CN111612135A - Method and device for information interaction - Google Patents
Method and device for information interaction Download PDFInfo
- Publication number
- CN111612135A CN111612135A CN202010439286.9A CN202010439286A CN111612135A CN 111612135 A CN111612135 A CN 111612135A CN 202010439286 A CN202010439286 A CN 202010439286A CN 111612135 A CN111612135 A CN 111612135A
- Authority
- CN
- China
- Prior art keywords
- model
- identification user
- user
- information
- identification
- 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 58
- 230000003993 interaction Effects 0.000 title claims abstract description 38
- 230000004044 response Effects 0.000 claims abstract description 56
- 238000012790 confirmation Methods 0.000 claims abstract description 15
- 238000011161 development Methods 0.000 claims description 42
- 238000012360 testing method Methods 0.000 claims description 42
- 238000012549 training Methods 0.000 claims description 13
- 238000005516 engineering process Methods 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 8
- 230000018109 developmental process Effects 0.000 description 34
- 238000010586 diagram Methods 0.000 description 15
- 238000013473 artificial intelligence Methods 0.000 description 9
- 239000003795 chemical substances by application Substances 0.000 description 9
- 238000004891 communication Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 6
- 238000012545 processing Methods 0.000 description 5
- 238000007906 compression Methods 0.000 description 4
- 230000006835 compression Effects 0.000 description 4
- 238000013527 convolutional neural network Methods 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 238000012417 linear regression Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000003709 image segmentation Methods 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013144 data compression Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000013441 quality evaluation Methods 0.000 description 1
- 238000013515 script Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The embodiment of the disclosure discloses a method and a device for information interaction. One embodiment of the method comprises: responding to a received first model obtaining request of a first identification user, forwarding the first model obtaining request to a second identification user corresponding to a target model pointed by the first model obtaining request, wherein the model in the model base is generated based on a model operation instruction of the second identification user; in response to receiving a confirmation instruction of a second identification user for the first model acquisition request, acquiring a target model and corresponding metadata from a model library; and based on the target model and the corresponding metadata, generating an image file and sending the image file to a position specified by the first identification user. And the demand side and the supply side of the model are connected, so that the application range of the model can be improved.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to a method and a device for information interaction.
Background
Nowadays, under the double promotion of policies related to Artificial Intelligence (AI) and advanced technologies, great enterprises and capital disputes increase the strength of the layout AI industry, so that talents in the AI field need blowout, while the cultivation of AI talents is relatively lagged, and "AI talents are barren". The model is an algorithm model constructed based on an AI technology, and when an enterprise needs to rapidly construct a multi-scenario model to meet business development, a large amount of talents with AI professional knowledge are needed.
Disclosure of Invention
The embodiment of the disclosure provides a method and a device for information interaction.
In a first aspect, an embodiment of the present disclosure provides a method for information interaction, where the method includes: responding to a received first model obtaining request of a first identification user, and forwarding the first model obtaining request to a second identification user corresponding to a target model pointed by the first model obtaining request, wherein the model in the model base is generated based on a model operation instruction of the second identification user; in response to receiving a confirmation instruction of a second identification user for the first model acquisition request, acquiring a target model and corresponding metadata from a model library; and generating an image file based on the target model and the corresponding metadata, and sending the image file to a position specified by the first identification user.
In some embodiments, generating an image file based on the target model and corresponding metadata includes: constructing a container environment based on the metadata corresponding to the target model; testing the target model in a container environment; and if the target model passes the test, generating an image file based on the container environment, the target model and the corresponding metadata.
In some embodiments, the model library further comprises: the second identification user uploads a pre-trained model and corresponding metadata; and in response to receiving a second request instruction identifying the user push model, presenting description information of the model to which the request instruction points.
In some embodiments, the method further comprises: in response to receiving a query instruction of a first identified user, presenting description information of a model pointed by the query instruction to a second identified user; and presenting the model requirement information in response to receiving a request instruction of the first identification user for pushing the model requirement information.
In some embodiments, the method further comprises: acquiring model demand information in a preset time period; generating model demand heat information based on the acquired model demand information, wherein the model demand heat information is used for representing the demand degree of each type of model in the time period; and pushing the model demand heat information to the first identification user and the second identification user.
In some embodiments, the method further comprises: acquiring a transaction record of the model in a preset time period; generating model value attribute information based on the acquired transaction records, wherein the model value attribute information is used for representing model value attribute trends of various types of models in the time period; and pushing the model value attribute information to the first identification user and the second identification user.
In some embodiments, the method further comprises: determining a technical identifier of the second identification user based on the model stored in the model base by the second identification user, wherein the technical identifier is used for representing the model which is good for development by the second identification user; in response to receiving a request instruction of pushing model demand information by a first identification user, determining a second identification user matched with the model demand information based on the model demand information and a technical identification; and pushing the matched information of the second identification user to the first identification user.
In some embodiments, the method further comprises: responding to a request instruction of pushing the development team demand information by a second identification user; determining a second identification user matched with the development team demand information based on the development team demand information and the technology identification; and pushing the information of the second identification user matched with the requirement information of the development team to the second identification user.
In some embodiments, the method further comprises: in response to receiving a pre-test request instruction of a first identification user, obtaining a model pointed by a pre-test request from a model library; inputting the test data of the first identification user into the model pointed by the test request instruction to obtain a test result corresponding to the test data; the test results are presented to the first identified user.
In some embodiments, the method further comprises: responding to a second model acquisition request of the first identification user, and forwarding the second model acquisition request to a second identification user corresponding to a model pointed by the second model acquisition request, wherein the first model acquisition request represents the privatization permission of the first identification user for requesting to acquire the model, and the second model acquisition request represents the permission of the first identification user for requesting to acquire the online service of the model; and in response to receiving a confirmation instruction aiming at the second model acquisition request, opening the API interface of the target model pointed by the second model acquisition request to the second identification user.
