Internet of Things data aggregation calculation method and device, electronic equipment and medium

Abstract

The invention provides an Internet of Things data aggregation calculation method and device, electronic equipment and a medium, and belongs to the field of Internet of Things data processing.The method comprises the steps that obtained multi-source Internet of Things data are subjected to standardization processing to obtain standardized data, the standardized data are subjected to multi-level storage to obtain storage data, and the storage data are stored in a database; constructing a multi-dimensional bitmap index of the stored data based on a bitmap index strategy; and determining a query condition according to the obtained aggregation query request, performing index analysis matching on the query condition and the multi-dimensional bitmap index to obtain matched data, and performing parallel aggregation calculation on the matched data to obtain an aggregation calculation result. According to the method, the multi-dimensional bitmap index of the stored data is established, and the matched data meeting the condition is queried in an index analysis matching mode, so that the scanned data volume can be effectively reduced, and the aggregation calculation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things data processing, and in particular to an Internet of Things data aggregation calculation method, device, electronic equipment and medium. Background Art

[0002] With the development of information technology, IoT devices are widely distributed in every corner, collecting and transmitting a large amount of IoT data with time tags and geographic location tags in real time. These data not only contain basic information such as device status and environmental parameters, but also contain rich spatiotemporal behavior characteristics, such as indicators, time, space, level, and monitoring objects. Effective processing and analysis of these data can provide decision support for smart cities, intelligent transportation, environmental monitoring and other fields, among which aggregate computing is a common means of IoT data analysis.

[0003] When performing aggregate calculations on IoT data, existing IoT data processing technologies usually analyze the user's aggregate query conditions first, then traverse all data and perform logical judgments on the data and each query condition to find data that meets the conditions, and then perform aggregate calculations on the data that meets the conditions. However, due to the characteristics of IoT data such as multi-source heterogeneity, high-dimensional complexity, and massive dispersion, existing aggregate calculation methods need to scan a large amount of data, which is time-consuming and has low efficiency in data aggregation calculations.

[0004] Therefore, the existing technology has technical problems that aggregation calculation requires scanning a large amount of data, which is time-consuming and the efficiency of data aggregation calculation is low, which needs to be improved. Summary of the invention

[0005] In view of this, it is necessary to provide an Internet of Things data aggregation calculation method, device, electronic device and medium to solve the technical problems existing in the prior art that aggregation calculation requires scanning a large amount of data, which is time-consuming and has low efficiency of data aggregation calculation.

[0006] In order to solve the above technical problems, on the one hand, the present invention provides an IoT data aggregation calculation method, comprising: The acquired multi-source IoT data is standardized to obtain standardized data, the standardized data is multi-level stored to obtain storage data, and a multi-dimensional bitmap index of the storage data is constructed based on a bitmap index strategy; The query condition is determined according to the obtained aggregation query request, the query condition and the multi-dimensional bitmap index are subjected to index analysis and matching to obtain matching data, and parallel aggregation calculation is performed on the matching data to obtain the aggregation calculation result.

[0007] In a possible implementation, the acquired multi-source IoT data is standardized to obtain standardized data, including: The multi-source IoT data are cleaned, parsed, timestamp corrected and encapsulated in sequence to obtain standardized data.

[0008] In a possible implementation, the standardized data is stored in multiple levels to obtain stored data, including: An initial memory table is constructed, and standardized data is written into the initial memory table until the data volume of the initial memory table reaches a preset data volume threshold to obtain a memory table to be stored, and the memory table to be stored is stored in a database to obtain stored data.

[0009] In a possible implementation, a multi-dimensional bitmap index for storing data is constructed based on a bitmap index strategy, including: Construct an initial dimension dictionary for storing each dimension of data; A mapping relationship between each data value and the stored data is constructed in the initial dimension dictionary, and a multi-dimensional bitmap index is established based on the mapping relationship.

