Data acquisition method and device, electronic equipment and readable storage medium

Abstract

The invention discloses a data acquisition method and device, electronic equipment and a readable storage medium, and belongs to the technical field of data. The data acquisition method comprises the following steps: acquiring identity information of an object, and outputting at least one piece of equipment information associated with the identity information according to the identity information; receiving a first input for at least one piece of device information; in response to the first input, determining target equipment information in the at least one piece of equipment information; and obtaining and outputting target object data associated with the object according to the object identity information and the target equipment information.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and specifically relates to a data acquisition method, device, electronic device and readable storage medium. Background Art

[0002] When users log in to platforms such as browsers, they will fill in their login account and password, or they can fill in their phone number and verification code, or even the platform will require users to fill in their ID number for real-name authentication. Based on the above login methods, it can be ensured that the user's data in the platform will not be leaked.

[0003] It can be seen that the above login protection measures are all from the user level, and the existing technology may result in situations where someone other than the user logs in, resulting in incomplete protection of user data. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a data acquisition method that can solve the problem that the login measures in the prior art do not provide comprehensive protection for user data.

[0005] In a first aspect, an embodiment of the present application provides a data acquisition method, the method comprising:

[0006] Acquire the identity information of the object, and output at least one device information associated with the identity information according to the identity information; receive a first input of the at least one device information; determine the target device information in the at least one device information in response to the first input; acquire and output the target object data associated with the object according to the object identity information and the target device information.

[0007] In a second aspect, an embodiment of the present application provides a data acquisition device, the device comprising:

[0008] A first acquisition module, used to acquire identity information of an object, and output at least one device information associated with the identity information according to the identity information;

[0009] A receiving module, configured to receive a first input of the at least one device information;

[0010] a determination module, configured to determine target device information in the at least one device information in response to the first input;

[0011] The second acquisition module is used to acquire and output target object data associated with the object according to the object identity information and the target device information.

[0012] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps described in the first aspect are implemented.

[0013] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0014] In a fifth aspect, an embodiment of the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the method described in the first aspect.

[0015] In a sixth aspect, an embodiment of the present application provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the method described in the first aspect.

[0016] In an embodiment of the present application, the identity information of the object is obtained, at least one device information associated with the identity information is output according to the identity information of the object, a first input of at least one device information is received, the first input is used to confirm the device being used by the user, and in response to the first input, the target device information of the device being used by the user can be determined in the at least one device information, and the target object data associated with the object is obtained and output according to the object identity information and the target device information. In this way, the identity information of the object can be effectively identified and verified, ensuring that only authorized devices can access the target object data, thereby preventing the situation where a non-object logs in, and improving the security of data protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of a data acquisition method in some embodiments of the present application;

[0018] Figure 2 is a schematic diagram illustrating a data acquisition method of some embodiments of the present application;

[0019] Figure 3 is a flow chart of a data acquisition method in some embodiments of the present application;

[0020] Figure 4 is a schematic diagram illustrating a data acquisition method of some embodiments of the present application;

[0021] Figure 5 is a schematic diagram illustrating a data acquisition method of some embodiments of the present application;

[0022] Figure 6is a schematic diagram illustrating a data acquisition method of some embodiments of the present application;

[0023] Figure 7 is a schematic diagram illustrating a data acquisition method of some embodiments of the present application;

[0024] Figure 8 is a flow chart of a data acquisition method in some embodiments of the present application;

[0025] Fig. 9 is a block diagram of a data acquisition device in some embodiments of the present application;

[0026] Fig.10 Schematic diagram of the hardware structure of the electronic device of some embodiments of the present application;

[0027] Fig.11 It is a schematic diagram of the hardware structure of an electronic device of some embodiments of the present application. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings of the embodiments of the present application to clearly describe the technical solutions of the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present application belong to the scope of protection of this application.

[0029] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally a class, and the number of objects is not limited. For example, the target object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0030] The data acquisition method provided in the embodiment of the present application is described in detail below through specific embodiments and their application scenarios in conjunction with the accompanying drawings.

[0031] like Figure 1 FIG. 1 is a flowchart of a data acquisition method according to an embodiment of the present application. The method is applied to an electronic device as an example. The method includes:

[0032] Step 110: Acquire identity information of the object, and output at least one device information associated with the identity information according to the identity information;

[0033] Object, the user or entity that needs to perform identity and device authentication. For example, user: Miya; device: Phone 13.

[0034] Identity information, data used to confirm the user's identity, such as user name, password, mobile phone number, ID number, etc. For example, user name: Miya; password: Password123; mobile phone number: +1234567890; ID number: 123456789012345.

[0035] Device information is used to confirm the data of the device used by the user, such as device ID, device fingerprint, device model, etc. For example, device ID: A1B2C3D4E5; device fingerprint: 1a2b3c4d5e6f7g8h9i0j; device model: Phone 13.

