Data backtracking output method and device, electronic device, and storage medium

Abstract

The present disclosure provides a data backtracking output method and device, electronic device, and storage medium, and relates to the field of computer technology. The data backtracking output method includes: initializing a target data structure and receiving event data; extracting dimension data from the event data, and determining index data corresponding to the dimension data, and storing the index data in a dimension indexer; storing the dimension data in a feature domain corresponding to the target data structure based on the index data; if it is detected that the target dimension data in the feature domain is greater than or equal to the statistical threshold, backtracking and outputting all dimension data corresponding to the target dimension data from the target data structure. The technical solution of the embodiment of the present disclosure can improve the storage efficiency of the dimension data by storing the dimension data in the event data in the target data structure, and can also backtrack the dimension data from the target data structure according to the index data, thereby improving the backtracking and output efficiency of the dimension data.

Description

Background Art

[0002] With the rapid development of the Internet, how to efficiently store data and extract real-time features of data to analyze data based on its real-time features and thus mine more valuable information has become the goal of today's Internet development.

[0003] At present, in relevant data storage solutions, data storage space can be reduced by compressing data, but it is impossible to quickly convert compressed data into original data, that is, the existing relevant data storage solutions cannot support the function of data backtracking. Therefore, in relevant data storage solutions, it is impossible to realize real-time feature extraction and analysis of data.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of the embodiments of the present disclosure is to provide a data output method, a data output device, an electronic device, and a computer-readable storage medium, thereby overcoming the problem that existing related data storage solutions cannot support data backtracking, at least to a certain extent.

[0006] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0007] According to a first aspect of an embodiment of the present disclosure, a data backtracking output method is provided, comprising: initializing a target data structure and receiving event data; the target data structure comprises a dimension indexer and a feature domain; extracting dimension data from the event data, determining index data corresponding to the dimension data, and storing the index data in the dimension indexer; storing the dimension data in the feature domain based on the index data; if it is detected that the target dimension data in the feature domain is greater than or equal to a statistical threshold, backtrackingly outputting all dimension data corresponding to the target dimension data from the target data structure.

[0008] In some example embodiments of the present disclosure, based on the above solution, extracting dimension data from the event data and determining index data corresponding to the dimension data includes:

[0009] Calculate the storage address mapping value corresponding to the dimension data, and detect whether there is a target storage address mapping value identical to the storage address mapping value in the dimension indexer; if there is a target storage address mapping value identical to the storage address mapping value in the dimension indexer, use the coordinate information corresponding to the storage address mapping value as the index data of the dimension data; if there is no target storage address mapping value identical to the storage address mapping value in the dimension indexer, calculate the coordinate information corresponding to the dimension data, and use the coordinate information as the index data of the dimension data.

[0010] In some example embodiments of the present disclosure, based on the above solution, the feature domain includes a dimension domain and a parameter domain, and storing the dimension data in the feature domain based on the index data includes:

[0011] Based on the index data, detect whether the dimension value in the dimension domain corresponding to the dimension data is a null value; if it is detected that the dimension value in the dimension domain corresponding to the dimension data is a null value, store the dimension value corresponding to the dimension data in the dimension domain, and store the parameter value corresponding to the dimension data in the parameter domain according to the dimension value.

[0012] In some example embodiments of the present disclosure, based on the above solution, if it is detected that the dimension value in the dimension domain corresponding to the dimension data is a null value, the dimension value corresponding to the dimension data is stored in the dimension domain, further comprising:

[0013] If it is detected that the dimension value in the dimension domain corresponding to the index data is not a null value, the dimension value stored in the dimension domain is compared with the dimension value corresponding to the dimension data to obtain a comparison result; if the comparison result is the same, the parameter value corresponding to the dimension data is stored in the parameter domain according to the dimension value stored in the dimension domain.

[0014] In some example embodiments of the present disclosure, based on the above solution, if the comparison result is the same, the parameter value corresponding to the dimension value in the dimension data is stored in the parameter domain, further comprising:

[0015] If the comparison result is not the same, the dimension data is updated and a new storage address mapping value corresponding to the updated dimension data is calculated.

[0016] In some example embodiments of the present disclosure, based on the above solution, the parameter domain includes an event unit, and storing the parameter value corresponding to the dimensional data in the parameter domain according to the dimensional value stored in the dimensional domain includes:

[0017] The parameter values ​​corresponding to the dimensional data are classified according to the dimensional values ​​stored in the dimensional domain to obtain a plurality of event units; and the event units are allocated and stored in the parameter domain according to the amount of data corresponding to the event units.

[0018] In some example embodiments of the present disclosure, based on the above scheme, if it is detected that the target dimension data in the feature domain is greater than or equal to the statistical threshold, all dimension data corresponding to the target dimension data are backtracked and output from the target data structure, including:

[0019] If it is detected that the parameter value corresponding to the target dimensional data in the feature domain is greater than or equal to the statistical threshold, the target index data corresponding to the target dimensional data is obtained; based on the target index data, all dimensional data corresponding to the target dimensional data are obtained from the target data structure; and all the dimensional data are output to a preset data evaluation system to perform security level assessment on all the dimensional data.

[0020] According to a second aspect of an embodiment of the present disclosure, a data backtracking output device is provided, comprising: an initialization module for initializing a target data structure and receiving event data; the target data structure comprises a dimension indexer and a feature domain; an index data determination module for extracting dimension data from the event data, determining index data corresponding to the dimension data, and storing the index data in the dimension indexer; a dimension data storage module for storing the dimension data in the feature domain based on the index data; and a backtracking output module for backtrackingly outputting all dimension data corresponding to the target dimension data from the target data structure if it is detected that the target dimension data in the feature domain is greater than or equal to a statistical threshold.

[0021] In some example embodiments of the present disclosure, based on the aforementioned scheme, the index data determination module includes an index data determination unit, which is used to calculate the storage address mapping value corresponding to the dimension data, and detect whether there is a target storage address mapping value that is identical to the storage address mapping value in the dimension indexer; if there is a target storage address mapping value that is identical to the storage address mapping value in the dimension indexer, coordinate information corresponding to the storage address mapping value is used as index data of the dimension data; if there is no target storage address mapping value that is identical to the storage address mapping value in the dimension indexer, coordinate information corresponding to the dimension data is calculated, and the coordinate information is used as index data of the dimension data.

