Message notification methods, devices, storage media, and electronic devices

Abstract

This invention provides a message prompting method, apparatus, storage medium, and electronic device. The method includes: when it is determined that target processing needs to be performed on target data in a first database, determining a second database in the system that has a target association relationship with the first database, wherein the system includes multiple databases, including the first database and the second database, and the target association relationship is used to indicate that similar data between the databases with a similarity greater than a predetermined threshold meets the target condition; synchronizing a target prompt message to the second database, the target prompt message being used to indicate whether target processing needs to be performed on the target data in the second database. This invention solves the problem of low efficiency in effectively synchronizing similar databases in related technologies, achieving the effect of improving the efficiency of effectively synchronizing similar databases and improving resource utilization.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of artificial intelligence technology, and more specifically, to a message prompting method, apparatus, storage medium, and electronic device. Background Technology

[0002] With the continuous development of technology, big data is widespread, such as enterprise data, scientific data, medical data, security data, and Internet data. As the volume and types of big data grow rapidly, the storage, retrieval, and application of data are facing enormous challenges.

[0003] In related technologies, when a system is used by multiple departments, each department will establish its own database within the system to store data corresponding to its department. Due to factors such as poor communication between these departments or the need for confidentiality, each department may repeatedly perform the same operation on the same data, resulting in low efficiency in effectively processing the same data. Furthermore, performing this operation requires certain resources (hardware resources, storage resources, etc.), but resources are limited, thus leading to unnecessary waste and low resource utilization.

[0004] There is currently no effective solution to the problem of low efficiency in synchronizing similar databases in related technologies. Summary of the Invention

[0005] The present invention provides a message notification method, apparatus, storage medium, and electronic device to at least solve the problem of low efficiency in effectively processing the same data in related technologies.

[0006] According to an embodiment of the present invention, a message prompting method is provided, comprising: when it is determined that target processing needs to be performed on target data in a first database, determining a second database in the system that has a target association relationship with the first database, wherein the system includes multiple databases, and the multiple databases include the first database and the second database, the target association relationship being used to indicate that similar data between the databases with a similarity greater than a predetermined threshold has reached a target condition; synchronizing a target prompting message to the second database, wherein the target prompting message is used to prompt whether the target processing needs to be performed on the target data in the second database.

[0007] In one exemplary embodiment, the method further includes: acquiring first data to be added to the first database; determining whether a third database exists in the plurality of databases, including second data whose similarity to the first data is greater than the predetermined threshold; and, if the third database exists in the plurality of databases, establishing a first association relationship between the first database and the third database, wherein the first association relationship is established by recording an association identifier and the location information of the second data in the third database in the first database, the association identifier being used to associate the first data with the second data.

[0008] In one exemplary embodiment, the method further includes: determining the first data as the target data when it is determined that the third database does not exist in the plurality of databases; determining that an add operation needs to be performed on the target data in the first database, wherein the target processing includes the add operation.

[0009] In an exemplary embodiment, determining a second database included in the system that has a target association relationship with the first database includes: performing the following operations on each of the N databases included in the system other than the first database to determine the association degree between each of the N databases and the first database, thereby obtaining N association degrees, wherein each of the N databases is the current database when performing the following operations: determining a first data volume of data whose similarity between the first database and the current database is greater than a predetermined threshold, a second data volume of all data stored in the first database, and a third data volume of all data stored in the current database; determining the association degree between the current database and the first database based on the first data volume, the second data volume, and the third data volume, wherein N is a positive integer greater than or equal to 1; and determining the second database from the N databases based on the N association degrees.

[0010] In an exemplary embodiment, determining the second database from the N databases based on the N correlation degrees includes: if there is a first correlation degree among the N correlation degrees that is greater than a target preset threshold, determining the database in the N databases that corresponds to the first correlation degree as the second database.