In some embodiments, the model in the model library is generated based on the model operation instructions of the second identified user, including: in response to receiving a second instruction to create a model identifying the user, generating an initial model based on the instruction to create the model; opening the permission of obtaining the sample data to the second identification user in response to receiving the sample obtaining instruction of the second identification user; and in response to receiving a model training instruction of the second identification user, training the initial model based on sample data acquired by the second identification user to obtain a pre-trained model.
In a second aspect, an embodiment of the present disclosure provides an apparatus for information interaction, the apparatus including: the information receiving unit is configured to respond to receiving a first model obtaining request of a first identification user, and forward the first model obtaining request to a second identification user corresponding to a target model pointed by the first model obtaining request, wherein the model in the model base is generated based on a model operation instruction of the second identification user; the model acquisition unit is configured to respond to a confirmation instruction of a second identification user for the first model acquisition request, and acquire a target model and corresponding metadata from the model library; and the data sending unit is configured to generate an image file based on the target model and the corresponding metadata and send the image file to the position specified by the first identification user.
In some embodiments, the data sending unit includes an image generating unit configured to generate the image file via: constructing a container environment based on the metadata corresponding to the target model; testing the target model in a container environment; and if the target model passes the test, generating an image file based on the container environment, the target model and the corresponding metadata.
In some embodiments, the model library further comprises: the second identification user uploads a pre-trained model and corresponding metadata; the device also includes: a model information pushing unit configured to: in response to receiving a second request instruction identifying a model pushed by the user, presentation of description information of the model to which the request instruction points.
In some embodiments, the apparatus further comprises a user information pushing unit configured to: determining a technical identifier of the second identification user based on the model stored in the model base by the second identification user, wherein the technical identifier is used for representing the model which is good for development by the second identification user; in response to receiving a request instruction of pushing model demand information by a first identification user, determining a second identification user matched with the model demand information based on the model demand information and a technical identification; and pushing the matched information of the second identification user to the first identification user.
In some embodiments, the apparatus further comprises a team information push unit configured to: responding to a request instruction of pushing the development team demand information by a second identification user; determining a second identification user matched with the development team demand information based on the development team demand information and the technology identification; and pushing the information of the second identification user matched with the requirement information of the development team to the second identification user.
In some embodiments, the apparatus further comprises a model generation unit configured to obtain a model in the model library via: in response to receiving a second instruction to create a model identifying the user, generating an initial model based on the instruction to create the model; opening the permission of obtaining the sample data to the second identification user in response to receiving the sample obtaining instruction of the second identification user; and in response to receiving a model training instruction of the second identification user, training the initial model based on sample data acquired by the second identification user to obtain a pre-trained model.
According to the method and the device for information interaction, on one hand, service support is provided for the demander of the model, so that the demander of the model can obtain an expected model according to the requirement of the demander, on the other hand, data support is provided for the supplier of the model, the supplier of the model can generate the model according to the requirement, the model is provided for the adaptive demander, the demander and the supplier of the model can be better connected, and the application range of the model is favorably improved.
Drawings
Other features, objects and advantages of the disclosure will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which some embodiments of the present disclosure may be applied;
FIG. 2 is a flow diagram for one embodiment of a method for information interaction, according to the present disclosure;
FIG. 3 is a flow diagram of yet another embodiment of a method for information interaction according to the present disclosure;
FIG. 4 is a flow diagram of yet another embodiment of a method for information interaction according to the present disclosure;
FIG. 5 is a schematic diagram of one application scenario of a method for information interaction, in accordance with an embodiment of the present disclosure;
FIG. 6 is a flow diagram of yet another embodiment of a method for information interaction according to the present disclosure;
FIG. 7 is a flow diagram of yet another embodiment of a method for information interaction according to the present disclosure;
FIG. 8 is a schematic block diagram illustrating one embodiment of an apparatus for information interaction according to the present disclosure;
FIG. 9 is a schematic structural diagram of an electronic device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
The present disclosure is described in further detail below with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
In the disclosed embodiment, the models involved include AI models, a first identifying user characterizing users having a need for the model, i.e., the demander of the model, and a second identifying user characterizing users providing the model, i.e., the supplier of the model. It will be appreciated that in some scenarios, the first identified user and the second identified user may be the same person or group. As an example, the first identified user may be an individual who has a need for the model or an enterprise who has a need for the model; the second identified user may be an independent model developer or a development team consisting of multiple model developers. When a certain developer or development team has a model requirement, the developer or the development team can have two kinds of identity identifications of a first identification user and a second identification user at the same time.
Fig. 1 illustrates an exemplary system architecture 100 of a method for information interaction or an apparatus for information interaction to which embodiments of the present disclosure may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
Users (including a first identification user and a second identification user) perform information interaction with a server 105 through terminal devices 101, 102 and 103 via a network 104, wherein a model library is stored in the server 105, and the first identification user can browse information of each model in the model library of the server 105 through the terminal devices to select a target model meeting own requirements; the second identified user may send a model operation instruction to the server 105 through the terminal device to generate a model, and store the generated model in the model library.
The terminal devices 101, 102, 103 may be various electronic devices that support information interaction, including but not limited to smart phones, tablet computers, e-book readers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server that provides various services to a developer or operator of application software, for example, a data processing server that presents information of each model in the model library to the terminal devices 101, 102, 103.
It should be noted that the method for information interaction provided by the embodiment of the present disclosure may be executed by the server 105. Accordingly, means for information interaction may be provided in the server 105.
The server may be hardware or software. When the server is hardware, it may be implemented as a distributed server cluster formed by multiple servers, or may be implemented as a single server. When the server is software, it may be implemented as multiple pieces of software or software modules, for example, to provide distributed services, or as a single piece of software or software module. And is not particularly limited herein.