[0022] In some example embodiments of the present disclosure, based on the aforementioned scheme, the dimensional data storage module includes a dimensional data storage unit, and the dimensional data storage unit is used to detect whether the dimensional value in the dimensional domain corresponding to the dimensional data is a null value based on the index data; if it is detected that the dimensional value in the dimensional domain corresponding to the dimensional data is a null value, the dimensional value corresponding to the dimensional data is stored in the dimensional domain, and the parameter value corresponding to the dimensional data is stored in the parameter domain according to the dimensional value.

[0023] In some example embodiments of the present disclosure, based on the aforementioned scheme, the dimensional data storage module also includes a comparison processing unit, and the comparison processing unit is used to compare the dimension value stored in the dimensional domain with the dimension value corresponding to the dimensional data to obtain a comparison result if it is detected that the dimension value in the dimensional domain corresponding to the index data is not a null value; if the comparison result is the same, the parameter value corresponding to the dimensional data is stored in the parameter domain according to the dimension value stored in the dimensional domain.

[0024] In some example embodiments of the present disclosure, based on the aforementioned scheme, the dimensional data storage module further includes an update unit, wherein the update unit is used to update the dimensional data if the comparison result is not the same, and calculate a new storage address mapping value corresponding to the updated dimensional data.

[0025] In some example embodiments of the present disclosure, based on the aforementioned scheme, the dimensional data storage module also includes an event unit, and the event unit is used to classify the parameter values ​​corresponding to the dimensional data according to the dimensional values ​​stored in the dimensional domain to obtain multiple event units; according to the amount of data corresponding to the event unit, the event unit is allocated and stored in the parameter domain.

[0026] In some example embodiments of the present disclosure, based on the aforementioned scheme, the data backtracking output module includes a data backtracking output unit, and the data backtracking output unit is used to obtain target index data corresponding to the target dimensional data if it is detected that the parameter value corresponding to the target dimensional data in the feature domain is greater than or equal to a statistical threshold; based on the target index data, obtain all dimensional data corresponding to the target dimensional data from the target data structure; and output all dimensional data to a preset data evaluation system to perform security level assessment on all dimensional data.

[0027] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the data backtracking output methods described above is implemented.

[0028] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the data backtracking output method according to any one of the above items is implemented.

[0029] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects:

[0030] The data backtracking output method in the example embodiment of the present disclosure initializes a target data structure and receives event data; the target data structure includes a dimension indexer and a feature domain; dimension data is extracted from the event data, and index data corresponding to the dimension data is determined, and the index data is stored in the dimension indexer; the dimension data is stored in the feature domain based on the index data; if it is detected that the target dimension data in the feature domain is greater than or equal to a statistical threshold, all dimension data corresponding to the target dimension data are backtracked and output from the target data structure. On the one hand, by initializing the target data structure, extracting the dimension data corresponding to the received event data and determining the index data corresponding to the dimension data, the index data is then stored in the dimension indexer, and the dimension data is stored in the feature domain associated with the index data, thereby improving the accuracy of storing the dimension data; at the same time, it is not necessary to store the complete event data, only the dimension data extracted from the event data needs to be stored in the target data structure, thereby improving the storage efficiency of the dimension data; on the other hand, by detecting the size relationship between the target dimension data and the statistical threshold in the feature domain, if the target dimension data is greater than or equal to the statistical threshold, all dimension data corresponding to the target dimension data can be obtained from the target data structure according to the index data, thereby realizing the backtracking of the dimension data corresponding to the event data, and at the same time avoiding the restoration processing of the dimension data such as decompression processing, thereby improving the output efficiency of the dimension data.

[0031] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0033] Figure 1 A schematic diagram schematically illustrates a data backtracking output method according to some embodiments of the present disclosure;

[0034] Figure 2 A schematic diagram schematically shows a structure of a target data structure according to some embodiments of the present disclosure;

[0035] Figure 3 A schematic diagram schematically illustrates a process of determining index data according to some embodiments of the present disclosure;

[0036] Figure 4 A schematic diagram schematically illustrates a process of storing dimensional data according to some embodiments of the present disclosure;

[0037] Figure 5 A schematic diagram schematically illustrates the structure of a dimensional domain according to some embodiments of the present disclosure;

[0038] Figure 6 A schematic diagram schematically illustrates the structure of a parameter domain according to some embodiments of the present disclosure;

[0039] Figure 7 A schematic diagram schematically illustrates a process of storing parameter values ​​according to some embodiments of the present disclosure;

[0040] Figure 8 A schematic diagram schematically illustrates a process of allocating storage to an event unit according to some embodiments of the present disclosure;

[0041] Fig. 9 A schematic diagram schematically shows a process of dimensional data backtracking output according to some embodiments of the present disclosure;

[0042] Fig.10 A schematic diagram schematically shows another process of dimensional data backtracking output according to some embodiments of the present disclosure;

[0043] Fig.11 A schematic diagram schematically shows a data backtracking output device according to some embodiments of the present disclosure;

[0044] Fig.12 A schematic diagram schematically shows a structure of a computer system of an electronic device according to some embodiments of the present disclosure;

[0045] Fig.13 A schematic diagram of a computer-readable storage medium according to some embodiments of the present disclosure is schematically shown.

[0046] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts. DETAILED DESCRIPTION

[0047] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more comprehensive and complete and will fully convey the concept of the example embodiments to those skilled in the art.

[0048] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0049] Furthermore, the drawings are only schematic illustrations and are not necessarily drawn to scale. The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0050] In this example embodiment, a data backtracking output method is first provided. The data backtracking output method can be applied to terminal devices, such as electronic devices such as mobile phones and computers, and can also be applied to servers. Figure 1 The following is a schematic diagram showing the flow of a data traceback output method according to some embodiments of the present disclosure. Figure 1 As shown, the data backtracking output method may include the following steps:

[0051] Step S110, initializing a target data structure and receiving event data; the target data structure includes a dimension indexer and a feature domain;

[0052] Step S120, extracting dimension data from the event data, determining index data corresponding to the dimension data, and storing the index data in the dimension indexer;

[0053] Step S130, storing the dimension data in the feature domain based on the index data;

[0054] Step S140: If it is detected that the target dimensional data in the feature domain is greater than or equal to the statistical threshold, all dimensional data corresponding to the target dimensional data are back-tracked and output from the target data structure.

