CN113836418A - Data pushing method and device, electronic equipment and storage medium - Google Patents
Data pushing method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a data pushing method, a data pushing device, electronic equipment and a storage medium, and relates to the technical field of machine learning, in particular to the technical field of machine learning, wherein a target user appearing in a preset distance range is monitored in real time, a biological characteristic image of the target user is collected, the biological characteristic image is input into a local characteristic recognition model, a first target characteristic similar to the biological characteristic image and output by the local characteristic recognition model is obtained, the biological characteristic image and the first target characteristic are sent to a server, the server inputs the biological characteristic image into a network characteristic recognition model to obtain a second target characteristic output by the network characteristic recognition model, a display target is pushed to the electronic equipment according to the first target characteristic and the second target characteristic, the display target pushed by the server is received and displayed, compared with the related technology, the embodiment can be identified by the biological characteristic image, and high-precision customized pushing is performed on the target user corresponding to the biological characteristic image, so that the user experience is improved.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to the field of machine learning technologies, and in particular, to a data pushing method and apparatus, an electronic device, and a storage medium.
Background
When the intelligent electronic equipment with the screen is in standby, the single background is dull, and the screen protection program (hereinafter referred to as screen protector) can effectively solve aesthetic fatigue relatively, so that the intelligent voice equipment with the screen is more interesting.
At present, the content presented by the screen saver of the intelligent electronic device with a screen is relatively fixed and is generally updated in units of days, and the content recommended by the screen saver is also relatively fixed, so that the use experience of a user is reduced.
Disclosure of Invention
The disclosure provides a data pushing method and device, electronic equipment and a storage medium.
According to an aspect of the present disclosure, a method for pushing data is provided, where the method includes:
monitoring a target user appearing in a preset distance range in real time, and acquiring a biological characteristic image of the target user;
inputting the biological characteristic image into a local characteristic recognition model, and acquiring a first target characteristic which is output by the local characteristic recognition model and is similar to the biological characteristic image;
sending the biological characteristic image and the first target characteristic to a server so that the server can input the biological characteristic image into a network characteristic recognition model to obtain a second target characteristic output by the network characteristic recognition model, and pushing a display target to the electronic equipment according to the first target characteristic and the second target characteristic;
and receiving and displaying the display target pushed by the server.
According to another aspect of the present disclosure, there is provided a data pushing device, including:
receiving a biological characteristic image and a first target characteristic sent by electronic equipment, wherein the first target characteristic is obtained by identifying the biological characteristic image by the electronic equipment based on a local characteristic identification model;
inputting the biological characteristic image into a network characteristic recognition model to obtain a second target characteristic similar to the biological characteristic image;
performing weighted calculation according to the first target characteristic and the second target characteristic, and searching a corresponding display target according to the target characteristic after weighted calculation;
pushing the display target to the electronic equipment.
According to another aspect of the present disclosure, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of one or more of the preceding aspects.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of the foregoing one or another aspect.
According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements a method as set forth in the preceding aspect or another aspect.
The data pushing method, the data pushing device, the electronic device and the storage medium provided by the disclosure monitor a target user appearing in a preset distance range in real time, acquire a biological feature image of the target user, input the biological feature image to a local feature recognition model, acquire a first target feature output by the local feature recognition model and similar to the biological feature image, send the biological feature image and the first target feature to a server, so that the server inputs the biological feature image into a network feature recognition model to obtain a second target feature output by the network feature recognition model, push a display target to the electronic device according to the first target feature and the second target feature, receive and display the display target pushed by the server, compared with the related technology, the embodiment can recognize the biological feature image, and high-precision customized pushing is performed on the target user corresponding to the biological characteristic image, so that the user experience is improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is a schematic flowchart of a data pushing method according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of another data pushing method provided in the embodiment of the present disclosure;
fig. 3 is a schematic diagram of an electronic device interacting with a server according to an embodiment of the present disclosure;
fig. 4 is a flowchart illustrating a third data pushing method according to an embodiment of the disclosure;
fig. 5 is a schematic structural diagram of a first data pushing device according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a second data pushing device according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of a third data pushing device according to an embodiment of the present disclosure;
fig. 8 is a schematic structural diagram of a fourth data pushing device according to an embodiment of the present disclosure;
fig. 9 is a schematic block diagram of an example electronic device 900 provided by embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The push method, the push apparatus, the electronic device, and the storage medium of data according to the embodiments of the present disclosure are described below with reference to the drawings.
Fig. 1 is a schematic flow chart of a data pushing method according to an embodiment of the present disclosure.
