Real-time processing method and device for equipment operation data of Internet of Things

Abstract

The invention discloses a real-time processing method and device for equipment operation data of the Internet of Things. The real-time processing method for the equipment operation data of the Internet of Things comprises the following steps: sending real-time data generated by operation of equipment of the Internet of Things to message middleware, and sending the real-time data to a distributed processing architecture through the message middleware; the distributed stream processing architecture performs real-time processing on the real-time data, wherein the processing mode comprises any one or more of a multi-time window aggregation mode and a multi-dimensional data analysis mode; and storing a result processed by the distributed stream processing architecture in a preset real-time data warehouse. According to the technical scheme, the data of the corresponding time window or the data of the corresponding dimension can be directly loaded from the preset real-time data warehouse, the corresponding data does not need to be extracted from the original data again and processed, the feedback efficiency and timeliness are greatly improved, and the use experience of a user is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things data processing, and in particular to a method, device, equipment and storage medium for real-time processing of device operation data of the Internet of Things. Background Art

[0002] The amount of operating data of IoT devices reaches hundreds of millions. When viewing the data indicator trend chart of IoT devices according to the time window, the conventional technical solution is to query through the database, which will consume a lot of server resources. As the window query range expands, the massive amount of data will cause a huge amount of calculation, resulting in low feedback efficiency of query results. In the conventional technical solution, when analyzing the operating data of IoT devices, it can only be calculated in batches within a smaller range according to the computing power limitation, and continuously polling and summarizing, which greatly occupies system resources and its timeliness cannot meet the needs of users. How to properly solve the above problems has become a topic that needs to be solved urgently in the industry. Summary of the invention

[0003] The present invention provides a real-time processing method, device, equipment and storage medium for device operation data of the Internet of Things, which are used to directly load data of a corresponding time window or data of a corresponding dimension from a preset real-time data warehouse, greatly enhancing feedback efficiency and timeliness, and improving user experience.

[0004] According to a first aspect of the present invention, a method for real-time processing of device operation data of an Internet of Things is provided, and the method for real-time processing of device operation data of the Internet of Things comprises:

[0005] Sending real-time data generated by the operation of the IoT device to the message middleware, and sending the real-time data to the distributed processing architecture through the message middleware;

[0006] The distributed stream processing architecture processes the real-time data in real time, and the processing method includes any one or more of a multi-time window aggregation method and a multi-dimensional data analysis method;

[0007] The results processed by the distributed stream processing architecture are stored in a preset real-time data warehouse.

[0008] In one embodiment, the distributed stream processing architecture processes the real-time data in real time, including:

[0009] The distributed stream processing architecture obtains the timestamp and the preset unit time window of the real-time data;

[0010] Process the data of each device within any unit time window in real time;

[0011] Aggregate the results within at least two unit time windows, with the results having a time index.

[0012] In one embodiment, the distributed stream processing architecture processes the real-time data in real time, further comprising:

[0013] The distributed stream processing architecture performs data mining and data analysis on the real-time data according to at least one preset dimension indicator;

[0014] A mapping relationship between the preset dimensional indicators and the corresponding data mining and data analysis results is established, and the results carry the dimensional indicators.

[0015] In one embodiment, it further includes:

[0016] When receiving a query instruction, analyzing the index of the query instruction;

[0017] According to the index of the query instruction, the corresponding result is searched in the preset real-time data warehouse;

[0018] The results are displayed in real time in a multimedia manner, wherein the multimedia manner includes any one or more of charts, animations, texts or voices.

[0019] According to a second aspect of the present invention, a real-time processing device for device operation data of an Internet of Things is provided, comprising:

[0020] A sending module, used to send the real-time data generated by the operation of the IoT device to the message middleware, and send the real-time data to the distributed processing architecture through the message middleware;

[0021] A processing module, used for real-time processing of the real-time data in a distributed stream processing architecture, wherein the processing method includes any one or more of a multi-time window aggregation method and a multi-dimensional data analysis method;

[0022] The storage module is used to store the results processed by the distributed stream processing architecture in a preset real-time data warehouse.