With continued reference to FIG. 2, a flow 200 of one embodiment of a method for information interaction in accordance with the present disclosure is shown. The method for information interaction comprises the following steps:
In this embodiment, an execution subject (for example, a server shown in fig. 1) of the method for information interaction may perform information interaction with a terminal device through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection means may include, but is not limited to, 2G/3G/4G connection, WiFi connection, bluetooth connection, WiMAX connection, Zigbee connection, uwb (ultra wideband) connection, and other wireless connection means now known or developed in the future.
In this embodiment, the model stored in the model library of the execution subject is a model constructed by the second identified user, and is used for selecting the target model from the model library to the first identified user according to the requirement of the first identified user.
In this embodiment, the first model retrieval request indicates that the first identified user desires to retrieve the target model. As an example, the first identified user may send a first model acquisition request to the executing agent through the terminal device.
It can be understood that the first identification user acquisition model needs to pay a certain fee to the corresponding second identification user, and the charging modes corresponding to different acquisition modes are different. Thus, the method for information interaction of the present disclosure includes different model acquisition requests.
In some embodiments, the model in the model library is generated based on the model operation instructions of the second identified user. With further reference to fig. 3, fig. 3 illustrates an implementation manner of building a model based on a model operation instruction of a second identified user in the present embodiment. In the process 300 illustrated in FIG. 3, the executive agent obtains the model via the following steps:
in response to receiving a second instruction to create a model identifying a user, an initial model is generated based on the instruction to create the model, step 301.
As an example, each type of initial model, such as an initial convolutional neural network model, an initial area generation network, and the like, may be pre-stored in the execution main body, the second identification user selects an initial model that meets the needs of the user, and sends the initial parameters of the selected initial model to the execution main body, and the execution main body constructs the initial model according to the received initial parameters.
Alternatively, the second identified user may create the model based on the structure of the empirically designed model and submit to the executing agent. The executing agent receives the second identified user-submitted model as an initial model.
In this implementation, the execution principal may further store sample data in advance, so that the second identifier user may directly invoke the required sample data from the execution principal. As an example, after the permission to acquire the sample data is opened to the second identifier user, the second identifier user may browse the sample data pre-stored in the execution main body, and select the sample data meeting the own requirement from the sample data.
In this implementation manner, based on the initial model obtained in step 301 and the sample data obtained in step 302, the execution subject may input the sample data into the initial model according to the instruction of the second identified user, automatically execute the training step of the initial model, obtain the pre-trained model and the corresponding metadata, and store the pre-trained model and the metadata in the model library.
In the implementation manner, the second identification user can acquire data support from the execution main body by performing information interaction with the execution main body, so that the efficiency of developing the model by the second identification user is improved.
It should be noted that, when providing data service for the second identified user, the fee to be paid by the second identified user may be determined according to the data amount.
In other optional implementations of this embodiment, the models in the model library further include: and secondly, identifying the pre-trained model uploaded by the user and the corresponding metadata. Namely, the second identification user can directly upload the model which is developed by the second identification user to the execution main body and store the model into the model base in the execution main body without building the model by means of the data service provided by the execution main body.
In some optional implementation manners of this embodiment, the following steps may also be included: in response to receiving a pre-test request instruction of a first identification user, obtaining a model pointed by a pre-test request from a model library; inputting the test data of the first identification user into the model pointed by the test request instruction to obtain a test result corresponding to the test data; the test results are presented to the first identified user. Thus, before determining the target model, the first identified user may pretest the model of interest to determine whether the model can meet his or her needs.
Generally, a first identification user browses information of various types of models in a model library of an execution main body through terminal equipment to select a target model meeting own requirements from the information, then a first model acquisition request corresponding to the target model is sent to the execution main body, and the execution main body forwards the first model acquisition request to a corresponding second identification user to request the second identification user to agree with the first identification user to privatize the model.
In this embodiment, after receiving the first model acquisition request forwarded by the execution subject in step 201, if the privatization request of the first identity user is approved, the second identity user sends a confirmation instruction to the execution subject through the terminal device. And after receiving the confirmation instruction of the second identification user, the execution main body acquires the target model and the corresponding metadata pointed by the first model acquisition request from the model library. The metadata may include information such as reasoning scripts, the environment on which the model runs, the manner in which the model is invoked, etc.
In this embodiment, the image file includes a target model and corresponding metadata, and the first identifier user can complete the privatized deployment of the target model by only installing the received image file on the device specified by the first identifier user.
With further reference to FIG. 4, FIG. 4 illustrates one implementation of generating an image file in the present embodiment. In the flow 400 shown in FIG. 4, the executing agent generates an image file via the following steps:
In some optional implementation manners of this embodiment, before generating the container environment, the target model and the corresponding metadata into the image file, the method may further include: in response to receiving a compression instruction of a second identified user, the target model and corresponding metadata are compressed. As an example, when the storage space in the device specified by the first identification user is insufficient, a compression instruction may be sent to the execution subject, and the execution subject may perform lossless compression on the container environment and the target model and corresponding metadata, and then regenerate the image file to reduce the occupied space of the image file.
In some optional implementation manners of the foregoing embodiment, the first model obtaining request represents a privatization permission of the first identity user requesting to obtain the model. Specifically, the first model acquisition request indicates that the first identified user desires to fully deploy the target model to the device specified by the first identified user.
In some optional implementations of this embodiment, the flow of the method for information interaction may further include: responding to a second model acquisition request of the first identification user, forwarding the second model acquisition request to a second identification user corresponding to a model pointed by the second model acquisition request, wherein the second model acquisition request represents the authority of the first identification user for requesting to acquire the online service of the model; and in response to receiving a confirmation instruction aiming at the second model acquisition request, opening an API (Application Programming Interface) Interface of the target model pointed by the second model acquisition request to a second identification user.