As shown in fig. 1, the method comprises the following steps:
The biometric image described in this embodiment includes, but is not limited to, at least one image of a face, a fingerprint, an iris, and a voiceprint, and accurate identification of a target user is facilitated by using at least one image of a face, a fingerprint, an iris, and a voiceprint.
The preset distance range is an experimental value, and may be adaptively set according to different application scenarios, for example, the preset distance range is set to be 20 centimeters, or 15 centimeters, and the like, which is not specifically limited in the embodiment of the present disclosure.
When the biological characteristic image is a face image, the camera based on the electronic equipment acquires the biological characteristic image of the target user, so that the related display target is recommended after the biological characteristic image is identified.
The local feature recognition model is used for performing coarse-grained recognition on a target user, for example, the gender, age, and the like of the target user can be recognized.
103, sending the biological feature image and the first target feature to a server, so that the server inputs the biological feature image into a network feature recognition model to obtain a second target feature output by the network feature recognition model, and pushing a display target to the electronic device according to the first target feature and the second target feature.
In order to realize customized push for a target user, the embodiment of the disclosure provides a network feature recognition model, which can perform fine recognition on a biological feature image acquired by an electronic device based on the network feature recognition model, search a display target (interest content) of the target user based on a network according to a recognition result, and send the display target to the electronic device.
And 104, receiving and displaying the display target pushed by the server.
The electronic device receives the display target sent by the server and then displays the display target, and as an implementation manner of this embodiment, the received display target is displayed as a screen saver of the electronic device.
The data pushing method provided by the disclosure monitors a target user appearing in a preset distance range in real time, collects a biological feature image of the target user, inputs the biological feature image into a local feature recognition model, acquires a first target feature output by the local feature recognition model and similar to the biological feature image, sends the biological feature image and the first target feature to a server, so that the server inputs the biological feature image into a network feature recognition model to obtain a second target feature output by the network feature recognition model, pushes a display target to an electronic device according to the first target feature and the second target feature, receives and displays the display target pushed by the server, compared with the related technology, the embodiment can recognize the biological feature image, and high-precision customized pushing is performed on the target user corresponding to the biological characteristic image, so that the user experience is improved.
In practical application, the electronic device may have abnormal network connection, and in order not to affect the user personalized display of the electronic device, before the step 102 is executed, whether the network connection of the electronic device is normal is determined; if the network connection is abnormal, determining a display target from a display target library according to the first target characteristics, wherein the first target characteristics comprise the age group and/or gender of a target user, and displaying the display target determined from the display target library. And if the network connection is determined to be normal, sending the biological characteristic image and the first target characteristic to a server.
The display target library can be an album of the electronic equipment and can also be a display target library created by the user, and the display target library can be edited or updated regularly to enhance the use viscosity of the user. The mode of locally acquiring the display target from the electronic equipment can also realize the display of the user customized content.
When the electronic equipment does not perform biological feature recording of a target user, outputting prompt information for acquiring a training biological feature image, acquiring the training biological feature image according to the prompt information, and sending the training biological feature image to the server, so that the server can train the network feature recognition model based on the training biological feature image. In order to make the training of the network feature recognition model more accurate, when the training biological feature images are collected, a plurality of training biological feature images can be collected to increase the training accuracy. For the training method, reference may be made to any one of the related technologies, and details are not repeated in this embodiment.
An embodiment of the present invention further provides a data pushing method, which is applied to a server side, and as shown in fig. 2, the method includes:
To more clearly illustrate the embodiment, as shown in fig. 3, fig. 3 is a schematic diagram of interaction between an electronic device and a server according to the embodiment of the present disclosure, after a biometric image is collected at an electronic device side, coarse-grained recognition of a local feature recognition model of the electronic device is performed to obtain a first target feature, and the first target feature and a primitive biometric image are sent to the server, so that the server performs detail recognition, and customized push of a target user is achieved.
The first target feature is sent to the server in order to save processing resources of the network feature recognition model.
And taking the biological characteristic image as the input of the network characteristic recognition model, and outputting a second target characteristic after the processing of the network characteristic recognition model.
And 204, pushing the display target to the electronic equipment.
The data pushing method provided by the disclosure monitors a target user appearing in a preset distance range in real time, collects a biological feature image of the target user, inputs the biological feature image into a local feature recognition model, acquires a first target feature output by the local feature recognition model and similar to the biological feature image, sends the biological feature image and the first target feature to a server, so that the server inputs the biological feature image into a network feature recognition model to obtain a second target feature output by the network feature recognition model, pushes a display target to an electronic device according to the first target feature and the second target feature, receives and displays the display target pushed by the server, compared with the related technology, the embodiment can recognize the biological feature image, and high-precision customized pushing is performed on the target user corresponding to the biological characteristic image, so that the user experience is improved.