[0023] In one embodiment, the processing module also includes the distributed stream processing architecture obtaining the timestamp and preset unit time window of the real-time data; processing the data of each device within any unit time window in real time; and aggregating the results within at least two unit time windows, wherein the results have time indicators.

[0024] In one embodiment, the processing module also includes the distributed stream processing architecture performing data mining and data analysis on the real-time data according to at least one preset dimensional indicator; establishing a mapping relationship between the preset dimensional indicator and the corresponding data mining and data analysis results, and the results carry the dimensional indicator.

[0025] In one embodiment, it further includes:

[0026] An analysis module, configured to analyze the index of the query instruction when receiving the query instruction;

[0027] A query module, used to query corresponding results in a preset real-time data warehouse according to the index of the query instruction;

[0028] A display module is used to display the results in real time through multimedia, and the multimedia includes any one or more of charts, animations, text or voice. In one embodiment, it also includes:

[0029] According to a third aspect of the present invention, there is provided an electronic device, the electronic device comprising: a communication interface, a processor, and a memory;

[0030] Among them, the memory is used to store program instructions, and when the program instructions are executed by the processor that is communicatively connected to the memory through the communication interface, any of the above-mentioned methods for real-time processing of device operation data of the Internet of Things is implemented.

[0031] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a computer (e.g., a processor in a computer), any of the above-mentioned methods for real-time processing of device operation data of the Internet of Things is implemented.

[0032] In summary, the present invention provides a method and apparatus for real-time processing of device operation data of the Internet of Things, the method comprising: sending the real-time data generated by the operation of the Internet of Things device to the message middleware, and sending the real-time data to the distributed processing architecture through the message middleware; the distributed stream processing architecture processes the real-time data in real time, and the processing method includes any one or more of a multi-time window aggregation method and a multi-dimensional data analysis method; the results processed by the distributed stream processing architecture are stored in a preset real-time data warehouse. The technical solution of the present application can directly load the data of the corresponding time window or the data of the corresponding dimension from the preset real-time data warehouse, without re-extracting and processing the corresponding data from the original data, which greatly enhances the feedback efficiency and timeliness, and improves the user experience.

[0033] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0034] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 A flowchart of a method for real-time processing of device operation data of an Internet of Things provided by an embodiment of the present invention;

[0037] Figure 2 A flowchart of step S12 of a method for real-time processing of device operation data of an Internet of Things provided by an embodiment of the present invention;

[0038] Figure 3 A flowchart of step S12 of another method for real-time processing of device operation data of the Internet of Things provided by an embodiment of the present invention;

[0039] Figure 4 A flowchart of a method for real-time processing of device operation data of the Internet of Things provided by an embodiment of the present invention;

[0040] Figure 5 A structural diagram of a real-time processing device for operating data of an IoT device provided by an embodiment of the present invention;

[0041] Figure 6 A structural diagram of a real-time processing device for operating data of another IoT device provided by an embodiment of the present invention;

[0042] Figure 7 A structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0044] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0045] like Figure 1 As shown, the present invention provides a real-time processing method for device operation data of the Internet of Things, and the real-time processing method for device operation data of the Internet of Things includes:

[0046] In step S11, the real-time data generated by the operation of the IoT device is sent to the message middleware, and the real-time data is sent to the distributed processing architecture through the message middleware;

[0047] In step S12, the distributed stream processing architecture processes the real-time data in real time, and the processing method includes any one or more of a multi-time window aggregation method and a multi-dimensional data analysis method;

[0048] In step S13, the results processed by the distributed stream processing architecture are stored in a preset real-time data warehouse.