[0036] Obtain the user's identity information, such as username or password, and output at least one device information associated with the user based on this information, such as device ID or device fingerprint. For example, user Miya logs in to the platform, obtains his identity information, and outputs the device information associated with him, such as device ID A1B2C3D4E5.

[0037] The electronic device used in this embodiment may be a server of a certain platform.

[0038] The application scenario of this embodiment is, for example, when a user logs in to a platform on a device, he inputs the identity information of the object on the device, so that the platform server obtains the identity information of the object.

[0039] At the same time, the identity information of an object may log in to the platform on multiple devices. In this embodiment, the platform server can lock at least one of the devices as a device associated with the identity information of the object, thereby obtaining the device information of the associated device.

[0040] For example, use the identity information of an object to log in to a browser on a mobile phone; at the same time, use the identity information of the object to log in to the browser on a tablet. Based on this embodiment, the identity information of the object can be locked with the mobile phone, or the identity information of the object can be locked with the platform.

[0041] Wherein, step 110 may specifically include the following steps:

[0042] Obtain the identity information of the object, which at least includes the login account, login password, and bound phone number;

[0043] According to the identity information of the object, obtaining device information of at least one device logged in based on the identity information of the object;

[0044] Output at least one device information.

[0045] When a user logs in to a browser, the user enters the login account and password; further, considering the browser's cache settings, the login state of the user account has a certain timeliness. When the user performs any data operation in the browser, including exporting and deleting personal data, the user is required to verify the phone number bound to the login account, thereby completing the first two-factor verification.

[0046] After completing the authentication of user information through two-factor verification, the browser server calls the account server to pull the device information of the active devices of the logged-in user account and display it on the browser client. Finally, the browser server outputs multiple device information to the browser client.

[0047] Step 120: receiving a first input of the at least one device information;

[0048] Receive the user's selection or input of device information. For example, the user Miya selects device ID A1B2C3D4E5 in the device list.

[0049] Step 130: In response to the first input, determining target device information in the at least one device information;

[0050] According to the user's input, the target device information selected by the user is determined in the device information list. For example, according to the user's selection, the target device information is determined to be device ID A1B2C3D4E5.

[0051] like Figure 2 As shown, a device identification corresponding to at least one device information is displayed.

[0052] In response to a first input of the at least one device information, target device information is determined in the at least one device information.

[0053] In this step, the browser user terminal receives user input for at least one device identifier, and in response to the user input, the browser user terminal returns the device information corresponding to the device identifier selected by the user input to the browser server, so that the browser server determines the device information corresponding to the device identifier selected by the user input as the device information associated with the user information.

[0054] In this embodiment, an associated device is determined among multiple devices logged in based on user information, so that data retrieval can be performed based on the associated device, making the data retrieval more accurate.

[0055] In a possible embodiment, step 130 includes:

[0056] In response to the first input, determining associated device information in the at least one device information;

[0057] Sending verification information to the associated device according to the associated device information;

[0058] Receiving verification feedback information sent by the associated device;

[0059] When the verification feedback information indicates that the associated device verification is passed, the associated device information is determined as the target device information.

[0060] Associated device information refers to device information associated with user identity information, for example, device ID: A1B2C3D4E5.

[0061] Verification information, information used to verify the identity of the device, such as a verification code, device fingerprint, etc. For example, the verification code is: 123456.

[0062] Verification feedback information, which is sent by the device after receiving the verification information, is used to confirm the result of the device identity authentication. For example, verification feedback information: verification passed.

[0063] According to the user's input, the associated device information selected by the user is determined in the device information list. For example, the user Miya selects the device ID A1B2C3D4E5 in the device list, and the device is determined as the associated device information.

[0064] According to the associated device information, send verification information to the device to verify the device identity. For example, send verification code 4567 to device ID A1B2C3D4E5. Figure 3 As shown, the browser server returns the displayed random code to the browser client, so the browser client displays the random code.

[0065] After the user confirms on the browser client that the random verification information will be sent to the device selected by the user, the account server sends a command to the device, such as Figure 4 As shown, on the device, a pop-up window is used to prompt the user to enter random verification information. Further, after the user correctly enters the random verification information on the device, the random verification information and device information are returned by the account server to the browser server, and the browser server verifies that the two match and confirms that they are associated devices.

[0066] like Figure 5 As shown, a prompt message indicating successful verification is displayed on the browser user side.

[0067] In this embodiment, the device selected by the user is further verified to ensure that the device is currently being used by the user, thereby preventing user data leakage. In addition, the double-end random code verification link shown in this embodiment can be replaced by a more secure and reliable level trust code issuance mechanism.

[0068] Receive verification feedback information sent by the associated device to confirm the result of device identity authentication. For example, the system receives verification feedback information "Verification passed" sent by device ID A1B2C3D4E5.

[0069] If the verification feedback information indicates that the device verification is passed, the associated device information is determined as the target device information. For example, when the verification feedback information "verification passed" is received, the device ID A1B2C3D4E5 is determined as the target device information.

