Data analysis method, device and equipment and computer storage medium

Abstract

The invention belongs to the technical field of intelligent household appliances, and particularly relates to a data analysis method and device, equipment and a computer storage medium. The method comprises the following steps: establishing a data processing platform in advance, and inputting to-be-processed data into the data processing platform; performing stage decomposition on the to-be-processed data by using the data processing platform, and performing data analysis by using a corresponding function calculation container according to a decomposition result; and summarizing the data analysis results in real time by using the analysis summarization function until the analysis of the to-be-processed data is completed. According to the method, the defect that traditional data analysis is difficult to parallelize is overcome, data accumulation generated by online analysis is avoided, the integrity and accuracy of real-time analysis are ensured, the calculation efficiency of data analysis is improved, the defects of resource waste and repeated calculation are avoided, and the user experience is improved.

Description

Technical Field

[0001] The present application belongs to the technical field of smart home appliances, and specifically relates to a data analysis method, device, equipment and computer storage medium. Background Art

[0002] With the rapid development of science and technology, smart home appliances are also constantly developing and gaining more favor among people. More and more people choose to use smart home appliances.

[0003] As the number of users of smart home appliances increases, the amount of data that needs to be processed is also increasing. In existing technologies, Lambda architecture is mostly used to process data for the problem of massive data processing. The traditional Lambda architecture combines offline batch processing and online streaming processing, providing comprehensive and accurate data processing and analysis through offline batch processing, and providing low-latency, fast-response data through online streaming processing. Applying this architecture during periodic analysis can effectively balance the accuracy of analysis and real-time requirements.

[0004] However, the offline processing process of the Lambda architecture is slow and consumes huge resources; online streaming processing can only ensure that data does not accumulate by simplifying the parsing logic, but the parsing results are easily overwritten by the results of subsequent offline processing, resulting in duplication and waste of computing resources. Summary of the invention

[0005] The present application provides a data analysis method, apparatus, device and computer storage medium to solve the problems of data accumulation, resource bottleneck and inability to process data in parallel by stages generated by online analysis.

[0006] In a first aspect, the present application provides a data parsing method, comprising:

[0007] Acquire data to be processed, and input the data to be processed into a data processing platform, wherein the data to be processed is operation data uploaded during the operation of the smart home appliance;

[0008] Controlling the data processing platform to parse the data to be processed according to the operation stage of the smart home appliance to obtain parsed data corresponding to multiple operation stages;

[0009] According to the parsed data, a parsing result of the data to be processed is determined.

[0010] Optionally, before obtaining the data to be processed, the method further includes:

[0011] Obtain the parsing logic in historical data;

[0012] According to the parsing logic, a plurality of independent function logics and operation groups are determined, wherein the independent function logic is used to indicate logic corresponding to a plurality of operation stages in the parsing logic, and the operation group is determined according to the operation relationship of the plurality of independent function logics;

[0013] The data processing platform is determined according to the independent function logic and the operation grouping.

[0014] Optionally, determining a plurality of independent function logics and operation groups according to the parsing logic includes:

[0015] According to the multiple operation stages of the smart home appliance, the analysis logic is processed to determine the independent function logic corresponding to each operation stage;

[0016] According to the parsing logic, obtaining the operation relationship between the multiple independent function logics;

[0017] The plurality of independent function logics are grouped according to the operation relationship to obtain the operation grouping.

[0018] Optionally, determining the data processing platform according to the independent function logic and the operation grouping includes:

[0019] According to the multiple independent function logics, generate multiple function computing containers corresponding to the multiple independent function logics;

[0020] Determining a grouping of the plurality of function computing containers according to the running grouping;

[0021] The data processing platform is determined according to the grouping of the multiple function computing containers.

[0022] Optionally, the controlling the data processing platform to parse the data to be processed according to the operation stage of the smart home appliance to obtain parsed data corresponding to multiple operation stages, including:

[0023] Controlling the data processing platform to decompose the data to be processed into stages according to the operation stage of the smart home appliance;

[0024] Acquire a first trigger event in the decomposed data, where the first trigger event is used to indicate stage information of different operation stages of the smart home appliance;

[0025] According to the first trigger event, matching the decomposed data with a plurality of function computing containers;

[0026] According to the matching results, the multiple function computing containers in the data processing platform are controlled to parse the decomposed data to obtain parsed data corresponding to multiple running stages.

[0027] Optionally, controlling the multiple function computing containers in the data processing platform to parse the decomposed data according to the matching result includes:

[0028] According to the matching result, respectively determine whether the matched multiple function computing containers belong to a first running group, where the first running group is used to indicate a group that does not receive parsing results output by other function computing containers;

[0029] If yes, then parse the data in the group that matches the first trigger event in parallel;

[0030] If not, obtaining a second trigger event, where the second trigger event is used to indicate the data parsing result output by the previous grouping;

[0031] Determine trigger information according to the second trigger event, where the trigger information is used to indicate address information in the data;

[0032] The decomposed data receiving the trigger information is parsed.