[0049] In one embodiment, the amount of operating data of IoT devices reaches hundreds of millions. When viewing the data indicator trend chart of IoT devices according to the time window, the conventional technical solution is to query through the database, which will consume a lot of server resources. As the window query range expands, the massive amount of data will cause a huge amount of calculation, resulting in low feedback efficiency of the query results. In the conventional technical solution, when analyzing the operating data of IoT devices, it can only be calculated in batches within a smaller range according to the computing power limitation, and continuously polling and summarizing, which greatly occupies system resources and its timeliness cannot meet the needs of users. The technical solution in this embodiment can properly solve the above problems.

[0050] IoT devices generate a large amount of real-time data during operation, which is first sent to the message middleware, a software used to transmit messages or data between different systems or services. It acts as a communication bridge between different components or services in distributed applications. Message middleware allows applications to exchange data in a decoupled manner without having to know the specific implementation details of the other party.

[0051] After receiving data from IoT devices, the message middleware forwards it to the distributed processing architecture. The message middleware distributes tasks across multiple physical or virtual computing resources to achieve higher performance, scalability, and fault tolerance. The distributed stream processing architecture is responsible for real-time processing of real-time data. Stream processing refers to the processing of continuous data streams. Stream processing is a computing model that can process continuous data streams in real time instead of waiting for data sets to accumulate to a certain amount before batch processing. Stream processing allows the system to analyze and respond to data instantly, and is suitable for scenarios that require rapid decision support, such as IoT data processing, financial transaction monitoring, and real-time social media analysis. The multi-time window aggregation method is to group data according to time windows, and then perform aggregation operations on the data in each time window, such as summing and averaging. The multi-dimensional data analysis method is to analyze data from multiple angles or dimensions, for example, real-time statistics of the total power generation of equipment, average power generation, etc. The processed results are stored in a preset real-time data warehouse, which is a system specially designed to store and manage large amounts of data, usually used to support complex queries and analysis.

[0052] Message middleware acts as a bridge for data transmission, ensuring that data can be transmitted from the source to the processing architecture. Distributed stream processing architecture is the core of data processing, which uses multi-time window aggregation and multi-dimensional data analysis technology to improve processing efficiency and analysis depth. The real-time data warehouse is the end point of the data, storing the processed results and providing support for subsequent queries and analysis.

[0053] The technical solution in this embodiment can directly load the data of the corresponding time window or the data of the corresponding dimension from the preset real-time data warehouse without re-extracting and processing the corresponding data from the original data, which greatly enhances the feedback efficiency and timeliness and improves the user experience.

[0054] In one embodiment, Figure 2 As shown, step S12 includes the following steps S21-S23:

[0055] In step S21, the distributed stream processing architecture obtains the timestamp and the preset unit time window of the real-time data;

[0056] In step S22, the data of each device within any unit time window is processed in real time;

[0057] In step S23, the results within at least two unit time windows are aggregated, and the results are provided with a time index.

[0058] In one embodiment, the distributed stream processing architecture first obtains the timestamp of the real-time data. The timestamp is the specific time when the data point is generated, which is crucial for real-time data processing and allows the system to sort and analyze the data according to the time order. The data is organized using a preset unit time window, which is the time range for data aggregation and analysis, which can be seconds, minutes, hours, etc. Through the time window, the system can process the data in batches to achieve real-time data analysis. Within each unit time window, the distributed stream processing architecture processes data from each device in real time. This means that the system can continuously receive data and process it immediately at the end of each time window. Aggregation of results across time windows, the system aggregates the results within at least two unit time windows. This aggregation can provide a more comprehensive data perspective, such as trend analysis, pattern recognition, etc. Aggregation operations may include statistical methods such as sum, average, maximum, minimum, etc. During the aggregation process, the results will be accompanied by time indicators, which means that the aggregated data still retains time-related information. This allows users or systems to consider time factors when analyzing and perform time series analysis or other analysis that requires time information.

[0059] From acquiring timestamps to real-time data processing, and then to cross-window aggregation, the use of time windows allows data to be processed and analyzed within a limited, controllable time period, while aggregation operations provide a deeper understanding of the data. Through this distributed stream processing architecture, large-scale real-time data streams can be effectively processed and analyzed, providing fast and accurate support for decision-making.