[0070] By sending verification information to the associated device and receiving verification feedback information, the device identity can be confirmed more accurately to ensure the accuracy of the data source. The device authentication process adds an extra layer of security to prevent unauthorized devices from accessing user data and improve security.

[0071] Through device authentication, we can avoid incomplete or over-inventory problems caused by incorrect device identity, and ensure the accuracy of data inventory. Through the device authentication process, users can more conveniently select and manage data associated with their devices, improving user experience and satisfaction.

[0072] Therefore, by sending verification information to the associated device and receiving verification feedback information, the device identity can be confirmed more accurately, ensuring the accuracy of the data source while enhancing security. This solution not only improves the accuracy of data inventory, but also avoids data inventory errors, improving user experience and satisfaction.

[0073] In another embodiment of the present application, Figure 6 As shown in the figure, a reference data processing flow is provided. First, starting from the browser user end, the user logs in with the account and password, and completes the two-factor verification method in combination with the user's SMS verification code. Then, the browser server calls the account server to return the device list of the logged-in user information, so that the user can confirm the device that needs to exercise data rights on the browser user end, that is, the associated device.

[0074] Based on this, the browser server generates a random code to prompt the user to enter the random code in the pop-up window of the selected device. Finally, the browser server completes the verification based on the random code and device information entered in the selected device, so that the browser server retrieves the user data based on the user account and the device information of the associated device.

[0075] Step 140: Acquire and output target object data associated with the object according to the object identity information and the target device information.

[0076] Target device information: specific device information selected or determined by the user.

[0077] For example, target device ID: A1B2C3D4E5;

[0078] Target object data, which is specific data associated with the user and target device.

[0079] For example, the reading record of user Miya on device A1B2C3D4E5.

[0080] According to the user's identity information and the target device information, obtain and output specific data associated with the user and the device. For example, according to the user Miya's identity information and the target device ID A1B2C3D4E5, obtain and output Miya's reading records on the device.

[0081] By combining identity and device authentication, the user's identity and data source device can be confirmed more accurately, ensuring that the data inventory is complete and consistent with user cognition; searching for user data only by account ID may not accurately lock the device of the data source, resulting in incomplete data inventory or excessive inventory. Combining device authentication can avoid this situation. By providing more accurate data inventory and exercise channels, the GDPR's requirements on the rights of data subjects can be better respected, avoiding the problems of excessive requests for user information and restrictions on exercise channels.

[0082] Users can more easily select and manage data associated with their devices, improving user experience and satisfaction; by combining identity and device authentication, the user's identity and data source device can be more accurately confirmed, ensuring that the data inventory is complete and consistent with user cognition, while respecting GDPR's requirements on data subject rights. This solution not only improves security, but also avoids the problems of excessive requests for user information and restrictions on exercise channels, improving user experience and satisfaction.

[0083] In a possible embodiment, step 140 includes:

[0084] Step 210, obtaining object data associated with the object;

[0085] Step 220, determining matching information in the object identity information and the target device information according to the business type of the object data;

[0086] Step 230, comparing the matching information with preset matching information of the object data;

[0087] Step 240: acquiring and outputting the target object data from the object data according to the comparison result.

[0088] Involving step 210, various data associated with the user are obtained, such as reading records, purchase records, browsing history, etc.

[0089] For example, obtain the reading records of user Miya on device A1B2C3D4E5.

[0090] Step 220 involves determining matching information, such as a user ID and a device ID, in the user identity information and the target device information according to the business type of the object data.

[0091] For example, according to the business type of the reading record, matching information is determined in the identity information of the user Miya and the device ID A1B2C3D4E5.

[0092] Involving step 230: comparing the determined matching information with the preset matching information of the object data to confirm the accuracy of the data matching.

[0093] For example, user ID Miya and device ID A1B2C3D4E5 are compared with preset user ID Miya and preset device ID A1B2C3D4E5.

[0094] Object data refers to various data associated with users, such as reading records, purchase records, browsing history, etc. For example, the reading records of user Miya on device A1B2C3D4E5

[0095] Business type refers to the business category to which the object data belongs, such as reading, purchasing, browsing, etc.

[0096] Matching information refers to data used to match object data and device information, such as user ID, device ID, etc. For example, user ID: Miya; device ID: A1B2C3D4E5.

[0097] Preset matching information refers to pre-set data used to match object data and device information.

[0098] For example, preset user ID: Miya; preset device ID: A1B2C3D4E5;

[0099] Involving step 240: according to the comparison result, obtaining and outputting the target object data associated with the user and the device in the object data.

[0100] For example, according to the comparison result, the target reading record associated with the device A1B2C3D4E5 is obtained and output in the reading record of the user Miya.

[0101] By comparing the matching information with the preset matching information, the accuracy of the data matching can be confirmed more accurately, ensuring the accuracy of the acquired target object data. Through data matching and comparison, the problem of incomplete data inventory or over-inventory due to data matching errors can be avoided, ensuring the accuracy of data inventory. Through preset matching information and automatic comparison, the process of data matching and acquiring target object data can be handled more efficiently, improving data processing efficiency. Through the data matching and comparison process, users can more conveniently acquire and manage data associated with their devices, improving user experience and satisfaction.