[0033] Optionally, determining the parsing result of the data to be processed according to the parsed data includes:

[0034] When a starting event is received, the parsed data is aggregated in real time through a parsing aggregation function on a data processing platform;

[0035] When receiving the end event, determining whether the function computing container has completed data parsing;

[0036] If yes, determining the parsing result of the data to be processed;

[0037] If not, the resource coordinator is called to expand the resources, and after the expanded function computing container completes data parsing, the parsing result of the data to be processed is determined.

[0038] In a second aspect, the present application provides a data parsing device, comprising:

[0039] The acquisition module is used to acquire the data to be processed, where the data to be processed is the operation data uploaded during the operation of the smart home appliance.

[0040] The processing module is used to input the data to be processed into a data processing platform.

[0041] The processing module is also used to control the data processing platform to parse the data to be processed according to the operation stage of the smart home appliance, so as to obtain parsed data corresponding to multiple operation stages.

[0042] A determination module is used to determine the parsing result of the data to be processed according to the parsed data.

[0043] Optionally, the acquisition module is also used to acquire parsing logic in historical data.

[0044] The determination module is also used to determine multiple independent function logics and operation groups according to the parsing logic, the independent function logic is used to indicate the logic corresponding to multiple operation stages in the parsing logic, and the operation group is determined according to the operation relationship of the multiple independent function logics.

[0045] The determination module is further used to determine the data processing platform according to the independent function logic and the operation grouping.

[0046] Optionally, the processing module is further used to process the parsing logic according to multiple operating stages of the smart home appliance.

[0047] The determination module is also used to determine the independent function logic corresponding to each operation stage.

[0048] The acquisition module is further used to acquire the operating relationship between the multiple independent function logics according to the parsing logic.

[0049] The processing module is further used to group the multiple independent function logics according to the operation relationship to obtain the operation grouping.

[0050] Optionally, the data analysis device also includes: a generation module.

[0051] The generating module is used to generate a plurality of function computing containers corresponding to the plurality of independent function logics according to the plurality of independent function logics.

[0052] The determining module is further configured to determine the grouping of the multiple function computing containers according to the running grouping.

[0053] The determination module is further configured to determine the data processing platform according to the grouping of the multiple function calculation containers.

[0054] Optionally, the processing module is further used to control the data processing platform to decompose the data to be processed into stages according to the operation stage of the smart home appliance.

[0055] The acquisition module is further used to acquire a first trigger event in the decomposed data, where the first trigger event is used to indicate stage information of different operating stages of the smart home appliance.

[0056] The processing module is further configured to match the decomposed data with a plurality of function computing containers according to the first triggering event.

[0057] The processing module is further used to control the multiple function computing containers in the data processing platform to parse the decomposed data according to the matching results.

[0058] The acquisition module is also used to obtain parsing data corresponding to multiple running stages.

[0059] Optionally, the data analysis device further includes: a judgment module.

[0060] The judgment module is used to judge, according to the matching result, whether the matched multiple function computing containers belong to a first running group, where the first running group is used to indicate a group that does not receive parsing results output by other function computing containers.

[0061] The processing module is further used to parse in parallel the data in the group that matches the first trigger event.

[0062] The acquisition module is further used to acquire a second trigger event, where the second trigger event is used to indicate a data parsing result output by the previous grouping.

[0063] The determining module is further used to determine trigger information according to the second trigger event, where the trigger information is used to indicate address information in the data. The processing module is further used to parse the decomposed data of the received trigger information.

[0064] Optionally, the processing module is further used to summarize the parsed data in real time through a parsing summary function on a data processing platform when a start event is received.

[0065] The judgment module is further configured to judge whether the function calculation container has completed data parsing when receiving an end event.

[0066] The determination module is further used to determine the parsing result of the data to be processed.

[0067] The processing module is also used to call the resource coordinator to expand resources.

[0068] In a third aspect, the present application provides a data parsing device, comprising:

[0069] Memory;

[0070] processor;

[0071] Wherein, the memory stores computer-executable instructions;

[0072] The processor executes the computer-executable instructions stored in the memory to implement the data parsing method as described in the first aspect and various possible implementations of the first aspect.

[0073] In a fourth aspect, the present application provides a computer storage medium having computer execution instructions stored thereon, wherein the computer execution instructions are executed by a processor to implement the data parsing method as described in the first aspect and various possible implementations of the first aspect.