[0055] According to the data backtracking output method in this example embodiment, on the one hand, by initializing the target data structure, extracting the dimension data corresponding to the received event data and determining the index data corresponding to the dimension data, and then storing the index data in the dimension indexer, and storing the dimension data in the feature domain associated with the index data, the accuracy of storing the dimension data is improved; at the same time, it is not necessary to store the complete event data, only the dimension data extracted from the event data needs to be stored in the target data structure, which improves the storage efficiency of the dimension data; on the other hand, by detecting the size relationship between the target dimension data and the statistical threshold in the feature domain, if the target dimension data is greater than or equal to the statistical threshold, all dimension data corresponding to the target dimension data can be obtained from the target data structure according to the index data, thereby realizing the backtracking of the dimension data corresponding to the event data, and at the same time, the restoration processing of the dimension data such as decompression processing can be avoided, thereby improving the output efficiency of the dimension data.

[0056] The data backtracking output method in this example embodiment will be further described below.

[0057] In step S110, a target data structure is initialized and event data is received; the target data structure includes a dimension indexer and a feature field.

[0058] In an example embodiment of the present disclosure, a dimension indexer may refer to an indexer for determining unique identification data of data to be stored. For example, when the unique identification data of the data to be stored is a mapping value corresponding to storage address data, the dimension indexer may be an indexer for determining the mapping value corresponding to storage address data of the data to be stored. Of course, the dimension indexer may also be an indexer for determining other unique identification data of the data to be stored, which is not specifically limited in this example embodiment. A feature domain may refer to a logical set in which data to be stored is stored in a computer according to the feature attributes of the data.

[0059] The target data structure may refer to a data structure for storing data, for example, the target data structure is a data storage structure composed of a dimension indexer and a feature domain, such as determining the location information corresponding to the data to be stored in the feature domain through the dimension indexer, and then dividing the parameter values ​​corresponding to the data into multiple event units according to the characteristics of the data, and storing the data in the form of event units. Of course, the target data structure may also be other forms of data structures for storing data, and this embodiment does not specifically limit this.

[0060] Event data may refer to user behavior data. For example, event data may be behavior data of a user clicking on a web page, event data may be behavior data of a user scrolling a page, or behavior data of a user registering an account. Of course, event data may also be other user behavior data, and this embodiment does not specifically limit this.

[0061] After initializing the target data structure and receiving event data, the event data can be classified according to its characteristics, the core data in the event data can be extracted, and the unique identification data of the core data can be determined, and then the index data of the core data in the target data structure can be determined based on the unique identification data.

[0062] In step S120, dimension data is extracted from the event data, index data corresponding to the dimension data is determined, and the index data is stored in the dimension indexer.

[0063] In an example embodiment of the present disclosure, dimension data may refer to data containing characteristic dimension data of event data and parameter values ​​corresponding to the characteristic dimension data. For example, event data may be account registration behavior data such as mobile phone number data, verification data, password data, user's IP address data, mobile phone number location, account registration time data, and account data. According to the characteristics of the account registration behavior data, the user's IP address data is the IP address dimension data of the account registration behavior data, the mobile phone number location is the location dimension data of the number registration behavior data, and the user's IP address data such as IP:123.45.82.80 is the parameter value corresponding to the IP address dimension data. Of course, dimension data may also be data containing other characteristic dimension data and parameter values ​​corresponding to other characteristic dimension data, and this example embodiment does not specifically limit this.

[0064] Index data may refer to unique identification data of dimension data in the feature domain. For example, index data may be storage location data corresponding to dimension data in the feature domain, or identification data corresponding to storage location data of dimension data in the feature domain. Of course, index data may also be other unique identification data of dimension data in the feature domain, and this embodiment does not impose any special limitation on this.

[0065] Preferably, the event data can be classified according to the characteristic dimensions of the event data, and then the dimension data can be screened from the event data, and the index data corresponding to the dimension data, such as a hash value, can be calculated, and the index data corresponding to the dimension data can be stored in a dimension indexer to detect whether there is initial index data identical to the index data in the dimension indexer. If it is detected that there is initial index data identical to the index data in the dimension indexer, the dimension data can be stored in a characteristic domain associated with the index data.

[0066] In step S130, the dimensional data is stored in the feature domain based on the index data.

[0067] In an example embodiment of the present disclosure, the feature domain includes a dimension domain and a parameter domain. The dimension domain may refer to a logical set for storing feature dimension data in the dimension data. For example, when the event data is account registration behavior data, and the dimension data includes IP address dimension data and mobile phone number dimension data, the first dimension value in the dimension domain may be IP address dimension data, and the second dimension value may be mobile phone number dimension data. Of course, the dimension domain may also be a logical set for storing other feature dimension data in the dimension data, and this example embodiment does not specifically limit this.

[0068] The parameter domain may refer to a logical data set used to store parameter values ​​of characteristic dimension data in the dimension domain. For example, when the event data is account registration behavior data, the dimension data corresponding to the event data includes a first dimension value which is IP address dimension data, and a second dimension value which is mobile phone number dimension data. The parameter value corresponding to the IP address dimension data is IP:123.45.82.80, and the parameter value corresponding to the mobile phone number dimension data is 123456789123. Then, the first parameter value corresponding to the first dimension value is IP:123.45.82.80, and the second parameter value corresponding to the second dimension value is 123456789123. Of course, the parameter domain may also be a logical data set used to store other parameter values ​​of other characteristic dimension data in the dimension domain, and this embodiment does not specifically limit this.

[0069] Based on the index data corresponding to the dimensional data, the characteristic domain for storing the dimensional data can be determined, and then the characteristic dimensional data in the dimensional data can be stored in the dimensional domain in the characteristic domain, and the parameter values ​​corresponding to the dimensional data of the dimensional data can be stored in the parameter domain in the characteristic domain, thereby realizing the storage of the dimensional data in the characteristic domain corresponding to the index data.

[0070] In step S140, if it is detected that the target dimensional data in the feature domain is greater than or equal to the statistical threshold, all dimensional data corresponding to the target dimensional data are back-tracked and output from the target data structure.

[0071] In an example embodiment of the present disclosure, the target dimension data may refer to the target data obtained by filtering from the dimension data. For example, when the first dimension value of the dimension data is the IP address dimension data, the second dimension value is the mobile phone number location dimension data, and the parameter value corresponding to the IP address dimension data is IP:123.45.82.80, and the parameter value corresponding to the location dimension data is a certain province, a certain city, and a certain district, the target dimension data may be the IP address dimension data, and the parameter value IP:123.45.82.80 corresponding to the IP address dimension data. The target dimension data may also be the mobile phone number location dimension data, and the parameter value corresponding to the mobile phone number location dimension data is a certain province, a certain city, and a certain district. Of course, the target dimension data may also be other target data corresponding to the dimension data, such as the number of times the same IP address data appears or the number of times the same mobile phone number location appears. This example embodiment does not specifically limit this.