In this implementation, the first identified user may perform information interaction with the target model running in the execution subject through the API interface, so as to obtain the online service of the model. The whole model does not need to be deployed on the equipment established by the user, so that the cost for obtaining the model can be reduced.
With continued reference to fig. 5, fig. 5 is a schematic diagram of an application scenario of the method for information interaction according to the present embodiment. In the application scenario 500 of fig. 5, the execution body 503 constructs a virtual platform facing the model demander and supplier based on the method for information interaction of the present disclosure, and can provide support for development and transaction of the model. Through the virtual platform, the demander of the model can obtain the information of the model stored in the virtual platform. For example, the first identified user 501 may interact with the execution subject 503 through the terminal device 502 (e.g., a smartphone), and the execution subject sends information of each model in the model library to the terminal device, and the terminal device 502 presents the information to the first identified user.
Then, the demand side and the supply side of the model can complete the transaction of the model through the virtual platform. For example, after the first identified user selects the target model, the first model acquisition request may be sent to the execution subject, and forwarded by the execution subject to the second identified user; and after receiving the confirmation instruction of the second identification user, the execution main body extracts the target model and the corresponding metadata from the model library, generates the image file and sends the image file to the position specified by the first identification user, and completes the service support for the model demand party and the model supply party.
In addition, the virtual platform can also provide data support for a supplier development model of the model, for example, the execution subject can also receive a model operation instruction of the second identified user 504, provide data support for the second identified user, generate the model according to the instruction of the second identified user, and then store the generated model into the model library by the execution subject, thereby completing the data support for the supplier of the model.
According to the method provided by the embodiment of the disclosure, on one hand, service support is provided for the demander of the model, so that the demander of the model can obtain an expected model according to the own requirements, on the other hand, data support is provided for the supplier of the model, the supplier of the model can generate the model according to the requirements and provide the model for the adapted demander, the demander and the supplier of the model can be better connected, and the application range of the model is favorably improved.
In addition, the above-mentioned embodiment of the method for information interaction may further include a flow illustrated in fig. 6, where the flow 600 includes the following steps:
When the model in the model library cannot meet the requirement of the first identification user, the first identification user may generate model requirement information according to the actual requirement of the first identification user, for example, the AI requirement information includes information such as the use, the field, the subject type, the performance requirement, the value attribute, and the like of the model, and then send a request instruction for pushing the model requirement information to the execution main body, so that the execution main body presents the model requirement information. Therefore, the second identification user can know the requirement information of the model in time, and then develop the relevant model in a more targeted manner, so that the requirement of the first identification user on the model is met, and the development work of the second identification user on the model is more targeted.
In a specific example, the user a needs a convolutional neural network model for image recognition, and the user a may generate model requirement information according to its detailed requirements, which may include: name: a convolutional neural network; topic type: image recognition; the payment mode is as follows: a face is advised; the method comprises the following steps: the accuracy is high and the operation speed is high; the contact way is as follows: XXX. And then the user A sends a request instruction for pushing the model demand information to the execution main body, and after receiving the request instruction, the execution main body sends the model demand information to each terminal device interacting with the execution master and the execution master to be presented to the user. After the user B sees the model requirement information, the user B considers that the professional skills of the user B can meet the requirements of the user A, and then the user B can contact the user A and follow the construction task of the model. Therefore, the connection between the demand side and the supply side of the model can be more convenient.
In this embodiment, the model demand heat information is used to characterize the demand degree of the first identified user for each type of model in the time period.
In a specific example, the execution subject may generate the demand heat curve with time as a dimension according to the quantity of the demand information of each type of model. With a more accurate understanding of the demand of the first identified user for the model, the executing agent may generate the model demand heat information based on the model demand information in a certain time period acquired in step 602.
And step 604, pushing the model demand heat information to the first identification user and the second identification user.
According to the model demand heat degree information, the first identification user and the first identification can more accurately grasp the demand degree of the model in the time period.
In the implementation shown in fig. 6, the executive agent can make the connection between the demander and supplier of the model more direct by pushing the model requirement information; by pushing the model heat information, the user can be helped to more accurately grasp the demand degree of each type of model in the time period.
In another specific example of the implementation shown in fig. 6, the following steps may be further included: acquiring a transaction record of the model in a preset time period; generating model value attribute information based on the acquired transaction records, wherein the model value attribute information is used for representing the value attribute trend of each type of model in the time period; and pushing the model value attribute information to the first identification user and the second identification user. The model value attribute information is helpful for the user to more accurately know the price value attribute information of each type of model in the time period.
With further reference to fig. 7, on the basis of the flow shown in fig. 6, the above-mentioned embodiment of the method for information interaction may further include a flow 700, and specifically, the flow 700 may include the following steps:
In this implementation, the technical identification is used to describe the technical area in which the second identification is good for the user.
As an example, the models of the execution subject in which the execution subject detects the user C in the model library include two types, a convolutional neural network and a linear regression model, which are respectively used for image recognition and image segmentation, so that the technical identification of the user C can be set as "good field: image recognition and image segmentation; the main body excels in: convolution application network, linear regression model ".
Further, the executive agent may also determine the technical identity of the second identified user based on the transaction data of the user and the rating of the first identified user for which the model was obtained. For example, expected information of the user on model prices can be determined according to transaction data, quality evaluation of a model built by the user can be determined according to evaluation of a first identified user for which the model is obtained, and the like.
Continuing with the example in step 701, user D indicates in the model requirement information that a linear regression model for image recognition is required, matching the technical identification of user C, and thus, user C may be determined to be the second identified user matching the model requirement information. It will be appreciated that the same model requirement information may include a plurality of second identified users matched thereto.
And step 703, pushing the matched information of the second identified user to the first identified user. In this way, the connection between the demander and the supplier of the model can be made more targeted.
