Point location data management method and system and electronic equipment

Abstract

The invention relates to a point location data management method and system and electronic equipment, and the method comprises the steps: obtaining point location data and at least two data types of the point location data, processing the point location data according to a preset data structure and the data types, and obtaining substructure data, the index offset of each piece of point location data in the corresponding substructure data is obtained; determining a data part according to the substructure data, and obtaining an index part according to the data type of each point location data and the corresponding index offset; and determining a target data structure according to the index part and the data part, and storing the point data according to the target data structure.

Description

Technical Field

[0001] This application relates to the field of Internet of Things data management, and particularly to a method, system, and electronic device for point data management. Background Art

[0002] In the field of the Internet of Things, a "point" refers to a data source formed by various sensors and actuators used for data collection, monitoring, and control on Internet of Things devices, and is one of the most important basic data units in Internet of Things application scenarios. In the application scenarios of the Internet of Things field, a device often has multiple points, and these points often have different data types and different sampling frequencies. Different types of point data are uploaded separately, thus increasing the difficulty of data modeling and data application.

[0003] If the existing wide table is used to model and store point data, many empty table cells will be stored, causing serious data sparsity problems and wasting storage space. The more points a device has, the more obvious the data sparsity problem becomes. The narrow table modeling scheme includes maintaining a data table for each point with a different data type, and uniformly converting all point types into string types for storage. Among them, the method of maintaining a data table for each point with a different data type requires multiple independent data tables to be managed and stored when there are multiple data types of points. Although the data sparsity problem is solved, the number of data tables to be maintained will increase with the increase in the number of point types, bringing inconvenience to data management, maintenance, and use. If all point types are uniformly converted into string types for storage, although only one table object needs to be accessed when users perform write and query operations, greatly improving the usability, but converting the data into strings not only increases the storage cost but also reduces the query and calculation efficiency.

[0004] The existing point data management method has a high storage cost and low storage efficiency, and an efficient point data management method is needed to improve the management ability of devices. Summary of the Invention

[0005] Embodiments of this application provide a method, system, and electronic device for point data management to at least solve the problem of high storage cost and low storage efficiency in the related art of point data management methods.

[0006] In a first aspect, embodiments of this application provide a method for point data management, including: Obtain point data and at least two data types of the point data, process the point data according to a preset data structure and the data types to obtain sub-structure data, and the index offset of each point data in the corresponding sub-structure data; Determine the data part according to the sub-structure data, and obtain the index part according to the data type of each point position data and the corresponding index offset; Determine the target data structure according to the index part and the data part, and store the point position data in the target data structure.

[0007] In one embodiment, the processing of the point position data according to the preset data structure and the data type to obtain the sub-structure data, and the index offset of each point position data in the corresponding sub-structure data includes: Store the point position data of each data type according to the preset data structure respectively, to obtain the sub-structure data corresponding to the data type, and the index offset of each point position data in the sub-structure data corresponding to its data type.

[0008] In one embodiment, after obtaining the sub-structure data corresponding to the data type, the method further includes: Sort and group the sub-structure data according to the preset sorting conditions to obtain the secondary grouped structure data, and sequentially compress and store the secondary grouped structure data according to the sorting result.

[0009] In one embodiment, the storing the point position data in the target data structure includes: Store the point position data according to the preset storage order, and determine the subscript of each piece of data in the data part in the target data structure according to the storage order.

[0010] In one embodiment, the method further includes querying the target data, and the querying the target data includes: Obtain the target subscript of the target data in the target data structure, and determine the target data type and the target index offset according to the target subscript; Determine the target data from the sub-structure data according to the target data type and the target index offset.

[0011] In one embodiment, the querying the target data includes: In response to the same data type of the target data, return the target data in the preset data structure; In response to the different data types of the target data, return the target data in the target data structure.

[0012] In a second aspect, an embodiment of the present application provides a point position data management system, including: An acquisition module: configured to acquire point position data and at least two data types of the point position data, process the point position data according to a preset data structure and the data type to obtain sub-structure data, and the index offset of each point position data in the corresponding sub-structure data; Determination module: configured to determine the data part according to the sub-structure data, and obtain the index part according to the data type of each point data and the corresponding index offset; Storage module: configured to determine the target data structure according to the index part and the data part, and store the point data in the target data structure.

[0013] In one embodiment, the acquisition module includes: configured to store the point data of each data type respectively according to the preset data structure, obtain the sub-structure data corresponding to the data type, and the index offset of each point data in the sub-structure data corresponding to its data type.