[0074] The data analysis method provided by the present application is to establish a data processing platform in advance and input the data to be processed into the data processing platform; use the data processing platform to decompose the data to be processed in stages, and use the corresponding function calculation container to perform data analysis based on the decomposition results; use the analysis summary function to summarize the data analysis results in real time until the analysis of the data to be processed is completed. This method solves the defect that traditional data analysis is difficult to parallelize, avoids the data accumulation generated by online analysis, ensures the integrity and accuracy of real-time analysis, improves the calculation efficiency of data analysis, avoids the defects of resource waste and repeated calculation, and improves user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0076] Figure 1 It is a scenario diagram of the data parsing method provided by this application;

[0077] Figure 2 This is the process of the data analysis method provided by this application Figure 1 ;

[0078] Figure 3 This is the process of the data analysis method provided by this application Figure 2 ;

[0079] Figure 4 This is the process of the data analysis method provided by this application Figure 3 ;

[0080] Figure 5 This is the process of the data analysis method provided by this application Figure 4 ;

[0081] Figure 6 It is a structural schematic diagram of the data parsing device provided by the present application;

[0082] Figure 7 It is a structural schematic diagram of the data analysis device provided in this application.

[0083] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0084] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0085] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein, for example.

[0086] In the embodiments of the present application, the words "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0087] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0088] With the rapid development of science and technology, smart home appliances are also constantly developing and gaining more favor among people. More and more people choose to use smart home appliances.

[0089] Smart home appliances can connect to the network wirelessly via WIFI and report various status information during each operation process to the data center. The data center regards each operation process as a cycle and can parse, summarize, and calculate various valuable data information for each operation cycle from the received user operation data (such as changes and summaries of various state quantities, changes and summaries of various operation abnormalities, etc.), providing a basis for subsequent intelligent analysis.

[0090] As the number of users of smart home appliances increases, the amount of data that needs to be processed is also increasing. In existing technologies, Lambda architecture is mostly used to process data for the problem of massive data processing. The traditional Lambda architecture combines offline batch processing and online streaming processing, providing comprehensive and accurate data processing and analysis through offline batch processing, and providing low-latency, fast-response data through online streaming processing. Applying this architecture during periodic analysis can effectively balance the accuracy of analysis and real-time requirements.

[0091] However, since there are many online users at the same time during the operation of smart home appliances, the sensor information monitored during the operation process is diverse, and there are interdependencies between the various operation stages, it is difficult to perform online analysis of the operation cycle of all users in real time. The main difficulties currently exist are:

[0092] 1. Since periodic analysis has complex analysis logic dependencies, it is difficult to parallelize by stage. Therefore, it is impossible to handle the delay and data accumulation caused by real-time processing of complex calculation logic, which seriously affects the normal progress of the analysis process.

[0093] 2. Online analysis can only perform coarse-grained resource coordination based on the number of online washing machine models, which is difficult to effectively resolve resource bottlenecks. The only way to improve processing speed is to simplify the online analysis logic.

[0094] 3. The resource coordination of serverless function computing is still based on specific function computing. When smart home appliances process data in stages, the resource requirements in different stages are uneven and need to be analyzed on demand. For example, the duration and data volume of the rinsing stage are higher than those of the dehydration stage.

[0095] In response to the above problems, the present application provides a data analysis method.

[0096] First, the implementation scenarios involved in this application are described.

[0097] Serverless computing is a new way to host applications on infrastructure that does not require end-user management. It is the next stage in the evolution of IaaS (Infrastructure as a Service). It separates the underlying infrastructure from developers, essentially virtualizing the runtime (a type of virtual machine, generally referring to a process-level virtual machine) and operational management.

[0098] Serverless computing is a cloud service where a hosting provider allocates sufficient resources in real time, without the need to pay for dedicated servers or capacity in advance. Serverless computing does not mean that servers are not needed (serverless literally means, don't worry about servers), but it builds a new abstraction layer on top of the cloud infrastructure to execute the code written by developers using only the very precise computing resources required to complete the task. The serverless platform executes the task when the preset event that triggers the code occurs. The end user does not need to tell the serverless provider how many times the event or function will occur. Serverless computing can also be called function as a service (function as a service) or event-driven computing. From an infrastructure perspective, it has different abstraction layers that developers can interact with, such as bare metal, virtual machines, and containers. For some workloads, all management can be abstracted so that users can execute code without worrying about the infrastructure or management of the server.

[0099] like Figure 1 As shown, the smart home appliance 1 is in communication connection with the server 2. The smart home appliance 1 provides its own operation data to the server 2. The server 2 can process the acquired data. For example, the smart home appliance 1 can be a smart clothing processing device, and the server 2 can be configured with a data processing platform. The data processing platform on the server 2 can acquire the clothing processing data uploaded by the smart clothing processing device and perform real-time analysis and processing on the data.

[0100] The data parsing method provided by the present application decomposes the data parsing logic and generates multiple independent functions for calculation and coordination; generates event-driven independent function calculation units according to the operating relationship between multiple independent functions; and dynamically deploys and coordinates resources for the data to be processed through the data processing platform, thereby realizing parallel parsing of data. This method solves the defect that traditional data parsing is difficult to parallelize, avoids the data accumulation generated by online parsing, ensures the integrity and accuracy of real-time parsing, improves the computational efficiency of data parsing, avoids the defects of resource waste and repeated calculation, and improves user experience.

