User equipment login bitmap data storage method, device, electronic device and medium

Abstract

The embodiments of the present disclosure disclose a method, apparatus, electronic device and medium for storing user device login bitmap data. A specific implementation of the method includes: obtaining a user device login information set; generating a data space correlation set; deduplicating the user device login information set and mapping it with the user identity to obtain a user identity information set; filtering and performing data structure transformation on the deduplicated user device login information set to obtain a user device login bitmap data set; matching the data statistics request instruction with the user device login bitmap data set to obtain a user device login matching bitmap data set; performing aggregate query processing on the user device login matching bitmap data set to obtain a login aggregate query information set; encrypting the login aggregate query information set to obtain an encrypted login aggregate query information set. This implementation can reduce the waste of storage resources, improve query efficiency, and improve user experience.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of computer technology, and in particular to a method, device, electronic device, and medium for storing user equipment login bitmap data. Background Art

[0002] With the development of the Internet, the amount of login information of different users on different devices is increasing. The correlation analysis of login data of multiple users and multiple devices and the problem of cross-device access are attracting more and more attention. The statistical analysis and storage of login data of multiple users and multiple devices are often adopted as follows: the user-device correlation mapping is performed on the login data of multiple users and multiple devices to obtain the correlation mapping data table. The correlation mapping data table is statistically queried using structured query language to obtain the statistical query result set, and the statistical query result set is stored.

[0003] However, it is found in practice that when the above-mentioned method is used to store and statistically analyze the login data of multiple users and multiple devices, the following technical problems often occur: user-device association mapping of login data of multiple users and multiple devices results in large data volume, low user-device association efficiency and long association time. Statistical queries using structured query statements require a large amount of computing resources, resulting in a waste of computing resources and storage resources, extending query time and reducing user experience.

[0004] In the process of adopting technical solutions to solve the above-mentioned technical problem 1, the following technical problem 2 is often accompanied: there is a large amount of potential noise data and redundant data in the user device login information set, and a large amount of storage resources will be wasted when storing the user device login information set. For the above-mentioned technical problem 2, the conventional solution is generally: determine the similarity value of the user device login information in the user device login information set to obtain a similarity value set. Then, at least one similarity value greater than or equal to the preset similarity threshold is screened out from the above-mentioned similarity value set as the target similarity value set. Finally, the user device login information corresponding to the target similarity value set is removed from the above-mentioned user device login information set to obtain the user device login information set after removal, and the data storage format is converted and stored. However, the above-mentioned conventional solution still has the following problems: only by similarity to determine whether there is redundant data in the user device login information, the influencing factors considered are too single, and the potential redundant information cannot be identified, resulting in low deduplication effect, poor quality of user device login information and waste of storage resources.

[0005] In the process of adopting technical solutions to solve the above-mentioned technical problem one, the following technical problem three is often accompanied: Since there is user privacy data in the login aggregate query information set, how to ensure the security of the login aggregate query information set during the transmission process, prevent the leakage of the login aggregate query information set, and reduce the economic losses of individuals or enterprises. In response to the above-mentioned technical problem three, the conventional solution is generally: fully encrypt the login aggregate query information set to obtain the encrypted query data. Then, the encrypted query data is sent to the user terminal. However, the above conventional solution still has the following problems: Since the login aggregate query information set is fully encrypted, the importance of different types of data is not reflected. When the encryption key is cracked, the login aggregate query information set will become plaintext, resulting in privacy data leakage, and the amount of encrypted data encrypted by full encryption is large, requiring a large amount of computing resources and transmission resources, resulting in low data security, increased encryption memory overhead and waste of transmission resources.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0007] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0008] Some embodiments of the present disclosure propose a method, apparatus, electronic device, and medium for storing user device login bitmap data to solve one or more of the technical problems mentioned in the above background technology section.

[0009] In a first aspect, some embodiments of the present disclosure provide a method for storing user device login bitmap data, including: obtaining a user device login information set of a user information set of each login device in a login device set to obtain a user device login information group set; generating a data space correlation set between the user device login information included in the above user device login information group set; performing deduplication processing on the above user device login information set according to the above data space correlation set to obtain a deduplicated user device login information set; performing user identity mapping on the above deduplicated user device login information set to obtain a user identity mapping information set; performing screening processing on the above deduplication user device login information set according to the above user identity mapping information set to obtain a screened user device login information set. The method comprises the steps of: performing data structure transformation processing on the filtered user device login information set to obtain a user device login bitmap data set; in response to detecting a data statistics request instruction, matching the data statistics request instruction with the user device login bitmap data set to obtain a user device login matching bitmap data set; performing aggregate query processing on the user device login matching bitmap data set to obtain a login aggregate query information set; encrypting the login aggregate query information set to obtain an encrypted login aggregate query information set, storing the encrypted login aggregate query information set and the user device login bitmap data set, and sending the encrypted login aggregate query information set to the terminal of the data statistics instruction and displaying it.

[0010] In a second aspect, some embodiments of the present disclosure provide a user device login bitmap data storage device, including: an acquisition unit, configured to acquire a user device login information set of a user information set of each login device in a login device set, and obtain a user device login information group set; a generation unit, configured to generate a data space correlation set between the user device login information included in the above user device login information group set; a deduplication unit, configured to perform deduplication processing on the above user device login information set according to the above data space correlation set, and obtain a deduplicated user device login information set; a user identity mapping unit, configured to perform user identity mapping on the above deduplicated user device login information set, and obtain a user identity mapping information set; a screening unit, configured to perform screening processing on the above deduplication user device login information set according to the above user identity mapping information set, and obtain a screened user A device login information set; a data structure transformation unit, configured to perform data structure transformation processing on the above-screened user device login information set to obtain a user device login bitmap data set; a matching unit, configured to match the above-mentioned data statistics request instruction and the above-mentioned user device login bitmap data set in response to detecting a data statistics request instruction, to obtain a user device login matching bitmap data set; an aggregate query unit, configured to perform aggregate query processing on the above-mentioned user device login matching bitmap data set to obtain a login aggregate query information set; a storage unit, configured to encrypt the above-mentioned login aggregate query information set to obtain an encrypted login aggregate query information set, store the above-mentioned encrypted login aggregate query information set and the above-mentioned user device login bitmap data set, and send the above-mentioned encrypted login aggregate query information set to the terminal of the above-mentioned data statistics instruction and display it.

[0011] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner in the first aspect.

[0012] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method described in any implementation manner in the first aspect is implemented.

