN115495570A (en) | Application user classification method, application user evaluation method, application user classification device, application user evaluation device and application user evaluation equipment | |
| CN111241382A (en) | Data processing method and device, storage medium and electronic equipment | |
| CN116108132B (en) | Method and device for auditing text of short message | |
| US20220067655A1 (en) | Crowdsourced Insights About Merchant Shipping Methods | |
| CN116911913B (en) | Method and device for predicting interaction result | |
| CN117217852B (en) | Behavior recognition-based purchase willingness prediction method and device | |
| CN117743673A (en) | Resource recall method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: Room 221, 2 / F, block C, 18 Kechuang 11th Street, Daxing District, Beijing, 100176 Applicant after: Jingdong Technology Holding Co.,Ltd. Address before: Room 221, 2 / F, block C, 18 Kechuang 11th Street, Daxing District, Beijing, 100176 Applicant before: Jingdong Digital Technology Holding Co.,Ltd. Address after: Room 221, 2 / F, block C, 18 Kechuang 11th Street, Daxing District, Beijing, 100176 Applicant after: Jingdong Digital Technology Holding Co.,Ltd. Address before: Room 221, 2 / F, block C, 18 Kechuang 11th Street, Daxing District, Beijing, 100176 Applicant before: JINGDONG DIGITAL TECHNOLOGY HOLDINGS Co.,Ltd. |
|
| CB02 | Change of applicant information | ||
| GR01 | Patent grant |