[0014] In a third aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements a point data management method as described in the first aspect above.

[0015] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements a point data management method as described in the first aspect above.

[0016] A point data management method, system, and electronic device provided by an embodiment of the present application have at least the following technical effects.

[0017] The present application uniformly manages different types of point data through the target data structure, stores different types of point data in the Internet of Things in the same data structure. Compared with the traditional scheme of using strings to simulate the storage of point data, it not only improves the storage density, but also effectively reduces the memory usage. When storing, the point data is grouped by type and then stored, ensuring the consistency of data types within each group, thereby improving the storage compression rate of point data and effectively reducing the storage cost.

[0018] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more concise and understandable. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments and descriptions thereof of the present application are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings: Figure 1 is a flowchart of a point data management method shown according to an embodiment of the present application; Figure 2It is a schematic structural diagram of an IOTANY Vector shown according to an exemplary embodiment; Figure 3 It is a schematic diagram of a point data grouping process shown according to an exemplary embodiment; Figure 4 It is a flowchart of point data persistence shown according to an exemplary embodiment; Figure 5 It is a structural block diagram of a point data management system shown according to an embodiment of the present application; Figure 6 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0020] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be described and explained below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments provided in the present application without creative efforts fall within the scope of protection of the present application.

[0021] Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present application. For those of ordinary skill in the art, the present application can be applied to other similar scenarios based on these drawings without creative efforts. In addition, it can also be understood that although the efforts made in this development process may be complex and lengthy, for those of ordinary skill in the art related to the content disclosed in the present application, some design, manufacturing or production changes made based on the technical content disclosed in the present application are only conventional technical means and should not be understood as the content disclosed in the present application being insufficient.

[0022] Referring to "embodiments" in the present application means that specific features, structures or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase appears in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those of ordinary skill in the art explicitly and implicitly understand that the embodiments described in the present application can be combined with other embodiments without conflict.

[0023] Unless otherwise defined, the technical terms or scientific terms involved in this application shall have the ordinary meanings understood by those with ordinary skills in the technical field to which this application pertains. The words such as "a", "an", "one", "the" and the like involved in this application do not indicate a limitation in quantity and may represent a singular or plural number. The terms "including", "comprising", "having" and any variations thereof involved in this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may further include unlisted steps or units, or may further include other steps or units inherent to these processes, methods, products or devices. The words such as "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "plurality" involved in this application means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, "A and / or B" may represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the front and rear associated objects. The terms "first", "second", "third" and the like involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0024] Uploading different types of point data separately increases the difficulty for users to model and use the data. Efficient point data management helps enterprises achieve equipment monitoring, fault warning and real-time monitoring, thereby enhancing the management ability of the equipment. Taking the vehicle networking scenario as an example, multiple sensors are used to collect data such as battery usage, vehicle speed, and engine status. Among them, the data type of battery usage (elec) is integer, the data type of vehicle speed (speed) is double-precision floating-point type, and the data type of engine status (engineStatus) is string. The prior art usually performs data modeling through wide tables or narrow tables.

[0025] (1) Table 1 is a data table obtained by modeling in the wide table mode.

[0026] Table 1

[0027] As shown in Table 1, each time series forms a separate row. The wide table modeling has the problem of data sparsity and wastes storage space. And as the number of points of the equipment increases, the problem of data sparsity becomes more and more obvious.

[0028] (2)Maintain a data table for each point of different data types. Table 2, Table 3, and Table 4 are the battery power table (integer type points), vehicle speed table (double precision floating point type points), and engine status table (string type points) respectively.

[0029] Table 2

[0030] Table 3

[0031] Table 4

[0032] As shown in Tables 2 to 4, different data tables are maintained to store point data of different types and frequencies of use. Although the problem of data sparsity is solved, the number of tables to be maintained will increase with the increase in the number of point types, which brings inconvenience to management, maintenance, and use. For example, when querying different points, different table objects need to be accessed.

[0033] (3)Convert all point types into strings for storage. In this way, only one table needs to be maintained to manage all point data, which is a common solution for current point data storage. Table 5 is the data table obtained by converting all point types into strings for storage.

[0034] Table 5

[0035] As shown in Table 5, by adopting the method of converting all point types into strings for storage, when users perform write and query operations, they only need to access one table object, which greatly improves the usability. However, converting the data into strings will increase the storage cost and reduce the query and calculation efficiency.

[0036] Therefore, an efficient and flexible point management method is needed to improve the efficiency of point data management for Internet of Things enterprises.