[0101] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0102] Figure 2 The process of the data analysis method provided in the embodiment of the present application Figure 1 The execution subject of this embodiment may be, for example, a server. Figure 2As shown, the data parsing method provided in this embodiment includes:

[0103] S201: Acquire data to be processed and input the data to be processed into a data processing platform, wherein the data to be processed is operation data uploaded during the operation of the smart home appliance.

[0104] The data processing platform refers to a platform that is built based on the historical operation data of smart home appliances and can analyze real-time data online. Different smart home appliances have different corresponding data processing platforms.

[0105] The smart home appliance is connected to the server in communication, so the server can obtain the real-time operation data uploaded by the smart home appliance, and the smart home appliance that uploads the real-time operation data can be one or more.

[0106] After the server obtains the data to be processed uploaded by the smart home appliance, the data to be processed is input into a data processing platform configured on the server, and the data to be processed is analyzed in real time using the data processing platform.

[0107] For example, during operation, multiple smart clothing processing devices upload operation data to a server configured with a serverless computing platform for processing data for the current smart clothing processing devices, and the server controls the serverless computing platform to process the acquired data.

[0108] S202: Control the data processing platform to parse the data to be processed according to the operation stage of the smart home appliance, and obtain parsed data corresponding to multiple operation stages.

[0109] The operation phase refers to the different calculation phases obtained by dividing the data analysis process according to the process of processing historical data by the smart home appliance. Different smart home appliances and different operation programs have different operation phases.

[0110] The control data processing platform decomposes the data to be processed according to the operation stage of the smart home appliance, so that the phased data to be processed corresponding to the operation stage of the smart home appliance can be obtained. Then, the data processing platform parses the obtained phased data to be processed, so as to obtain the parsed data corresponding to the operation stage of the smart home appliance.

[0111] For example, the current operation phases of the intelligent clothing processing device may include: soaking phase, washing phase, dehydration phase, and drying phase. The operation data uploaded by the intelligent clothing processing device is divided according to the operation phases of "soaking phase, washing phase, dehydration phase, and drying phase", so that multiple phases of data to be processed can be obtained, and the corresponding analysis data can be obtained by processing these data.

[0112] S203: Determine the parsing result of the data to be processed according to the parsed data.

[0113] The currently acquired parsed data is data that has been decomposed according to the operation stage of the smart home appliance, which indicates that these parsed data correspond to the decomposed data to be processed. Then, the obtained multiple parsed data are sorted and combined according to the current operation stage of the smart home appliance, and the re-sorted and combined multiple parsed data are summarized, and the obtained new parsed data is the parsing result of the data to be processed.

[0114] It can be understood that the amount of parsed data corresponds to the number of operating stages of the current smart home appliance.

[0115] For example, the serverless computing platform divides and analyzes the data to be processed online according to the operating stages of "soaking stage, washing stage, dehydration stage, and drying stage". The analyzed data obtained at this time is also stage-by-stage data. The multiple analyzed data obtained are sorted and combined again according to the operating stages of "soaking stage, washing stage, dehydration stage, and drying stage". Finally, the sorted and combined analyzed data are summarized together, which is the analysis result of the operating data uploaded by the intelligent clothing processing device. This application does not impose any special restrictions on the specific division of the operating stages.

[0116] The data analysis method provided in this embodiment obtains the data to be processed and inputs the data to be processed into a data analysis model; then, the data to be processed is analyzed and processed according to the operation stage of the smart home appliance to obtain analysis data corresponding to multiple operation stages; and the analysis result of the data to be processed is determined according to the analysis data. This method solves the defect that traditional data analysis is difficult to parallelize, avoids the data accumulation caused by online analysis, and improves the user experience.

[0117] Figure 3 The process of the data analysis method provided in the embodiment of the present application Figure 2 .like Figure 3 As shown, in this embodiment Figure 2 Based on the embodiment, the data analysis method is described in detail. The data analysis method shown in this embodiment includes:

[0118] S301: Obtain the parsing logic in the historical data.

[0119] Among them, historical data refers to the data information corresponding to different operating processes of different smart home appliances in the database, and parsing logic refers to the data processing rules when parsing the operating data of smart home appliances.

[0120] Obtain the historical data of the operation process of smart home appliances in the database, and then extract the data processing rules in the processing of these historical data.

[0121] It is understandable that different intelligent clothes processing devices have different corresponding analysis logics in different operation stages. For example, the data processing rules in the washing stage and the drying stage are different.

[0122] S302: Determine a plurality of independent function logics and operation groups according to the parsing logic.

[0123] The independent function logic is used to indicate the logic corresponding to multiple operation stages in the parsing logic, and the operation grouping is determined according to the operation relationship between the multiple independent function logics.

[0124] According to the parsing logic of the current data to be processed, the parsing logic is decomposed by stages using the data processing platform, and the obtained multiple parsing logics after being decomposed by stages are independent function logics.