[0010] In a possible implementation, a query condition is determined according to the obtained aggregate query request, and the query condition and the multi-dimensional bitmap index are subjected to index analysis and matching to obtain matching data, including: Determine a number of query conditions according to the aggregate query request, and determine a number of corresponding query indexes according to the query conditions; Perform logical operations on each query index to obtain a target index, and determine matching data based on the target index.

[0011] In a possible implementation, performing parallel aggregation calculation on the matching data to obtain an aggregate calculation result includes: Divide the matching data into several aggregate groups; Perform aggregation calculation on each aggregation group to obtain several group aggregation results; Combine the aggregation results of each group to get the aggregate calculation result.

[0012] In a possible implementation, the IoT data aggregation calculation method further includes: Based on the data dimension statistics and aggregation calculation results, the multi-dimensional analysis results of the aggregate query are obtained.

[0013] On the other hand, the present invention also provides an IoT data aggregation computing device, comprising: A data storage unit, used for standardizing the acquired multi-source IoT data to obtain standardized data, performing multi-level storage on the standardized data to obtain stored data, and constructing a multi-dimensional bitmap index for the stored data based on a bitmap index strategy; The aggregation calculation unit is used to determine the query condition according to the obtained aggregation query request, perform index analysis and matching on the query condition and the multi-dimensional bitmap index to obtain matching data, and perform parallel aggregation calculation on the matching data to obtain the aggregation calculation result.

[0014] On the other hand, the present invention also provides an electronic device, including a memory and a processor, wherein: Memory for storing computer programs; The processor is coupled to the memory and is used to execute a computer program to implement the steps in the above-mentioned IoT data aggregation calculation method.

[0015] On the other hand, the present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned Internet of Things data aggregation calculation method are implemented.

[0016] The beneficial effects of the present invention are as follows: the IoT data aggregation calculation method provided by the present invention first standardizes the acquired multi-source IoT data to obtain standardized data, performs multi-level storage on the standardized data to obtain storage data, and constructs a multi-dimensional bitmap index of the storage data based on a bitmap index strategy; then determines the query condition according to the acquired aggregation query request, performs index analysis and matching on the query condition and the multi-dimensional bitmap index to obtain matching data, and performs parallel aggregation calculation on the matching data to obtain an aggregation calculation result. The present invention can effectively reduce the amount of data scanned and improve the efficiency of aggregation calculation by establishing a multi-dimensional bitmap index for storage data and querying matching data that meets the conditions through index analysis and matching. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A schematic diagram of a flow chart of an embodiment of the method for computing Internet of Things data aggregation provided by the present invention; Figure 2 A schematic diagram of a process for constructing a multi-dimensional bitmap index according to an embodiment of the present invention; Figure 3 A schematic diagram of the index analysis and matching process of an embodiment of the present invention; Figure 4 A schematic diagram of a process of parallel aggregation calculation according to an embodiment of the present invention; Figure 5 A schematic diagram of the structure of an embodiment of the Internet of Things data aggregation computing device provided by the present invention; Figure 6 A schematic structural diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0020] In the description of the embodiments of the present invention, unless otherwise specified, "multiple" means two or more than two. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" may mean: A exists alone, A and B exist at the same time, and B exists alone.

[0021] The descriptions of "first", "second", etc. involved in the embodiments of the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the technical features defined as "first" or "second" may explicitly or implicitly include at least one of the features.

[0022] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0023] Before presenting the embodiments, the following names are explained.

[0024] Aggregate calculation: refers to the aggregate query task for the user, such as the average and minimum value of all data that meet condition A, querying the corresponding data in the database and performing corresponding calculations to obtain the target result.

[0025] The present invention provides an Internet of Things data aggregation calculation method, device, electronic device and medium, which are described below respectively.