[0072] The statistical threshold may refer to the threshold of the parameter value corresponding to the target dimension data. For example, when the target dimension data is the number of times the same IP address and mobile phone number are used to register an account in the same application, the statistical threshold may be a threshold of the number of times the same IP address and mobile phone number are used to register an account in the same application, such as 25 times. Of course, the statistical threshold may also be a threshold of the parameter value corresponding to other target dimension data, and this embodiment does not specifically limit this.

[0073] Preferably, it is possible to detect whether the target dimensional data is greater than or equal to a statistical threshold. When the target dimensional data is greater than the statistical threshold, all dimensional data corresponding to the target dimensional data can be traced back from the feature domain of the target data structure according to the index data, and then the dimensional data can be output to the data evaluation system to perform a security level evaluation on the dimensional data of the event data.

[0074] Figure 2 Schematically shows a structural diagram of a target data structure according to some embodiments of the present disclosure; Figure 2 As shown, the target data structure 200 includes a dimension indexer 210 and a feature field 220 .

[0075] The dimension indexer includes a dimension Hash value such as dim0Hash and a storage space index 0, and the feature domain 220 includes a dimension domain 221 and a parameter domain 222. The dimension indexer 210 is used to store the dimension Hash value of the dimension data and the storage dimension index corresponding to the dimension Hash value, and the feature domain 220 is used to store the feature dimension data of the dimension data and the parameter value corresponding to the feature dimension data; the dimension domain 221 is used to store the feature dimension data of the dimension data, and the parameter domain 222 is used to store the parameter value corresponding to the feature dimension data.

[0076] According to the index data of the dimensional data, the characteristic dimensional data of the dimensional data can be stored in the dimensional domain in the characteristic domain, and the parameter values ​​corresponding to the characteristic dimensional data can be stored in the parameter domain in the characteristic domain, and then the dimensional data can be stored based on the target data structure to improve the storage efficiency of the dimensional data.

[0077] Figure 3 A schematic diagram schematically illustrates a process of determining index data according to some embodiments of the present disclosure; Figure 3 As shown, the index data determination method may include the following steps:

[0078] In step S310, a storage address mapping value corresponding to the dimension data is calculated, and it is detected whether there is a target storage address mapping value identical to the storage address mapping value in the dimension indexer;

[0079] In step S320, if there is a target storage address mapping value that is the same as the storage address mapping value in the dimension indexer, the coordinate information corresponding to the storage address mapping value is used as the index data of the dimension data;

[0080] In step S330, if there is no target storage address mapping value identical to the storage address mapping value in the dimension indexer, coordinate information corresponding to the dimension data is calculated, and the coordinate information is used as index data of the dimension data.

[0081] Among them, the storage address mapping value may refer to the mapping value of the index data corresponding to the dimension data in the dimension indexer. For example, the storage address mapping value may be the mapping value obtained by hashing the dimension data. Of course, the storage address mapping value may also be the mapping value of the index data obtained by performing other forms of processing on the dimension data. This embodiment does not make any special limitation to this.

[0082] The coordinate information may refer to the index information of the storage address mapping value in the feature domain. For example, the coordinate information may be the same one-dimensional coordinate information as the storage address mapping value. Of course, the coordinate information may also be other multi-dimensional coordinate information associated with the storage address mapping value. This embodiment does not specifically limit this.

[0083] Preferably, the hash value of the dimension data can be calculated, and the hash value of the dimension data can be used as the storage address mapping value of the dimension data, and it can be detected whether there is a target storage address mapping value that is the same as the storage address mapping value in the dimension indexer; if there is a target storage address mapping value that is the same as the storage address mapping value of the dimension data in the dimension indexer, the coordinate information corresponding to the storage address mapping value of the dimension data is used as the index data corresponding to the dimension data; if there is no target storage address mapping value that is the same as the storage address mapping value of the dimension data in the dimension indexer, the coordinate information of the dimension data is calculated, and the calculated dimension coordinate information is used as the index data of the dimension data; therefore, the uniqueness of the index data corresponding to the dimension data is guaranteed by the storage address mapping value and coordinate information of the dimension data, and the accuracy of the index data of the dimension data is improved.

[0084] Figure 4 A schematic diagram schematically shows a process of storing dimensional data according to some embodiments of the present disclosure; Figure 4 As shown, the dimension data storage method may include the following steps:

[0085] In step S410, based on the index data, it is detected whether the dimension value in the dimension domain corresponding to the dimension data is a null value;

[0086] In step S420, if it is detected that the dimension value in the dimension domain corresponding to the dimension data is a null value, the dimension value corresponding to the dimension data is stored in the dimension domain, and the parameter value corresponding to the dimension data is stored in the parameter domain according to the dimension value.

[0087] Among them, the dimension value can refer to the characteristic dimension value of the dimension data. For example, when the dimension data is the data submitted by the user for an order, such as delivery address data, mobile phone number data, and item attribute data, the dimension data can have three characteristic dimension values, namely, the first dimension value is the delivery address data, the second dimension value is the mobile phone number data, and the third dimension value is the item attribute data. Of course, the dimension value can also refer to other characteristic dimension values ​​of the dimension data, and this embodiment does not make any special limitation on this.

[0088] The parameter value may refer to the numerical value corresponding to the dimension value. For example, the first dimension value is the delivery address data, the second dimension value is the mobile phone number data, and the third dimension value is the item attribute data. The delivery address is Street D, District C, City B, Province A, the mobile phone number data is 12345678912, and the item attribute data is 1. Then the parameter value corresponding to the first dimension value is Street D, District C, City B, Province A, the parameter value corresponding to the second dimension value is 12345678912, and the parameter value corresponding to the third dimension value is 1. Of course, the parameter value may also be other data corresponding to the dimension value, and this embodiment does not make any special limitation to this.

[0089] A dimension domain may refer to a logical data set for storing dimension values ​​of dimension data, wherein the dimension domain consists of a control header and a dimension value. A parameter domain may refer to a logical data set for storing data corresponding to a dimension value, wherein the parameter domain consists of a control header and a parameter value.

[0090] Preferably, when the dimension value in the dimension domain corresponding to the target storage address mapping value in the dimension indexer is a null value, the dimension value corresponding to the dimension data can be stored in the dimension domain corresponding to the target storage address mapping value, and then the parameter value corresponding to the dimension value can be stored in the parameter domain associated with the dimension domain, thereby achieving efficient storage of dimensional data.