[0125] Since the operation process of smart home appliances is a process with a sequence and the operation process is irreversible. Then, the multiple independent function logics obtained also have a corresponding sequence, and the multiple independent function logics have an association relationship, and the association relationship is mostly a chain relationship. According to the sequence and association relationship of the independent function logic, it can be grouped to obtain multiple operation groups, and the independent function logic in each operation group can be carried out in parallel.

[0126] It is understandable that the number of operation groups is related to the order and association of independent function logics, and there is no fixed number. The number of operation groups corresponding to the same operation program of the same smart home appliance is consistent.

[0127] For example, the analysis logic of the current data to be processed can be: first the soaking stage, then the washing stage, then the dehydration stage, and finally the drying stage. After decomposing the analysis logic, the data processing rules corresponding to the four "soaking stage, washing stage, dehydration stage, and drying stage" can be obtained. Since the operation order between these four data processing rules is irreversible and the data processing rules between two adjacent stages are related, the four stages can be grouped into the first operation group, the second operation group, the third operation group, and the fourth operation group.

[0128] S303: Determine a data processing platform according to the independent function logic and the operation grouping.

[0129] The logic of each independent function is abstracted and constructed into a corresponding data processing function, that is, the parsing logic is functionalized to facilitate online parsing of the acquired data to be processed.

[0130] Then, according to the corresponding operation grouping, the operation process of the data processing function is controlled, and the obtained platform that can analyze the operation data online is the corresponding data processing platform.

[0131] For example, the serverless computing platform abstracts the data processing rules corresponding to the "immersion stage, washing stage, dehydration stage, and drying stage" into corresponding mathematical functions; and the groups corresponding to these four stages are the first operation group, the second operation group, the third operation group, and the fourth operation group; at this time, there are corresponding mathematical functions in these groups, and the operation process is carried out in the order of the operation groups. At this time, the four operation groups and the corresponding mathematical functions in the groups are set on the serverless computing platform, and the corresponding data processing platform can be obtained.

[0132] S304: Acquire the data to be processed, and input the data to be processed into the data processing platform.

[0133] Step S304 is similar to the above step S201 and will not be described again.

[0134] S305: Control the data processing platform to decompose the data to be processed into stages according to the operation stage of the smart home appliance.

[0135] Different operating programs of smart home appliances correspond to different operating stages. The corresponding operating stage can be determined based on the operating data uploaded by the current smart home appliance. The data to be processed is divided according to the acquired operating stage through the data processing platform corresponding to the current smart home appliance, and the number of the divided data to be processed is consistent with the number of the current operating stage.

[0136] It is understandable that there are multiple data processing platforms, and different smart home appliances correspond to different data processing platforms. At this time, the data processing platform that performs online analysis on the operation data uploaded by the current smart home appliance corresponds to the current smart home appliance.

[0137] For example, the operation phases corresponding to the current intelligent clothing processing device may be: "immersion phase, washing phase, dehydration phase, drying phase". According to these four operation phases, the data to be processed is divided into soaking phase data, washing phase data, dehydration phase data, and drying phase data using the data processing platform.

[0138] S306: Acquire the first trigger event in the decomposed data.

[0139] S307: According to the first triggering event, match the decomposed data with multiple function computing containers.

[0140] The first trigger event is used to indicate the stage information of different operation stages of the smart home appliance, and the stage information may include, for example, soaking, washing, dehydration, and drying. The function computing container refers to a computing unit that can perform data analysis in each group in the data processing platform.

[0141] The data after stage decomposition and the function computing container both have stage information. Therefore, the stage information can be used to match the two. The matching is based on whether the stage information of the two is consistent.

[0142] The first trigger event corresponding to the data after stage decomposition is obtained, and the first trigger event is compared with the first trigger event corresponding to each function computing container, and the number of the currently obtained first trigger events is consistent with the number of the data after the current stage decomposition.

[0143] For example, the data to be processed is divided into soaking stage data, washing stage data, dehydration stage data, and drying stage data, and the corresponding stage information is: soaking, washing, dehydration, and drying. The current data processing platform is function computing container 1, function computing container 2, function computing container 3, and function computing container 4, and the corresponding stage information is soaking, washing, dehydration, and drying. At this time, the stage information of the two is matched, and the stage information of function computing container 1 is consistent with that of the soaking stage data, the stage information of function computing container 2 is consistent with that of the washing stage data, the stage information of function computing container 3 is consistent with that of the dehydration stage data, and the stage information of function computing container 4 is consistent with that of the drying stage data.

[0144] S308: According to the matching result, control the multiple function computing containers in the data processing platform to parse the decomposed data to obtain parsed data corresponding to multiple running stages.

[0145] When the comparison result of the first trigger event of the data after stage decomposition is consistent with the first trigger event of the function computing container, it indicates that the function computing container with the same first trigger event can be used to parse the data after the corresponding stage decomposition, thereby obtaining the parsed data corresponding to multiple first trigger events.

