Data storage method, system, device and medium based on label calculation

Abstract

The application discloses a data storage method, system and device based on label calculation and a medium. The data storage method based on label calculation comprises the following steps: obtaining a label script from a front-end page and translating the label script into distributed calculation logic for data analysis to obtain a plurality of basic labels, derivative labels, a first label rule and a second label rule; obtaining original data from a preset database; and using a distributed calculation engine to perform reverse sequence arrangement calculation on the basic labels, the derivative labels, the first label rule, the second label rule and the original data in the subsequent step, and storing the label calculation result in at least one storage format, so that the label can be added infinitely through page configuration without affecting the structure and performance of a database table, thereby meeting the requirement of a large amount of distributed storage.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data storage, and in particular to a data storage method based on label calculation, a data storage system based on label calculation, an electronic device and a storage medium. BACKGROUND

[0002] From the birth of Hadoop (a distributed system infrastructure) to now, the data storage format has undergone several generations of development, from the initial txt file (a storage format), to the later sequence file (another storage format) and rcfile (another storage format), to the current ORC (a columnar storage format) and Parquet (another columnar storage format) and other columnar storage files, the data storage format has undergone earth-shattering changes, with better performance and higher compression ratio. However, the development of data organization methods has been quite slow.

[0003] Hive (a data warehouse tool) proposes the concept of partitioning, which uses certain columns (columns in a data table) as partition values to organize data, which can effectively filter out data that does not need to be read. This partitioning is reflected in physical storage as partitioning (organizing) data by folders. Using folders to organize data has natural affinity with the file system structure of HDFS (Hadoop Distributed File System), so this method has been used down.

[0004] However, with the combination of big data and cloud computing, more and more underlying storage systems are shifting from distributed file systems to storage systems on the cloud. This way of organizing data using folders has encountered great challenges. As the amount of data continues to grow, traditional file systems and relational databases cannot meet the requirements of high capacity, high performance and high scalability, such as being easily affected by database table fields, and being unable to frequently add and delete labels. SUMMARY

[0005] The technical problem to be solved by the present application is to overcome the above-mentioned defects in the prior art, and to provide a data storage method based on label calculation, a data storage system based on label calculation, an electronic device and a storage medium.

[0006] The present application solves the above technical problems by the following technical solutions:

[0007] The present application solves the above technical problems by the following technical solutions:

[0008] The present application provides a data storage method based on label calculation, which comprises:

[0009] acquire a label script from a front-end page, and translate the label script into distributed computing logic for data analysis to obtain a plurality of basic labels, at least one derived label corresponding to the basic labels, and a first label rule corresponding to the basic labels, and a second label rule corresponding to the derived label;

[0010] acquire original data from a preset database;

[0011] perform reverse-order arrangement calculation on the basic labels, the derived labels, the first label rule, the second label rule, and the original data using a distributed computing engine to obtain a label calculation result;

[0012] store the label calculation result in at least one storage format.

[0013] Preferably, the storing the label calculation result in at least one storage format comprises:

[0014] acquire the number of labels, and determine whether the number is greater than a preset number threshold;

[0015] if not, store the label calculation result in a first storage format;

[0016] if yes, store the label calculation result in a second storage format.

[0017] Preferably, the method further comprises:

[0018] store the stored label calculation result into a query analysis system in a narrow table to wide table manner to perform label query and / or label analysis on the labels or the derived labels.

[0019] Preferably, before the acquiring the label script from the front-end page, the method further comprises:

[0020] configure the label script on the front-end page through a combination of a preset syntax and online filtering logic operation.

[0021] The application further provides a data storage system based on label calculation, which comprises:

[0022] a label analysis module configured to acquire a label script from a front-end page, and translate the label script into distributed computing logic for data analysis to obtain a plurality of basic labels, at least one derived label corresponding to the basic labels, and a first label rule corresponding to the basic labels, and a second label rule corresponding to the derived label;

[0023] an acquisition module configured to acquire original data from a preset database;

[0024] a computing module, performing reverse sequence calculation on the base label, the derived label, the first label rule, the second label rule and the original data using a distributed computing engine to obtain a label calculation result;

[0025] a storage module, storing the label calculation result in at least one storage format.

[0026] Preferably, the storage module acquires the number of labels and determines whether the number is greater than a preset number threshold.

[0027] If not, the label calculation result is stored in a first storage format.