[0102] In a possible embodiment, before step 210, the method further includes:

[0103] Obtaining object training data, a reference code corresponding to the object training data, and a first model;

[0104] Using a first model to identify the object training data and a reference code corresponding to the object training data, to obtain an identification code of the object training data;

[0105] Using the reference code and the identification code to adjust the first model to obtain a second model;

[0106] The object data is identified using the second model to obtain the business type of the object model.

[0107] Object training data, data used to train the model, is usually historical data related to the object.

[0108] For example, user Miya's historical reading records

[0109] Reference encoding, the standard encoding used to train the model, is usually a pre-defined encoding rule or standard.

[0110] For example, the reference codes for reading records are: 1 and 0; 1 is used to indicate that it has been read, and 0 is used to indicate that it has not been read;

[0111] The first model, an initial machine learning model, is used to perform preliminary recognition on the object training data. For example, the first model may be an initial reading record recognition model.

[0112] Recognition code: the code generated by the first model after recognizing the object training data.

[0113] For example, identification codes: 1 and 0; 1 is used to indicate read, and 0 is used to indicate unread;

[0114] The second model, the adjusted machine learning model, is used to more accurately identify the object data. For example, the second model can be an adjusted reading record recognition model.

[0115] Obtain object training data for training the model, as well as reference codes corresponding to these data and an initial first model. For example, obtain the historical reading records of user Miya, as well as the reference codes of these records, and obtain an initial reading record recognition model.

[0116] The object training data is identified using the first model, and an identification code is generated. For example, the historical reading records of Miya are identified using the initial reading record identification model, and an identification code is generated.

[0117] According to the difference between the reference code and the identification code, the first model is adjusted to obtain a more accurate second model. For example, according to the difference between the reference code and the identification code, the initial reading record recognition model is adjusted to obtain an adjusted reading record recognition model.

[0118] The object data is identified using the adjusted second model to determine the service type of the object data. For example, the current reading record of Miya is identified using the adjusted reading record identification model to determine the service type of the reading record.

[0119] Specifically, object training data with legitimate sources and after desensitization can be obtained, and the object training data can be input into the base of the pre-trained first model for learning. Among them, manual participation in the supervised training process, that is, manual sampling and labeling of the object training data to input the reference code of the object training data, so that the recognition code output by the first model is close to the reference code based on the training.

[0120] like Figure 7 As shown, according to the category names of the object training data, the object training data under different category names are category-coded.

[0121] The first model is trained based on the reference code and the recognition code so that the recognition code output by the first model is close to the reference code.

[0122] By adjusting the first model using reference codes and identification codes, the recognition accuracy of the model can be improved, ensuring the accuracy of business type identification of object data; by adjusting the model, the adaptability of the model to different types of data can be enhanced, ensuring that the model can accurately identify object data of various business types; by using the adjusted second model, the business type identification of object data can be processed more efficiently, improving data processing efficiency; through the model adjustment and identification process, users can more conveniently obtain and manage data associated with their business types, thereby improving user experience and satisfaction.

[0123] Therefore, by adjusting the first model using the reference code and the identification code, the recognition accuracy of the model can be improved, ensuring the accuracy of the business type recognition of the object data. This solution not only improves the accuracy of data processing, but also enhances the adaptability of the model, improves data processing efficiency and user experience.

[0124] The first model is adjusted by using the reference code and the identification code to obtain the second model, including:

[0125] Comparing the reference code with the recognition code to obtain the recognition accuracy of the first model;

[0126] When the recognition accuracy is less than a preset accuracy threshold, detecting structured data and unstructured data in the object training data;

[0127] Performing regression annotation on the structured data and input annotation on the unstructured data to obtain annotated object training data;

[0128] The first model is fine-tuned using the labeled object training data to obtain the second model.

[0129] Recognition accuracy rate, the accuracy of the first model in recognizing the object training data, usually expressed as a percentage. For example, recognition accuracy rate: 90%.

[0130] The preset accuracy threshold refers to the pre-set recognition accuracy standard, which is used to determine whether the model needs to be adjusted. For example, the preset accuracy threshold is 95%;

[0131] Structured data refers to data with a fixed format and structure, such as table data, database records, etc. For example, a phone number: 11 consecutive digits starting with 1; a geographic location: data with a field name of loc.

[0132] Unstructured data refers to data without a fixed format and structure, such as text, images, audio, etc. Unstructured data: data with no fixed structure, no fixed data structure and field names, and data that cannot be identified using privacy detection algorithms, such as user clicks, exposure information, user browsing history, etc.

[0133] Regression annotation is used to annotate structured data to improve the regression prediction ability of the model. For example, regression annotation is performed on the data in the reading record table.

[0134] Input annotation is used to annotate unstructured data to improve the model's input recognition capabilities. For example, input annotation is used to annotate reading note text.