Optionally, the flow 700 may further include:
In this implementation, the method for information interaction may also provide information support for the second identified user organization team, facilitating collaborative communication between model providers.
As an example, when a development team of a certain model needs a technician in a certain specific field, the development team requirement information may be generated according to the actual requirement of the user, and a request instruction for pushing the development team requirement information is sent to the execution main body, and the execution main body may send the development team requirement information to a terminal interacting with the execution main body for presentation to the user.
Through steps 705 and 706, the executing agent may automatically identify a second identified user that may satisfy the requirements of the development team and push information of the second identified user to the development team. Therefore, the convenience of contact and communication between model suppliers can be improved, and different second identification users can collaboratively complete the development of the model.
In the implementation shown in fig. 7, through steps 701 to 703, information of a second identified user can be pushed to a first identified user, which is helpful to improve the pertinence of the contact between the model demander and the supplier; through steps 704 through 706, the second identified user may be pushed to the model development team, facilitating communication and collaboration between model suppliers.
With further reference to fig. 8, as an implementation of the method shown in the above-mentioned figures, the present disclosure provides an embodiment of an apparatus for information interaction, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable to various electronic devices.
As shown in fig. 8, the apparatus 800 for information interaction of the present embodiment includes: the device includes: an information receiving unit 801 configured to, in response to receiving a first model obtaining request of a first identified user, forward the first model obtaining request to a second identified user corresponding to a target model to which the first model obtaining request is directed, where models in a model library are generated based on a model operation instruction of the second identified user; a model obtaining unit 802 configured to obtain a target model and corresponding metadata from a model library in response to receiving a confirmation instruction of a second identified user for the first model obtaining request; a data sending unit 803 configured to generate an image file based on the target model and the corresponding metadata, and send the image file to a location specified by the first identified user.
In the present embodiment, the data transmission unit 803 includes an image generation unit configured to generate an image file via the steps of: constructing a container environment based on the metadata corresponding to the target model; testing the target model in a container environment; and if the target model passes the test, generating an image file based on the container environment, the target model and the corresponding metadata.
In this embodiment, the data sending unit 803 further includes a data compression unit configured to: and before the container environment, the target model and the corresponding metadata are generated into the mirror image file, the target model and the corresponding metadata are compressed in response to receiving a compression instruction of a second identification user.
In this embodiment, the model library further includes: and secondly, identifying the pre-trained model uploaded by the user and the corresponding metadata.
In this embodiment, the apparatus further includes a model information pushing unit configured to: in response to receiving a second request instruction identifying the user push model, presentation of description information of the model to which the request instruction is directed.
In this embodiment, the apparatus further includes a model information query unit configured to: in response to receiving a query instruction of a first identified user, presentation is made to a second identified user of description information of the model to which the query instruction points.
In this embodiment, the apparatus further includes a demand information pushing unit configured to: and presenting the model demand information in response to receiving a request instruction of the first identification user for pushing the model demand information.
In this embodiment, the apparatus further includes a demand heat information unit configured to: acquiring model demand information in a preset time period; generating model demand heat information based on the acquired model demand information, wherein the model demand heat information is used for representing the demand degree of each type of model in the time period; and pushing the model demand heat information to the first identification user and the second identification user.
In this embodiment, the apparatus further includes a model value attribute information unit configured to: acquiring a transaction record of the model in a preset time period; generating model value attribute information based on the acquired transaction records, wherein the model value attribute information is used for representing the value attribute trend of each type of model in the time period; and pushing the model value attribute information to the first identification user and the second identification user.
In this embodiment, the apparatus further includes a user information pushing unit configured to: determining a technical identifier of the second identification user based on the model stored in the model base by the second identification user, wherein the technical identifier is used for representing the model which is good for development by the second identification user; in response to receiving a request instruction of pushing model demand information by a first identification user, determining a second identification user matched with the model demand information based on the model demand information and a technical identification; and pushing the matched information of the second identification user to the first identification user.
In this embodiment, the apparatus further includes a team information pushing unit configured to: responding to a request instruction of pushing the development team demand information by a second identification user; determining a second identification user matched with the development team demand information based on the development team demand information and the technology identification; and pushing the information of the second identification user matched with the requirement information of the development team to the second identification user.
In this embodiment, the apparatus further comprises a pretest unit configured to: in response to receiving a pre-test request instruction of a first identification user, obtaining a model pointed by a pre-test request from a model library; inputting the test data of the first identification user into the model pointed by the test request instruction to obtain a test result corresponding to the test data; the test results are presented to the first identified user.
In this embodiment, the information receiving unit 801 is further configured to: responding to a second model acquisition request of the first identification user, and forwarding the second model acquisition request to a second identification user corresponding to a model pointed by the second model acquisition request, wherein the first model acquisition request represents the privatization permission of the first identification user for requesting to acquire the model, and the second model acquisition request represents the permission of the first identification user for requesting to acquire the online service of the model; the apparatus also includes an interface opening unit configured to: and in response to receiving a confirmation instruction aiming at the second model acquisition request, opening the API interface of the target model pointed by the second model acquisition request to the second identification user.
In this embodiment, the apparatus further comprises a model generation unit configured to obtain the model in the model library via the following steps: in response to receiving a second instruction to create a model identifying the user, generating an initial model based on the instruction to create the model; opening the permission of obtaining the sample data to the second identification user in response to receiving the sample obtaining instruction of the second identification user; and in response to receiving a model training instruction of the second identification user, training the initial model based on sample data acquired by the second identification user to obtain a pre-trained model.
Referring now to fig. 9, shown is a schematic diagram of an electronic device (e.g., terminal device in fig. 1) 900 suitable for use in implementing embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like. The terminal device shown in fig. 9 is only an example, and should not bring any limitation to the functions and the use range of the embodiments of the present disclosure.