[0060] In one embodiment, Figure 3 As shown, step S12 includes the following steps S31-S32:

[0061] In step S31, the distributed stream processing architecture performs data mining and data analysis on the real-time data according to at least one preset dimension indicator;

[0062] In step S32, a mapping relationship between the preset dimensional indicators and the corresponding data mining and data analysis results is established, and the results carry dimensional indicators.

[0063] In one embodiment, the distributed stream processing architecture first performs data mining and data analysis on real-time data based on preset dimensional indicators. Dimensional indicators are the basis of data analysis, and they are the characteristics of measuring and observing data. Data mining and analysis are not single-dimensional, but multi-dimensional, and data is comprehensively analyzed from different angles and dimensions to obtain more comprehensive and in-depth information. During the processing, a mapping relationship between preset dimensional indicators and data mining and analysis results is established. The system associates the analysis results with specific dimensions of the original data to ensure the traceability and interpretability of the results, not only based on dimensional indicators, but also time indicators. The entire processing process is real-time, ensuring the timeliness of the data. The processed results are usually stored in a data warehouse for further analysis and decision support. The distributed stream processing architecture can effectively process and analyze large-scale real-time data streams.

[0064] In one embodiment, Figure 4 As shown, the following steps S41-S43 are also included:

[0065] In step S41, when a query instruction is received, an indicator of the query instruction is analyzed;

[0066] In step S42, according to the index of the query instruction, the corresponding result is queried in the preset real-time data warehouse;

[0067] In step S43, the result is displayed in real time in a multimedia manner, and the multimedia manner includes any one or more of charts, animations, texts or voices.

[0068] In one embodiment, the process starts with receiving a query instruction for requesting specific data or information. After receiving the query instruction, it is necessary to analyze the indicators contained in the instruction. Indicators are key parameters for measuring and evaluating data, and are the basis for understanding query requirements and determining the scope of data retrieval. According to the analyzed indicators, query operations are performed in a preset real-time data warehouse to retrieve data matching the query instruction. The real-time data warehouse is designed to quickly access a large amount of real-time generated data to ensure the efficiency of query operations and the timeliness of results. The data obtained by the query needs to be displayed in a user-understandable way. The results are displayed in real time in a multimedia way, which enhances the accessibility and intuitiveness of the data. The multimedia way includes charts, animations, text or voice. Different display methods are suitable for different scenarios and user needs, such as bar charts, line charts, etc., which are suitable for displaying data trends and comparisons. For example, animations can dynamically display data changes and are suitable for displaying time series data. For example, text can directly display data in text form, which is suitable for detailed data reports, such as voice broadcast data, which is suitable for scenarios without visual interaction. From the analysis of query instructions to the real-time display of data, real-time and interactivity are reflected, and users can obtain intuitive displays of required data in a timely manner.

[0069] In one embodiment, Figure 5 FIG. 1 is a block diagram of a real-time processing device for operating data of an IoT device according to an exemplary embodiment. Figure 5 As shown, the real-time processing device of the device operation data of the Internet of Things includes a sending module 51, a processing module 52 and a storage module 53.

[0070] The sending module 51 is used to send the real-time data generated by the operation of the IoT device to the message middleware, and send the real-time data to the distributed processing architecture through the message middleware;

[0071] The processing module 52 is used for real-time processing of the real-time data in a distributed stream processing architecture, wherein the processing method includes any one or more of a multi-time window aggregation method and a multi-dimensional data analysis method;

[0072] The storage module 53 is used to store the results processed by the distributed stream processing architecture in a preset real-time data warehouse.

[0073] The processing module 52 also includes the distributed stream processing architecture obtaining the timestamp and preset unit time window of the real-time data; processing the data of each device within any unit time window in real time; and aggregating the results within at least two unit time windows, wherein the results have time indicators.

[0074] The processing module 52 also includes the distributed stream processing architecture performing data mining and data analysis on the real-time data according to at least one preset dimensional indicator; establishing a mapping relationship between the preset dimensional indicator and the corresponding data mining and data analysis results, and the results carry the dimensional indicator.