[0037] Based on the above situation, the embodiments of the present application provide a point data management method, system, and electronic device.

[0038] In the first aspect, the embodiments of the present application provide a point data management method, Figure 1 which is a flowchart of a point data management method shown according to the embodiments of the present application. As Figure 1 shown, the method includes: Step S101, obtain point data and at least two data types of the point data, process the point data according to a preset data structure and data type to obtain sub-structure data, and the index offset of each point data in the corresponding sub-structure data.

[0039] Optionally, the preset data structure is the unified basic data structure Vector inside DolphinDB. By grouping the point data according to the data type, the consistency of the data type within each group is ensured, which is beneficial to improving the storage compression ratio of the point data and effectively reducing the storage cost.

[0040] In one example, step S101 includes: storing the point data of each data type respectively according to the preset data structure to obtain sub-structure data corresponding to the data type, and the index offset of each point data in the sub-structure data corresponding to its data type.

[0041] Optionally, multiple Vector data structures are used to store the data of each basic data type (such as strings, floating-point numbers, etc.) respectively, and the index of the point data in the corresponding Vector structure data is obtained.

[0042] Step S102, determine the data part according to the sub-structure data, and obtain the index part according to the data type and the corresponding index offset of each point data.

[0043] Optionally, all the Vector structure data are used as the data part of the target structure data, and then the data of different types in the same logical row are associated by introducing an index (the data in the same logical row may be stored in different Vector data structures), so as to facilitate storing the point data of different data types in the same structure.

[0044] Step S103, determine the target data structure according to the index part and the data part, and store the point data in the target data structure.

[0045] Optionally, the target data structure is the self-named IOTANY type, which represents any data type in the Internet of Things (IOT) scenario. The IOTANY Vector is divided into two parts: Data and Index. The data structure of the IOTANY Vector type can provide efficient physical storage support for any type. A new data type IOTANY is proposed, which stores different types of point data in the Internet of Things in the same data structure Vector and uses subscript indexing for access. Compared with the traditional scheme of using strings to simulate the storage of point data, this design not only improves the storage density but also effectively reduces the memory usage.

[0046] For example, strings, floating-point numbers, and integers can be placed in the same storage format with high efficiency. Figure 2 is a schematic structural diagram of an IOTANY Vector shown according to an exemplary embodiment, asFigure 2 As shown, this data structure can aggregate and store data of the same type in the same basic data structure Vector, forming a sub-array (SubVector) in the IOTANY Vector. The 0th, 1st, and 4th rows of the IOTANY Vector are of INT type and are sequentially recorded in the INT-type sub-array; the 2nd, 3rd, and 5th rows are of DOUBLE type and are sequentially recorded in the DOUBLE-type sub-array.

[0047] When writing a new element into the IOTANY Vector, taking Figure 2 the last element 21.49 as an example, first determine that it should be written into the DOUBLE-type sub-array according to the data type (DOUBLE) and become the last element of this sub-array (i.e., the subscript is 2 in the sub-array), then write the type information of DOUBLE into the Types column in the subscript index and write the subscript 2 into the Indices column, and finally write 21.49 into the DOUBLE sub-array.

[0048] In this way, through the newly proposed target data type IOTANY, different types of point data in the Internet of Things are uniformly stored in the same data structure Vector, and accessed using the index part containing data types and indices. Compared with the traditional solution of using strings to simulate the storage of point data, it not only improves the storage density but also effectively reduces memory usage.

[0049] In an example, after obtaining the sub-structure data corresponding to the data type, the method further includes: sorting and grouping the sub-structure data according to a preset sorting condition to obtain secondary grouped structure data, and sequentially compressing and storing the secondary grouped structure data according to the sorting result.

[0050] Optionally, the point data is stored in the data file Level File of TSDB (a time series data storage engine independently developed by DolphinDB based on the LSM-Tree architecture), and the Level File is a data storage file based on the Partitioned Attributes Across (PAX) format. In the original persistence process, the data to be flushed to disk will be sorted and grouped according to the sort key, then each group will be evenly chunked by size and a Zone Map index will be established, and then each data chunk will be compressed and written to disk column by column to generate the Level File. The Zone Map index records the minimum and maximum values of multiple groups, and by comparing whether an input key value is between the minimum and maximum values of a group, it is determined whether the key value is in the group, achieving the purpose of indexing.