[0028] If yes, the label calculation result is stored in a second storage format.

[0029] Preferably, the system further comprises:

[0030] a conversion module, storing the stored label calculation result into a query analysis system in a narrow table to wide table manner to perform label query and / or label analysis on the label or the derived label.

[0031] Preferably, the system further comprises:

[0032] a configuration module, configuring the label script on the front-end page through a combination of preset syntax and online filtering logic operation.

[0033] The application further provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the data storage method based on label calculation as described above when executing the computer program.

[0034] The application further provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executable on a processor to implement the data storage method based on label calculation as described above.

[0035] The positive progress effect of the application is that by configuring a label script on a front-end page and using a distributed computing engine to perform reverse sequence calculation on a base label, a derived label, a first label rule, a second label rule and original data in subsequent processes, and storing a label calculation result in at least one storage format, it is realized that labels can be added infinitely through page configuration without affecting the structure and performance of a database table, thereby meeting the requirement of a large amount of distributed storage. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1A flowchart of a data storage method based on tag calculation provided by the embodiment.

[0037] Figure 2 Another flowchart of a data storage method based on tag calculation provided by the embodiment.

[0038] Figure 3 A module diagram of a data storage system based on tag calculation provided by the embodiment.

[0039] Figure 4 A structural diagram of an electronic device for implementing a data storage method based on tag calculation provided by the embodiment. DETAILED DESCRIPTION

[0040] The application will be further described in the following embodiments, but the application is not limited to the scope of the embodiments.

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some examples or embodiments of the present application, and for those skilled in the art, the present application can be applied to other similar scenarios without creative labor. Unless it is clear from the language context or otherwise indicated, the same reference numbers in the drawings represent the same structure or operation.

[0042] In this document, reference to“an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase“in an embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily all directed to the same embodiment, or to a single alternative embodiment. It is explicitly contemplated that embodiments described herein can be combined with each other.

[0043] It should be understood that the terms“system”,“apparatus”,“unit” and / or“module” used herein are a method for distinguishing different components, elements, parts, sections or assemblies of different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0044] As shown herein, unless the context clearly indicates otherwise, the words“a”,“an”,“one”, and / or“this” do not mean a single number but can include a plurality. Generally, the terms“comprise” and“include” only indicate the inclusion of the steps and elements explicitly identified, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements.

[0045] The terms "have," "may have," "include," or "may include" as used herein indicate the presence of the corresponding function, operation, element, etc. described in the specification and do not limit the existence of other one or more functions, operations, elements, etc. In addition, it should be understood that the terms "include" or "have" as used herein indicate the presence of the features, numbers, steps, operations, elements, components, or combinations thereof described in the specification, without excluding the presence or addition of one or more other features, numbers, steps, operations, elements, components, or combinations thereof.

[0046] The terms "A or B," "at least one of A and / or B," or "one or more of A and / or B" as used herein include any and all combinations of one or more of the words enumerated together. For example, "A or B," "at least one of A and B," or "at least one of A or B" means (1) including at least one A, (2) including at least one B, or (3) including both at least one A and at least one B.

[0047] The terms "first," "second," etc. as used herein, are used to describe objects only for the purpose of distinguishing the objects from one another, and do not indicate or imply a specific order or order of sequence. For example, a first element can be termed a second element, and similarly, a second element can be termed a first element without departing from the scope of the present disclosure, and the like.

[0048] The term "connected to" or "coupled to" as used herein, will be understood to mean that a connection or a coupling between elements (e.g., a first element) and another element (e.g., a second element) can be direct or indirect, and that there can be intermediate elements (e.g., a third element) between the elements and another element. In contrast, the term "directly connected to" or "directly coupled to" as used herein will be understood to mean that there are no intermediate elements (e.g., a third element) between the elements (e.g., a first element) and another element (e.g., a second element).

[0049] Flowcharts are used herein to illustrate the operations performed by systems according to embodiments of the present disclosure. It should be understood that the preceding or following operations are not necessarily performed in the order as shown. Rather, the steps can be processed in reverse order or simultaneously. Also, other operations can be added to or removed from these processes, or one or more steps can be removed from these processes.

[0050] With the combination of big data and cloud computing, more and more underlying storage systems are shifting from distributed file systems to storage systems on the cloud. This way of organizing data using folders has encountered great challenges. As the amount of data continues to grow, traditional file systems and relational databases cannot meet the requirements of high capacity, high performance and high scalability. For example, they are easily affected by the limitations of database table fields and cannot frequently add and delete tags.