[0013] The above-mentioned embodiments of the present disclosure have the following beneficial effects: the user device login bitmap data storage method of some embodiments of the present disclosure can reduce the waste of storage space and improve the query efficiency of data joint aggregation query, reduce data query time and improve user query experience. Specifically, the waste of related computing resources and storage resources, the extension of query time, and the reduction of user experience are caused by: the user device association mapping of the login data of multiple users and multiple devices has the problems of large data volume, low user device association efficiency and long association time. The statistical query through the structured query statement requires a large amount of computing resources, resulting in a waste of computing resources and storage resources, extending the query time and reducing the user experience. Based on this, the user device login bitmap data storage method of some embodiments of the present disclosure can first obtain the user device login information set of the user information set of each login device in the login device set to obtain the user device login information group set. Here, the user device login information group set is convenient for the subsequent generation of data space correlation. Secondly, the data space correlation set between the user device login information included in the above user device login information group set is generated. Here, the data space correlation can characterize the association relationship of the storage space storing the user device login information, which is convenient for subsequent deduplication. Again, according to the data space correlation set, the user device login information set is deduplicated to obtain a deduplicated user device login information set. Here, redundant error data in the user device login information set can be removed, the amount of data can be reduced, and the data quality can be improved. Next, the user identity mapping is performed on the deduplicated user device login information set to obtain a user identity mapping information set. Here, the data barriers of the user device login information of multiple devices obtained can be eliminated to a certain extent, data fusion can be promoted, and the association between users and devices can be improved. Subsequently, according to the user identity mapping information set, the deduplicated user device login information set is screened to obtain a screened user device login information set. Here, the amount of data in the user device login information set can be reduced and the data quality can be improved. Afterwards, the data structure transformation is performed on the screened user device login information set to obtain a user device login bitmap data set. Here, the screened user device login information set is converted into bitmap type data, which can compress the data and save a lot of storage resources. Then, in response to detecting the data statistics request instruction, the data statistics request instruction and the user device login bitmap data set are matched to obtain the user device login matching bitmap data set. Here, the filtered user device login bitmap set related to the data statistics request instruction can be determined to narrow the query scope and reduce the waste of computing resources. Then, the user device login matching bitmap data set is aggregated and queried to obtain a login aggregate query information set. Here, the query efficiency can be improved, the query time can be shortened, and the user experience can be improved.Finally, the login aggregate query information set is encrypted to obtain the encrypted login aggregate query information set, and the encrypted login aggregate query information set and the user device login bitmap data set are stored, and the encrypted login aggregate query information set is sent to the terminal of the data statistics instruction and displayed. It can be obtained that the user device login bitmap data storage method can compress the user device login information set by converting the user device login information set into a user device login information set in bitmap format, thereby reducing the waste of storage resources, and can improve query efficiency, reduce query time, and improve user experience by performing aggregate query on the user device login information set in bitmap format. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0015] Figure 1 is a flowchart of some embodiments of the method for storing user equipment login bitmap data according to the present disclosure;

[0016] Figure 2 is a schematic diagram of the structure of some embodiments of the user equipment login bitmap data storage device according to the present disclosure;

[0017] Figure 3 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0018] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0019] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0020] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0021] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0022] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0023] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0024] Figure 1 The process 100 of some embodiments of the method for storing bitmap data for user equipment login according to the present disclosure is shown. The method for storing bitmap data for user equipment login includes the following steps:

[0025] Step 101, obtaining a user device login information set of a user information set of each login device in a login device set, and obtaining a user device login information group set.

[0026] In some embodiments, the execution subject (e.g., electronic device) of the above-mentioned user device login bitmap data storage method can obtain the user device login information set of the user information set of each login device in the login device set through a wired connection method or a wireless connection method to obtain the user device login information group set. Among them, the above-mentioned login device can be a hardware device for logging in user information. For example, the above-mentioned login device can be but not limited to one of the following: a mobile phone, a tablet, a computer. The user information in the above-mentioned user information set can be information that can identify the user's identity information. For example, the above-mentioned user information can include but is not limited to at least one of the following: user account information, user password, verification information. The user device login information in the above-mentioned user device login information group set can be information for recording the login of user information on the above-mentioned login device. For example, the above-mentioned user device login information can include but is not limited to at least one of the following: user information, login time, login device identification information, login status, login duration. The above-mentioned login device identification information can be information that uniquely identifies the login device. For example, the above-mentioned login device identification information can be a login device number.

[0027] Step 102: Generate a data space correlation set between the user equipment login information included in the user equipment login information group set.

[0028] In some embodiments, the execution subject may generate a data space correlation set between the user device login information included in the user device login information set, wherein the data space correlation in the data space correlation set may characterize the distribution characteristics and correlation relationship of the spatial data between the stored user device login information.

[0029] In some optional implementations of some embodiments, generating a data space correlation set between the user equipment login information included in the user equipment login information group set may include the following steps:

[0030] The first step is to preprocess the user equipment login information set to obtain a preprocessed user equipment login information set. The preprocessing may include at least one of the following: missing value filling, data standardization, and data cleaning.

[0031] The second step is to determine the login data spatial correlation set of the above-mentioned pre-processed user device login information group set. Among them, the login data spatial correlation in the above-mentioned login data spatial correlation group set can characterize the spatial correlation, spatiotemporal correlation and dependency between the above-mentioned pre-processed user device login information. In practice, the above-mentioned execution subject can determine the Euclidean distance value of any two pre-processed user device login information in the above-mentioned pre-processed user device login information group set, and obtain the Euclidean distance value set as the login data spatial correlation set.

[0032] The third step is to construct a spatial model of the server storing the pre-processed user device login information set based on the login data spatial correlation set. The spatial model can be a model that can dynamically adjust storage space resources by characterizing the data fluctuation and data deduplication uncertainty of the pre-processed user device login information set.

[0033] As an example, the execution subject may first determine the standard deviation of the preprocessed user device login information set as the data fluctuation coefficient of the preprocessed user device login information set. Then, determine the mean of the login data spatial correlation set as the login data spatial correlation mean. Finally, determine the product of the exponential function of the data fluctuation coefficient of the preset threshold and the login data spatial correlation mean as the spatial model. The preset threshold may be a preset value.

[0034] The fourth step is to map the pre-processed user equipment login information set to the spatial model to obtain a model user equipment login information set.

[0035] The fifth step is to generate a data space correlation set between the model user equipment login information included in the above model user equipment login information set. In practice, the above execution subject can use a radial basis function to generate a data space correlation set between the model user equipment login information included in the above model user equipment login information set.

[0036] Step 103: De-duplicate the user equipment login information set according to the data space correlation set to obtain a de-duplicated user equipment login information set.

[0037] In some embodiments, the execution subject may perform deduplication processing on the user device login information set according to the data space correlation set to obtain a deduplicated user device login information set. The deduplicated user device login information set may be a data set obtained by removing redundant information from the user device login information set.

[0038] As an example, the execution subject may first input the data space correlation set and the user device login information set into a pre-trained redundant data prediction model to obtain a user device login redundant data set. The redundant data prediction model may be a model for predicting redundant information based on the input data space correlation and the time series information of the user device login information. The redundant data prediction model may be an autoregressive model. Then, the user device login redundant data set is removed from the user device login information set to obtain a deduplicated user device login information set.