[0091] Figure 5 A schematic diagram schematically shows the structure of a dimensional domain according to some embodiments of the present disclosure; Figure 5 As shown, the dimension field 500 mainly includes a control header 510 and a dimension value 520 .

[0092] The control header 510 mainly includes the magic number, total length, number of dimensions, and dimension value length. The magic number occupies 4 bytes and is a summary of all values ​​in the entire dimension domain except the magic number itself, which is used to verify the data in the dimension domain; the total length occupies 2 bytes and describes the total length of the entire dimension domain (in actual applications, the combined value of the dimension of the feature will not exceed 256). 2 Bytes); Number of dimensions: occupies 1 byte, describing the number of dimensions stored in the current dimension domain (in actual applications, the number of dimensions of a feature will not exceed 256); Dimension value length (dimXSize): describes the length of the Xth dimension value, organized in the order of the dimension values ​​in the dimension data, and each item occupies 1 byte (in actual applications, the value of each dimension of a feature will not exceed 256 bytes). In the target data structure, the number of bytes S occupied by the control header of each dimension domain is determined by formula (1):

[0093] S=4+2+1+1*dim Xsize (1)

[0094] In the dimension domain, the length of the dimension value part is not fixed, and the relative position of each dimension value is consistent with the order of appearance of each dimension value in the dimension data; the control header describes the storage method of all dimension values ​​of the dimension data. Then, the dimension values ​​of the dimension data are stored in the dimension domain.

[0095] Figure 6 A schematic diagram schematically shows the structure of a parameter domain according to some embodiments of the present disclosure; Figure 6 As shown, the parameter domain 600 mainly includes a control header 610 and a parameter value 620 .

[0096] Among them, the control header 610 mainly includes the number of parameters and the length of parameter values. The number of parameters in the control header occupies 1 byte, which describes the number of parameter values ​​in the current event unit (in actual applications, the number of parameter values ​​will not exceed 256); the parameter value length (argSizeX) of the control header occupies 2 bytes, which describes the space occupied by each parameter in the current event unit. The relative position of each parameter value length corresponds to the order in which each dimension value appears in the dimension data (in actual use, the value of each parameter in the feature will not exceed 256). 2 The parameter value part 620 stores the real parameter value, the length of which is not fixed, and the relative position of each parameter value is consistent with the order of each dimension value in the dimension data.

[0097] In the target data structure, the number of bytes S occupied by each control header in the parameter field is determined by formula (2):

[0098] S=1+1*argValueX (2)

[0099] Among them, argValueX represents the number of parameter values ​​in the parameter domain.

[0100] Parameter values ​​are stored in the parameter domain in the form of event units. Event units corresponding to multiple events are independent of each other in the parameter domain. Usually a dimension value contains multiple parameter values, such as statistical quantity, event time, etc. For example, if the dimension value is dim1Value, dim1Value contains argValue1 (parameter value 1) and argValue2 (parameter value 2). Figure 2 , argValue1 can be stored in the first event unit Args[1, 1] whose index data of the parameter domain is 1, and argValue2 can be stored in the second event unit Args[1, 2] whose index data of the parameter domain is 1. Therefore, according to the storage order of parameter values ​​in the dimension domain, the parameter values ​​corresponding to the dimension values ​​can be stored in the parameter domain in sequence.

[0101] Figure 7 A schematic diagram schematically shows a process of storing parameter values ​​according to some embodiments of the present disclosure; Figure 7 As shown, the parameter value storage method may include the following steps:

[0102] In step S710, if it is detected that the dimension value in the dimension domain corresponding to the index data is not a null value, the dimension value stored in the dimension domain is compared with the dimension value corresponding to the dimension data to obtain a comparison result;

[0103] In step S720, if the comparison result is the same, the parameter value corresponding to the dimensional data is stored in the parameter field according to the dimensional value stored in the dimensional field.

[0104] Among them, the comparison result may refer to whether the dimension value in the dimension indexer matches the dimension value of the dimension data under the condition that the index data is the same. For example, the comparison result may be the result that the dimension value in the dimension indexer matches the dimension value of the dimension data, or it may be the result that the dimension value in the dimension indexer does not match the dimension value of the dimension data. This embodiment does not make any special limitation on this.

[0105] It can detect whether the dimension value of the dimension data is the same as the dimension value in the dimension indexer. If it is detected that there is index data corresponding to the dimension data in the dimension indexer, and there is a dimension value in the dimension domain associated with the index data, compare whether the dimension value already existing in the dimension indexer is the same as the dimension value of the dimension data; if the dimension value already existing in the dimension indexer is the same as the dimension value of the dimension data, directly store the parameter value corresponding to the dimension value in the dimension data into the parameter domain to realize the storage of the parameter value of the dimension data.

[0106] In one embodiment of the present disclosure, if it is detected that the dimension value of the dimension data is different from the dimension value already existing in the dimension indexer, the dimension data is updated and a new storage address mapping value corresponding to the updated dimension data is calculated.

[0107] Among them, the dimension data can be hashed to achieve the update of the dimension data. For example, based on the storage address mapping value corresponding to the dimension data, the storage dimension data can be hashed, such as performing a round count value + 1 operation on the storage address mapping value, and the initial value of the calculated value is 0. Every time an updated storage address mapping value of the dimension data is obtained, the coordinate information corresponding to the updated storage address mapping value is used as the index data of the dimension data, and the dimension value of the index data in the dimension indexer is compared with the dimension value of the dimension data. If it is detected that the dimension value in the dimension indexer is different from the dimension value of the dimension data, the count value is increased by 1, and the dimension data is hashed again until the dimension value identical to the dimension data can be matched in the dimension indexer, and the update processing of the dimension data is terminated, and the index data corresponding to the dimension value in the dimension indexer when the dimension value is the same as the dimension value in the dimension data is determined, and the storage address mapping value of the dimension data corresponding to the index data is used as the new storage address mapping value after the dimension data is updated.

[0108] The problem that the index data is the same but the dimension value in the dimension indexer is different from the dimension value corresponding to the dimension data can be understood as a hash conflict problem, and then the dimension data is updated based on the method of resolving hash conflicts, such as re-square detection, pseudo-random detection, and chain address method in the open address method, establishing a common overflow area, and re-hashing method. This example embodiment does not make any special limitations on this.