[0075] In one embodiment, Figure 6 As shown, the real-time processing device for the device operation data of the Internet of Things also includes an analysis module 61, a query module 62 and a display module 63.

[0076] The analysis module 61 is used to analyze the index of the query instruction when receiving the query instruction;

[0077] The query module 62 is used to query the corresponding results in the preset real-time data warehouse according to the index of the query instruction;

[0078] The display module 63 is used to display the result in real time through multimedia, and the multimedia includes any one or more of charts, animations, texts or voices.

[0079] like Figure 7 As shown, the present invention provides an electronic device 700, which includes: a communication interface, a processor 701, and a memory 702;

[0080] Among them, the memory 702 is used to store program instructions. When the program instructions are executed by the processor 701 that is communicatively connected to the memory 702 through the communication interface, the real-time data generated by the operation of the Internet of Things device is sent to the message middleware, and the real-time data is sent to the distributed processing architecture through the message middleware; the distributed stream processing architecture processes the real-time data in real time, and the processing method includes any one or more of a multi-time window aggregation method and a multi-dimensional data analysis method; the results processed by the distributed stream processing architecture are stored in a preset real-time data warehouse.

[0081] The present invention provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, real-time data generated by the operation of an Internet of Things device is sent to a message middleware, and the real-time data is sent to a distributed processing architecture through the message middleware; the distributed stream processing architecture processes the real-time data in real time, and the processing method includes any one or more of a multi-time window aggregation method and a multi-dimensional data analysis method; and the results processed by the distributed stream processing architecture are stored in a preset real-time data warehouse.

[0082] It should be understood that the specific features, operations and details described hereinabove about the method of the present invention may also be similarly applied to the device and system of the present invention, or, vice versa. In addition, each step of the method of the present invention described above may be performed by the corresponding parts or units of the device or system of the present invention.

[0083] It should be understood that each module / unit of the device of the present invention can be implemented in whole or in part by software, hardware, firmware or a combination thereof. Each module / unit can be embedded in the processor of the computer device in the form of hardware or firmware or independent of the processor, or can be stored in the memory of the computer device in the form of software for the processor to call to perform the operation of each module / unit. Each module / unit can be implemented as an independent component or module, or two or more modules / units can be implemented as a single component or module.

[0084] In one embodiment, a computer device is provided, which includes a memory and a processor, and the memory stores computer instructions executable by the processor, and the computer instructions instruct the processor to execute each step of the method of the embodiment of the present invention when executed by the processor. The computer device can be a server, a terminal, or any other electronic device with necessary computing and / or processing capabilities in a broad sense. In one embodiment, the computer device may include a processor, a memory, a network interface, a communication interface, etc. connected through a system bus. The processor of the computer device can be used to provide necessary computing, processing and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and an internal memory. An operating system, a computer program, etc. may be stored in or on the non-volatile storage medium. The internal memory can provide an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface and the communication interface of the computer device can be used to connect and communicate with external devices through a network. The steps of the method of the present invention are executed by the processor.

[0085] The present invention may be implemented as a computer-readable storage medium having a computer program stored thereon, which causes the steps of the method of an embodiment of the present invention to be executed when executed by a processor. In one embodiment, the computer program is distributed on a plurality of computer devices or processors coupled to a network so that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be performed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be performed by one or more computer devices or processors, and one or more other method steps / operations may be performed by one or more other computer devices or processors. One or more computer devices or processors may perform a single method step / operation, or perform two or more method steps / operations.