[0146] For example, when function computing container 1 is consistent with the phase information of the soaking phase data, function computing container 2 is consistent with the phase information of the washing phase data, function computing container 3 is consistent with the phase information of the dehydration phase data, and function computing container 4 is consistent with the phase information of the drying phase data, function computing container 1 can parse the soaking phase data online, function computing container 2 can parse the washing phase data online, function computing container 3 can parse the dehydration phase data online, and function computing container 4 can parse the drying phase data online.

[0147] S309: When a start event is received, the parsing data is summarized in real time through the parsing summary function on the data processing platform.

[0148] The start event refers to the state at the start of any operation process of the smart home appliance, for example, the smart clothing processing device may be turned on.

[0149] The operation process of smart home appliances is a process with a start and an end. When the smart home appliances start running, the corresponding operation data can be obtained.

[0150] When the data processing platform receives the start event of the current smart home appliance, the smart home appliance starts to run the corresponding program, and continuously generates operation data during the operation, and uploads the operation data to the data processing platform and starts online analysis. At this time, the analysis and summary function on the data processing platform completes the real-time summary of the analyzed data.

[0151] It is understandable that the data processing platform's analysis of operating data is a process of decomposition followed by aggregation. Decomposition is done to enable parallel processing of operating data, and aggregation is done because the decomposed data cannot accurately and completely reflect the corresponding operating process. Therefore, it is necessary to aggregate the decomposed operating data uploaded by the same smart home appliance, so as to obtain the parsed data of the current smart home appliance operation process. The order in which the parsing and aggregation functions obtain the parsed data is related to the order in which the function computing container completes the data parsing.

[0152] For example, when the data processing platform receives the start event, i.e., "the smart clothing processing device is turned on", the parsing summary function starts to summarize the parsed data in real time. When function computing container 1 completes the online parsing of the soaking stage data, the parsing summary function obtains the parsed data; when function computing container 2 completes the online parsing of the washing stage data, the parsing summary function obtains the parsed data. At this time, the summary function includes: the start event, the parsed data of the soaking stage, and the parsed data of the washing stage.

[0153] S310: When an end event is received, determine whether the function computing container has completed data parsing; if so, execute step S311; if not, execute step S312.

[0154] The end event refers to the state of any end time of the operation process of the smart home appliance, for example, the end of the current clothing processing program.

[0155] The purpose of this step of judging whether the function computing container has completed data parsing is to determine whether there is data accumulation.

[0156] If the function computing container completes data parsing, it means that there is no data accumulation. The function computing container currently performing data parsing has completed data parsing when the end event is obtained. At this time, the parsing summary function is used to aggregate the parsed data of the function computing container performing data parsing, which is the parsing result of the data to be processed.

[0157] If the function computing container has not completed data parsing, it indicates that there is data accumulation. The function computing container currently performing data parsing still has unfinished data parsing when the end event is obtained. At this time, the resource coordinator can send a resource expansion instruction to the container resource coordinator, that is, the function computing container that has completed data parsing or the function computing container in an idle state is transferred to the function computing container that has not completed data parsing, so as to improve the parsing speed of the computing unit that has not completed data parsing and jointly complete the corresponding data parsing.

[0158] The container resource coordinator is used to coordinate the function computing containers in the data processing platform, and the resource coordinator is used to coordinate resources within the server to avoid data accumulation and resource duplication.

[0159] For example, when the data processing platform receives an end event, that is, "the current clothing processing program ends", the parsing summary function begins to summarize the parsed data in real time. When the parsing summary function includes: the start event, the parsed data of the soaking stage, the parsed data of the washing stage, the parsed data of the dehydration stage, the parsed data of the drying stage, and the end event, the function calculation container completes the data parsing at this time, and the summary result is the parsing result. When the parsing summary function includes: the start event, the parsed data of the soaking stage, the parsed data of the washing stage, the parsed data of the dehydration stage, and the end event, it indicates that the parsed data of the drying stage has not been received. At this time, the function calculation container has not completed the data parsing, and the function calculation containers of the soaking stage, the washing stage, and the dehydration stage can be called for data parsing in the drying stage until the parsing summary function receives the parsed data of the drying stage. At this time, the summary result is the parsing result.

[0160] S311: Determine the parsing result of the data to be processed.

[0161] S312: calling a resource coordinator to expand resources, and after the expanded function computing container completes data parsing, determining the parsing result of the data to be processed.

[0162] The data analysis method provided in this embodiment establishes a data processing platform in advance and inputs the data to be processed into the data processing platform; uses the data processing platform to decompose the data to be processed in stages, and uses the corresponding function computing container to perform data analysis based on the decomposition results; uses the analysis summary function to summarize the data analysis results in real time until the analysis of the data to be processed is completed. This method solves the defect that traditional data analysis is difficult to parallelize, avoids the data accumulation generated by online analysis, ensures the integrity and accuracy of real-time analysis, improves the calculation efficiency of data analysis, avoids the defects of resource waste and repeated calculation, and improves user experience.