[0011] In an exemplary embodiment, determining the correlation between the current database and the first database based on the first data volume, the second data volume, and the third data volume includes: determining the numerical relationship between the second data volume and the third data volume when both the second data volume and the third data volume are greater than a quantity threshold; and determining the correlation between the current database and the first database based on the first data volume and the numerical relationship.

[0012] In an exemplary embodiment, determining the correlation between the current database and the first database based on the first data volume and the numerical size relationship includes: when the numerical size relationship indicates that the second data volume is less than the third data volume, determining the correlation as the ratio between the first data volume and the second data volume; and when the numerical size relationship indicates that the second data volume is greater than or equal to the third data volume, determining the correlation as the ratio between the first data volume and the third data volume.

[0013] In one exemplary embodiment, the method further includes: determining that the correlation between the current database and the first database is 0 when it is determined that the second data volume and / or the third data volume is less than a quantity threshold.

[0014] In one exemplary embodiment, after synchronizing the target prompt message to the second database, the method further includes: upon receiving a confirmation instruction, performing the target processing on the target data in the second database.

[0015] According to another embodiment of the present invention, a message prompting device is provided, comprising: a first determining module, configured to determine a second database in the system that has a target association relationship with the first database when it is determined that target processing needs to be performed on target data in a first database, wherein the system includes multiple databases, and the multiple databases include the first database and the second database, and the target association relationship is used to indicate that similar data between the databases with a similarity greater than a predetermined threshold has reached a target condition; and a synchronization module, configured to synchronize a target prompting message to the second database, wherein the target prompting message is used to prompt whether the target processing needs to be performed on the target data in the second database.

[0016] According to yet another embodiment of the present invention, a computer-readable storage medium is also provided, wherein a computer program is stored therein, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed.

[0017] According to yet another embodiment of the present invention, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0018] This invention, when it is determined that target processing needs to be performed on target data in a first database, identifies a second database within the system that has a target association relationship with the first database. This target association relationship indicates that similar data in the second database and the first database with a similarity greater than a predetermined threshold meets the target condition. A target prompt message is then synchronized to the second database, indicating whether target processing needs to be performed on the target data in the second database. Using this method, target processing can be synchronously performed on target data in at least two databases with a target association relationship, solving the problem of low efficiency in effectively synchronizing similar databases in related technologies. This improves the efficiency of effectively synchronizing similar databases and enhances resource utilization. Attached Figure Description

[0019] Figure 1 This is a hardware structure block diagram of a mobile terminal for a message notification method according to an embodiment of the present invention.

[0020] Figure 2 This is a flowchart of a message prompting method according to an embodiment of the present invention;

[0021] Figure 3 This is a flowchart illustrating the addition of features to the system deployment library according to a specific embodiment of the present invention;

[0022] Figure 4 This is a flowchart illustrating the deletion feature of the system deployment library according to a specific embodiment of the present invention;

[0023] Figure 5 This is a structural block diagram of a message notification device according to an embodiment of the present invention. Detailed Implementation

[0024] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings and examples.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0026] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1This is a hardware structure block diagram of a mobile terminal for a message notification method according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0027] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the message prompting method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0028] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0029] This embodiment provides a message prompting method. Figure 2 This is a flowchart of a message prompting method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0030] S202, if it is determined that target processing needs to be performed on target data in the first database, a second database in the system that has a target association relationship with the first database is determined, wherein the system includes multiple databases, and the multiple databases include the first database and the second database, and the target association relationship is used to indicate that similar data between databases with a similarity greater than a predetermined threshold have reached the target condition;

[0031] S204, synchronize the target prompt message to the second database, wherein the target prompt message is used to prompt whether the target processing needs to be performed on the target data in the second database.

[0032] The device performing the above operations can be a controller or control system, a device with control capabilities, a processor installed in a smart device, or other processing devices or processing units with similar processing capabilities.