[0026] Figure 1 A flow chart of an embodiment of the method for computing the Internet of Things data aggregation provided by the present invention is shown in FIG. Figure 1 As shown, the IoT data aggregation calculation method includes: S101, standardizing the acquired multi-source IoT data to obtain standardized data, performing multi-level storage on the standardized data to obtain stored data, and constructing a multi-dimensional bitmap index for the stored data based on a bitmap index strategy; S102: Determine a query condition according to the obtained aggregate query request, perform index analysis and matching on the query condition and the multi-dimensional bitmap index to obtain matching data, and perform parallel aggregate calculation on the matching data to obtain an aggregate calculation result.

[0027] Specifically, the IoT data aggregation calculation method provided by the present invention first standardizes the acquired multi-source IoT data to obtain standardized data, performs multi-level storage on the standardized data to obtain storage data, and constructs a multi-dimensional bitmap index of the storage data based on a bitmap index strategy; then, the query condition is determined according to the acquired aggregation query request, the query condition and the multi-dimensional bitmap index are subjected to index analysis and matching to obtain matching data, and the matching data are subjected to parallel aggregation calculation to obtain an aggregation calculation result. The present invention can effectively reduce the amount of data scanned and improve the efficiency of aggregation calculation by establishing a multi-dimensional bitmap index for storage data and querying matching data that meets the conditions through index analysis and matching.

[0028] In some embodiments of the present invention, the obtained multi-source IoT data is standardized to obtain standardized data, including: The multi-source IoT data are cleaned, parsed, timestamp corrected and encapsulated in sequence to obtain standardized data.

[0029] Specifically, in the embodiment, multi-source IoT data is data collected by various IoT devices, including but not limited to temperature sensors, gas sensors, air quality detectors, water quality detectors or pressure sensors. In the embodiment, a data acquisition module supporting multiple communication protocols is deployed to receive original multi-temporal and spatiotemporal data sent by sensors and smart devices of different brands or models.

[0030] Since these data sources are complex and the data quality varies, they need to be standardized first. During the standardization process, the embodiment removes invalid and abnormal data through data cleaning, converts binary or specific format data into structured data through data parsing, and performs timestamp correction on data from various sources. The embodiment ensures the accuracy and consistency of the data through standardization, and then encapsulates the data to obtain standardized data.

[0031] In some embodiments of the present invention, the standardized data is stored in multiple levels to obtain stored data, including: An initial memory table is constructed, and standardized data is written into the initial memory table until the data volume of the initial memory table reaches a preset data volume threshold to obtain a memory table to be stored, and the memory table to be stored is stored in a database to obtain stored data.

[0032] Specifically, in the data storage process, in order to improve the data storage efficiency, the embodiment optimizes the data storage based on the LSM-TREE method. First, the embodiment now creates an initial memory table in the memory, and then writes the standardized data into the initial memory table. When the number in the initial memory table reaches the preset data volume threshold, the memory table filled with data is written into the database on the disk, and then a new initial memory table is created to continue the writing process. By forming multi-level storage in this way, the embodiment can effectively utilize the efficient processing power of the memory and improve the data storage efficiency.

[0033] In some embodiments of the present invention, Figure 2 FIG. 1 is a flow chart of constructing a multi-dimensional bitmap index according to an embodiment of the present invention. Figure 2 The multi-dimensional bitmap index for storing data is constructed based on the bitmap index strategy, including: S201, constructing an initial dimension dictionary for storing each dimension of data; S202: Construct a mapping relationship between each data value and the stored data in the initial dimension dictionary, and establish a multi-dimensional bitmap index according to the mapping relationship.

[0034] Specifically, in order to improve the efficiency of querying target data during the aggregation calculation process, the embodiment constructs a multi-dimensional bitmap index of the data after storing the data in the database, and quickly and efficiently finds data that meets the conditions in the aggregation calculation by analyzing the multi-dimensional bitmap index.

[0035] In the process of constructing a multi-dimensional bitmap index, the embodiment first creates an initial dimension dictionary for each dimension of the stored data, and stores the mapping relationship between the data value and the stored data ID in the dimension dictionary, and based on the mapping relationship, generates a bitmap index for each data value under each dimension, and records which stored data contains the value.