As shown in fig. 9, the electronic device 900 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 901 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)902 or a program loaded from a storage means 908 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data necessary for the operation of the electronic apparatus 900 are also stored. The processing apparatus 901, the ROM 902, and the RAM 903 are connected to each other through a bus 904. An input/output (I/O) interface 905 is also connected to bus 904.
Generally, the following devices may be connected to the I/O interface 905: input devices 906 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 907 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; and a communication device 909. The communication device 909 may allow the electronic apparatus 900 to perform wireless or wired communication with other apparatuses to exchange data. While fig. 9 illustrates an electronic device 900 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 9 may represent one device or may represent multiple devices as desired.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication device 909, or installed from the storage device 908, or installed from the ROM 902. The computer program, when executed by the processing apparatus 901, performs the above-described functions defined in the methods of the embodiments of the present disclosure. It should be noted that the computer readable medium of the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In embodiments of the present disclosure, however, a computer readable signal medium may comprise a propagated data signal with computer 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 computer readable signal medium may also be any computer readable medium that is not a computer 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 computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the terminal; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: responding to a received first model obtaining request of a first identification user, forwarding the first model obtaining request to a second identification user corresponding to a target model pointed by the first model obtaining request, wherein the model in the model base is generated based on a model operation instruction of the second identification user; in response to receiving a confirmation instruction of a second identification user for the first model acquisition request, acquiring a target model and corresponding metadata from a model library; and generating an image file based on the target model and the corresponding metadata, and sending the image file to a position specified by the first identification user.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a data acquisition unit, a data storage unit, a sequence generation unit, and a task execution unit. Where the names of these units do not in some cases constitute a limitation on the units themselves, for example, the information receiving unit may also be described as a "unit that, in response to receiving a first model acquisition request of a first identified user, forwards the first model acquisition request to a second identified user corresponding to a target model to which the first model acquisition request is directed".
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (19)
1. A method for information interaction, comprising:
responding to a first model obtaining request of a first identification user, forwarding the first model obtaining request to a second identification user corresponding to a target model pointed by the first model obtaining request, wherein the model in the model base is generated based on a model operation instruction of the second identification user;
in response to receiving a confirmation instruction of the second identification user for the first model acquisition request, acquiring the target model and corresponding metadata from a model library;
and generating an image file based on the target model and the corresponding metadata, and sending the image file to a position specified by the first identification user.
2. The method of claim 1, wherein generating an image file based on the object model and corresponding metadata comprises:
constructing a container environment based on the metadata corresponding to the target model;
testing the target model in the container environment;
and if the target model passes the test, generating a mirror image file based on the container environment, the target model and the corresponding metadata.
3. The method of claim 1, wherein the model library further comprises: the pre-trained models and corresponding metadata uploaded by the second identification user; and the number of the first and second groups,
in response to receiving the second request instruction for identifying the user to push the model, presenting the description information of the model pointed by the request instruction.
4. The method of claim 1, further comprising:
in response to receiving a query instruction of the first identified user, presenting description information of a model pointed to by the query instruction to the first identified user; and the number of the first and second groups,
and presenting the model demand information in response to receiving a request instruction of the first identification user for pushing the model demand information.
5. The method of claim 4, further comprising:
acquiring model demand information in a preset time period;
generating model demand heat information based on the acquired model demand information, wherein the model demand heat information is used for representing the demand degree of each type of model in the time period;
and pushing the model demand heat information to the first identification user and the second identification user.
6. The method of claim 4, further comprising:
acquiring a transaction record of the model in a preset time period; generating model value attribute information based on the acquired transaction records, wherein the model value attribute information is used for representing the value attribute trend of each type of model in the time period;
pushing the model value attribute information to the first identified user and the second identified user.
7. The method of claim 4, further comprising:
determining a technical identification of the second identification user based on the model stored in the model base by the second identification user, wherein the technical identification is used for representing the model which is developed by the second identification user and is good at;
in response to receiving a request instruction of pushing model demand information by the first identification user, determining a second identification user matched with the model demand information based on the model demand information and the technical identification;
and pushing the information of the matched second identification user to the first identification user.
8. The method of claim 6, further comprising:
responding to a request instruction of pushing development team demand information by the second identification user; and the number of the first and second groups,
determining a second identification user matched with the development team requirement information based on the development team requirement information and the technology identification;
and pushing information of the second identification user matched with the development team requirement information to the second identification user.
9. The method of claim 1, further comprising:
in response to receiving a pre-test request instruction of the first identification user, obtaining a model pointed by the pre-test request from the model library;
inputting the test data of the first identification user into the model pointed by the test request instruction to obtain a test result corresponding to the test data;
presenting the test result to the first identified user.
10. The method of claim 1, further comprising:
responding to a second model obtaining request of the first identification user, and forwarding the second model obtaining request to a second identification user corresponding to a model pointed by the second model obtaining request, wherein the first model obtaining request represents the privatization permission of the first identification user for obtaining the model, and the second model obtaining request represents the permission of the first identification user for obtaining the online service of the model;
and in response to receiving a confirmation instruction aiming at the second model acquisition request, opening the API interface of the target model pointed by the second model acquisition request to the second identification user.
11. The method of one of claims 1 to 10, wherein the model in the model library is generated based on a second identified user's model operation instructions, comprising:
in response to receiving the second model-creating instruction identifying the user, generating an initial model based on the model-creating instruction;
opening the permission of acquiring the sample data to the second identification user in response to receiving the sample acquisition instruction of the second identification user;
and in response to receiving a model training instruction of the second identification user, training the initial model based on sample data acquired by the second identification user to obtain the pre-trained model.