[0039] In some optional implementations of some embodiments, the deduplication processing of the user equipment login information set according to the data space correlation set to obtain the deduplication user equipment login information set may include the following steps:

[0040] The first step is to determine at least one pre-processed user device login information greater than the upper limit of the preset model and at least one pre-processed user device login information less than the lower limit of the preset model in the pre-processed user device login information set as the upper limit login information set and the lower limit login information set. The upper limit of the preset model can be the maximum amount of pre-processed user device login information that can be stored in the preset spatial model. The lower limit of the preset model can be the minimum amount of pre-processed user device login information that can be stored in the preset spatial model.

[0041] In the second step, based on the data space correlation set, weighted denoising is performed on the upper limit login information set and the lower limit login information set to obtain a denoised upper limit login information set and a denoised lower limit login information set.

[0042] As an example, the execution subject may first select at least one data space correlation related to the upper limit login information set from the data space correlation set to obtain the upper limit data space correlation set. Secondly, using the KNN (K-Nearest Neighbors) algorithm, the upper limit login information set is subjected to weighted denoising according to the upper limit data space correlation set to obtain the denoised upper limit login information set. Thirdly, at least one data space correlation related to the lower limit login information set is selected from the data space correlation set to obtain the lower limit data space correlation set. Then, the ratio of each lower limit data space correlation in the lower limit data space correlation set to the sum of the lower limit data space correlation set is determined as the spatial weight value to obtain the spatial weight value set. Finally, using the weighted moving average filtering algorithm, the lower limit login information set is subjected to weighted average denoising according to the spatial weight value set to obtain the denoised lower limit login information set.

[0043] In the third step, at least one pre-processed user device login information, the denoised upper limit login information set and the denoised lower limit login information set located between the above upper limit login information set and the above lower limit login information set are determined as the target login information set.

[0044] The fourth step is to determine the data autocorrelation statistical distribution sequence of the above target login information set. Among them, the data autocorrelation statistical distribution sequence in the above data autocorrelation statistical distribution sequence can characterize the degree of correlation between the target login information set and itself at different time points. In practice, the above execution entity can use ACF (Autocorrelation Function) to determine the data autocorrelation statistical distribution sequence of the above target login information set.

[0045] The fifth step is to process the target login information set in blocks according to the above-mentioned data autocorrelation statistical distribution sequence, and obtain the target login information set after block processing as the target block login information set. In practice, the above-mentioned execution subject can first determine the partial autocorrelation value set of the above-mentioned target login information set through the partial autocorrelation function (PACF). Then, using the autoregressive moving average model, the autocorrelation order set of the above-mentioned target login information set is determined according to the above-mentioned data autocorrelation statistical distribution sequence and the above-mentioned partial autocorrelation value set. Among them, the autocorrelation order in the above-mentioned autocorrelation order set can characterize the dependency range of the target login information, that is, the correlation between the target login information and the target login information located before and after the target login information. Finally, the above-mentioned target login information set is processed in blocks through the above-mentioned autocorrelation order set to obtain the target login information set after block processing as the target block login information set.

[0046] The sixth step is to determine a multidimensional scaling feature vector set of the target block login information set. The multidimensional scaling feature vector in the multidimensional scaling feature vector set can represent feature information of multiple dimensions of the target block login information. In practice, the execution subject can use a multiple feature decomposition algorithm to perform linear superposition processing on the target block login information set to obtain a multidimensional scaling feature vector set.

[0047] In the seventh step, the target login information set is matched with multidimensional scaling according to the multidimensional scaling feature vector set to obtain a login association feature vector set, wherein the login association feature vector in the login association feature vector set can represent the association relationship between the target login information set and the multidimensional scaling feature vector set.

[0048] As an example, the execution subject may use a matching algorithm to perform multi-dimensional scaling matching on the target login information set according to the multi-dimensional scaling feature vector set to obtain a login-related feature vector set. The matching algorithm may be a similarity-based matching algorithm.

[0049] In the eighth step, clustering is performed on the target login information set according to the login-related feature vector set to obtain a clustered user device login information class set.

[0050] As an example, the execution subject may utilize a hierarchical clustering algorithm to perform clustering processing on the target login information set according to the login-related feature vector set to obtain a clustered user device login information class set.

[0051] In the ninth step, for each cluster of the user device login information in the above clustered user device login information cluster set, perform the following decomposition and reconstruction steps:

[0052] Sub-step 1: time series decomposition of the target login information group included in the clustered user device login information cluster to obtain the decomposed target login information group. In practice, the execution subject may use a fast wavelet decomposition algorithm to time series decompose the target login information group included in the clustered user device login information cluster to obtain the login decomposition information group.

[0053] Sub-step 2, performing data reconstruction processing on the above-mentioned login decomposition information group to obtain a reconstructed login decomposition information group.

[0054] Sub-step 3, using the autoregressive redundant data prediction model, perform step-by-step prediction on the reconstructed login decomposition information set to obtain a predicted deduplicated login information set. The autoregressive redundant data prediction model can be an autoregressive model that performs redundant data prediction on the input target login information set.

[0055] The tenth step is to remove the obtained predicted deduplicated login information group set from the above preprocessed user device login information set to obtain a deduplicated user device login information set, and store the above deduplicated user device login information set.

[0056] The above-mentioned first to tenth steps and their related contents serve as an inventive point of an embodiment of the present disclosure, which solves the second technical problem mentioned in the background technology: "Only by similarity is it determined whether there is redundant data in the user device login information, the influencing factors considered are too single, and the potential redundant information cannot be identified, resulting in low deduplication effect, poor quality of user device login information and waste of storage resources." The factors that lead to low deduplication effect, poor quality of user device login information and waste of storage resources are often as follows: Only by similarity is it determined whether there is redundant data in the user device login information, the influencing factors considered are too single, and the potential redundant information cannot be identified, resulting in low deduplication effect, poor quality of user device login information and waste of storage resources. If the above factors are solved, the effect of improving the deduplication effect, improving the quality of user device login information and reducing the waste of storage resources can be achieved. In order to achieve this effect, the present disclosure firstly performs denoising on the upper limit login information set and the lower limit login information set respectively according to the above data space correlation set. The data space correlation characterizes the spatial distribution characteristics and mutual relations of the user device login information after preprocessing. By performing weighted denoising on the neighboring data, the denoising efficiency and accuracy can be improved, as well as the quality of the user device login information after denoising. Secondly, the target login information is divided into blocks and multi-dimensional feature vectors are extracted. Blocking can improve the accuracy of deduplication processing. The extraction of multi-dimensional feature vectors can extract the potential feature information in the target login information set, so as to have a more comprehensive understanding of the target login information. Subsequently, the target login information set is matched and clustered through the multi-dimensional scale feature vector set. The matching process can extract the intrinsic correlation between the target login information, which is convenient for identifying the potential redundant data between the target login information. The clustering process determines the target login information with high similarity as a cluster, which is convenient for subsequent deduplication processing in a cluster and improves the deduplication granularity. Then, the target login information set included in each cluster is decomposed and reconstructed, and the target login information set is converted into a feature representation that is easier to analyze and process, which is convenient for extracting key features of the data, and redundant data prediction is performed on the reconstructed target login information set through an autoregressive redundant data prediction model, which can improve the accuracy of redundant data prediction. Finally, the predicted deduplication login information group set is removed from the preprocessed user device login information set to obtain the deduplication user device login information set, and the deduplication user device login information set is stored, which can improve the deduplication efficiency and the data quality of the deduplication user device login information set and reduce the waste of storage resources.