[0051] In addition, centralized metadata services (such as Hive MetaStore) can better manage the metadata information of the entire data warehouse, but also bring some problems. First, the separate storage of metadata and data can easily lead to inconsistency, that is, the modification of data cannot be timely reflected in the metadata. For example, some data is deleted or modified on HDFS, but MetaStore cannot perceive the change of the data, causing inconsistency between the two. Second, centralized metadata management can easily become a single bottleneck for large-scale enterprise applications. That is, all metadata access needs to be accessed through centralized metadata services, which can increase the pressure on the metadata service and thus prolong the query plan time.

[0052] Based on this, the embodiment provides a data storage method based on label calculation, as shown in Figure 1 The data storage method based on label calculation of the embodiment includes:

[0053] S101, obtain a label script from a front-end page, and translate the label script into distributed computing logic for data analysis to obtain a plurality of basic labels, at least one derived label corresponding to the basic labels, and a first label rule corresponding to the basic labels, and a second label rule corresponding to the derived label.

[0054] The label script obtained from the front-end page is parsed and translated into executable distributed computing logic, and the translated distributed computing logic is used for data analysis to extract a plurality of basic labels and derived labels related thereto.

[0055] The translation process enables the label script to be understood and executed by the subsequent computing engine. During data analysis, if the calculation of the basic label triggers the calculation logic of the derived label, at least one derived label related to the basic label will be calculated according to the definition of the derived label. In this way, more rich label information can be obtained for subsequent data analysis and decision-making. By parsing the label script, the system can extract the first label rule corresponding to the basic label and the second label rule corresponding to the derived label. These rules describe the calculation logic and conditions of the labels. The extracted label rules provide accurate basis for subsequent calculation and analysis processes and provide necessary data basis for subsequent calculation, storage and analysis.

[0056] As an optional implementation, before step S101, the data storage method based on label calculation of the embodiment further comprises:

[0057] S100, configuring a label script on a front-end page through a combination of preset syntax and online filtering logic operation.

[0058] On the front-end page, the user can flexibly configure the label script through the preset syntax and online filtering logic operation. This allows the user to intuitively define the filtering rules and select different conditions and logical operators to create complex filtering logic according to needs. Furthermore, the user can highly customize the label script configuration according to business needs and data characteristics.

[0059] S102, obtaining original data from a preset database.

[0060] The embodiment obtains the original data stored in the database through connection with the preset database, thereby providing a data source for subsequent label calculation.

[0061] S103, performing reverse-order arrangement calculation on the basic label, the derived label, the first label rule, the second label rule, and the original data using a distributed computing engine to obtain a label calculation result.

[0062] The reverse-order arrangement calculation on the basic label, the derived label, the label rule, and the original data using the distributed computing engine ensures the accuracy and consistency of the calculation, can significantly improve the calculation efficiency, and can obtain the label calculation result more quickly when processing large-scale data, thereby realizing efficient distributed label calculation.

[0063] S104, storing the label calculation result in at least one storage format.

[0064] It should be further noted that the data storage method based on label calculation provided by the embodiment can be executed in a smart terminal, a computer terminal, a network device, a chip, a chip module, or a similar computing device.

[0065] As an optional implementation, as shown in Figure 2 S104 of the embodiment comprises:

[0066] S1041, obtaining the number of labels and determining whether the number is greater than a preset number threshold. If not, step S1042 is executed, and if yes, step S1043 is executed.

[0067] S1042, storing the label calculation result in a first storage format.

[0068] S1043, store the label calculation result in the second storage format.

[0069] When the number of labels is less than the preset number threshold, in order to save storage resources and achieve high performance and compression ratio, the label calculation result can be stored in a first storage format, such as a Hive Parquet format for data storage.

[0070] When the number of labels is greater than the preset number threshold, that is, in the case of a large number of labels, in order to avoid performance problems caused by a large number of folders formed by label partitioning, the label calculation result can be stored in a second storage format, such as an iceberg table management storage format.

[0071] Further, the embodiment can not only use the corresponding storage format according to the number of labels, but also meet the demand of a large number of distributed storage without affecting the table structure, thereby ensuring the reliability and consistency of the data.