The foregoing embodiment describes in detail a method for pushing data on an electronic device side and a server side, and as shown in fig. 4, the method further includes:
step 401, the electronic device outputs prompt information for acquiring a training biological characteristic image, and acquires the training biological characteristic image according to the prompt information.
Step 402, the electronic device sends the training biometric image to the server.
And 403, the server receives the training biological feature image sent by the electronic device, and trains the network feature recognition model according to the training biological feature image to obtain a trained network feature recognition model.
Step 404, the electronic device monitors a target user appearing within a preset distance range in real time, and acquires a biometric image of the target user.
Step 405, the electronic device inputs the biometric image to a local feature recognition model, and obtains a first target feature output by the local feature recognition model and similar to the biometric image.
In step 406, the electronic device sends the biometric image and the first target feature to a server.
Step 407, the server receives the biometric image and the first target feature sent by the electronic device, and inputs the biometric image to the network feature recognition model to obtain a second target feature similar to the biometric image.
Step 408, the server performs weighted calculation according to the first target feature and the second target feature, searches for a corresponding display target according to the target feature after weighted calculation, and pushes the display target to the electronic device.
And step 409, the electronic equipment receives and displays the display target pushed by the server.
The method described in fig. 4 is a data interaction description of the electronic device and the server, and please refer to the detailed description of the above embodiments for a specific implementation process of each step, which is not repeated herein.
Fig. 5 is a schematic structural diagram of a data pushing apparatus according to an embodiment of the present disclosure, as shown in fig. 5, including: a monitoring unit 51, an input unit 52, a first sending unit 53 and a first presentation unit 54, wherein,
the monitoring unit 51 is used for monitoring a target user appearing in a preset distance range in real time and acquiring a biological characteristic image of the target user;
an input unit 52, configured to input the biometric image to a local feature recognition model, and acquire a first target feature output by the local feature recognition model and similar to the biometric image;
a first sending unit 53, configured to send the biometric image and the first target feature to a server, so that the server inputs the biometric image into a network feature recognition model to obtain a second target feature output by the network feature recognition model, and pushes a display target to the electronic device according to the first target feature and the second target feature;
the first display unit 54 is configured to receive and display the display target pushed by the server.
Further, in a possible implementation manner of this embodiment, as shown in fig. 6, the apparatus further includes: the monitoring unit 61, the input unit 62, the first sending unit 63, and the first displaying unit 64, wherein the description of the monitoring unit 61, the input unit 62, the first sending unit 63, and the first displaying unit 64 refers to the detailed description of the monitoring unit 51, the input unit 52, the first sending unit 53, and the first displaying unit 53 in fig. 5.
A confirming unit 65, configured to confirm whether the network connection of the electronic device is normal before the first sending unit 63 sends the biometric image and the first target feature to a server;
a determining unit 66, configured to determine, when it is determined that the network connection is abnormal, a display target from a display target library according to the first target feature, where the first target feature includes an age group and/or a gender of a target user;
the second display unit 67 is configured to display the display target determined from the display target library.
Further, in a possible implementation manner of this embodiment, as shown in fig. 6, the first sending unit 63 is further configured to send the biometric image and the first target feature to a server when it is determined that the network connection is normal.
Further, in a possible implementation manner of this embodiment, as shown in fig. 6, the apparatus further includes:
an output unit 68, configured to output prompt information for acquiring a training biometric image before the biometric image is input to the local feature recognition model;
an acquisition unit 69, configured to acquire the training biometric image according to the prompt information;
a second sending unit 610, configured to send the training biometric image acquired by the acquisition unit to the server, so that the server trains the network feature recognition model based on the training biometric image.
Further, in a possible implementation manner of this embodiment, the biometric features include at least one of a human face, a fingerprint, an iris, and a voiceprint.
Further, an embodiment of the present disclosure further provides a data pushing device, as shown in fig. 7, which is applied to a server side, and includes: a first receiving unit 71, an input unit 72, a calculating unit 73, a searching unit 74 and a pushing unit 75.