[0135] The reference code and the recognition code are compared to calculate the recognition accuracy of the first model. For example, the reference code and the recognition code are compared to calculate the recognition accuracy of the initial reading record recognition model.

[0136] If the recognition accuracy of the first model is less than the preset accuracy threshold, the structured data and unstructured data in the object training data are detected. For example, if the recognition accuracy (90%) is less than the preset accuracy threshold (95%), Miya's reading record form and reading note text are detected.

[0137] Regression annotation is performed on structured data, and input annotation is performed on unstructured data to obtain annotated object training data. For example, regression annotation is performed on the data in the reading record table, and input annotation is performed on the reading note text to obtain annotated object training data.

[0138] The first model is fine-tuned using the labeled object training data to obtain a more accurate second model. For example, the initial reading record recognition model is fine-tuned using the labeled reading record and reading note data to obtain an adjusted reading record recognition model.

[0139] By comparing the reference code and the identification code, the recognition accuracy of the model can be calculated, and the model can be adjusted according to the accuracy to ensure the accuracy of model recognition; by detecting and labeling structured data and unstructured data, the adaptability of the model to different types of data can be enhanced to ensure that the model can accurately identify object data of various business types; by fine-tuning the model using labeled object training data, the business type identification of object data can be more efficiently processed, thereby improving data processing efficiency; through the model adjustment and identification process, users can more conveniently obtain and manage data associated with their business types, thereby improving user experience and satisfaction.

[0140] Therefore, by comparing the reference code and the recognition code, the recognition accuracy of the model can be calculated, and the model can be adjusted according to the accuracy to ensure the accuracy of model recognition. This solution not only improves the accuracy of data processing, but also enhances the adaptability of the model, improves data processing efficiency and user experience.

[0141] In this embodiment, after the first model is trained based on the training data, the data discovery capability of the model is tested by manual verification. If the recognition rate of structured data such as telephone numbers does not meet the quality standard, a privacy data detection algorithm can be used for detection and regression annotation; if unstructured data such as user behavior data does not meet the quality standard, the manual annotation ratio is increased.

[0142] In this embodiment, based on the training of the first model, the output of the first model is made close to the manually annotated code. During the training process, when the recognition rate of the first model does not meet the quality standard, the first model can be fine-tuned with the help of algorithms and manual annotation until the recognition rate meets the quality standard.

[0143] In another embodiment of the present application, the second model identifies all data in the business database, marks the data fields, and identifies them in the form shown in Table 1 below:

[0144]

[0145] Table 1

[0146] For the unrecognized data, manual verification is required to complete the recognition results, as shown in Table 2:

[0147]

[0148] Table 2

[0149] In addition, if the identification results of a business data are all " / ", then the data is non-personal business data.

[0150] like Figure 8 As shown, based on the training data input into the model base, a large data recognition model, i.e., the first model, is obtained. Manual supervision training is performed to obtain a large data recognition model, i.e., the second model. The second model is used to identify business data to identify personal business data. Furthermore, the identification results are manually labeled.

[0151] In a possible embodiment, step 220 includes:

[0152] When the business type of the object data is an object type, determining the object identity information as matching information;

[0153] When the business type of the object data is a device type, determining the target device information as matching information;

[0154] When the business type of the object data is an object type and a device type, the object information and the target device information are determined as matching information.

[0155] Object type refers to the data type associated with user identity information, such as user ID, user name, etc.

[0156] For example, User ID: Miya

[0157] Device type, the data type associated with device information, such as device ID, device fingerprint, etc.

[0158] For example, device ID: A1B2C3D4E5

[0159] Among them, according to the business type of the object data, the matching information is determined in the user identity information and the target device information. Specifically, it includes the following situations:

[0160] First, if the business type of the object data is an object type, such as a user ID or a user name, the user identity information is determined as the matching information. For example, if the business type of the object data is an object type, the user ID Miya is determined as the matching information.

[0161] Second, if the business type of the object data is a device type, such as a device ID or a device fingerprint, the target device information is determined as matching information; for example, if the business type of the object data is a device type, the device ID A1B2C3D4E5 is determined as matching information.

[0162] Third, if the business type of the object data includes both the object type and the device type, the user identity information and the target device information are determined as matching information. For example, if the business type of the object data is the object type and the device type, the user ID Miya and the device ID A1B2C3D4E5 are determined as matching information.

[0163] By determining matching information based on the business type of the object data, different types of data matching requirements can be handled more flexibly to ensure the accuracy of data matching; by determining matching information based on the business type, the problem of incomplete or excessive data inventory due to data type mismatch can be avoided to ensure the accuracy of data inventory; by automatically determining matching information based on the business type, the process of data matching and obtaining target object data can be handled more efficiently, thereby improving data processing efficiency; through the process of determining matching information based on business type, users can more conveniently obtain and manage data associated with their identities and devices, thereby improving user experience and satisfaction.

[0164] Specifically, in the object data of the user type, the object data associated with the user information is returned.