[0072] As an optional implementation, the data storage method based on label calculation of the embodiment further includes:

[0073] S105, store the stored label calculation result into the query analysis system in a narrow table to wide table manner, to perform label query and / or label analysis on the label or derived label.

[0074] The stored label calculation result is subjected to a narrow table to wide table operation, and the result is converted from a narrow table form in which each record contains only one label to a wide table form in which each record contains multiple labels. Such conversion manner can improve the efficiency of query and analysis. By storing the label calculation result into the query analysis system, the user can conveniently perform label query and analysis, obtain statistical information, trend analysis, etc. about a specific label or derived label, thereby supporting business decision-making and insight discovery.

[0075] The embodiment realizes that unlimited labels can be added through page configuration without affecting the structure and performance of the database table, and further meets the requirement of a large number of distributed storage, by configuring a label script on a front-end page and using a distributed computing engine to perform reverse-order arrangement calculation on the basic label, the derived label, the first label rule, the second label rule, and the original data, and storing the label calculation result in at least one storage format.

[0076] Corresponding to the data storage method based on label calculation described above, the embodiment further provides a data storage system based on label calculation, which will be introduced respectively. Specifically, as shown in Figure 3 The data storage system based on label calculation of the embodiment includes:

[0077] The tag parsing module 1 obtains a tag script from a front-end page and translates the tag script into distributed computing logic for data parsing to obtain a plurality of basic tags, at least one derived tag corresponding to the basic tags, and a first tag rule corresponding to the basic tags and a second tag rule corresponding to the derived tags.

[0078] The tag script obtained from the front-end page is parsed and translated into executable distributed computing logic, and the translated distributed computing logic is used for data parsing to extract a plurality of basic tags and derived tags related thereto.

[0079] The above translation process enables the tag script to be understood and executed by a subsequent computing engine. During data parsing, if the calculation of a basic tag triggers the calculation logic of a derived tag, at least one derived tag related to the basic tag is calculated according to the definition of the derived tag. In this way, more rich tag information can be obtained for subsequent data analysis and decision-making. Furthermore, by parsing the tag script, the system can extract a first tag rule corresponding to the basic tag and a second tag rule corresponding to the derived tag. These rules describe the calculation logic and conditions of the tags, and the extracted tag rules provide accurate basis for subsequent calculation and analysis processes and provide necessary data basis for subsequent calculation, storage and analysis.

[0080] As an optional implementation, the data storage system based on tag calculation of the embodiment further comprises:

[0081] The configuration module 0 configures the tag script on the front-end page through a combination of preset syntax and online filtering logic operations.

[0082] On the front-end page, the user can flexibly configure the tag script through the preset syntax and online filtering logic operations. This enables the user to intuitively define filtering rules and select different conditions and logic operators to create complex filtering logic according to needs. Furthermore, the user can highly customize the tag script configuration according to business needs and data characteristics.

[0083] The acquisition module 2 acquires original data from a preset database.

[0084] The embodiment acquires original data stored in the database through connection with the preset database, thereby providing a data source for subsequent tag calculation.

[0085] The computing module 3 uses a distributed computing engine to perform reverse-order arrangement calculation on the basic tags, the derived tags, the first tag rule, the second tag rule and the original data to obtain a tag calculation result.

[0086] The distributed computing engine is used to perform reverse sequence calculation on the basic label, the derived label, the label rule and the original data, so as to ensure the accuracy and consistency of the calculation, significantly improve the calculation efficiency, and obtain the label calculation result more quickly when processing large-scale data, thereby realizing efficient distributed label calculation.

[0087] The storage module 4 is configured to store the label calculation result in at least one storage format.

[0088] As an optional implementation, the storage module 4 of the embodiment is configured to acquire the number of labels and determine whether the number is greater than a preset number threshold.

[0089] If not, the label calculation result is stored in a first storage format.

[0090] If yes, the label calculation result is stored in a second storage format.

[0091] When the number is less than the preset number threshold, in order to save storage resources, realize high performance and compression ratio, the label calculation result can be stored in the first storage format, for example, the Parquet format of Hive.

[0092] When the number of labels is greater than the preset number threshold, that is, in the case of a large number of labels, in order to avoid performance problems caused by a large number of folders formed by label partitioning, the label calculation result can be stored in the second storage format, for example, the table management storage format of iceberg.