[0057] Step 104: Perform user identity mapping on the deduplicated user device login information set to obtain a user identity information set.

[0058] In some embodiments, the execution subject may perform user identity mapping on the deduplicated user device login information set to obtain a user identity information set. The user identity information in the user identity information set may represent unique identification information of the same user on the same login device. In practice, the execution subject may perform user identity mapping on the deduplicated user device login information set using an ID-Mapping algorithm to obtain a user identity information set.

[0059] In some optional implementations of some embodiments, performing user identity mapping on the deduplicated user device login information set to obtain the user identity mapping information set may include the following steps:

[0060] The first step is to extract the user device association information set from the above-mentioned user device login information set after deduplication. The user device association information in the above-mentioned user device association information set can represent the association relationship between the user and the login device. The above-mentioned association relationship can be but not limited to one of the following: login, non-login. In practice, the above-mentioned execution subject can use a relationship extraction model to extract the user device association information set from the above-mentioned user device login information set after deduplication.

[0061] The second step is to generate a user device relationship map based on the user device association information set and the deduplicated user device login information set. The user device relationship map can be a relationship network for displaying the association between user information and login devices, as well as the attribute information of users and login devices. The attribute information of the user can include but is not limited to at least one of the following: user identification information, user login password, user phone number. The attribute information of the login device can include but is not limited to at least one of the following: login device identification information, login device location information, login device operation status information.

[0062] As an example, the execution subject may construct triples of the user device association information set and the deduplicated user device login information set to obtain a user device triple set. Then, the user device triple set is input into a graph database to obtain a user device relationship graph.

[0063] The third step is to perform community discovery processing on the user device relationship graph to obtain a target relationship graph set. The target relationship graph in the target relationship graph set may be a subgraph of the user device relationship graph obtained by clustering the node set and edge set in the user device relationship graph. In practice, the execution subject may use the Louvain algorithm to perform community discovery processing on the user device relationship graph to obtain a target relationship graph set.

[0064] The fourth step is to perform feature extraction processing on each target relationship graph in the target relationship graph set to obtain a node feature vector set. The node feature vector in the node feature vector set can represent the node's own attributes and the feature information of the relationship between the node and the neighboring nodes. In practice, the execution subject can use the graph neural network GNN to perform feature extraction processing on each target relationship graph in the target relationship graph set to obtain a node feature vector set.

[0065] The fifth step is to determine the feature similarity value of the node feature vector set to obtain a feature similarity value set. The feature similarity in the feature similarity value set can represent the degree of similarity between the node feature vectors. The feature similarity value can be the cosine similarity between any two node feature vectors in the node feature vector set.

[0066] The sixth step is to perform user identity mapping on the above-mentioned deduplicated user device login information set according to the above-mentioned feature similarity value set to obtain a user identity mapping information set. In practice, the above-mentioned execution subject can perform the following steps for each user device login information in the above-mentioned user device login information set: The first step is to filter out the various feature similarity values ​​corresponding to the above-mentioned deduplicated user device login information from the above-mentioned feature similarity value set to generate a user feature similarity value group. The second step is to filter out at least one user feature similarity value greater than or equal to a preset similarity threshold from the above-mentioned user feature similarity value group as the target feature similarity value group. The third step is to determine the user information group corresponding to the above-mentioned target feature similarity value group as the same user identity information as the user identity mapping information.

[0067] Step 105 , filtering the deduplicated user device login information set according to the user identity mapping information set to obtain a filtered user device login information set.

[0068] In some embodiments, the execution subject may filter the deduplicated user device login information set according to the user identity mapping information set to obtain a filtered user device login information set.

[0069] As an example, the execution subject may first modify the user information groups included in the user login information representing the same user identity into user identity mapping information through the user identity mapping information set to obtain a modified user device login information set. Then, the modified user device login information set is deduplicated to obtain deduplicated login information as the filtered user device login information set.

[0070] Step 106: Perform data structure transformation processing on the filtered user equipment login information set to obtain a user equipment login bitmap data set.

[0071] In some embodiments, the execution subject may perform data structure transformation processing on the filtered user device login information set to obtain a user device login bitmap data set. The user device login bitmap data in the user device login bitmap data set may be data storing the filtered user device login information in a bitmap storage format. The bitmap may be a data structure that uses one bit to mark the data storage status.

[0072] In some optional implementations of some embodiments, performing data structure transformation processing on the filtered user equipment login information set to obtain a user equipment login bitmap data set may include the following steps:

[0073] The first step is to determine the login key field information set that needs to undergo data structure transformation in the above-screened user device login information set. The login key field information in the above-screened user device login information set can be the field name in the above-screened user device login information set. For example, the above-screened login key field information can be the login device name.

[0074] In the second step, for each login key field information in the above login key field information set, perform the following storage steps:

[0075] Sub-step 1, in response to determining that the above-mentioned login key field information is a non-numeric login key field information, dictionary encoding is performed on each login attribute value in the login attribute value set corresponding to the above-mentioned login key field information to generate an encoded login attribute value, and obtain an encoded login attribute value set. Among them, the above-mentioned encoded login attribute value can be an attribute value that represents the login key field information in the form of a numerical value. The login attribute value in the above-mentioned login attribute value set can be the specific data corresponding to the above-mentioned login key field. For example, the login attribute value corresponding to the above-mentioned login device can be a mobile phone model.

[0076] Sub-step 2: determining the number of the encoded login attribute values ​​included in the encoded login attribute value set as the login attribute number.

[0077] Sub-step 3, for each encoded login attribute value in the encoded login attribute value set, perform the following determination steps:

[0078] The first sub-step is to determine the preset format attribute value of the encoded login attribute value. The preset format attribute value may be a hexadecimal representation of the encoded login attribute value. For example, the encoded login attribute value is 191037. Then the preset format attribute value is 0002EA3D.

[0079] In the second sub-step, the preset format attribute value is divided to obtain a first divided attribute value and a second divided attribute value. The first divided attribute value may be an attribute value corresponding to the first half of the preset format attribute value. The second divided attribute value may be an attribute value corresponding to the second half of the preset format attribute value. For example, the first divided attribute value of the preset format attribute value of 0002EA3D is 0002. The second divided attribute value of the preset format attribute value of 0002EA3D is EA3D.