[0036] In some embodiments of the present invention, Figure 3 FIG. 1 is a flow chart of index analysis and matching according to an embodiment of the present invention. Figure 3 As shown, the query condition is determined according to the obtained aggregate query request, and the query condition and the multi-dimensional bitmap index are subjected to index analysis and matching to obtain matching data, including: S301, determining a number of query conditions according to the aggregate query request, and determining a number of corresponding query indexes according to the query conditions; S302: Perform a logical operation on each query index to obtain a target index, and determine matching data according to the target index.

[0037] Specifically, considering that the existing technology needs to scan a large amount of data when searching for data that meets the aggregation query conditions through traversal and logical judgment, which leads to low efficiency, this embodiment proposes an index analysis and matching method. By performing logical analysis and matching search on the index, the amount of data scanned can be effectively reduced, thereby improving the efficiency of data search in aggregate calculations.

[0038] First, the embodiment parses the query condition according to the aggregate query request. For example, if the aggregate query request is: query the average and minimum value of the value B in the IoT data with the value A being value1 and the value C being value2, the query condition obtained by parsing can be expressed as A="value1" AND C="value2", and then in the dimension dictionary, the data IDs corresponding to the two conditions A="value1" and C="value2" are searched, such as IdA and IdC. Then, the bitmap indexes corresponding to IdA and IdC are found as query indexes, such as BitmapA and BitmapC.

[0039] Then, the embodiment performs a logical operation on the query index to obtain a target index, such as performing a logical AND operation on BitmapA and BitmapC to obtain a target index BitmapFinal, and then determines matching data that meets all query conditions according to the target index.

[0040] Compared with the prior art, the embodiment of the present invention searches for stored data that meets various query conditions through bitmap indexes and performs logical analysis on the query conditions at the index level, which can effectively reduce the amount of data scanned during the query process and improve the efficiency of aggregate calculations.

[0041] In some embodiments of the present invention, Figure 4 FIG. 1 is a flow chart of parallel aggregation calculation according to an embodiment of the present invention. Figure 4 As shown, parallel aggregation calculation is performed on the matching data to obtain the aggregation calculation results, including: S401, dividing the matching data into several aggregation groups; S402, performing aggregation calculation on each aggregation group to obtain several group aggregation results; S403: Combine the aggregation results of each group to obtain an aggregate calculation result.

[0042] Specifically, in aggregate computing, the embodiments optimize data computing efficiency by placing data in memory as much as possible, performing computing in memory, and performing parallel computing.

[0043] In parallel aggregation calculation, it is necessary to divide the matching data into several aggregation groups, and then perform parallel aggregation operations on each aggregation group, and then merge the aggregation results of each group and return the aggregate calculation result. In an embodiment, the matching data can be grouped according to the data source and then aggregated. The aggregation function can simultaneously calculate the aggregation function values ​​corresponding to multiple data sources. Commonly used aggregation function values ​​include count, sum, average, minimum, and maximum. The embodiment performs parallel aggregation on the matching data of each data source, and then merges the aggregation results of each group to obtain the aggregate calculation result. For example, for calculating the average value, after obtaining the aggregation results of each group, the aggregation results of each group are averaged again to obtain the final average value, or for the minimum value, the aggregation results of each group are minimized again to obtain the final minimum value.

[0044] In addition, in the embodiment, if there is a newly added query task, and the newly added query task can use the aggregated calculation results of the historical query task, for example, the same query task: query the average and minimum value of the B value in the IoT data with the A value of value1 and the C value of value2, but a new data source is added. The embodiment queries the results of the historical aggregation, uses the results of the historical aggregation as the grouped aggregation results of the corresponding data source, and then merges them with the grouped aggregation results of the newly added data source to obtain the aggregated calculation results of the newly added query task.

[0045] In some embodiments of the present invention, the IoT data aggregation calculation method further includes: Based on the data dimension statistics and aggregation calculation results, the multi-dimensional analysis results of the aggregate query are obtained.