[0109] Preferably, when the index data is the same but the dimension value in the dimension indexer is different from the dimension value of the dimension data, the dimension data can be updated, and a new storage address mapping value corresponding to the updated dimension data can be calculated, and then based on the new storage address mapping value, it is detected whether there is a target storage address mapping value that is the same as the new storage address mapping value in the dimension indexer. If there is a target storage address mapping value that is the same as the new storage address mapping value in the dimension indexer, the coordinate information corresponding to the new storage address mapping value is used as the index data corresponding to the updated dimension data, and based on the index data, it is detected whether the dimension value in the dimension indexer is a null value. If the dimension value in the dimension indexer is null, the dimension value of the updated dimension data is stored in the dimension domain, and then the parameter value corresponding to the dimension value of the updated dimension data is stored in the parameter domain; if it is detected that the dimension value in the dimension indexer is not a null value, the dimension value in the dimension indexer is compared with the dimension value of the updated dimension data to see if they are the same. If the comparison results are the same, the parameter value of the updated dimension data is stored in the parameter domain. If the comparison results are not the same, the dimension data continues to be updated, thereby improving the accuracy of the parameter value corresponding to the stored dimension data.

[0110] Figure 8 A schematic diagram schematically shows a process of allocating storage to an event unit according to some embodiments of the present disclosure; Figure 8 As shown, the method for allocating storage for an event unit may include the following steps:

[0111] In step S810, the parameter values ​​corresponding to the dimensional data are classified according to the dimensional values ​​stored in the dimensional domain to obtain a plurality of event units;

[0112] In step S820, the event unit is allocated and stored in the parameter domain according to the data volume corresponding to the event unit.

[0113] Among them, the event unit can refer to a basic unit that describes event data based on dimensional values. For example, when the event data is the submission of order data, the dimensional value is order information. The event unit can be a delivery address, mobile phone number, order number, item attributes, and the dimensional value can also be payment information. The event unit can also be the payment amount, the corresponding time of order submission completion. Of course, the event unit can also be a basic unit that describes event data based on other dimensional values. This example embodiment does not make any special limitations on this.

[0114] The dimensional data corresponding to the event data can be classified according to the dimensional value, and then the parameter values ​​corresponding to the dimensional data can be classified to obtain multiple event units, so that the parameter values ​​can be stored in the parameter domain in the form of event units according to the byte size corresponding to different parameter values. That is, according to the byte size of the parameter value corresponding to the event unit, the event unit can be allocated on demand, and the allocated event unit can be stored in the parameter domain, thereby improving the storage efficiency of the event units corresponding to the dimensional data.

[0115] Fig. 9 A schematic diagram schematically shows the process of dimensional data backtracking output according to some embodiments of the present disclosure; Fig. 9 As shown, the method for tracing back and outputting dimension data may include the following steps:

[0116] In step S910, if it is detected that the parameter value corresponding to the target dimension data in the feature domain is greater than or equal to the statistical threshold, the target index data corresponding to the target dimension data is obtained;

[0117] In step S920, based on the target index data, all dimension data corresponding to the target dimension data are acquired from the target data structure; and

[0118] In step S930, all the dimensional data are output to a preset data evaluation system to perform a security level assessment on all the dimensional data.

[0119] Among them, the statistical threshold can refer to the maximum statistical value corresponding to the parameter value of the target dimension data. For example, the dimension data is account login data, and the target dimension data is the number of times the same account logs in within 1 hour. The statistical threshold can be 5. When the same account logs in 6 times within 1 hour, the account may be at risk of being stolen, and the account may be locked. Of course, the statistical threshold can also be the maximum statistical value corresponding to other target dimension data, and this example embodiment does not make any special limitations on this.

[0120] For example, taking the account registration scenario, the dimensional data is a certain IP address data, a certain mobile phone location, a registered account, and the number of registered account times. The target dimensional data is the number of registered account times, and the statistical threshold is 25. When it is detected that the number of registered accounts with the same IP address data and the same mobile phone location is greater than or equal to the statistical threshold, these registered accounts are considered to be risky and need to be handled in different dimensions. According to the index data of the dimensional domain corresponding to the number of registered account times, all dimensional values ​​and parameter values ​​corresponding to the dimensional values ​​that belong to the same dimensional domain as the number of registered account times can be traced back from the target data structure, such as the IP address of the registered account, the mobile phone location, the registered account, etc.

[0121] Target dimensional data can be selected from the dimensional data, and whether the parameter value corresponding to the target dimensional data is greater than or equal to the statistical threshold is detected. If the parameter value corresponding to the target dimensional data is greater than or equal to the statistical threshold, the target index data corresponding to the target dimensional data is obtained, and then all dimensional data corresponding to the target dimensional data are traced back according to the target index data, and then all dimensional data traced back from the target data structure are output to the data evaluation system, thereby realizing the security level evaluation of all dimensional data and improving the security of dimensional data.

[0122] Fig.10 A schematic diagram schematically shows another process of dimensional data backtracking output according to some embodiments of the present disclosure; Fig.10 As shown, the method for tracing back and outputting dimension data may include the following steps:

[0123] In step S1010, the target data structure is initialized and event data is received;

[0124] In step S1011, dimension data is extracted from the event data, and a storage address mapping value corresponding to the dimension data is determined;

[0125] In step S1012, it is detected whether there is a target storage address mapping value identical to the storage address mapping value in the dimension indexer. If there is a target storage address mapping value identical to the storage address mapping value in the dimension indexer, step S1013 is executed, otherwise, the process jumps to step S1022;

[0126] In step S1013, the coordinate information of the storage address mapping value is used as index data corresponding to the dimension data;

[0127] In step S1014, based on the index data, the dimension value in the dimension indexer is determined;

[0128] In step S1015, it is detected whether the dimension value in the dimension indexer is a null value. If the dimension value in the dimension indexer is a null value, step S1016 is executed, otherwise, the process jumps to step S1023;

[0129] In step S1016, the dimension value of the dimension data is stored in the dimension field;

[0130] In step S1017, the parameter values ​​in the dimension data are divided into a plurality of data units, and the event units are allocated and stored in the parameter domain as required;

[0131] In step S1018, it is detected whether the target data corresponding to the dimension data is greater than or equal to the statistical threshold. If the target data is greater than or equal to the statistical threshold, step S1019 is executed, otherwise step S1020 is executed;

[0132] In step S1019, based on the target index data, all dimension data corresponding to the target dimension data are obtained from the target data structure, and all the dimension data are output to a preset data evaluation system;

[0133] In step S1020, it is detected whether it is necessary to exit the data evaluation system. If it is necessary to exit the data evaluation system, step S1021 is executed, otherwise step S1025 is executed;

[0134] In step S1021, exit the data evaluation system;

[0135] In step S1022, coordinate information corresponding to the dimension data is calculated, and the coordinate information is used as index data of the dimension data;

[0136] In step S1023, it is detected whether the dimension value in the dimension indexer is the same as the dimension value of the dimension data. If the dimension value in the dimension indexer is the same as the dimension value of the dimension data, step S1017 is executed, otherwise, the process jumps to step S1024;

[0137] In step S1024, the dimension data is updated, and a new storage address mapping value corresponding to the updated dimension data is calculated, and the process jumps to step S1011;

[0138] In step S1025, the expired dimension data is cleared and the process jumps to step S1010.