[0080] The third sub-step is to determine the bucket number of the storage bucket of the first partitioned attribute value. The storage bucket may be a storage space for storing the encoded login attribute value. For example, the bucket number is 0002.

[0081] In a fourth sub-step, in response to determining that the number of the logged attributes is less than a preset number threshold, the storage container corresponding to the bucket number is determined to be an array type storage container. The preset number threshold may be a pre-set threshold for determining the type of storage container. For example, the preset number threshold may be 4096.

[0082] The fifth sub-step is to use the second divided attribute value to determine the first storage position of the encoded logged attribute value in the array type storage container as the attribute storage position.

[0083] Sub-step 4: storing the login attribute value set corresponding to the above login key field information into the obtained attribute storage location set to obtain the user device login bitmap data.

[0084] Optionally, after dividing the second attribute value and determining the storage position of the encoded logged attribute value in the array type storage container, the method may further include the following steps:

[0085] In the first step, in response to determining that the number of the above-mentioned login attributes is greater than or equal to a preset number threshold, the storage container corresponding to the above-mentioned bucket number is determined to be a bitmap storage container. The above-mentioned bitmap storage container can be a storage data structure that marks the data storage state by a binary bit. If the binary bit is marked as 1, it means that data is stored in this storage location. If the binary bit is marked as 0, it means that there is no data in this storage location.

[0086] In the second step, the second partition attribute value is used to determine the second storage position of the encoded login attribute value in the bitmap storage container as the attribute storage position. In practice, the encoded login attribute value is 821697800. The preset format attribute value is 30FA1D08. The first partition attribute value is 30FA, and the second partition attribute value is 1D08. The storage space corresponding to the position 1D08 is determined in the bitmap storage container, and the storage space corresponding to 1D08 is set to 1. 1 indicates that this storage space stores data.

[0087] In the third step, in response to determining that the login attribute value is greater than or equal to a preset number threshold, and the login attribute value set has continuous attribute values, the storage container corresponding to the bucket number is determined as a trip step type storage container. The trip step type storage container may be a storage container that stores data with continuity through step compression.

[0088] The fourth step is to determine at least one login attribute value group having continuous attribute values ​​as a continuous login attribute value group set.

[0089] The fifth step is to determine the second division attribute value of the continuous login attribute value located at the initial position in each continuous login attribute value group in the above-mentioned continuous login attribute value group set and the number of continuous login attribute values ​​included in the above-mentioned continuous login attribute value group as the continuous division attribute value and the continuous quantity, and obtain the continuous division attribute value set and the continuous quantity set.

[0090] Step 6: For each continuous division attribute value in the continuous division attribute value set, determine the continuous storage position of the continuous login attribute value group corresponding to the continuous division attribute value as the attribute storage position, and store the difference between the continuous number and the preset value in the continuous storage position. The preset value may be a preset threshold. For example, the preset value may be 1.

[0091] Step 107 , in response to detecting the data statistics request instruction, matching the data statistics request instruction with the user equipment login bitmap data set to obtain the user equipment login matching bitmap data set.

[0092] In some embodiments, the execution subject may, in response to detecting a data statistics request instruction, match the data statistics request instruction with the user device login bitmap data set to obtain a user device login matching bitmap data set. The data statistics request instruction may be a request instruction message sent by a user to perform statistical calculations on the user device login bitmap data set. The user device login matching bitmap data in the user device login matching bitmap data set may be the user device login bitmap data associated with the data statistics request instruction.

[0093] Step 108: performing aggregate query processing on the user device login matching bitmap data set to obtain a login aggregate query information set.

[0094] In some embodiments, the execution subject may perform aggregate query processing on the user device login matching bitmap data set to obtain a login aggregate query information set. The login aggregate query information in the login aggregate query information set may be information obtained after performing relevant statistical operations on the user device login matching bitmap data set and used to return to the user end. The aggregate query processing may be a bit operation. For example, the bit operation may be an intersection or union operation.

[0095] In some optional implementations of some embodiments, performing aggregate query processing on the user device login matching bitmap data set to obtain a login aggregate query information set may include the following steps:

[0096] The first step is to determine the type of storage container of each user device login matching bitmap data in the user device login matching bitmap data set to generate a bitmap container type and obtain a bitmap container type set.

[0097] In the second step, in response to determining that each bitmap container type in the bitmap container type set is an array type, and the sum of the cardinalities of the above-mentioned user device login matching bitmap data sets is greater than a preset quantity threshold, a merge, sort and union operation is performed on the above-mentioned user device login matching bitmap data sets to obtain an array union query result as a login aggregation query information set.

[0098] In practice, in response to determining that each bitmap container type in the bitmap container type set is an array type, and the sum of the cardinalities of the above-mentioned user device login matching bitmap data sets is greater than a preset quantity threshold, the above-mentioned execution entity can use a merge sort algorithm to perform a merge sort and union operation on the above-mentioned user device login matching bitmap data sets to obtain an array union query result as a login aggregation query information set.

[0099] In the third step, in response to determining that each bitmap container type in the bitmap container type set is an array type, and the sum of the cardinalities of the above-mentioned user device login matching bitmap data sets is less than or equal to a preset quantity threshold, each user device login matching bitmap data in the above-mentioned user device login matching bitmap data set is mapped to user device login matching bitmap data of a bitmap type to obtain a target user device login matching bitmap data set.

[0100] The fourth step is to perform a bitmap union operation on the target user device login matching bitmap data set to obtain a login aggregation query information set.

[0101] Step 5: Perform path merging operation on the user device login matching bitmap data set to obtain a login aggregation query information set. The path merging operation may be performed using a Galloping algorithm.

[0102] In a sixth step, in response to determining that each bitmap type in the bitmap container type set is a bitmap container type, a Boolean intersection and union operation is performed on the user device login matching bitmap data set to obtain a login aggregate query information set.

[0103] In the seventh step, in response to determining that the bitmap container type set contains array type and bitmap type, each user device login matching bitmap data in the user device login matching bitmap data set is traversed and de-duplicated intersection and union operations are performed to obtain a login aggregation query information set.

[0104] Step 109, encrypt the login aggregate query information set to obtain the encrypted login aggregate query information set, store the encrypted login aggregate query information set and the user device login bitmap data set, and send the encrypted login aggregate query information set to the terminal of the data statistics instruction and display it.

[0105] In some embodiments, the above-mentioned execution entity may encrypt the above-mentioned login aggregate query information set to obtain an encrypted login aggregate query information set, store the above-mentioned encrypted login aggregate query information set and the above-mentioned user device login bitmap data set, and send the above-mentioned encrypted login aggregate query information set to the terminal of the above-mentioned data statistics instruction and display it.