[0046] Specifically, in the aggregation application, the embodiment receives the aggregation query request from the user through the interface, and after completing the aggregation calculation, in addition to returning the corresponding aggregation calculation result, the aggregation calculation result is also counted in different data dimensions to obtain the multi-dimensional analysis result of the aggregation query. For example, in the intelligent fire protection scenario, the multi-dimensional analysis results of each key indicator (such as air quality index and fire risk level) in different dimensions (such as time and space) are counted, including the average, percentile, maximum and minimum values, etc.

[0047] In summary, in order to reduce the amount of data scanned and improve the efficiency of data aggregation calculation, the present invention first standardizes the acquired multi-source IoT data to obtain standardized data, performs multi-level storage on the standardized data to obtain storage data, and constructs a multi-dimensional bitmap index of the storage data based on a bitmap index strategy; then, the query condition is determined according to the acquired aggregation query request, the query condition and the multi-dimensional bitmap index are subjected to index analysis and matching to obtain matching data, and the matching data are subjected to parallel aggregation calculation to obtain the aggregation calculation result. The present invention can effectively reduce the amount of data scanned and improve the efficiency of aggregation calculation by establishing a multi-dimensional bitmap index for storage data and querying matching data that meets the conditions through index analysis and matching.

[0048] In order to better implement the Internet of Things data aggregation calculation method in the embodiment of the present invention, based on the Internet of Things data aggregation calculation method, correspondingly, Figure 5 As shown, the present invention also provides an Internet of Things data aggregation computing device, and the Internet of Things data aggregation computing device 500 includes: The data storage unit 501 is used to perform standardization processing on the acquired multi-source IoT data to obtain standardized data, perform multi-level storage on the standardized data to obtain stored data, and construct a multi-dimensional bitmap index for the stored data based on a bitmap index strategy; The aggregation calculation unit 502 is used to determine the query condition according to the obtained aggregation query request, perform index analysis and matching on the query condition and the multi-dimensional bitmap index to obtain matching data, and perform parallel aggregation calculation on the matching data to obtain the aggregation calculation result.

[0049] The Internet of Things data aggregation computing device 500 provided in the above embodiment can implement the technical solution described in the above Internet of Things data aggregation computing method embodiment. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above Internet of Things data aggregation computing method embodiment, which will not be repeated here.

[0050] like Figure 6 As shown, the present invention also provides an electronic device 600. The electronic device 600 includes a processor 601, a memory 602 and a display 603. Figure 6 Only some components of the electronic device 600 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0051] In some embodiments, the processor 601 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 602, such as the IoT data aggregation calculation method of the present invention.

[0052] In some embodiments, the processor 601 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processor 601 may be local or remote. In some embodiments, the processor 601 may be implemented in a cloud platform. In one embodiment, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0053] In some embodiments, the memory 602 may be an internal storage unit of the electronic device 600, such as a hard disk or memory of the electronic device 600. In other embodiments, the memory 602 may also be an external storage device of the electronic device 600, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 600.

[0054] Furthermore, the memory 602 may include both an internal storage unit of the electronic device 600 and an external storage device. The memory 602 is used to store application software installed in the electronic device 600 and various data.

[0055] In some embodiments, the display 603 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 603 is used to display information of the electronic device 600 and to display a visual user interface. The components 601-603 of the electronic device 600 communicate with each other through a system bus.

[0056] In one embodiment, when the processor 601 executes the IoT data aggregation computing program in the memory 602, the following steps may be implemented: The acquired multi-source IoT data is standardized to obtain standardized data, the standardized data is multi-level stored to obtain storage data, and a multi-dimensional bitmap index of the storage data is constructed based on a bitmap index strategy; The query condition is determined according to the obtained aggregation query request, the query condition and the multi-dimensional bitmap index are subjected to index analysis and matching to obtain matching data, and parallel aggregation calculation is performed on the matching data to obtain the aggregation calculation result.