[0139] Preferably, the target data structure can be initialized first, and event data can be obtained, and dimension data can be extracted from the event data, the dimension data including dimension values ​​and parameter values, and the target data structure including a dimension indexer and a feature domain; the storage address mapping value of the dimension data is calculated, and it is detected whether there is target storage address index data that is the same as the storage address index data in the dimension indexer; if there is target storage address index data that is the same as the storage address mapping value of the dimension data in the dimension indexer, the coordinate information of the storage address index data is used as the index data corresponding to the dimension data; if there is no target storage address mapping value that is the same as the storage address mapping value of the dimension data in the dimension indexer, the coordinate information of the dimension data is calculated, and the coordinate information is used as the index data of the dimension data.

[0140] Secondly, if it is detected that the dimension value in the dimension indexer is not a null value, then continue to detect whether the existing dimension value in the dimension indexer is the same as the dimension value of the dimension data. If the existing dimension value in the dimension indexer is the same as the dimension value of the dimension data, then the dimension value of the dimension data is stored in the dimension domain; if it is detected that the existing dimension value in the dimension indexer is different from the dimension value of the dimension data, then update the dimension data such as performing hash calculation on the dimension data to obtain a new storage address mapping value of the updated dimension data, and continue to detect whether there is a target storage address mapping value that is the same as the new storage address mapping value in the dimension indexer. If there is no target storage address mapping value that is the same as the new storage address index data of the updated dimension data in the dimension indexer, then continue to update the dimension data, and use the coordinate information corresponding to the new storage address mapping value as the index data of the updated dimension data, and at the same time continue to detect whether the dimension value in the dimension indexer is a null value. If the dimension value in the dimension indexer is a null value, then the dimension value of the updated dimension data is also stored in the dimension domain.

[0141] Next, the parameter values ​​corresponding to the dimension data are divided into multiple event units according to the storage order of the dimension values ​​in the dimension domain, and the event units are allocated and stored in the parameter domain as needed according to the data volume, such as the byte size of the event unit or the size of the occupied storage space. When it is detected that the target dimension data corresponding to the dimension data is greater than or equal to the statistical threshold, all dimension data corresponding to the target dimension data are backtracked from the target data structure based on the index data, and the dimension data is output to the preset data evaluation system to achieve the security level evaluation of the dimension data.

[0142] Finally, after detecting the request message to exit the data evaluation system, you can directly exit the data evaluation system. After outputting the dimensional data to the data evaluation system to implement a security level assessment of the dimensional data, you can also clear expired data, further obtain new event data, and extract the dimensional data corresponding to the new event data, thereby realizing the storage and traceback output of the dimensional data.

[0143] It should be noted that, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0144] In addition, in this exemplary embodiment, a data traceback output device is also provided. Fig.11As shown, the data backtracking output device 1100 includes: an initialization module 1110, an index data determination module 1120, a dimension data storage module 1130, and a data backtracking output 1140. The initialization module 1110 is used to initialize the target data structure and receive event data; the target data structure includes a dimension indexer and a feature domain; the index data determination module 1120 is used to extract dimension data from the event data, determine the index data corresponding to the dimension data, and store the index data in the dimension indexer; the dimension data storage module 1130 is used to store the dimension data in the feature domain based on the index data; the data backtracking output module 1140 is used to backtrack and output all dimension data corresponding to the target dimension data from the target data structure if it is detected that the target dimension data in the feature domain is greater than or equal to the statistical threshold.

[0145] In some example embodiments of the present disclosure, based on the aforementioned scheme, the index data determination module 1120 includes an index data determination unit, which is used to calculate the storage address mapping value corresponding to the dimension data, and detect whether there is a target storage address mapping value that is identical to the storage address mapping value in the dimension indexer; if there is a target storage address mapping value that is identical to the storage address mapping value in the dimension indexer, the coordinate information corresponding to the storage address mapping value is used as the index data of the dimension data; if there is no target storage address mapping value that is identical to the storage address mapping value in the dimension indexer, the coordinate information corresponding to the dimension data is calculated, and the coordinate information is used as the index data of the dimension data.

[0146] In some example embodiments of the present disclosure, based on the aforementioned scheme, the dimensional data storage module 1130 includes a dimensional data storage unit, and the dimensional data storage unit is used to detect whether the dimensional value in the dimensional domain corresponding to the dimensional data is a null value based on the index data; if it is detected that the dimensional value in the dimensional domain corresponding to the dimensional data is a null value, the dimensional value corresponding to the dimensional data is stored in the dimensional domain, so as to store the parameter value corresponding to the dimensional data in the parameter domain according to the dimensional value.

[0147] In some example embodiments of the present disclosure, based on the aforementioned scheme, the dimensional data storage module 1130 also includes a comparison processing unit, and the comparison processing unit is used to compare the dimension value stored in the dimensional domain with the dimension value corresponding to the dimensional data to obtain a comparison result if it is detected that the dimension value in the dimensional domain corresponding to the index data is not a null value; if the comparison result is the same, the parameter value corresponding to the dimensional data is stored in the parameter domain according to the dimension value stored in the dimensional domain.

[0148] In some example embodiments of the present disclosure, based on the aforementioned scheme, the dimensional data storage module 1130 also includes an update unit, which is used to update the dimensional data if the comparison result is different, and calculate a new storage address mapping value corresponding to the updated dimensional data.

[0149] In some example embodiments of the present disclosure, based on the aforementioned scheme, the dimensional data storage module 1130 also includes an event unit, and the event unit is used to classify the parameter values ​​corresponding to the dimensional data according to the dimensional values ​​stored in the dimensional domain to obtain multiple event units; according to the amount of data corresponding to the event unit, the event unit is allocated and stored in the parameter domain.

[0150] In some example embodiments of the present disclosure, based on the aforementioned scheme, the data backtracking output module 1140 includes a data backtracking output unit, and the data backtracking output unit is used to obtain target index data corresponding to the target dimensional data if it is detected that the parameter value corresponding to the target dimensional data in the feature domain is greater than or equal to the statistical threshold; based on the target index data, obtain all dimensional data corresponding to the target dimensional data from the target data structure; and output all dimensional data to a preset data evaluation system to perform security level assessment on all dimensional data.