[0106] In some optional implementations of some embodiments, encrypting the login aggregate query information set to obtain the encrypted login aggregate query information set may include the following steps:

[0107] In the first step, the above-mentioned login aggregate query information set is input into the pre-trained user device privacy identification model to obtain a query privacy data set and a query non-privacy data set. Among them, the above-mentioned user device privacy identification model can be a model for identifying privacy data in the input login aggregate query information set. The above-mentioned user device privacy identification model may include: an unsupervised bidirectional language model based on deep learning, a fully connected layer and a CRF (Conditional Random Field) layer. The above-mentioned bidirectional language model can be BERT (Bidirectional Encoder Representation from Transformers, a bidirectional encoding representation model based on Transformers), ELMo (Embedding from Language Model), GPT (Generative Pre-training Transformer). The query privacy data in the above-mentioned query privacy data set can be data that brings harm to individuals or society after data leakage. For example, the above-mentioned query privacy data can be a user account password.

[0108] The second step is to determine the query privacy byte set of the query privacy data set to obtain a query sensitive byte set, wherein the query sensitive bytes in the query sensitive byte set may be ASCII code bytes corresponding to the query privacy data.

[0109] As an example, the execution subject may first perform clustering on the query privacy data set to obtain various query privacy data clusters. Then, a one-way function is used to determine the byte subsets corresponding to the various query privacy data clusters. Finally, the obtained multiple byte subsets are concatenated to obtain a query sensitive byte set.

[0110] The third step is to generate a privacy key vector matrix for the query sensitive byte set by using a preset key generation function. The privacy key vector matrix may be a parameter matrix for converting the query privacy data into ciphertext. The preset key generation function may be the Weierstrass equation. In practice, the query sensitive byte set is input into the Weierstrass equation to obtain the privacy key vector matrix.

[0111] The fourth step is to encrypt the privacy key vector matrix to obtain the privacy communication data key for the query sensitive byte set. The privacy communication data key may be an unordered check code used to encrypt and transmit the privacy key vector matrix. The encryption process may be encryption using an elliptic equation curve.

[0112] The fifth step is to perform hash value mapping processing on the above-mentioned private communication data key to obtain a privacy-encoded hash value for the query privacy data set. The above-mentioned privacy-encoded hash value can represent the hash value of the encoding position of the encrypted query privacy data during the transmission process. In practice, the above-mentioned execution subject can use the logistic map algorithm to perform hash value mapping processing on the above-mentioned private communication data key to obtain a privacy-encoded hash value for the query privacy data set.

[0113] Step 6: Generate a privacy transmission key arrangement diagram for the query sensitive byte set through the privacy encoded hash value. The privacy transmission key arrangement diagram may be a two-dimensional arrangement diagram for representing the transmission order of the query sensitive byte set.

[0114] As an example, the execution subject can determine the encrypted position corresponding to each query privacy data after encryption through the privacy coded hash value to obtain an encryption position set. Then, according to the encryption position set, the query sensitive byte set is arranged in two dimensions to obtain a privacy transmission key arrangement diagram.

[0115] In the seventh step, the query privacy data set is encrypted using the privacy transmission key arrangement diagram to obtain an encrypted query privacy data set.

[0116] In the eighth step, the encrypted query privacy data set and the encrypted query non-privacy data set are determined as an encrypted logged-in aggregate query information set, and the encrypted logged-in aggregate query information set is transmitted to the terminal of the data statistics instruction and displayed.

[0117] The above technical solution and its related contents, as an inventive point of an embodiment of the present disclosure, solve the technical problem three mentioned in the background technology: "Since the login aggregate query information set is fully encrypted, the importance of different types of data is not reflected. When the encryption key is cracked, the login aggregate query information set will become plain text, causing privacy data leakage, and the amount of encrypted data encrypted by the full encryption is large, requiring a large amount of computing resources and transmission resources, resulting in low data security, increased encryption memory overhead and waste of transmission resources." The factors that lead to low data security and increased encryption memory overhead and waste of transmission resources are often as follows: Since the login aggregate query information set is fully encrypted, the importance of different types of data is not reflected. When the encryption key is cracked, the login aggregate query information set will become plain text, causing privacy data leakage, and the amount of encrypted data encrypted by the full encryption is large, requiring a large amount of computing resources and transmission resources. If the above factors are solved, the effect of improving data security and reducing encryption memory overhead and waste of transmission resources can be achieved. In order to achieve this effect, the present disclosure first identifies sensitive data for the above login aggregate query information set, extracts sensitive data that needs to be encrypted, and reduces the amount of data that needs to be encrypted. Secondly, by determining the byte set of the above sensitive data set and generating a key vector matrix for encryption processing, the privacy communication data key is obtained, which can improve the security of data transmission and ensure the stability of transmission. Subsequently, the above privacy communication data key is processed by hash value mapping, and the privacy encoding hash value is used to generate a privacy transmission key arrangement diagram and encrypt it. By changing the transmission order of the transmitted encrypted data, the security of sensitive data encrypted transmission can be further improved. Finally, the above encrypted query privacy data set and the above query non-privacy data set are determined as the encrypted login aggregate query information set, and sent to the terminal. Only sensitive data is encrypted, which can reduce the amount of encrypted data, reduce encryption memory overhead, ensure the security of sensitive data, and reduce encryption resource overhead and transmission resource waste.

[0118] The above-mentioned embodiments of the present disclosure have the following beneficial effects: the user device login bitmap data storage method of some embodiments of the present disclosure can reduce the waste of storage space and improve the query efficiency of data joint aggregation query, reduce data query time and improve user query experience. Specifically, the waste of related computing resources and storage resources, the extension of query time, and the reduction of user experience are caused by: the user device association mapping of the login data of multiple users and multiple devices has the problems of large data volume, low user device association efficiency and long association time. The statistical query through the structured query statement requires a large amount of computing resources, resulting in a waste of computing resources and storage resources, extending the query time and reducing the user experience. Based on this, the user device login bitmap data storage method of some embodiments of the present disclosure can first obtain the user device login information set of the user information set of each login device in the login device set to obtain the user device login information group set. Here, the user device login information group set is convenient for the subsequent generation of data space correlation. Secondly, the data space correlation set between the user device login information included in the above user device login information group set is generated. Here, the data space correlation can characterize the association relationship of the storage space storing the user device login information, which is convenient for subsequent deduplication. Again, according to the data space correlation set, the user device login information set is deduplicated to obtain a deduplicated user device login information set. Here, redundant error data in the user device login information set can be removed, the amount of data can be reduced, and the data quality can be improved. Next, the user identity mapping is performed on the deduplicated user device login information set to obtain a user identity mapping information set. Here, the data barriers of the user device login information of multiple devices obtained can be eliminated to a certain extent, data fusion can be promoted, and the association between users and devices can be improved. Subsequently, according to the user identity mapping information set, the deduplicated user device login information set is screened to obtain a screened user device login information set. Here, the amount of data in the user device login information set can be reduced and the data quality can be improved. Afterwards, the data structure transformation is performed on the screened user device login information set to obtain a user device login bitmap data set. Here, the screened user device login information set is converted into bitmap type data, which can compress the data and save a lot of storage resources. Then, in response to detecting the data statistics request instruction, the data statistics request instruction and the user device login bitmap data set are matched to obtain the user device login matching bitmap data set. Here, the filtered user device login bitmap set related to the data statistics request instruction can be determined to narrow the query scope and reduce the waste of computing resources. Then, the user device login matching bitmap data set is aggregated and queried to obtain a login aggregate query information set. Here, the query efficiency can be improved, the query time can be shortened, and the user experience can be improved.Finally, the login aggregate query information set is encrypted to obtain the encrypted login aggregate query information set, and the encrypted login aggregate query information set and the user device login bitmap data set are stored, and the encrypted login aggregate query information set is sent to the terminal of the data statistics instruction and displayed. It can be obtained that the user device login bitmap data storage method can compress the user device login information set by converting the user device login information set into a user device login information set in bitmap format, thereby reducing the waste of storage resources, and can improve query efficiency, reduce query time, and improve user experience by performing aggregate query on the user device login information set in bitmap format.