[0033] In the above embodiments, there can be multiple target data, and target processing can be performed on these multiple target data in the first database. For example, when it is necessary to perform target processing on three target data (target data 1, target data 2, and target data 3) in the first database, the target processing on these three target data can be performed simultaneously in the first database. Alternatively, the order in which the target processing on the three target data is performed can be determined based on the priority of the three target data. For example, when the priority of the three target data is target data 1 > target data 2 > target data 3, the target processing on the target data with the higher priority can be performed first in the first database according to this priority, and so on. It should be noted that the above examples of target data are only exemplary implementations, and the target data are not limited to the above examples.

[0034] In the above embodiments, there may be multiple second databases that have a target association relationship with the first database. Therefore, multiple second databases with a target association relationship with the first database can be identified in the system, and the target prompt message can be synchronized to these multiple second databases. For example, when the system includes three second databases (second database 1, second database 2, and second database 3) with a target association relationship with the first database, the target prompt message can be synchronized to all three second databases simultaneously. Alternatively, the order in which the target prompt message is synchronized to the three second databases can be determined according to their priority. For example, when the priority of the three second databases is second database 1 > second database 2 > second database 3, the target prompt message can be synchronized to second database 1 first, then to second database 2, and finally to second database 3. Alternatively, the target prompt message can be synchronized to only at least one of the three second databases according to actual needs, and so on. It should be noted that the above examples of second databases are merely exemplary implementations, and the second databases are not limited to the above examples.

[0035] In the above embodiments, when it is determined that target processing needs to be performed on target data in the first database, a second database with a target association relationship with the first database is identified in the system. This target association relationship indicates that similar data in the second database and the first database with a similarity greater than a predetermined threshold (e.g., 95%, 99%, or other values) meet the target condition. A target prompt message is then synchronized to the second database, indicating whether target processing needs to be performed on the target data in the second database. Using the method of this invention, target processing can be performed synchronously on target data in at least two databases with a target association relationship, solving the problem of low efficiency in effectively synchronizing similar databases in related technologies. This achieves the effect of improving the efficiency of effectively synchronizing similar databases and increasing resource utilization.

[0036] In one exemplary embodiment, the method further includes: acquiring first data to be added to the first database; determining whether a third database exists in the plurality of databases, including second data whose similarity to the first data is greater than the predetermined threshold; and, if the third database exists in the plurality of databases, establishing a first association relationship between the first database and the third database, wherein the first association relationship is established by recording an association identifier and the location information of the second data in the third database in the first database, the association identifier being used to associate the first data with the second data. In this embodiment, if it is determined that a third database exists in multiple databases containing second data whose similarity to the first data is greater than a predetermined threshold, it is indicated that the first data and the second data are similar data. Then, the ID (Identity Document) of the first data can be associated with the ID of the second data through an association identifier. The association identifier and the position of the second data in the third database are recorded in the first database. This facilitates the simultaneous execution of corresponding processing on the associated data in multiple databases when it is necessary to perform corresponding processing on the associated data. This allows for rapid and effective processing of the associated data. Furthermore, the association identifier includes, but is not limited to, ID, serial number, and tag. It should be noted that the above-described association method between the first data and the second data, and the example of the association identifier, are only exemplary embodiments, and the association method between the first data and the second data, and the association identifier, are not limited to the above examples.

[0037] In the above embodiments, the predetermined threshold can be preset and can be set to 98%, 99%, 100%, etc. For example, when the predetermined threshold is 99%, it is determined whether there is a third database in multiple databases that includes second data with a similarity greater than 99% to the first data. It should be noted that the above example of the predetermined threshold is only an exemplary embodiment, and the predetermined threshold is not limited to the above example.