[0151] The specific details of each module of the above-mentioned data backtracking output device have been described in detail in the corresponding data backtracking output method, so they will not be repeated here.

[0152] It should be noted that although several modules or units of the data traceback output device are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0153] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above-mentioned data traceback output method is also provided.

[0154] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware embodiments, complete software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0155] Refer to the following Fig.12 1200 according to this embodiment of the present disclosure is described. Fig.12 The electronic device 1200 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0156] like Fig.12 As shown, the electronic device 1200 is in the form of a general computing device. The components of the electronic device 1200 may include, but are not limited to: the at least one processing unit 1210, the at least one storage unit 1220, a bus 1230 connecting different system components (including the storage unit 1220 and the processing unit 1210), and a display unit 1240.

[0157] The storage unit stores a program code, which can be executed by the processing unit 1210, so that the processing unit 1210 performs the steps according to various exemplary embodiments of the present disclosure described in the above “Exemplary Method” section of this specification. For example, the processing unit 1210 can perform the following steps: Figure 1 In step S110 shown in the figure, a target data structure is initialized and event data is received; the target data structure includes a dimension indexer and a feature domain; in step S120, dimension data is extracted from the event data, and index data corresponding to the dimension data is determined, and the index data is stored in the dimension indexer; in step S130, the dimension data is stored in the feature domain based on the index data; in step S140, if it is detected that the target dimension data in the feature domain is greater than or equal to a statistical threshold, all dimension data corresponding to the target dimension data are back-traced and output from the target data structure.

[0158] The storage unit 1220 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 1221 and / or a cache memory unit 1222 , and may further include a read-only memory unit (ROM) 1223 .

[0159] The storage unit 1220 may also include a program / utility 1224 having a set (at least one) of program modules 1225, such program modules 1225 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0160] Bus 1230 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0161] The electronic device 1200 may also communicate with one or more external devices 1270 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 1200, and / or communicate with any device that enables the electronic device 1200 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 1250. In addition, the electronic device 1200 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 1260. As shown, the network adapter 1260 communicates with other modules of the electronic device 1200 via a bus 1230. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 1200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0162] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0163] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible embodiments, various aspects of the present disclosure may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to perform the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of the present specification.

[0164] refer to Fig.13As shown, a program product 1300 for implementing the above-mentioned data traceback output method according to an embodiment of the present disclosure is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0165] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The 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 (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable 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.

[0166] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a 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.

[0167] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0168] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0169] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0170] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0171] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0172] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A data backtracking output method, characterized in that: include: Initialize a target data structure and receive event data; the target data structure includes a dimension indexer and a feature domain; Extracting dimension data from the event data, determining index data corresponding to the dimension data, and storing the index data in the dimension indexer; storing the dimensional data in the feature domain based on the index data; If it is detected that the parameter value corresponding to the target dimension data in the feature domain is greater than or equal to the statistical threshold, the target index data corresponding to the target dimension data is obtained; Based on the target index data, all dimensional data corresponding to the target dimensional data are acquired from the target data structure; and all the dimensional data are output to a preset data evaluation system to assess the security level of all the dimensional data.

2. The data backtracking output method according to claim 1, characterized in that: The extracting dimension data from the event data and determining index data corresponding to the dimension data includes: Calculate the storage address mapping value corresponding to the dimension data, and detect whether there is a target storage address mapping value that is the same as the storage address mapping value in the dimension indexer; If there is a target storage address mapping value identical to the storage address mapping value in the dimension indexer, the coordinate information corresponding to the storage address mapping value is used as the index data of the dimension data; If the target storage address mapping value identical to the storage address mapping value does not exist in the dimension indexer, coordinate information corresponding to the dimension data is calculated, and the coordinate information is used as index data of the dimension data.

3. The data backtracking output method according to claim 1, characterized in that: The feature domain includes a dimension domain and a parameter domain, and storing the dimension data in the feature domain based on the index data includes: Based on the index data, detecting whether a dimension value in the dimension domain corresponding to the dimension data is a null value; If it is detected that the dimension value in the dimension domain corresponding to the dimension data is a null value, the dimension value corresponding to the dimension data is stored in the dimension domain, and the parameter value corresponding to the dimension data is stored in the parameter domain according to the dimension value.

4. The data backtracking output method according to claim 3, characterized in that: If it is detected that the dimension value in the dimension domain corresponding to the dimension data is a null value, the dimension value corresponding to the dimension data is stored in the dimension domain, further comprising: If it is detected that the dimension value in the dimension domain corresponding to the index data is not a null value, the dimension value stored in the dimension domain is compared with the dimension value corresponding to the dimension data to obtain a comparison result; If the comparison result is the same, the parameter value corresponding to the dimensional data is stored in the parameter field according to the dimensional value stored in the dimensional field.

5. The data backtracking output method according to claim 4, characterized in that: If the comparison result is the same, then storing the parameter value corresponding to the dimension value in the dimension data into the parameter domain, further comprising: If the comparison result is not the same, the dimension data is updated and a new storage address mapping value corresponding to the updated dimension data is calculated.

6. The data backtracking output method according to any one of claims 3 to 5, characterized in that: The parameter domain includes an event unit, and storing the parameter value corresponding to the dimension data in the parameter domain according to the dimension value stored in the dimension domain includes: Classifying the parameter values ​​corresponding to the dimensional data according to the dimensional values ​​stored in the dimensional domain to obtain a plurality of event units; According to the data volume corresponding to the event unit, the event unit is allocated and stored in the parameter domain.

7. A data backtracking output device, characterized in that: include: An initialization module, used to initialize a target data structure and receive event data; the target data structure includes a dimension indexer and a feature domain; An index data determination module, configured to extract dimension data from the event data, determine index data corresponding to the dimension data, and store the index data in the dimension indexer; A dimensional data storage module, used for storing the dimensional data in the feature domain based on the index data; A data backtracking output module is used to obtain target index data corresponding to the target dimensional data if it is detected that the parameter value corresponding to the target dimensional data in the feature domain is greater than or equal to the statistical threshold; Based on the target index data, all dimensional data corresponding to the target dimensional data are acquired from the target data structure; and all the dimensional data are output to a preset data evaluation system to assess the security level of all the dimensional data.

8. An electronic device, comprising: processor; as well as A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the data traceback output method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the data backtracking output method according to any one of claims 1 to 6.

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