[0119] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a user equipment login bitmap data storage device, and these device embodiments are Figure 1 Corresponding to the method embodiments shown, the user equipment login bitmap data storage device can be specifically applied to various electronic devices.

[0120] like Figure 2As shown, a user device login bitmap data storage device 200 includes: an acquisition unit 201, a generation unit 202, a deduplication unit 203, a user identity mapping unit 204, a screening unit 205, a data structure transformation unit 206, a matching unit 207, an aggregate query unit 208 and a storage unit 209. Among them, the acquisition unit 201 is configured to: acquire the user device login information set of the user information set of each login device in the login device set to obtain the user device login information group set. The generation unit 202 is configured to: generate a data space correlation set between the user device login information included in the above user device login information group set. The deduplication unit 203 is configured to: perform deduplication processing on the above user device login information set according to the above data space correlation set to obtain the deduplication user device login information set. The user identity mapping unit 204 is configured to: perform user identity mapping on the above deduplication user device login information set to obtain the user identity mapping information set. The screening unit 205 is configured to: perform screening processing on the above-mentioned deduplicated user device login information set according to the above-mentioned user identity mapping information set to obtain the screened user device login information set. The data structure transformation unit 206 is configured to: perform data structure transformation processing on the above-mentioned screened user device login information set to obtain the user device login bitmap data set. The matching unit 207 is configured to: in response to detecting the data statistics request instruction, perform matching processing on the above-mentioned data statistics request instruction and the above-mentioned user device login bitmap data set to obtain the user device login matching bitmap data set. The aggregate query unit 208 is configured to: perform aggregate query processing on the above-mentioned user device login matching bitmap data set to obtain the login aggregate query information set. The storage unit 209 is configured to: perform encryption processing on the above-mentioned login aggregate query information set to obtain the encrypted login aggregate query information set, and store the above-mentioned encrypted login aggregate query information set and the above-mentioned user device login bitmap data set, and send the above-mentioned encrypted login aggregate query information set to the terminal of the above-mentioned data statistics instruction and display it.

[0121] It can be understood that the units recorded in the user equipment login bitmap data storage device 200 and the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the user equipment login bitmap data storage device 200 and the units contained therein, and will not be described in detail here.

[0122] Reference below Figure 3 , which shows a structural schematic diagram of an electronic device (eg, an electronic device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0123] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0124] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0125] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0126] It should be noted that the computer-readable medium in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0127] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (Hyper Text Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0128] The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist independently without being assembled into the electronic device. The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: obtains the user device login information set of the user information set of each login device in the login device set to obtain a user device login information group set; generates a data space correlation set between the user device login information included in the above-mentioned user device login information group set; performs deduplication processing on the above-mentioned user device login information set according to the above-mentioned data space correlation set to obtain a deduplicated user device login information set; performs user identity mapping on the above-mentioned deduplicated user device login information set to obtain a user identity mapping information set; performs screening processing on the above-mentioned deduplication user device login information set according to the above-mentioned user identity mapping information set to obtain a screened post-user device login information set; performing data structure transformation processing on the above-screened user device login information set to obtain a user device login bitmap data set; in response to detecting a data statistics request instruction, matching the above-mentioned data statistics request instruction and the above-mentioned user device login bitmap data set to obtain a user device login matching bitmap data set; performing aggregate query processing on the above-mentioned user device login matching bitmap data set to obtain a login aggregate query information set; encrypting the above-mentioned login aggregate query information set to obtain an encrypted login aggregate query information set, and storing the above-mentioned encrypted login aggregate query information set and the above-mentioned user device login bitmap data set, and sending the above-mentioned encrypted login aggregate query information set to the terminal of the above-mentioned data statistics instruction and displaying it.

[0129] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0130] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0131] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be arranged in a processor, for example, may be described as: a processor including an acquisition unit, a generation unit, a deduplication unit, a user identity mapping unit, a screening unit, a data structure transformation unit, a matching unit, an aggregate query unit and a storage unit. Among them, the names of these units do not constitute a limitation on the unit itself under certain circumstances. For example, the acquisition unit may also be described as "a unit that obtains a user device login information set of a user information set of each login device in a login device set, and obtains a user device login information group set".

[0132] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0133] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for storing user equipment login bitmap data, comprising: Obtain a user device login information set of a user information set of each login device in the login device set to obtain a user device login information group set; Generate a data space correlation set between the user equipment login information included in the user equipment login information group set; Deduplication processing is performed on the user equipment login information set according to the data space correlation set to obtain a deduplication user equipment login information set; Performing user identity mapping on the deduplicated user device login information set to obtain a user identity mapping information set; According to the user identity mapping information set, the deduplicated user device login information set is filtered to obtain a filtered user device login information set; The filtered user device login information set is subjected to data structure transformation processing to obtain a user device login bitmap data set, wherein the filtered user device login information set is subjected to data structure transformation processing to obtain a user device login bitmap data set, including: Determine a login key field information set that needs to undergo data structure transformation in the filtered user device login information set; For each login key field information in the login key field information set, the following storage steps are performed: In response to determining that the login key field information is non-numeric login key field information, dictionary encoding is performed on each login attribute value in the login attribute value set corresponding to the login key field information to generate an encoded login attribute value, thereby obtaining an encoded login attribute value set; Determining the number of encoded login attribute values ​​included in the encoded login attribute value set as the login attribute number; For each encoded login attribute value in the encoded login attribute value set, the following determination steps are performed: Determining a preset format attribute value of the encoded login attribute value; Performing division processing on the preset format attribute value to obtain a first division attribute value and a second division attribute value; Determine a bucket number of a storage bucket of the first partition attribute value; In response to determining that the number of the logged attributes is less than a preset number threshold, determining the storage container corresponding to the bucket number as an array type storage container; Determine the second divided attribute value as the first storage position of the encoded logged attribute value in the array type storage container as the attribute storage position; The login attribute value set corresponding to the login key field information is stored in the obtained attribute storage location set to obtain user equipment login bitmap data; In response to detecting the data statistics request instruction, matching the data statistics request instruction with the user equipment login bitmap data set to obtain a user equipment login matching bitmap data set; Performing aggregate query processing on the user device login matching bitmap data set to obtain a login aggregate query information set; The login aggregate query information set is encrypted to obtain an encrypted login aggregate query information set, the encrypted login aggregate query information set and the user device login bitmap data set are stored, and the encrypted login aggregate query information set is sent to the terminal of the data statistics request instruction and displayed.