[0165] In the object data of the device type, object data managed with the device information is returned.

[0166] In the object data of the user device type, object data associated with both user information and device information is returned.

[0167] In this step, after locking the user information and device information, the object data is retrieved in the business database. The object data includes the following categories: the first category is the type related only to the user, that is, the user type object data; the second category is the type related only to the device, that is, the device type object data; the third category is the type related to both the user and the device, that is, the user device type object data; the fourth category is the type not related to the user or the device, that is, other types of object data.

[0168] Based on this, the platform server retrieves object data based on the locked user information and device information. The retrieval rules are as follows:

[0169] If a certain object data in the object data of the user type is associated with the user information obtained in this embodiment, the object data is returned;

[0170] If a certain object data in the object data of the device type is associated with the device information obtained in this embodiment, the object data is returned;

[0171] If a certain object data in the object data of the user device type is associated with both the device information and the user information obtained in this embodiment, the object data is returned; wherein, if a certain object data in the object data of the user device type is only associated with the user information obtained in this embodiment or only associated with the device information obtained in this embodiment, the object data is not returned;

[0172] No other types of object data are returned.

[0173] Furthermore, the platform server performs export and deletion operations on the returned object data.

[0174] Therefore, by determining the matching information according to the business type of the object data, different types of data matching requirements can be handled more flexibly to ensure the accuracy of data matching. This solution not only improves the accuracy of data inventory, but also avoids data inventory errors, improves data processing efficiency and user experience.

[0175] In an embodiment of the present application, the identity information of the object is obtained, at least one device information associated with the identity information is output according to the identity information of the object, a first input of at least one device information is received, the first input is used to confirm the device being used by the user, and in response to the first input, the target device information of the device being used by the user can be determined in the at least one device information, and the target object data associated with the object is obtained and output according to the object identity information and the target device information. In this way, the identity information of the object can be effectively identified and verified, ensuring that only authorized devices can access the target object data, thereby preventing the situation where a non-object logs in, and improving the security of data protection.

[0176] The data acquisition method provided in the embodiment of the present application can be executed by a data acquisition device. In the embodiment of the present application, the data acquisition device provided in the embodiment of the present application is described by taking the data acquisition method executed by the data acquisition device as an example.

[0177] Fig. 9 A block diagram of a data acquisition device according to an embodiment of the present application is shown, the device comprising:

[0178] A first acquisition module 910 is used to acquire identity information of an object, and output at least one device information associated with the identity information according to the identity information;

[0179] The receiving module 920 is used to receive a first input of the at least one device information;

[0180] A determination module 930, configured to determine target device information in the at least one device information in response to the first input;

[0181] The second acquisition module 940 is used to acquire and output target object data associated with the object according to the object identity information and the target device information.

[0182] In a possible embodiment, the determination module 930 is specifically configured to:

[0183] In response to the first input, determining associated device information in the at least one device information;

[0184] Sending verification information to the associated device according to the associated device information;

[0185] Receiving verification feedback information sent by the associated device;

[0186] When the verification feedback information indicates that the associated device verification is passed, the associated device information is determined as the target device information.

[0187] In a possible embodiment, the second acquisition module 940 is specifically configured to:

[0188] Obtaining object data associated with the object;

[0189] Determining matching information in the object identity information and the target device information according to the business type of the object data;

[0190] comparing the matching information with preset matching information of the object data;

[0191] According to the comparison result, the target object data is acquired and outputted in the object data.

[0192] In a possible embodiment, the second acquisition module 940 is specifically configured to:

[0193] When the business type of the object data is an object type, determining the object identity information as matching information;

[0194] When the business type of the object data is a device type, determining the target device information as matching information;

[0195] When the business type of the object data is an object type and a device type, the object information and the target device information are determined as matching information.

[0196] In a possible embodiment, the device may further include:

[0197] A third acquisition module, used to acquire object training data, a reference code corresponding to the object training data, and a first model;

[0198] an identification module, configured to identify the object training data and a reference code corresponding to the object training data by using a first model, so as to obtain an identification code of the object training data;

[0199] an adjustment module, configured to adjust the first model using the reference code and the identification code to obtain a second model;

[0200] The identification module is further used to identify the object data using the second model to obtain the business type of the object model.

[0201] In a possible embodiment, the adjustment module is specifically used to:

[0202] Comparing the reference code with the recognition code to obtain the recognition accuracy of the first model;

[0203] When the recognition accuracy is less than a preset accuracy threshold, detecting structured data and unstructured data in the object training data;

[0204] Performing regression annotation on the structured data and input annotation on the unstructured data to obtain annotated object training data;

[0205] The first model is fine-tuned using the labeled object training data to obtain the second model.