The first receiving unit 71 is configured to receive a biometric image and a first target feature sent by an electronic device, where the first target feature is obtained by identifying, by the electronic device, the biometric image based on a local feature identification model;
an input unit 72, configured to input the biometric image to a network feature recognition model to acquire a second target feature similar to the biometric image;
a calculating unit 73, configured to perform weighting calculation according to the first target feature and the second target feature;
the searching unit 74 is used for searching the corresponding display target according to the target characteristics after the weighted calculation by the calculating unit;
the pushing unit 75 is configured to push the display target found by the finding unit to the electronic device.
Further, in a possible implementation manner of this embodiment, as shown in fig. 8, the apparatus further includes: the first receiving unit 81, the input unit 82, the calculating unit 83, the searching unit 84, and the pushing unit 85, wherein, with respect to one of the receiving unit 81, the input unit 82, the calculating unit 83, the searching unit 84, and the pushing unit 85, refer to the detailed descriptions of the first receiving unit 71, the input unit 72, the calculating unit 73, the searching unit 74, and the pushing unit 75,
a second receiving unit 86, configured to receive a training biometric image sent by the electronic device before the biometric image is input to the network feature recognition model by the input unit;
and the training unit 87 is configured to train the network feature recognition model according to the training biometric image to obtain a trained network feature recognition model.
Further, in a possible implementation manner of this embodiment, the biometric features include at least one of a human face, a fingerprint, an iris, and a voiceprint.
It should be noted that the foregoing explanation of the method embodiment is also applicable to the apparatus of the present embodiment, and the principle is the same, and the present embodiment is not limited thereto.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 9 illustrates a schematic block diagram of an example electronic device 900 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 9, the apparatus 900 includes a computing unit 901 that can perform various appropriate actions and processes in accordance with a computer program stored in a ROM (Read-Only Memory) 902 or a computer program loaded from a storage unit 908 into a RAM (Random Access Memory) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The calculation unit 901, ROM 902, and RAM 903 are connected to each other via a bus 904. An I/O (Input/Output) interface 905 is also connected to the bus 904.
A number of components in the device 900 are connected to the I/O interface 905, including: an input unit 906 such as a keyboard, a mouse, and the like; an output unit 907 such as various types of displays, speakers, and the like; a storage unit 908 such as a magnetic disk, optical disk, or the like; and a communication unit 909 such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 909 allows the device 900 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 901 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing Unit 901 include, but are not limited to, a CPU (Central Processing Unit), a GPU (graphics Processing Unit), various dedicated AI (Artificial Intelligence) computing chips, various computing Units running machine learning model algorithms, a DSP (Digital Signal Processor), and any suitable Processor, controller, microcontroller, and the like. The calculation unit 901 performs the respective methods and processes described above, such as a push method of data. For example, in some embodiments, the push method of data may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 908. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 900 via ROM 902 and/or communications unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more steps of the above described method may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to perform the aforementioned push method of data by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be realized in digital electronic circuitry, Integrated circuitry, FPGAs (Field Programmable Gate arrays), ASICs (Application-Specific Integrated circuits), ASSPs (Application Specific Standard products), SOCs (System On Chip, System On a Chip), CPLDs (Complex Programmable Logic devices), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory) or flash Memory, an optical fiber, a CD-ROM (Compact Disc Read-Only-Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a Display device (e.g., a CRT (Cathode Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), internet, and blockchain Network.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be noted that artificial intelligence is a subject for studying a computer to simulate some human thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware and software technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, machine learning/deep learning, a big data processing technology, a knowledge map technology and the like.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (19)
1. A data pushing method is applied to an electronic device side and comprises the following steps:
monitoring a target user appearing in a preset distance range in real time, and acquiring a biological characteristic image of the target user;
inputting the biological characteristic image into a local characteristic recognition model, and acquiring a first target characteristic which is output by the local characteristic recognition model and is similar to the biological characteristic image;
sending the biological characteristic image and the first target characteristic to a server so that the server can input the biological characteristic image into a network characteristic recognition model to obtain a second target characteristic output by the network characteristic recognition model, and pushing a display target to the electronic equipment according to the first target characteristic and the second target characteristic;
and receiving and displaying the display target pushed by the server.
2. The push method of claim 1, wherein prior to sending the biometric image and the first target feature to a server, the method further comprises:
confirming whether the network connection of the electronic equipment is normal;
if the network connection is abnormal, determining a display target from a display target library according to the first target characteristic, wherein the first target characteristic comprises the age group and/or the gender of a target user;
and displaying the display target determined from the display target library.
3. The push method of claim 2, wherein the sending the biometric image and the first target feature to a server comprises:
and if the network connection is determined to be normal, sending the biological characteristic image and the first target characteristic to a server.