[0051] Figure 3It is a schematic diagram of a point data grouping process shown according to an exemplary embodiment. As Figure 3 shown, considering that point data usually has different data types, when generating the Level File, grouping is first performed according to the type of point data. Figure 4 It is a flowchart of point data persistence shown according to an exemplary embodiment. As Figure 4 shown, after grouping according to the data type, within each group, sorting and grouping are performed based on the Sort Key (the sorting key specified when creating the TSDB distributed table) according to the original persistence process, and an index is established. By ensuring that the point data types within each group are consistent, the compression rate of the point data is optimized, thus significantly improving the storage efficiency. Moreover, a point data type index (Type Index) is introduced in the LevelFile. This index records the point data type and the offset (Offset) of the Zone Map index corresponding to the data of this type in the Level File.

[0052] In this way, when storing in the Level File, the point data is grouped according to the type and sorted according to the Sort Key, ensuring the consistency of the data types within each group, thereby improving the storage compression rate of the point data and effectively reducing the storage cost.

[0053] In one example, storing point data in the target data structure includes: storing the point data in the preset storage order, and determining the subscript of each piece of data in the data part in the target data structure according to the storage order. Optionally, it can be specified to store in the order of collection time or the order of receiving data when storing, generate data subscripts according to the storage order, and store the data and its subscripts.

[0054] In one example, the method further includes querying the target data. Querying the target data includes: Step S401, obtaining the target subscript of the target data in the target data structure, and determining the target data type and the target index offset according to the target subscript.

[0055] Step S402, determining the target data from the substructure data according to the target data type and the target index offset.

[0056] Optionally, when accessing the data in the IOTANY Vector through the subscript, the subscript of IOTANY is mapped to the subscript of the corresponding subarray through the subscript index. Referring to Figure 2 , in Figure 2 , when reading the second row of the IOTANY Vector, it will be found that it is of the INT type and the subscript in the INT subarray is 1. Then read the data 90 with the index 1 from the INT subarray.

[0057] Optionally, refer to Figure 4 , when reading point data from the Level File, first read the footer information at the end of the Level File to obtain the offset of the Type Index in the Level File. Then, locate and read all Type Indexes according to the offset to obtain the offset and data type of the Type Index. Next, locate and read the Zone Map according to the offset in the Type Index. Finally, determine the target data to be read based on the Zone Map in combination with the query conditions.

[0058] In one example, the query target data includes: In response to the same data type of the target data, return the target data in a preset data structure.

[0059] In response to different data types of the target data, return the target data in the target data structure.

[0060] Optionally, if the data types corresponding to multiple accessed subscripts are the same, return the base Vector of that type. If the data types are different, return a Vector of the IOTANY type. Refer to Figure 2 , when accessing the [2, 3, 5]th elements in the IOTANY Vector, it can be determined by retrieving the Types part in the subscript index that these subscripts correspond to the [0, 1, 2]th elements in the double-precision floating-point type (DOUBLE) subarray. Finally, the IOTANY Vector returns a Vector of type DOUBLE with element values [12.02, 9.97, 21.49]. When accessing the [1, 2, 3]th elements in the OTANY Vector, it can be determined by retrieving the type part in the subscript index that their types are INT, INT, and DOUBLE respectively, and they are the 0th, 1st subscripts in the INT subarray and the 0th subscript in the DOUBLE subarray respectively. Finally, return a new IOTANY type Vector with the stored data content [89, 90, 12.02].

[0061] Generally, the point data types of the same device are the same. Therefore, when extracting data from IOTANY Vector, if the data types are consistent, the result will be automatically converted into a normal Vector of that type, without involving additional data type conversion costs, but directly using the basic data types for calculation. In this way, during the query process, the data type consistency can be intelligently judged, and the data type can be converted into the most suitable data structure, effectively avoiding the performance impact on query and calculation caused by data type conversion. This not only improves the query performance but also better meets the actual application requirements of IoT point management.

[0062] As an example, performance testing is conducted on the IOTANY type management point data proposed in this application. The test hardware environment is CPU: Intel Core i5-12500; memory size: 64GiB; disk: PM9B1 NVMe Samsung 1TiB. The data scale involved in the test is a total of 143,856,000 pieces of data, among which the point data includes 27,000,000 pieces of integer type data, 21,600,000 pieces of long integer type data, 62,856,000 pieces of floating-point type data, and 32,400,000 pieces of double-precision floating-point type data. Table 6 is a comparison table of the write performance of storing point data using the IOTANY type and storing point data using string simulation.