[0086] It will be understood by those skilled in the art that the method steps of the present invention can be completed by instructing related hardware such as a computer device or a processor through a computer program, and the computer program can be stored in a non-temporary computer-readable storage medium, and the steps of the present invention are executed when the computer program is executed. Depending on the circumstances, any reference to memory, storage, database or other media herein may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (which can directly load data of the corresponding time window or data of the corresponding dimension from a preset real-time data warehouse without re-extracting and processing the corresponding data from the original data, greatly enhancing the feedback efficiency and timeliness, and improving the user's experience PROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0087] The various technical features described above can be combined arbitrarily. Although all possible combinations of these technical features are not described, any combination of these technical features should be considered to be covered by this specification as long as there is no contradiction in such combination.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for real-time processing of device operation data of the Internet of Things, characterized in that: include: Sending real-time data generated by the operation of the IoT device to the message middleware, and sending the real-time data to the distributed processing architecture through the message middleware; The distributed stream processing architecture processes the real-time data in real time, and the processing method includes any one or more of a multi-time window aggregation method and a multi-dimensional data analysis method; The results processed by the distributed stream processing architecture are stored in a preset real-time data warehouse.

2. The real-time processing method for device operation data of the Internet of Things according to claim 1, characterized in that: The distributed stream processing architecture processes the real-time data in real time, including: The distributed stream processing architecture obtains the timestamp and the preset unit time window of the real-time data; Process the data of each device within any unit time window in real time; Aggregate the results within at least two unit time windows, with the results having a time index.

3. The real-time processing method for device operation data of the Internet of Things according to claim 1, characterized in that: The distributed stream processing architecture processes the real-time data in real time, and further includes: The distributed stream processing architecture performs data mining and data analysis on the real-time data according to at least one preset dimension indicator; A mapping relationship between the preset dimensional indicators and the corresponding data mining and data analysis results is established, and the results carry the dimensional indicators.

4. The method for real-time processing of device operation data of the Internet of Things according to claim 1, characterized in that: Also includes: When receiving a query instruction, analyzing the index of the query instruction; According to the index of the query instruction, the corresponding result is searched in the preset real-time data warehouse; The results are displayed in real time in a multimedia manner, wherein the multimedia manner includes any one or more of charts, animations, texts or voices.

5. A real-time processing device for device operation data of the Internet of Things, characterized in that: include: A sending module, used to send the real-time data generated by the operation of the IoT device to the message middleware, and send the real-time data to the distributed processing architecture through the message middleware; A processing module, used for real-time processing of the real-time data in a distributed stream processing architecture, wherein the processing method includes any one or more of a multi-time window aggregation method and a multi-dimensional data analysis method; The storage module is used to store the results processed by the distributed stream processing architecture in a preset real-time data warehouse.

6. The real-time processing device for the operation data of the equipment of the Internet of Things according to claim 5, characterized in that: The processing module also includes the distributed stream processing architecture obtaining the timestamp and the preset unit time window of the real-time data; and processing the data of each device within any unit time window in real time; Aggregate the results within at least two unit time windows, with the results having a time index.

7. The real-time processing device for device operation data of the Internet of Things according to claim 5, characterized in that: Also includes: The processing module also includes the distributed stream processing architecture performing data mining and data analysis on the real-time data according to at least one preset dimensional indicator; establishing a mapping relationship between the preset dimensional indicator and the corresponding data mining and data analysis results, and the results carry the dimensional indicator.

8. The real-time processing device for device operation data of the Internet of Things according to claim 5, characterized in that: Also includes: An analysis module, configured to analyze the index of the query instruction when receiving the query instruction; A query module, used to query corresponding results in a preset real-time data warehouse according to the index of the query instruction; The display module is used to display the results in real time through multimedia, and the multimedia includes any one or more of charts, animations, texts or voices.

9. An electronic device, characterized in that: include: Communication interface, processor, memory; Wherein, the memory is used to store program instructions, and when the program instructions are executed by the processor that is communicatively connected to the memory through the communication interface, the electronic device implements the real-time processing method for device operation data of the Internet of Things as described in any one of claims 1 to 4.

10. A computer-readable storage medium having program instructions stored thereon, characterized in that: When the program instructions are executed by a computer, the computer implements the real-time processing method for device operation data of the Internet of Things as described in any one of claims 1 to 4.

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