4. The push method of claim 1, wherein prior to the inputting the biometric image to a local feature recognition model, the method further comprises:
outputting prompt information for acquiring a training biological characteristic image, and acquiring the training biological characteristic image according to the prompt information;
and sending the training biological feature image to the server so that the server can train the network feature recognition model based on the training biological feature image.
5. The push method according to any one of claims 1 to 4, wherein the biometric image includes at least one image of a human face, a fingerprint, an iris, and a voiceprint.
6. A data pushing method is applied to a server side and comprises the following steps:
receiving a biological characteristic image and a first target characteristic sent by electronic equipment, wherein the first target characteristic is obtained by identifying the biological characteristic image by the electronic equipment based on a local characteristic identification model;
inputting the biological characteristic image into a network characteristic recognition model to obtain a second target characteristic similar to the biological characteristic image;
performing weighted calculation according to the first target characteristic and the second target characteristic, and searching a corresponding display target according to the target characteristic after weighted calculation;
pushing the display target to the electronic equipment.
7. The recommendation method of claim 6, wherein prior to inputting the biometric image to a network feature recognition model, the method further comprises:
receiving a training biological characteristic image sent by the electronic equipment;
and training the network feature recognition model according to the training biological feature image to obtain the trained network feature recognition model.
8. The push method according to any one of claims 6 to 7, wherein the biometric image includes at least one image of a human face, a fingerprint, an iris, and a voiceprint.
9. A data pushing device is applied to an electronic device side and comprises:
the monitoring unit is used for monitoring a target user appearing in a preset distance range in real time and acquiring a biological characteristic image of the target user;
the input unit is used for inputting the biological characteristic image into a local characteristic recognition model and acquiring a first target characteristic which is output by the local characteristic recognition model and is similar to the biological characteristic image;
the first sending unit is used for sending the biological characteristic image and the first target characteristic to a server so that the server can input the biological characteristic image into a network characteristic recognition model to obtain a second target characteristic output by the network characteristic recognition model, and pushing a display target to the electronic equipment according to the first target characteristic and the second target characteristic;
the first display unit is used for receiving and displaying the display target pushed by the server.
10. The push device of claim 9, wherein the device further comprises:
a confirming unit, configured to confirm whether network connection of the electronic device is normal before the first sending unit sends the biometric image and the first target feature to a server;
the determining unit is used for determining a display target from a display target library according to the first target characteristic when the network connection is determined to be abnormal, wherein the first target characteristic comprises the age range and/or the gender of a target user;
and the second display unit is used for displaying the display target determined from the display target library.
11. The push device of claim 10, wherein the first sending unit is further configured to send the biometric image and the first target feature to a server when the network connection is determined to be normal.
12. The push device of claim 9, wherein the device further comprises:
the output unit is used for outputting prompt information for acquiring and training the biological characteristic image before the biological characteristic image is input into the local characteristic recognition model;
the acquisition unit is used for acquiring the training biological characteristic image according to the prompt information;
and the second sending unit is used for sending the training biological characteristic image acquired by the acquisition unit to the server so that the server can train the network characteristic recognition model based on the training biological characteristic image.
13. The push device of any one of claims 9 to 12, wherein the biometric image comprises at least one image of a human face, a fingerprint, an iris, and a voiceprint.
14. A data pushing device applied to a server side comprises:
the first receiving unit is used for receiving a biological characteristic image and a first target characteristic sent by electronic equipment, wherein the first target characteristic is obtained by identifying the biological characteristic image by the electronic equipment based on a local characteristic identification model;
an input unit, configured to input the biometric image to a network feature recognition model to acquire a second target feature similar to the biometric image;
the calculating unit is used for carrying out weighting calculation according to the first target characteristic and the second target characteristic;
the searching unit is used for searching the corresponding display target according to the target characteristics after the weighted calculation of the calculating unit;
and the pushing unit is used for pushing the display target searched by the searching unit to the electronic equipment.
15. The recommendation device of claim 14, wherein the device further comprises:
the second receiving unit is used for receiving the training biological characteristic image sent by the electronic equipment before the biological characteristic image is input to the network characteristic recognition model by the input unit;
and the training unit is used for training the network feature recognition model according to the training biological feature image to obtain the trained network feature recognition model.
16. The push device of any one of claims 14 to 15, wherein the biometric characteristic includes at least one of a human face, a fingerprint, an iris, and a voiceprint.
17. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 5 or 6 to 8.
18. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any of claims 1-5 or 6-8.
19. A computer program product comprising a computer program which, when executed by a processor, carries out the steps of the method according to any one of claims 1 to 5 or 6 to 8.
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