[0057] It should be understood that: when the processor 601 executes the Internet of Things data aggregation calculation program in the memory 602, in addition to the above functions, other functions can also be implemented. For details, please refer to the description of the corresponding method embodiment above.

[0058] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 600 mentioned, and the electronic device 600 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 600 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0059] Accordingly, an embodiment of the present application also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, the steps or functions in the Internet of Things data aggregation calculation method provided by the above-mentioned method embodiments can be implemented.

[0060] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0061] The IoT data aggregation calculation method, device, electronic device and storage medium provided by the present invention are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for aggregate calculation of Internet of Things data, characterized in that: include: Standardize the acquired multi-source IoT data to obtain standardized data, store the standardized data in multiple levels to obtain stored data, and construct a multi-dimensional bitmap index for the stored data based on a bitmap index strategy; The query condition is determined according to the obtained aggregate query request, the query condition and the multi-dimensional bitmap index are subjected to index analysis and matching to obtain matching data, and parallel aggregate calculation is performed on the matching data to obtain an aggregate calculation result.

2. The Internet of Things data aggregation calculation method according to claim 1 is characterized in that: The step of standardizing the acquired multi-source IoT data to obtain standardized data includes: The multi-source IoT data is sequentially cleaned, parsed, timestamp corrected and packaged to obtain standardized data.

3. The Internet of Things data aggregation calculation method according to claim 1 is characterized in that: The step of performing multi-level storage on the standardized data to obtain stored data includes: An initial memory table is constructed, the standardized data is written into the initial memory table until the data volume of the initial memory table reaches a preset data volume threshold to obtain a memory table to be stored, and the memory table to be stored is stored in a database to obtain stored data.

4. The Internet of Things data aggregation calculation method according to claim 1 is characterized in that: The step of constructing a multi-dimensional bitmap index of the stored data based on a bitmap index strategy includes: Constructing an initial dimension dictionary for each dimension of the stored data; A mapping relationship between each data value and the stored data is constructed in the initial dimension dictionary, and a multi-dimensional bitmap index is established according to the mapping relationship.

5. The Internet of Things data aggregation calculation method according to claim 1 is characterized in that: The determining of the query condition according to the obtained aggregate query request, and performing index analysis and matching on the query condition and the multi-dimensional bitmap index to obtain matching data includes: Determine a number of query conditions according to the aggregate query request, and determine a number of corresponding query indexes according to the query conditions; A logical operation is performed on each of the query indexes to obtain a target index, and matching data is determined according to the target index.

6. The Internet of Things data aggregation calculation method according to claim 1 is characterized in that: The performing parallel aggregation calculation on the matching data to obtain an aggregation calculation result includes: Dividing the matching data into a plurality of aggregate groups; Performing aggregation calculation on each of the aggregation groups to obtain a number of group aggregation results; The grouped aggregation results are combined to obtain an aggregate calculation result.

7. The Internet of Things data aggregation calculation method according to claim 1 is characterized in that: The Internet of Things data aggregation calculation method also includes: The aggregate calculation results are statistically analyzed based on the data dimensions to obtain the aggregate query multi-dimensional analysis results.

8. An IoT data aggregation computing device, characterized in that: include: A data storage unit, used for standardizing the acquired multi-source IoT data to obtain standardized data, performing multi-level storage on the standardized data to obtain stored data, and constructing a multi-dimensional bitmap index of the stored data based on a bitmap index strategy; The aggregation calculation unit is used to determine the query condition according to the obtained aggregation query request, perform index analysis and matching on the query condition and the multi-dimensional bitmap index to obtain matching data, and perform parallel aggregation calculation on the matching data to obtain the aggregation calculation result.

9. An electronic device, characterized in that: comprising a memory and a processor, wherein: The memory is used to store computer programs; The processor, coupled to the memory, is used to execute a computer program to implement the steps in the Internet of Things data aggregation calculation method described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the Internet of Things data aggregation calculation method described in any one of claims 1 to 7 are implemented.

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