[0038] In one exemplary embodiment, the method further includes: determining the first data as the target data when it is determined that the third database does not exist in the plurality of databases; determining that an add operation needs to be performed on the target data in the first database, wherein the target processing includes the add operation. In this embodiment, target processing includes, but is not limited to, add, delete, modify, and label operations. Different target processing can be performed on different target data in the first database. For example, when it is necessary to perform different target processing on three target data (target data 4, target data 5, and target data 6) in the first database, and it is necessary to perform an add operation on target data 4, a delete operation on target data 5, and a modify operation on target data 6 in the first database, the add, delete, and modify operations can be performed on the three target data simultaneously in the first database. Alternatively, the order of execution of the corresponding target processing on the three target data can be determined based on the priority of the three different types of target processing. For example, when the priority of the three different types of target processing is add operation > delete operation > modify operation, the add operation on target data 4 can be performed first in the first database according to this priority, then the delete operation on target data 5, and finally the modify operation on target data 6, and so on. It should be noted that the above examples of target processing are only exemplary implementations, and target processing is not limited to the above examples.

[0039] In an exemplary embodiment, determining a second database included in the system that has a target association relationship with the first database includes: performing the following operations on each of the N databases included in the system other than the first database to determine the association degree between each database in the N databases and the first database, obtaining N association degrees. During the operation, each database in the N databases is the current database: determining a first data volume of data whose similarity to the first database and the current database is greater than a predetermined threshold, a second data volume of all data stored in the first database, and a third data volume of all data stored in the current database; determining the association degree between the current database and the first database based on the first data volume, the second data volume, and the third data volume, where N is a positive integer greater than or equal to 1; and determining the second database from the N databases based on the N association degrees. In this embodiment, the association degree between each database in the N databases and the first database can be the affinity or similarity concentration between the two. Therefore, based on this affinity or similarity concentration, a database with a high affinity or similarity concentration to the first database can be determined from the N databases, and this database is determined as the second database, i.e., a database similar to the first database. The aforementioned correlation can be defined as the ratio between the smaller of the first data volume and the second and third data volumes. For example, when the second data volume is less than the third data volume, the correlation is equal to the ratio between the first and second data volumes, while when the second data volume is greater than or equal to the third data volume, the correlation is equal to the ratio between the first and third data volumes. The higher the correlation, the higher the affinity between the two databases (such as the current database and the first database mentioned above), or it can be understood as the higher the similarity between the two databases.

[0040] In an exemplary embodiment, determining the second database from the N databases based on the N correlation degrees includes: if a first correlation degree exists among the N correlation degrees that is greater than a target preset threshold, then the database corresponding to the first correlation degree among the N databases is determined as the second database. In this embodiment, based on the N correlation degrees, it is determined whether there exists a first correlation degree with a correlation degree greater than the target preset threshold (e.g., 0.8, 0.75, or other values). If so, the database corresponding to the first correlation degree is determined as the second database. It should be noted that the first correlation degree may include one or more correlation degrees, or the first correlation degree may be 0. That is, one or more databases among the N databases may have a correlation degree with the first database that is greater than the target preset threshold. In other words, the second database may be one or more databases, or there may be no database among the N databases with a correlation degree greater than the target preset threshold with the first database.

[0041] In an exemplary embodiment, determining the correlation between the current database and the first database based on the first data volume, the second data volume, and the third data volume includes: determining the numerical relationship between the second data volume and the third data volume when both the second data volume and the third data volume are greater than a quantity threshold; and determining the correlation between the current database and the first database based on the first data volume and the numerical relationship. In this embodiment, the quantity threshold can be preset and can be set to 4, 5, 6, etc. For example, when the quantity threshold is 5, when determining the second data volume (e.g., total)... a ) and third data volume (such as total) b If all values ​​are greater than 5, determine the total. a and total b Regarding the numerical relationship, it should be noted that the above examples of quantity thresholds are only exemplary embodiments, and the quantity thresholds are not limited to the above examples.