[0206] In an embodiment of the present application, the identity information of the object is obtained, at least one device information associated with the identity information is output according to the identity information of the object, a first input of at least one device information is received, the first input is used to confirm the device being used by the user, and in response to the first input, the target device information of the device being used by the user can be determined in the at least one device information, and the target object data associated with the object is obtained and output according to the object identity information and the target device information. In this way, the identity information of the object can be effectively identified and verified, ensuring that only authorized devices can access the target object data, thereby preventing the situation where a non-object logs in, and improving the security of data protection.

[0207] The device in the application embodiment can be an electronic device or a component in an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices other than a terminal. Exemplarily, the electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, a vehicle-mounted electronic device, a mobile Internet device (Mobile Internet Device, MID), an augmented reality (augmented reality, AR) / virtual reality (virtual reality, VR) device, a robot, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a personal digital assistant (personal digital assistant, PDA), etc. It can also be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (personal computer, PC), a television (television, TV), a teller machine or a self-service machine, etc., and the present application embodiment is not specifically limited.

[0208] The device of the embodiment of the present application may be a device having an action system. The action system may be an Android action system, an iOS action system, or other possible action systems, which are not specifically limited in the embodiment of the present application.

[0209] The device provided in the embodiment of the present application can implement each process implemented in the above method embodiment and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0210] Alternatively, if Fig.10As shown, an embodiment of the present application also provides an electronic device 100, including a processor 101, a memory 102, and a program or instruction stored in the memory 102 and executable on the processor 101. When the program or instruction is executed by the processor 101, each step of any of the above-mentioned data acquisition method embodiments is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0211] It should be noted that the electronic devices in the embodiments of the present application include the above-mentioned mobile electronic devices and non-mobile electronic devices.

[0212] Fig.11 A schematic diagram of the hardware structure of an electronic device to implement an embodiment of the present application.

[0213] The electronic device 1000 includes but is not limited to: a radio frequency unit 1001, a network module 1002, an audio output unit 1003, an input unit 1004, a sensor 1005, a display unit 1006, a user input unit 1007, an interface unit 1008, a memory 1009, a processor 1010, a camera 1011 and other components.

[0214] Those skilled in the art will appreciate that the electronic device 1000 may also include a power source (such as a battery) for supplying power to each component, and the power source may be logically connected to the processor 1010 through a power management system, thereby implementing functions such as managing charging, discharging, and power consumption management through the power management system. Fig.11 The electronic device structure shown in the figure does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently, which will not be described in detail here.

[0215] The processor 1010 is configured to obtain identity information of an object, and output at least one device information associated with the identity information according to the identity information;

[0216] The processor 1010 is further configured to receive a first input of the at least one device information;

[0217] The processor 1010 is further configured to determine target device information in the at least one device information in response to the first input;

[0218] The processor 1010 is further configured to acquire and output target object data associated with the object according to the object identity information and the target device information.

[0219] Optionally, the processor 1010 is further configured to determine associated device information in the at least one device information in response to the first input;

[0220] The processor 1010 is further configured to send verification information to the associated device according to the associated device information;

[0221] The processor 1010 is further configured to receive verification feedback information sent by the associated device;

[0222] The processor 1010 is further configured to determine the associated device information as the target device information when the verification feedback information indicates that the associated device verification is successful.

[0223] Optionally, the processor 1010 is further configured to obtain object data associated with the object;

[0224] The processor 1010 is further configured to determine matching information in the object identity information and the target device information according to a business type of the object data;

[0225] The processor 1010 is further configured to compare the matching information with preset matching information of the object data;

[0226] The processor 1010 is further configured to obtain and output the target object data from the object data according to the comparison result.

[0227] Optionally, the processor 1010 is further configured to determine the object identity information as matching information when the business type of the object data is an object type;

[0228] The processor 1010 is further configured to determine the target device information as matching information when the business type of the object data is a device type;

[0229] The processor 1010 is further configured to determine the object information and the target device information as matching information when the business type of the object data is an object type and a device type.

[0230] Optionally, the processor 1010 is further configured to obtain object training data, a reference code corresponding to the object training data, and a first model;

[0231] The processor 1010 is further configured to use the first model to identify the object training data and the reference code corresponding to the object training data to obtain the identification code of the object training data;

[0232] The processor 1010 is further configured to adjust the first model using the reference code and the identification code to obtain a second model;

[0233] The processor 1010 is further configured to use the second model to identify the object data to obtain a business type of the object model.

[0234] Optionally, the processor 1010 is further configured to compare the reference code with the recognition code to obtain a recognition accuracy rate of the first model;

[0235] The processor 1010 is further configured to detect structured data and unstructured data in the object training data when the recognition accuracy is less than a preset accuracy threshold;

[0236] The processor 1010 is further configured to perform regression annotation on the structured data and input annotation on the unstructured data to obtain annotated object training data;

[0237] The processor 1010 is further configured to use the labeled object training data to fine-tune the first model to obtain the second model.