[0063] Table 6

[0064] As shown in Table 6, compared with the traditional way of storing point data using string simulation, storing point data using the IOTANY type has a higher compression ratio and a higher throughput rate when writing data.

[0065] As an example, performance testing is conducted on the query performance of the IOTANY type management point data proposed in this application. The data scale involved in the test is a total of 9,000,000 pieces of data, among which there are 500 devices in total, each device has 300 points, and each point has 60 pieces of data. Table 7 is a comparison table of the query performance of DolphinDB and Apache IoTDB in the point management scenario, where DolphinDB stores and manages point data using the IOTANY type, and Apache IoTDB stores and manages point data using the string simulation method.

[0066] The query task is to perform aggregation calculations (such as calculating the average value) on the point data of certain devices at a specific time.

[0067] Table 7

[0068] As shown in Table 7, DolphinDB uses the IOTANY type to store and manage point data, which has obvious advantages in the query and calculation of point data.

[0069] In summary, the present application uniformly manages different types of point data through the target data structure, stores different types of point data in the Internet of Things in the same data structure Vector, and uses indexes for access. Compared with the traditional solution of using strings to simulate the storage of point data, it not only improves the storage density but also effectively reduces the memory usage. And using the target data structure can intelligently judge the data type consistency during the query process and convert the data type into the most suitable data structure, effectively avoiding the performance impact on query and calculation caused by data type conversion. It not only improves the query performance but also better meets the actual application requirements of Internet of Things point management. When storing in the Level File, the point data is grouped by type and then sorted according to the Sort Key, ensuring the data type consistency within each group, thereby improving the storage compression rate of the point data and effectively reducing the storage cost.

[0070] In a second aspect, an embodiment of the present application provides a point data management system. Figure 5 It is a structural block diagram of a point data management system shown according to an embodiment of the present application. As Figure 5 shown, the system includes: An acquisition module 100: configured to acquire point data and at least two data types of the point data, process the point data according to a preset data structure and data type to obtain sub-structure data, and the index offset of each point data in the corresponding sub-structure data.

[0071] A determination module 200: configured to determine the data part according to the sub-structure data, and obtain the index part according to the data type and the corresponding index offset of each point data.

[0072] A storage module 300: configured to determine the target data structure according to the index part and the data part, and store the point data in the target data structure.

[0073] In an example, the acquisition module 100 includes: configured to store the point data of each data type respectively according to the preset data structure to obtain sub-structure data corresponding to the data type, and the index offset of each point data in the sub-structure data corresponding to its data type.

[0074] In an example, after obtaining the sub-structure data corresponding to the data type, the system further includes: Sorting and grouping the sub-structure data according to the preset sorting conditions to obtain secondary grouped structure data, and sequentially compressing and storing the secondary grouped structure data according to the sorting result.

[0075] In one example, the storage module 300 includes: storing point data in a preset storage order, and determining the subscript of each piece of data in the data part in the target data structure according to the storage order.

[0076] In one example, the system is further configured to query target data, and querying the target data includes: Obtaining the target subscript of the target data in the target data structure, and determining the target data type and the target index offset according to the target subscript.

[0077] Determining the target data from the substructure data according to the target data type and the target index offset.

[0078] In one example, querying the target data includes: In response to the same data type of the target data, returning the target data in a preset data structure.

[0079] In response to different data types of the target data, returning the target data in the target data structure.

[0080] In summary, the present application uniformly manages different types of point data through the target data structure, stores different types of point data in the Internet of Things in the same data structure Vector, and accesses them using indexes. Compared with the traditional solution of using strings to simulate the storage of point data, it not only improves the storage density, but also effectively reduces the memory usage. And using the target data structure can intelligently judge the data type consistency during the query process, and convert the data type into the most suitable data structure, effectively avoiding the performance impact on querying and calculation caused by data type conversion, not only improving the query performance, but also more in line with the actual application requirements of Internet of Things point management. When storing in the Level File, the point data is grouped by type and then sorted according to the Sort Key, ensuring the data type consistency within each group, thereby improving the storage compression rate of the point data and effectively reducing the storage cost.

[0081] In a third aspect, an embodiment of the present application provides an electronic device, Figure 6 which is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements a point data management method provided in the first aspect. Figure 6 The displayed electronic device 60 is only an example and should not impose any restrictions on the functions and usage scope of the embodiments of the present application.

[0082] The electronic device 60 may be embodied in the form of a general-purpose computing device. For example, it may be a server device. The components of the electronic device 60 may include, but are not limited to: at least one of the above-mentioned processors 61, at least one of the above-mentioned memories 62, and a bus 63 that connects different system components (including the memory 62 and the processor 61).