[0042] In an exemplary embodiment, determining the correlation between the current database and the first database based on the first data volume and the numerical magnitude relationship includes: when the numerical magnitude relationship indicates that the second data volume is less than the third data volume, determining the correlation as the ratio between the first data volume and the second data volume; and when the numerical magnitude relationship indicates that the second data volume is greater than or equal to the third data volume, determining the correlation as the ratio between the first data volume and the third data volume. In this embodiment, a current database with a correlation (or affinity or similarity concentration) greater than 0.8 with the first database can be used as the second database. For example, when the second data volume (e.g., total) is greater than 0.8, the current database can be used as the second database. a The third data volume is 8, and the total data volume is 8. b The value is 10, and the first data volume (e.g., total) c When the value is 4, it indicates that the affinity or similarity concentration between the current database and the first database is 0.5, meaning that the correlation between the current database and the first database is not high, and the current database is not a similar database to the first database. a The total is 10. b It is 8, and total c A value of 4 indicates that the affinity or similarity concentration between the current database and the first database is 0.5, meaning that the correlation between the current database and the first database is not high, and the current database is not a similar database to the first database. When the total... a The total is 10. b The value is 12, and the total is... c A value of 9 indicates that the affinity or similarity concentration between the current database and the first database is 0.9, meaning the correlation between the current database and the first database is greater than 0.8, and the current database is a similar database to the first database. When the total... a The total is 12. b The value is 10, and the total is... c A value of 9 indicates that the affinity or similarity concentration between the current database and the first database is 0.9, the correlation between the current database and the first database is greater than 0.8, the current database is a similar database to the first database, and so on. It should also be noted that the above example of determining the correlation between the current database and the first database is only an exemplary embodiment, and the method of determining the correlation between the current database and the first database is not limited to the above example.

[0043] In one exemplary embodiment, the method further includes: determining that the correlation between the current database and the first database is 0 when it is determined that the second data volume and / or the third data volume is less than a quantity threshold. In this embodiment, when the quantity threshold is 5, the correlation between the current database and the first database is determined to be 0 when it is determined that the second data volume (e.g., total) is less than a quantity threshold. a ) and / or a third data volume (such as total) b If the correlation coefficient (affinity or similarity concentration) is less than 5, the correlation between the current database and the first database is determined to be 0. This means that the correlation (affinity or similarity concentration) between the current database and the first database determined by a smaller amount of second and / or third data is low. Therefore, determining the correlation between the current database and the first database using a smaller amount of second and / or third data is meaningless. To avoid meaningless operations leading to increased processing latency and wasted resources, the second data volume (total) is determined. a and / or third data volume total b If the number is less than the threshold, the correlation between the current database and the first database is determined to be 0.

[0044] In one exemplary embodiment, after synchronizing the target prompt message to the second database, the method further includes: upon receiving a confirmation instruction, performing the target processing on the target data in the second database. In this embodiment, the confirmation instruction can be triggered by an operator clicking a confirmation button with a mouse, or by an operator touching a confirmation key on a relevant display interface. Furthermore, the confirmation instruction can also be automatically triggered by a relevant system or smart device based on the target prompt message, etc. It should be noted that the above examples of target instruction triggering methods are only exemplary embodiments, and the triggering methods of target instructions are not limited to the above examples.

[0045] Obviously, the embodiments described above are only some embodiments of the present invention, and not all embodiments.

[0046] The present invention will now be described in detail with reference to specific embodiments (taking the database as the control database as an example):

[0047] Step 1, Method for Sharing Features Across Different Control Databases

[0048] When a member is added to the control database, the first feature (corresponding to the first data mentioned above) is extracted based on the issued photo. The first feature is compared with the features of all control databases in the system. When the similarity is greater than 99%, the second feature (corresponding to the second data mentioned above) is obtained. It is then assumed that the issued photo already exists in the system. The issued photo (corresponding to the first data mentioned above) and the second feature are associated through the ID (corresponding to the association identifier mentioned above). At this time, the control database member shares the feature (corresponding to the second data mentioned above) with other control database members in the system.

[0049] Step 2, Calculation formula for concentration of different control libraries

[0050] Following the method in step 1, the total number of features shared between different libraries is obtained. For example, if the system contains libraries A and B, with quantities of total features respectively... a (corresponding to the second data volume mentioned above), total b (Corresponding to the third data volume mentioned above), the total number of shared features between databases A and B is total. c (Corresponding to the first data volume mentioned above).