[0238] In an embodiment of the present application, the identity information of the object is obtained, at least one device information associated with the identity information is output according to the identity information of the object, a first input of at least one device information is received, the first input is used to confirm the device being used by the user, and in response to the first input, the target device information of the device being used by the user can be determined in the at least one device information, and the target object data associated with the object is obtained and output according to the object identity information and the target device information. In this way, the identity information of the object can be effectively identified and verified, ensuring that only authorized devices can access the target object data, thereby preventing the situation where a non-object logs in, and improving the security of data protection.

[0239] It should be understood that in the embodiment of the present application, the input unit 1004 may include a graphics processor (Graphics Processing Unit, GPU) 10041 and a microphone 10042, and the graphics processor 10041 processes the image data of the static picture or video image obtained by the image capture device (such as a camera) in the video image capture mode or the image capture mode. The display unit 1006 may include a display panel 10061, and the display panel 10061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 1007 includes a touch panel 10071 and at least one of other input devices 10072. The touch panel 10071 is also called a touch screen. The touch panel 10071 may include two parts: a touch detection device and a touch controller. Other input devices 10072 may include, but are not limited to, a physical keyboard, a function key (such as a volume control button, a switch button, etc.), a trackball, a mouse, and an action rod, which will not be repeated here. The memory 1009 can be used to store software programs and various data, including but not limited to applications and action systems. The processor 1010 may integrate an application processor and a modem processor, wherein the application processor mainly processes the action system, user pages and application programs, etc., and the modem processor mainly processes wireless communications. It is understandable that the modem processor may not be integrated into the processor 1010.

[0240] The memory 1009 can be used to store software programs and various data. The memory 1009 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 1009 may include a volatile memory or a non-volatile memory, or the memory 1009 may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM). The memory 1009 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0241] The processor 1010 may include one or more processing units; optionally, the processor 1010 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 1010.

[0242] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned data acquisition method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0243] The processor is the processor in the electronic device in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0244] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned data acquisition method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0245] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0246] An embodiment of the present application provides a computer program product, which is stored in a storage medium. The program product is executed by at least one processor to implement the various processes of the above-mentioned data acquisition method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0247] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0248] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of each embodiment of the present application.

[0249] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A data acquisition method, characterized in that: The method comprises: Acquire identity information of the object, and output at least one device information associated with the identity information according to the identity information; receiving a first input of the at least one device information; In response to the first input, determining target device information in the at least one device information; According to the object identity information and the target device information, target object data associated with the object is acquired and output.

2. The method according to claim 1, characterized in that The step of determining target device information in the at least one device information in response to the first input includes: In response to the first input, determining associated device information in the at least one device information; Sending verification information to the associated device according to the associated device information; Receiving verification feedback information sent by the associated device; When the verification feedback information indicates that the associated device verification is passed, the associated device information is determined as the target device information.

3. The method according to claim 1, characterized in that The acquiring and outputting target object data associated with the object according to the object identity information and the target device information includes: Obtaining object data associated with the object; Determining matching information in the object identity information and the target device information according to the business type of the object data; comparing the matching information with preset matching information of the object data; According to the comparison result, the target object data is acquired and outputted in the object data.

4. The method according to claim 3, characterized in that The determining matching information in the object identity information and the target device information according to the business type of the object data includes: When the business type of the object data is an object type, determining the object identity information as matching information; When the business type of the object data is a device type, determining the target device information as matching information; When the business type of the object data is an object type and a device type, the object information and the target device information are determined as matching information.

5. The method according to claim 3, characterized in that: Before obtaining the object data associated with the object, the method further includes: Obtaining object training data, a reference code corresponding to the object training data, and a first model; Using a first model to identify the object training data and a reference code corresponding to the object training data, to obtain an identification code of the object training data; Using the reference code and the identification code to adjust the first model to obtain a second model; The object data is identified using the second model to obtain the business type of the object model.

6. The method according to claim 5, characterized in that The step of adjusting the first model by using the reference code and the identification code to obtain a second model includes: Comparing the reference code with the recognition code to obtain the recognition accuracy of the first model; When the recognition accuracy is less than a preset accuracy threshold, detecting structured data and unstructured data in the object training data; Performing regression annotation on the structured data and input annotation on the unstructured data to obtain annotated object training data; The first model is fine-tuned using the labeled object training data to obtain the second model.

7. A data acquisition device, characterized in that: The device comprises: A first acquisition module, used to acquire identity information of an object, and output at least one device information associated with the identity information according to the identity information; A receiving module, configured to receive a first input of the at least one device information; a determination module, configured to determine target device information in the at least one device information in response to the first input; The second acquisition module is used to acquire and output target object data associated with the object according to the object identity information and the target device information.

8. The device according to claim 7, characterized in that The determining module is specifically used for: In response to the first input, determining associated device information in the at least one device information; Sending verification information to the associated device according to the associated device information; Receiving verification feedback information sent by the associated device; When the verification feedback information indicates that the associated device verification is passed, the associated device information is determined as the target device information.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the data acquisition method according to any one of claims 1 to 6 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or an instruction, and when the program or the instruction is executed by a processor, the steps of the data acquisition method according to any one of claims 1 to 6 are implemented.

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