12. An apparatus for information interaction, comprising:
the information receiving unit is configured to respond to receiving a first model obtaining request of a first identification user, forward the first model obtaining request to a second identification user corresponding to a target model pointed by the first model obtaining request, and the model in the model base is generated based on a model operation instruction of the second identification user;
a model obtaining unit configured to obtain the target model and corresponding metadata from a model library in response to receiving a confirmation instruction of the second identified user for the first model obtaining request;
and the data sending unit is configured to generate an image file based on the target model and the corresponding metadata and send the image file to the position specified by the first identification user.
13. The apparatus of claim 12, wherein the data sending unit comprises an image generating unit configured to generate an image file via:
constructing a container environment based on the metadata corresponding to the target model;
testing the target model in the container environment;
and if the target model passes the test, generating a mirror image file based on the container environment, the target model and the corresponding metadata.
14. The apparatus of claim 12, wherein the model library further comprises:
the pre-trained models and corresponding metadata uploaded by the second identification user; and the number of the first and second groups,
the apparatus further comprises a model information pushing unit configured to: in response to receiving the second request instruction for identifying the user to push the model, presenting the description information of the model pointed by the request instruction.
15. The apparatus of claim 12, the apparatus further comprising a user information pushing unit configured to:
determining a technical identification of the second identification user based on the model stored in the model base by the second identification user, wherein the technical identification is used for representing the model which is developed by the second identification user and is good at;
in response to receiving a request instruction of pushing model demand information by the first identification user, determining a second identification user matched with the model demand information based on the model demand information and the technical identification;
and pushing the information of the matched second identification user to the first identification user.
16. The apparatus of claim 15, the apparatus further comprising a team information push unit configured to:
responding to a request instruction of pushing development team demand information by the second identification user; and the number of the first and second groups,
determining a second identification user matched with the development team requirement information based on the development team requirement information and the technology identification;
and pushing information of the second identification user matched with the development team requirement information to the second identification user.
17. The apparatus according to one of claims 12 to 16, further comprising a model generation unit configured to derive the models in the model library via:
in response to receiving the second model-creating instruction identifying the user, generating an initial model based on the model-creating instruction;
opening the permission of acquiring the sample data to the second identification user in response to receiving the sample acquisition instruction of the second identification user;
and in response to receiving a model training instruction of the second identification user, training the initial model based on sample data acquired by the second identification user to obtain the pre-trained model.
18. A terminal, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-11.
19. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-11.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010439286.9A CN111612135B (en) | 2020-05-22 | 2020-05-22 | Method and device for information interaction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010439286.9A CN111612135B (en) | 2020-05-22 | 2020-05-22 | Method and device for information interaction |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111612135A true CN111612135A (en) | 2020-09-01 |
| CN111612135B CN111612135B (en) | 2024-04-16 |
Family
ID=72199481
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010439286.9A Active CN111612135B (en) | 2020-05-22 | 2020-05-22 | Method and device for information interaction |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111612135B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113485905A (en) * | 2021-02-26 | 2021-10-08 | 杜自然 | Test method, device, equipment and computer storage medium in data transaction |
| CN113824625A (en) * | 2021-01-29 | 2021-12-21 | 北京京东拓先科技有限公司 | Information interaction method and device |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102495845A (en) * | 2011-11-16 | 2012-06-13 | 陈宇 | Supply and demand information aggregation system based on data classification redirection transfer method |
| CN104717131A (en) * | 2015-02-13 | 2015-06-17 | 腾讯科技（深圳）有限公司 | Information interaction method and server |
| CN106156045A (en) * | 2015-03-28 | 2016-11-23 | 张佳伟 | Supply requirement Information Networking System and mode of doing business |
| CN106371840A (en) * | 2016-08-30 | 2017-02-01 | 北京航空航天大学 | Software development method and device based on crowdsourcing |
| TW201723951A (en) * | 2015-12-29 | 2017-07-01 | Zeroplus Technology Co Ltd | System and method for providing demand locally enabling the user to have dual identities of being a demand side and a supply side by means of linkage and mutual help |
| CN107688464A (en) * | 2017-06-28 | 2018-02-13 | 上海壹账通金融科技有限公司 | Software customized server, method and computer-readable recording medium |
| CN107767058A (en) * | 2017-10-26 | 2018-03-06 | 北京航空航天大学 | A kind of mass-rent software developer recommends method |
| CN107992979A (en) * | 2017-12-22 | 2018-05-04 | 山东师范大学 | A kind of mobile platform crowdsourcing task price optimization method and system |
| CN108647216A (en) * | 2017-03-16 | 2018-10-12 | 上海交通大学 | Software crowdsourcing task recommendation system and method based on developer's social networks |
| CN108846679A (en) * | 2018-05-22 | 2018-11-20 | 北京易知创新数据科技有限公司 | A kind of lead referral method applied to B2B enterprises service |
| CN109214773A (en) * | 2018-08-08 | 2019-01-15 | 胡雄风 | Software service business mode |