[0093] Further, the embodiment can not only use the corresponding storage format according to the number of labels, but also meet the demand of large-scale distributed storage without affecting the table structure, thereby ensuring the reliability and consistency of the data.

[0094] As an optional implementation, the data storage system based on label calculation of the embodiment further comprises:

[0095] The conversion module 5 is configured to store the stored label calculation result into a query analysis system in the form of narrow table to wide table, so as to perform label query and / or label analysis on the label or the derived label.

[0096] The stored label calculation result is subjected to narrow table to wide table operation, and the result is converted from the narrow table form in which each record contains only one label to the wide table form in which each record contains multiple labels. Such conversion manner can improve the efficiency of query and analysis. By storing the label calculation result into the query analysis system, the user can conveniently perform label query and analysis, obtain statistical information and trend analysis about a specific label or derived label, and thereby support business decision and insight discovery.

[0097] The embodiment realizes unlimited addition of tags through page configuration without affecting the structure and performance of the database table, and further meets the requirement of a large amount of distributed storage, by configuring a tag script on a front-end page, and performing reverse-order arrangement calculation on a basic tag, a derivative tag, a first tag rule, a second tag rule and original data using a distributed computing engine in subsequent calculation, and storing a tag calculation result in at least one storage format.

[0098] It should be noted that the data storage system based on tag calculation in the embodiment can be a single chip, a chip module or an electronic device, or a chip or a chip module integrated in an electronic device. Each module / unit contained in each device and product described in the above embodiments can be a software module / unit, or a hardware module / unit, or part of a software module / unit and part of a hardware module / unit. For example, for each device and product applied to or integrated in a chip, each module / unit contained therein can be realized in the form of hardware such as a circuit, or at least part of the modules / units can be realized in the form of a software program running on a processor integrated in the chip, and the remaining (if any) modules / units can be realized in the form of hardware such as a circuit. For each device and product applied to or integrated in a chip module, each module / unit contained therein can be realized in the form of hardware such as a circuit, and different modules / units can be located in the same component (for example, a chip, a circuit module, etc.) or different components of the chip module, or at least part of the modules / units can be realized in the form of a software program running on a processor integrated in the chip module, and the remaining (if any) modules / units can be realized in the form of hardware such as a circuit. For each device and product applied to or integrated in a terminal, each module / unit contained therein can be realized in the form of hardware such as a circuit, and different modules / units can be located in the same component (for example, a chip, a circuit module, etc.) or different components of the terminal, or at least part of the modules / units can be realized in the form of a software program running on a processor integrated in the terminal, and the remaining (if any) modules / units can be realized in the form of hardware such as a circuit.

[0099] In one specific example, the rule processing of the label is configured in the web rule configuration module, which can support SQL voice rules and combined filtering of online filtering logic operations, store the rules in the mysql database through filtering, process the calculation time of the label through the calculation engine, perform label calculation, support spark distributed calculation, and store the calculation result table into the hive. In order to solve the problem of data partitioning, the iceberg table format is used for storage. The narrow table type is used for storage. Finally, the data is stored into the ES through the conversion of the narrow table to the wide table. The web server uses the micro-service architecture to publish services and enter data, and stores the data into the rule engine server. The rule logic is processed and calculated through the distributed storage server, and is stored in the distributed server. Through the data row-to-column form, the data is stored into the ES server, and the label query analysis is performed.

[0100] Figure 4 A structural schematic diagram of an electronic device is provided for the embodiment. The electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the data storage method based on label calculation in the above-mentioned embodiments when executing the program. Figure 4 The electronic device 30 shown is merely an example and should not limit the functions and use range of the embodiments of the present application.

[0101] As shown in Figure 4 The electronic device 30 can be in the form of a general computing device, for example, it can be a server device. The components of the electronic device 30 can include but are not limited to the above-mentioned at least one processor 31, the above-mentioned at least one memory 32, and a bus 33 connecting different system components including the memory 32 and the processor 31.

[0102] The bus 33 includes a data bus, an address bus, and a control bus.

[0103] The memory 32 can include volatile memory, such as a random access memory (RAM) 321 and / or a cache memory 322, and can further include a read-only memory (ROM) 323.

[0104] The memory 32 can further include programs / utilities 325 having a set of (at least one) program modules 324, such as an operating system, one or more application programs, other program modules, and program data, each of which or a combination of which can include the implementation of a network environment.