[0051] The formula for calculating the affinity between A and B is as follows:

[0052]

[0053] Step 3: Add facial feature processing to the system's deployment database.

[0054] Figure 3 This is a flowchart illustrating the addition of features to the system deployment library according to a specific embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps:

[0055] S302, Begin;

[0056] S304, extract features from facial images;

[0057] Feature extraction is performed on the facial image of the person to be monitored, input by the user, to obtain the first feature.

[0058] S306, compare the first feature with the existing features in each control database;

[0059] The first feature is compared with the features of all control databases in the system to obtain the similarity.

[0060] S308, perform a first judgment to determine whether there is a feature whose similarity to the first feature exceeds 99% (corresponding to the above-mentioned predetermined threshold);

[0061] S310, if the first judgment result is yes, the user specifies a control library (which may also be a control library specified by the system or smart device, corresponding to the first database) and a control library (corresponding to the third database) that includes features with a similarity of more than 99% to the first feature to share features, and records the association relationship (corresponding to the first association relationship).

[0062] The first feature is not added to all control databases in the system. Only the association relationship and related information (name, ID number, etc.) are recorded in the user-specified control database.

[0063] S312, End;

[0064] S314, If the first judgment result is negative, add the feature (corresponding to the target data) to the user-specified control library and execute step S312;

[0065] Add features and related information to each of the other control libraries in the system, excluding the user-specified control library. Calculate the similarity concentration between the user-specified control library and each of the other control libraries in the system, according to the different control library concentration calculation formula described in step 2. When the similarity concentration is greater than 80%, the two control libraries are considered similar libraries. Then, the added personnel information (corresponding to the above-mentioned association and related information) is synchronized to the reminder list of other control libraries similar to the user-specified control library. The user can quickly add the personnel with one click.

[0066] Step 4: Deletion of facial features from the system's control database

[0067] Figure 4 This is a flowchart illustrating the deletion feature of the system deployment library according to a specific embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps:

[0068] S402, Begin;

[0069] S404, a user-issued feature to delete a specific person;

[0070] S406, Perform the first judgment to determine whether there is a related party relationship with the person;

[0071] S408, if the first judgment result is yes, clear the association and information of the specific person, and retain the characteristics;

[0072] Clear the associations and related information of specific personnel, while retaining their characteristics. Deleting personnel information is synchronized to a notification list in the corresponding similar control database (generated when adding the feature) recorded during feature addition. Users can quickly delete the personnel with one click. (For example, if there are three similar control databases for a given personnel with existing associations, the associations and related information in any two of these databases can be retained, while the personnel characteristics, associations, and related information in the third database can be cleared. Alternatively, depending on actual needs, the associations and related information in two specific databases can be retained, while the personnel characteristics, associations, and related information in the third database can be cleared, etc.).

[0073] S410, End;

[0074] S412, if the first judgment result is negative, clear the personnel characteristics and related information, and execute step S410.

[0075] As can be seen from the foregoing embodiments, when it is necessary to add features, the shared features are obtained by comparing them with the historical features that have already been added, and the similarity concentration between databases can not only help users quickly add, delete and modify similar database personnel, but also reduce resource consumption, further record the relationships between similar databases, and facilitate subsequent user operations.

[0076] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0077] This embodiment also provides a message prompting device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0078] Figure 5This is a structural block diagram of a message notification device according to an embodiment of the present invention, such as... Figure 5 As shown, the device includes:

[0079] The first determining module 52 is used to determine, when it is determined that target processing needs to be performed on target data in the first database, a second database in the system that has a target association relationship with the first database. The system includes multiple databases, and the multiple databases include the first database and the second database. The target association relationship is used to indicate that similar data between databases with a similarity greater than a predetermined threshold have reached the target condition.