[0083] The bus 63 includes a data bus, an address bus, and a control bus.

[0084] The memory 62 may include volatile memory, such as random access memory (RAM) 621 and / or cache memory 622, and may further include read-only memory (ROM) 623.

[0085] The memory 62 may also include a program / utility 625 having a set (at least one) of program modules 624. Such program modules 624 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0086] The processor 61 executes various functional applications and data processing by running computer programs stored in the memory 62, such as a point data management method provided in the first aspect of the present application.

[0087] The electronic device 60 may also communicate with one or more external devices 64 (such as a keyboard, a pointing device, etc.). Such communication may be carried out through an input / output (I / O) interface 65. Moreover, the device 60 for model generation may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 66. As shown in the figure, the network adapter 66 communicates with other modules of the device 60 for model generation through the bus 63. It should be understood that although not shown in the figure, other hardware and / or software modules may be used in combination with the device 60 for model generation, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.

[0088] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. In fact, according to the embodiments of the present invention, the features and functions of two or more of the above-mentioned units / modules may be embodied in one unit / module. Conversely, the features and functions of one unit / module described above may be further divided and embodied by multiple units / modules.

[0089] Fourthly, an embodiment of the present application provides a computer-readable storage medium, on which a program is stored. When the program is executed by a processor, it implements a point data management method provided in the first aspect.

[0090] Among them, the readable storage medium can more specifically include but is not limited to: portable disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0091] In a possible implementation manner, the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to enable the terminal device to execute the steps of implementing a point data management method provided in the first aspect.

[0092] Among them, the program code for executing the present invention can be written in any combination of one or more programming languages. The program code can be completely executed on a user device, partially executed on a user device, executed as an independent software package, partially executed on a user device and partially executed on a remote device, or completely executed on a remote device.

[0093] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as these combinations of technical features do not conflict, they should be considered as the scope described in this specification.

[0094] The above embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be understood as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A point data management method, characterized in that: include: Acquire point data and at least two data types of the point data, and process the point data according to a preset data structure and the data type to obtain substructure data and an index offset of each point data in the corresponding substructure data; Determine the data part according to the substructure data, and obtain the index part according to the data type of each point data and the corresponding index offset; A target data structure is determined according to the index part and the data part, and the point data is stored in the target data structure.

2. A point data management method according to claim 1, characterized in that: The step of processing the point data according to the preset data structure and the data type to obtain substructure data and an index offset of each point data in the corresponding substructure data includes: The point data of each data type are stored respectively according to the preset data structure to obtain substructure data corresponding to the data type and the index offset of each point data in the substructure data corresponding to its data type.

3. A point data management method according to claim 2, characterized in that: After obtaining the substructure data corresponding to the data type, the method further includes: The substructure data are sorted and grouped according to a preset sorting condition to obtain secondary grouping structure data, and the secondary grouping structure data are compressed and stored in sequence according to the sorting result.

4. A point data management method according to claim 1, characterized in that: The storing of the point data in the target data structure includes: The point data are stored in a preset storage order, and the subscript of each data in the data part in the target data structure is determined according to the storage order.

5. A point data management method according to claim 4, characterized in that: The method further includes querying target data, wherein the query target data includes: Obtain a target subscript of the target data in the target data structure, and determine a target data type and a target index offset according to the target subscript; Target data is determined from the substructure data according to the target data type and the target index offset.

6. A point data management method according to claim 5, characterized in that: The query target data includes: In response to the data types of the target data being the same, returning the target data in the preset data structure; In response to the data type of the target data being different, the target data is returned in the target data structure.

7. A point data management system, characterized in that: include: Acquisition module: used for acquiring point data and at least two data types of the point data, processing the point data according to a preset data structure and the data type to obtain substructure data, and an index offset of each point data in the corresponding substructure data; Determination module: used for determining the data part according to the substructure data, and obtaining the index part according to the data type of each point data and the corresponding index offset; Storage module: used for determining a target data structure according to the index part and the data part, and storing the point data in the target data structure.

8. A point data management system according to claim 7, characterized in that: The acquisition module comprises: It is used to store the point data of each data type according to the preset data structure, obtain the substructure data corresponding to the data type, and the index offset of each point data in the substructure data corresponding to its data type.

9. An electronic device, characterized in that: It comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a point data management method as claimed in any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a point data management method according to any one of claims 1 to 6 is implemented.

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