[0163] Figure 4 The process of the data analysis method provided in the embodiment of the present application Figure 3 .like Figure 4 As shown, in this embodiment Figure 3 Based on the embodiment, the process of determining the data processing platform is described in detail. The data parsing method shown in this embodiment includes:

[0164] S401: Process the parsing logic according to multiple operation stages of the smart home appliance to determine the independent function logic corresponding to each operation stage.

[0165] The currently acquired parsing logic corresponds to the current operation process of the smart home appliance. Different operation stages of the smart home appliance have different parsing logics. According to the different stages of the operation process of the smart home appliance, the current parsing logic is decomposed by stage, and the number of decomposed logics corresponds to the number of current operation stages.

[0166] For example, the analysis logic of the current data to be processed can be: first the soaking stage, then the washing stage, then the dehydration stage, and finally the drying stage. After decomposing the analysis logic, the data processing rules corresponding to the four "soaking stage, washing stage, dehydration stage, and drying stage" can be obtained.

[0167] S402: According to the parsing logic, obtain the operation relationship between multiple independent function logics.

[0168] The operation relationship refers to the order of operation between independent function logics and the relationship between two independent functions with adjacent operation orders. The relationship between two independent functions with adjacent operation orders can be, for example, that the latter obtains the parsed data output by the former or that the latter does not obtain the parsed data output by the former.

[0169] There are correlations between the multiple operating stages of the smart home appliance, and there are corresponding correlations between the corresponding multiple independent function logics.

[0170] The currently acquired parsing logic corresponds to the current operation process of the smart home appliance, and also reflects the operation relationship of each operation stage. Therefore, the operation relationship between the multiple independent function logics obtained is consistent with the operation relationship between each operation stage. At this time, the operation relationship obtained for each operation stage is the operation relationship between the multiple independent function logics.

[0171] For example, the operation phases corresponding to the current intelligent clothing processing device may be: "immersion phase, washing phase, dehydration phase, drying phase", and the order of these four operation phases is irreversible. After obtaining the analysis data of the previous operation phase, the data analysis of the next phase can be performed. Therefore, the data processing rules corresponding to these four operation phases are also the same.

[0172] S403: Grouping the multiple independent function logics according to the operation relationship to obtain the operation grouping.

[0173] According to the relationship between two independent functions in adjacent running order, for the two independent function logics where the latter needs to obtain the parsed data output by the former in order to perform data parsing, the current grouping remains unchanged; and the two independent function logics where the latter does not need to obtain the parsed data output by the former are merged into the same group.

[0174] At this time, the running relationship between these independent function logics can be constructed into multiple chain relationships according to whether the latter needs to obtain the front-end relationship of the parsed data output by the former. Then, according to the running order between the independent function logics, the independent function logics at the same level and the same running order in these multiple chain relationships are merged into the same group, so that a group of multiple independent function logics can be obtained, that is, the running group.

[0175] For example, according to the data processing rules corresponding to the four "soaking stage, washing stage, dehydration stage, and drying stage", since the operating order between these four data processing rules is irreversible, and there is a correlation between the data processing rules between two adjacent stages, that is, the latter between two adjacent stages needs to obtain the analytical data output by the former, then the groups corresponding to these four stages are the first operating group, the second operating group, the third operating group, and the fourth operating group, respectively.

[0176] S404: Generate multiple function computing containers corresponding to the multiple independent function logics according to the multiple independent function logics.

[0177] Independent function logic is only the underlying logic for data processing and cannot directly parse the acquired data. The acquired independent function logic is abstracted and constructed into corresponding data processing functions, and these functions are containerized to obtain multiple function computing containers corresponding to the independent function logic.

[0178] For example, the data processing rules of the immersion stage can be abstractly constructed into corresponding mathematical functions for data analysis. The principles of constructing functions in other stages are the same and will not be repeated here.

[0179] S405: Determine groups of the multiple function computing containers according to the running groups.

[0180] If multiple function computing containers are obtained based on the corresponding independent function logic, the running relationship between each function computing container is the same as the relationship between the corresponding independent function logic. In this case, the running grouping of the independent function logic can also be applied to the grouping of the function computing container. Then, the running grouping of multiple independent function logics is consistent with the running grouping of the corresponding function computing container.

[0181] For example, the operation groups of the independent function logic corresponding to the current data to be processed are: the first operation group (immersion stage), the second operation group (washing stage), the third operation group (dehydration stage), and the fourth operation group (drying stage). Then, the operation groups of the current function calculation container are: the first operation group (immersion stage), the second operation group (washing stage), the third operation group (dehydration stage), and the fourth operation group (drying stage).

[0182] S406: Determine a data processing platform according to the grouping of the multiple function calculation containers.

[0183] After multiple function computing containers are grouped, multiple running groups with a running order are obtained, and data analysis can be performed on the data to be processed. At this point, the basic framework of data analysis is formed.