[0080] The synchronization module 54 is used to synchronize the target prompt message to the second database, wherein the target prompt message is used to prompt whether the target processing needs to be performed on the target data in the second database.

[0081] In one exemplary embodiment, the above-described apparatus further includes:

[0082] The acquisition module is used to acquire the first data to be added to the first database;

[0083] The second determining module is used to determine whether there exists a third database in the plurality of databases that includes second data with a similarity greater than the predetermined threshold to the first data;

[0084] A module is established to establish a first association relationship between the first database and the third database when it is determined that the third database exists in the plurality of databases. The first association relationship is established by recording an association identifier and the location information of the second data in the third database in the first database. The association identifier is used to associate the first data with the second data.

[0085] In one exemplary embodiment, the above-described apparatus further includes:

[0086] The third determining module is used to determine the first data as the target data when it is determined that the third database does not exist in the multiple databases.

[0087] The fourth determining module is used to determine that an add operation needs to be performed on the target data in the first database, wherein the target processing includes the add operation.

[0088] In one exemplary embodiment, the first determining module 52 includes:

[0089] An execution submodule is configured to perform the following operations on all N databases included in the system, excluding the first database, to determine the correlation between each database in the N databases and the first database, thereby obtaining N correlation degrees. When performing the following operations, each database in the N databases is considered the current database: determining a first data volume of data whose similarity to the first database and the current database is greater than a predetermined threshold, a second data volume of all data stored in the first database, and a third data volume of all data stored in the current database; and determining the correlation degree between the current database and the first database based on the first data volume, the second data volume, and the third data volume, where N is a positive integer greater than or equal to 1.

[0090] The first determining submodule is used to determine the second database from the N databases based on the N correlation degrees.

[0091] In one exemplary embodiment, the first determining submodule includes:

[0092] The first determining unit is configured to determine the database corresponding to the first correlation degree among the N databases as the second database when there is a first correlation degree among the N correlation degrees that is greater than a target preset threshold.

[0093] In one exemplary embodiment, the above-mentioned execution submodule includes:

[0094] The second determining unit is used to determine the numerical relationship between the second data quantity and the third data quantity when both the second data quantity and the third data quantity are greater than the quantity threshold.

[0095] The third determining unit is used to determine the correlation degree between the current database and the first database based on the first data volume and the numerical size relationship.

[0096] In one exemplary embodiment, the third determining unit includes:

[0097] The first determining subunit is configured to determine the correlation degree as the ratio between the first data quantity and the second data quantity when the numerical relationship indicates that the second data quantity is less than the third data quantity;

[0098] The second determining subunit is used to determine the correlation degree as the ratio between the first data quantity and the third data quantity when the numerical relationship indicates that the second data quantity is greater than or equal to the third data quantity.

[0099] In one exemplary embodiment, the above-described apparatus further includes:

[0100] The fifth determining module is used to determine that the correlation between the current database and the first database is 0 when the second data volume and / or the third data volume are less than the quantity threshold.

[0101] In one exemplary embodiment, the above-described apparatus further includes:

[0102] The execution module is used to perform the target processing on the target data in the second database after synchronizing the target prompt message to the second database and receiving a confirmation instruction.

[0103] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0104] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed.

[0105] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0106] Embodiments of the present invention also provide an electronic device including a memory and a processor, the memory storing a computer program and the processor being configured to run the computer program to perform the steps in any of the above method embodiments.

[0107] In one exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0108] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0109] The message prompting method provided by this invention identifies identical images and shared features based on database feature comparison and similarity, enabling feature sharing across multiple databases and optimizing resource management. A similarity database is determined based on the similarity concentration between different databases, and then a synchronization method for rapid feature processing is provided through this similarity database, facilitating user operation.