| CN109445948A (en) * | 2018-11-15 | 2019-03-08 | 济南浪潮高新科技投资发展有限公司 | A kind of data mark crowdsourcing plateform system and crowdsourcing data mask method based on intelligent contract |
| CN109460962A (en) * | 2018-09-18 | 2019-03-12 | 中车工业研究院有限公司 | Design objective crowdsourcing management system and method based on full Photographing On-line platform |
| CN109740951A (en) * | 2019-01-09 | 2019-05-10 | 橙色云设计有限公司 | The method and synergistic mechanism that a kind of collaboration of industrial products is solved a problem |
-
2020
- 2020-05-22 CN CN202010439286.9A patent/CN111612135B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102495845A (en) * | 2011-11-16 | 2012-06-13 | 陈宇 | Supply and demand information aggregation system based on data classification redirection transfer method |
| CN104717131A (en) * | 2015-02-13 | 2015-06-17 | 腾讯科技（深圳）有限公司 | Information interaction method and server |
| CN106156045A (en) * | 2015-03-28 | 2016-11-23 | 张佳伟 | Supply requirement Information Networking System and mode of doing business |
| TW201723951A (en) * | 2015-12-29 | 2017-07-01 | Zeroplus Technology Co Ltd | System and method for providing demand locally enabling the user to have dual identities of being a demand side and a supply side by means of linkage and mutual help |
| CN106371840A (en) * | 2016-08-30 | 2017-02-01 | 北京航空航天大学 | Software development method and device based on crowdsourcing |
| CN108647216A (en) * | 2017-03-16 | 2018-10-12 | 上海交通大学 | Software crowdsourcing task recommendation system and method based on developer's social networks |
| CN107688464A (en) * | 2017-06-28 | 2018-02-13 | 上海壹账通金融科技有限公司 | Software customized server, method and computer-readable recording medium |
| CN107767058A (en) * | 2017-10-26 | 2018-03-06 | 北京航空航天大学 | A kind of mass-rent software developer recommends method |
| CN107992979A (en) * | 2017-12-22 | 2018-05-04 | 山东师范大学 | A kind of mobile platform crowdsourcing task price optimization method and system |
| CN108846679A (en) * | 2018-05-22 | 2018-11-20 | 北京易知创新数据科技有限公司 | A kind of lead referral method applied to B2B enterprises service |
| CN109214773A (en) * | 2018-08-08 | 2019-01-15 | 胡雄风 | Software service business mode |
| CN109460962A (en) * | 2018-09-18 | 2019-03-12 | 中车工业研究院有限公司 | Design objective crowdsourcing management system and method based on full Photographing On-line platform |
| CN109445948A (en) * | 2018-11-15 | 2019-03-08 | 济南浪潮高新科技投资发展有限公司 | A kind of data mark crowdsourcing plateform system and crowdsourcing data mask method based on intelligent contract |
| CN109740951A (en) * | 2019-01-09 | 2019-05-10 | 橙色云设计有限公司 | The method and synergistic mechanism that a kind of collaboration of industrial products is solved a problem |
Non-Patent Citations (2)
| Title |
|---|
| YURI: "逛超市一样法宝程序，码易做在线明码标价的软件众包平台", Retrieved from the Internet <URL:https//m.36kr.com/p/1720992448513> * |
| 大海: "码易平台全新改版，一切从客户需求出发", Retrieved from the Internet <URL:http://mp.wixin.qq.com/s/OW8ZAttOAfzgqJGKtxPtXg> * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113824625A (en) * | 2021-01-29 | 2021-12-21 | 北京京东拓先科技有限公司 | Information interaction method and device |
| CN113485905A (en) * | 2021-02-26 | 2021-10-08 | 杜自然 | Test method, device, equipment and computer storage medium in data transaction |
| CN113485905B (en) * | 2021-02-26 | 2023-09-05 | 杜自然 | Test method, device, equipment and computer storage medium in data transaction |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111612135B (en) | 2024-04-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109981787B (en) | Method and device for displaying information | |
| US11762931B2 (en) | Feedback method and apparatus based on online document comment, and non-transitory computer-readable storage medium | |
| CN111488995B (en) | Method, device and system for evaluating joint training model | |
| US11586772B2 (en) | Method and device for displaying information | |
| CN110619078B (en) | Method and device for pushing information | |
| US11763804B2 (en) | Leveraging dialogue history in updated dialogue | |
| CN111340558A (en) | Online information processing method, device, equipment and medium based on federal learning | |
| US20220366459A1 (en) | System and method for a professional services marketplace | |
| CN111061956A (en) | Method and apparatus for generating information | |
| CN111612135B (en) | Method and device for information interaction | |
| CN110392068A (en) | A kind of data transmission method, device and its equipment | |
| CN113505520A (en) | Method, device and system for supporting heterogeneous federated learning | |
| CN110827037A (en) | Data interaction method and device based on block chain | |
| CN111813685A (en) | Automatic testing method and device | |
| CN109840072B (en) | Information processing method and device | |
| CN115310582A (en) | Method and apparatus for training neural network models | |
| Shrivastava | Learning Salesforce Einstein | |
| KR102276230B1 (en) | Method for generating finite state machine, method for operating finite state machine, server and computer program for performing the same | |
| US20200311741A1 (en) | Methods and systems for validating order request | |
| KR20210080561A (en) | Consulting information processing method and device | |
| CN112308745A (en) | Method and apparatus for generating information | |
| US20230169617A1 (en) | Methods and devices for student admission management in hybrid customer relationship management (crm) system | |
| CN111125501A (en) | Method and apparatus for processing information | |
| US20170323363A1 (en) | Collaborative manipulation of media files | |
| Claudio | VZWAM Web-Based Lookup |
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 | ||
| CB02 | Change of applicant information |
Address after: Room 221, 2nd floor, Block C, 18 Kechuang 11th Street, Daxing Economic and Technological Development Zone, Beijing, 100176 Applicant after: Jingdong Technology Holding Co.,Ltd. Address before: Room 221, 2nd floor, Block C, 18 Kechuang 11th Street, Daxing Economic and Technological Development Zone, Beijing, 100176 Applicant before: Jingdong Digital Technology Holding Co.,Ltd. Address after: Room 221, 2nd floor, Block C, 18 Kechuang 11th Street, Daxing Economic and Technological Development Zone, Beijing, 100176 Applicant after: Jingdong Digital Technology Holding Co.,Ltd. Address before: Room 221, 2nd floor, Block C, 18 Kechuang 11th Street, Daxing Economic and Technological Development Zone, Beijing, 100176 Applicant before: JINGDONG DIGITAL TECHNOLOGY HOLDINGS Co.,Ltd. |
|
| GR01 | Patent grant |