2. The method according to claim 1, wherein: The step of generating a data space correlation set between the user equipment login information included in the user equipment login information set comprises: Preprocessing the user equipment login information set to obtain a preprocessed user equipment login information set; Determining a login data spatial correlation set of the preprocessed user equipment login information group set; Constructing a spatial model of a server storing the pre-processed user device login information set according to the login data spatial correlation set; Mapping the preprocessed user equipment login information set to the space model to obtain a model user equipment login information set; A data space correlation set between the model user equipment login information included in the model user equipment login information set is generated.

3. The method according to claim 1, wherein: After determining the second divided attribute value as the encoded logged attribute value at the first storage position of the array type storage container, the method further includes: In response to determining that the number of the login attributes is greater than or equal to a preset number threshold, determining the storage container corresponding to the bucket number as a bitmap storage container; Using the second divided attribute value to determine the encoded logged attribute value at a second storage location of the bitmap storage container as an attribute storage location; In response to determining that the number of the logged attributes is greater than or equal to a preset number threshold, and the logged attribute value set has continuous attribute values, determining the storage container corresponding to the bucket number as a trip step type storage container; Determine at least one login attribute value group having continuous attribute values ​​as a continuous login attribute value group set; Determine the second division attribute value of the continuous login attribute value located at the initial position in each continuous login attribute value group in the continuous login attribute value group set and the number of continuous login attribute values ​​included in the continuous login attribute value group as the continuous division attribute value and the continuous number, and obtain a continuous division attribute value set and a continuous number set; For each continuous division attribute value in the continuous division attribute value set, determine the continuous storage position of the continuous login attribute value group corresponding to the continuous division attribute value as the attribute storage position, and store the difference between the continuous number and the preset value in the continuous storage position.

4. The method according to claim 1, wherein: The performing user identity mapping on the deduplicated user device login information set to obtain a user identity mapping information set includes: Extracting a user device associated information set from the deduplicated user device login information set; Generate a user device relationship graph according to the user device association information set and the deduplicated user device login information set; Performing community discovery processing on the user device relationship graph to obtain a target relationship graph set; Performing feature extraction processing on each target relationship graph in the target relationship graph set to obtain a node feature vector set; Determine the feature similarity value of the node feature vector set to obtain a feature similarity value set; According to the feature similarity value set, user identity mapping is performed on the deduplicated user device login information set to obtain a user identity mapping information set.

5. The method according to claim 1, wherein: The performing aggregate query processing on the user equipment login matching bitmap data set to obtain a login aggregate query information set includes: Determine the type of storage container of each user device login matching bitmap data in the user device login matching bitmap data set to generate a bitmap container type, and obtain a bitmap container type set; In response to determining that each bitmap container type in the bitmap container type set is an array type, and the sum of the cardinalities of the user device login matching bitmap data sets is greater than a preset quantity threshold, performing a merge sort and union operation on the user device login matching bitmap data sets to obtain an array union query result as a login aggregate query information set; In response to determining that each bitmap container type in the bitmap container type set is an array type, and the sum of the cardinalities of the user device login matching bitmap data sets is less than or equal to a preset quantity threshold, mapping each user device login matching bitmap data in the user device login matching bitmap data set to user device login matching bitmap data of a bitmap type, to obtain a target user device login matching bitmap data set; Performing a bitmap union operation on the target user equipment login matching bitmap data set to obtain a login aggregation query information set; Performing a path merging operation on the user device login matching bitmap data set to obtain a login aggregation query information set; In response to determining that each bitmap type in the bitmap container type set is a bitmap container type, performing a Boolean intersection and union operation on the user device login matching bitmap data set to obtain a login aggregate query information set; In response to determining that the bitmap container type set includes an array type and a bitmap type, each user device login matching bitmap data in the user device login matching bitmap data set is traversed and deduplicated intersection and union operations are performed to obtain a login aggregation query information set.

6. A user equipment login bitmap data storage device, comprising: An acquisition unit is configured to acquire a user device login information set of a user information set of each login device in the login device set to obtain a user device login information group set; A generating unit configured to generate a data space correlation set between the user equipment login information included in the user equipment login information group set; a deduplication unit configured to perform deduplication processing on the user equipment login information set according to the data space correlation set to obtain a deduplicated user equipment login information set; A user identity mapping unit is configured to perform user identity mapping on the deduplicated user device login information set to obtain a user identity mapping information set; a screening unit configured to screen the deduplicated user device login information set according to the user identity mapping information set to obtain a screened user device login information set; The data structure transformation unit is configured to perform data structure transformation processing on the filtered user device login information set to obtain a user device login bitmap data set, wherein the data structure transformation processing on the filtered user device login information set to obtain a user device login bitmap data set includes: determining a login key field information set that needs to be transformed in the filtered user device login information set; for each login key field information in the login key field information set, performing the following storage steps: in response to determining that the login key field information is a non-numeric type of login key field information, performing dictionary encoding on each login attribute value in the login attribute value set corresponding to the login key field information to generate an encoded login attribute value to obtain an encoded login attribute value set; determining the encoded login attribute value set included in the encoded login attribute value set The number of encoded login attribute values ​​is used as the number of login attributes; for each encoded login attribute value in the encoded login attribute value set, the following determination steps are performed: determining a preset format attribute value of the encoded login attribute value; dividing the preset format attribute value to obtain a first divided attribute value and a second divided attribute value; determining a bucket number of a storage bucket of the first divided attribute value; in response to determining that the number of login attributes is less than a preset number threshold, determining the storage container corresponding to the bucket number as an array storage container; determining the second divided attribute value as the first storage position of the encoded login attribute value in the array storage container as an attribute storage position; storing the login attribute value set corresponding to the login key field information in the obtained attribute storage position set to obtain user device login bitmap data; a matching unit configured to, in response to detecting a data statistics request instruction, match the data statistics request instruction with the user equipment login bitmap data set to obtain a user equipment login matching bitmap data set; An aggregate query unit, configured to perform aggregate query processing on the user equipment login matching bitmap data set to obtain a login aggregate query information set; The storage unit is configured to encrypt the login aggregate query information set to obtain the encrypted login aggregate query information set, store the encrypted login aggregate query information set and the user device login bitmap data set, and send the encrypted login aggregate query information set to the terminal of the data statistics request instruction and display it.

7. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

8. A computer readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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