[0110] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0111] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for displaying a message, characterized in that, include: If it is determined that target processing needs to be performed on the target data in the first database. Determining a second database in the system that has a target association with the first database includes: performing the following operations on each of the N databases in the system other than the first database to determine the association degree between each of the N databases and the first database, resulting in N association degrees. When performing the following operations, each of the N databases is considered the current database: determining a first data volume of data with a similarity greater than a predetermined threshold between the first database and the current database, a second data volume of all data stored in the first database, and a third data volume of all data stored in the current database; determining the association degree between the current database and the first database based on the first data volume, the second data volume, and the third data volume, where N is a positive integer greater than or equal to 1; and determining the second database from the N databases based on the N association degrees. The system includes multiple databases, and these multiple databases include both the first database and the second database. The target association relationship is used to indicate that similar data between databases with a similarity greater than the predetermined threshold meets the target condition. The target prompt message is synchronized to the second database, wherein the target prompt message is used to prompt whether the target processing needs to be performed on the target data in the second database.

2. The method according to claim 1, characterized in that, The method further includes: Retrieve the first data to be added to the first database; Determine whether a third database exists among the multiple databases, containing second data whose similarity to the first data is greater than the predetermined threshold; If the existence of the third database is determined in multiple databases, a first association relationship is established between the first database and the third database. The first association relationship is established by recording an association identifier and the location information of the second data in the third database in the first database. The association identifier is used to associate the first data with the second data.

3. The method according to claim 2, characterized in that, The method further includes: If it is determined that the third database does not exist in any of the multiple databases, the first data is determined as the target data; It is determined that an add operation needs to be performed on the target data in the first database, wherein the target processing includes the add operation.

4. The method according to claim 1, characterized in that, Based on the N correlation degrees, the second database is determined from the N databases as including: If, among the N correlation degrees, there exists a first correlation degree greater than a target preset threshold, the database corresponding to the first correlation degree among the N databases is determined as the second database.

5. The method according to claim 1, characterized in that, Determining the correlation between the current database and the first database based on the first data volume, the second data volume, and the third data volume includes: If both the second data quantity and the third data quantity are greater than the quantity threshold, determine the numerical relationship between the second and third data quantities. The correlation between the current database and the first database is determined based on the first data volume and the numerical relationship.

6. The method according to claim 5, characterized in that, Determining the correlation between the current database and the first database based on the first data volume and the numerical relationship includes: When the numerical relationship indicates that the second data volume is less than the third data volume, the correlation degree is determined to be the ratio between the first data volume and the second data volume; When the numerical relationship indicates that the second data quantity is greater than or equal to the third data quantity, the correlation degree is determined to be the ratio between the first data quantity and the third data quantity.

7. The method according to claim 1, characterized in that, The method further includes: If the second data volume and / or the third data volume are determined to be less than the quantity threshold, the correlation between the current database and the first database is determined to be 0.

8. The method according to claim 1, characterized in that, After synchronizing the target notification message to the second database, the method further includes: Upon receiving a confirmation instruction, the target processing is performed on the target data in the second database.

9. A message notification device, characterized in that, include: A first determining module is configured to, when determining that target processing needs to be performed on target data in a first database, determine a second database in the system that has a target association relationship with the first database. This includes: performing the following operations on each of the N databases in the system (excluding the first database) to determine the association degree between each of the N databases and the first database, resulting in N association degrees. During the operation, each of the N databases is considered the current database: determining a first data volume of data with a similarity greater than a predetermined threshold between the first database and the current database, a second data volume of all data stored in the first database, and a third data volume of all data stored in the current database; determining the association degree between the current database and the first database based on the first data volume, the second data volume, and the third data volume, where N is a positive integer greater than or equal to 1; and determining the second database from the N databases based on the N association degrees. The system includes multiple databases, including both the first database and the second database. The target association relationship indicates that similar data between databases with a similarity greater than the predetermined threshold meets the target condition. A synchronization module is used to synchronize target prompt messages to the second database, wherein the target prompt messages are used to prompt whether the target processing needs to be performed on the target data in the second database.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of claims 1 to 8.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 8.

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