[0105] The processor 31 performs various function applications and data processing by running the computer program stored in the memory 32, such as the above-mentioned data storage method based on label calculation of the present application.

[0106] The electronic device 30 can also communicate with one or more external devices 34 such as a keyboard or a pointing device, among others. Such communication can occur through Input / Output (I / O) interfaces 35. Still yet, such a model generating device 30 can communicate to one or more networks such as a local area network (LAN), a wide area network (WAN), and / or the Internet through a network adapter 36. As Figure 4 illustrated, the network adapter 36 communicates to the other modules of the model generating device 30 through the bus 33. It should be appreciated that the model generating device 30 can be a part of another device or can be a stand-alone device. It also should be appreciated that the model generating device 30 can be connected to one or more devices in addition to or in lieu of the bus 33. In this regard, the functionality of the model generating device 30 can be implemented by using one or more devices located remotely from the model generating device 30.

[0107] It should be noted that although the above detailed description refers to several units / modules or sub-units / modules of the electronic device, such a division is merely exemplary and not mandatory. Indeed, according to embodiments of the application, the features and functionalities of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functionalities of one unit / module described above can be further divided into several units / modules embodied by.

[0108] The embodiments also provide a computer readable storage medium, having stored thereon a computer program, which when executed by a processor, implements the steps of the above described data storage method based on label calculation.

[0109] Wherein the readable storage medium can take more specific forms, which can include but are not limited to: portable disc, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device or any suitable combination of the above.

[0110] In possible embodiments, the application can also be implemented in the form of a program product, which includes program codes for causing a terminal device to execute the steps of the above described data storage method based on label calculation when the program product is run on the terminal device.

[0111] Wherein the program codes for executing the application can be written in any combination of one or more programming languages, which can be executed entirely on the user device, partially on the user device, as a stand-alone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0112] Although the specific embodiments of the present application have been described above, it is understood by those skilled in the art that the present application is only illustrated by way of example, and the scope of protection of the present application is defined by the appended claims. Those skilled in the art can make various changes or modifications to the embodiments without departing from the principles and essence of the present application, and such changes and modifications fall within the scope of protection of the present application.

Claims

1. A data storage method based on tag computing, characterized in that, The method includes: The tag script is obtained from the front-end page and translated into distributed computing logic for data parsing to obtain several basic tags, at least one derived tag corresponding to the basic tags, a first tag rule corresponding to the basic tags, and a second tag rule corresponding to the derived tags. Retrieve raw data from the preset database; The distributed computing engine is used to perform reverse-order calculations on the base tags, the derived tags, the first tag rules, the second tag rules, and the original data to obtain the tag calculation results; The label calculation results are stored using at least one storage format; The storage of the tag calculation results using at least one storage format includes: Obtain the number of tags and determine whether the number is greater than a preset number threshold; If not, the first storage format is used to store the tag calculation results; If so, the label calculation result is stored using the second storage format; The method further includes: The stored tag calculation results are stored in the query analysis system by converting narrow tables to wide tables, so as to perform tag query and / or tag analysis on the tags or the derived tags; Prior to retrieving the tag script from the front-end page, the method further includes: The tag script is configured on the front-end page by combining preset syntax and online filtering logic operations.

2. A data storage system based on tag computing, characterized in that, The system includes: The tag parsing module obtains tag scripts from the front-end page and translates the tag scripts into distributed computing logic for data parsing to obtain several basic tags, at least one derived tag corresponding to the basic tags, a first tag rule corresponding to the basic tags, and a second tag rule corresponding to the derived tags. The acquisition module retrieves raw data from a preset database; The calculation module uses a distributed computing engine to perform reverse-order calculations on the base tags, the derived tags, the first tag rules, the second tag rules, and the original data to obtain the tag calculation results; The storage module stores the tag calculation results using at least one storage format; The storage module acquires the number of tags and determines whether the number is greater than a preset number threshold. If not, the first storage format is used to store the tag calculation results; If so, the label calculation result is stored using the second storage format; The system also includes: The conversion module stores the calculated tag results into the query analysis system by converting a narrow table to a wide table, so as to perform tag query and / or tag analysis on the tag or the derived tag; The system also includes: The configuration module configures the tag script on the front-end page by combining preset syntax and online filtering logic operations.

3. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes a computer program, it implements the tag-based data storage method as described in claim 1.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the tag-based data storage method as described in claim 1.

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