[0184] It is understandable that the parsing data obtained at this time is obtained in stages, so it is also necessary to set up a parsing summary function to summarize these parsing data so as to obtain a complete parsing result. At this time, the platform that can perform the data parsing process is the data processing platform.

[0185] For example, the operation groups of the obtained function computing container are divided into: the first operation group (soaking stage), the second operation group (washing stage), the third operation group (dehydration stage), and the fourth operation group (drying stage), and are set on the serverless computing platform. At this time, the operation data uploaded by the smart clothing processing device can be analyzed online; and then the analysis summary function is set on the platform. At this time, the serverless computing platform is the data processing platform corresponding to the current smart clothing processing device.

[0186] The data parsing method provided in this embodiment decomposes the parsing logic according to the operation stage of the smart home appliance to obtain the independent function logic corresponding to each operation stage and the operation relationship between multiple independent function logics; multiple function computing containers corresponding to multiple independent function logics are grouped according to the operation relationship to obtain operation groups and determine the data processing platform. This method solves the defect that traditional data parsing is difficult to parallelize by running data groups, avoids the defects of resource waste and repeated calculations, improves the efficiency of data processing, and improves the user experience.

[0187] Figure 5 The process of the data analysis method provided in the embodiment of the present application Figure 2 .like Figure 5 As shown, in this embodiment Figure 3 Based on the embodiment, controlling the multiple function computing containers in the data processing platform to parse the decomposed data according to the matching results is described in detail. The data parsing method shown in this embodiment includes:

[0188] S501: According to the matching results, determine whether the matched multiple function computing containers belong to the first running group; if so, execute step S502; if not, execute step S503.

[0189] The first running group refers to the group of function computing containers at the first level of multiple chain relationships when the function computing containers are grouped according to the running relationship, that is, the container group that starts computing first after obtaining the data to be processed.

[0190] The function computing container set on the current data processing platform can parse the data of all running programs of the current smart home appliance. Therefore, the matching result of the first trigger event of the current smart home appliance and the first trigger event of the function computing container is only consistent. The first trigger event is only used to accurately allocate the data to be processed to the corresponding function computing container, thereby ensuring the accuracy and timeliness of data analysis.

[0191] If the matched multiple function computing containers belong to the first running group, it indicates that the currently matched function computing containers can perform data parsing at the same time. At this time, the function computing containers in the group that match the first triggering event are run to parse the corresponding stage-decomposed data in parallel.

[0192] If the matched multiple function computing containers do not belong to the first running group, it means that the function computing containers that do not belong to the first running group need to receive the parsed data output from the previous stage before they can run. At this time, it is necessary to further judge the function computing containers that do not belong to the first running group to determine the running group of the function computing containers.

[0193] For example, the operation groups of the function computing container corresponding to the current data to be processed are: the first operation group (immersion stage), the second operation group (washing stage), the third operation group (dehydration stage), and the fourth operation group (drying stage). At this time, the matching function computing containers belong to different operation groups, and the operation order of the operation groups is irreversible and cannot be exchanged. The function computing container in the first group is first run to obtain the parsed data of the immersion stage.

[0194] S502: Parsing in parallel the data in the group that matches the first trigger event.

[0195] S503: Acquire a second trigger event.

[0196] S504: Determine trigger information according to the second trigger event.

[0197] S505: parse the decomposed data of the received trigger information.

[0198] The second trigger event is used to indicate the data parsing result output by the previous group, and the trigger information is used to indicate the address information in the data.

[0199] When the matched multiple function computing containers do not belong to the first running group, a second triggering event is obtained, including but not limited to parsed data of the first running group.

[0200] After obtaining the parsed data, including but not limited to the function computing container of the second running group, by obtaining the trigger information of the two, it is determined that the two belong to the data to be processed of the same smart home appliance, and then the function computing container in the current group is used to parse the corresponding data.

[0201] It can be understood that in the process of online parsing of the data to be processed, the data to be processed obtained may come from one smart home appliance or from multiple smart home appliances.

[0202] For example, the operation groups of the function computing container corresponding to the current data to be processed are: the first operation group (immersion stage), the second operation group (washing stage), the third operation group (dehydration stage), and the fourth operation group (drying stage). After obtaining the parsed data of the soaking stage, it is necessary to determine whether the IP address of the parsed data is the same as the IP addresses of multiple function computing containers in the second operation group, that is, the operation data belonging to the same intelligent clothing processing device. When the IP addresses of the two are consistent, the function computing container in the second operation group starts data parsing to obtain the parsed data of the washing stage, and inputs the parsed data into the next operation group, and runs with the same logic until the data parsing is completed.

[0203] The data parsing method provided in this embodiment determines whether the multiple function computing containers that respond belong to the first running group after the multiple function computing containers respond to the decomposed data through the first trigger event; if so, the data in the group can be parsed in parallel; if not, the second trigger event and the corresponding trigger information are obtained, and the data with consistent trigger information is parsed. This method solves the defect that traditional data parsing is difficult to parallelize, ensures the integrity and accuracy of real-time parsing, improves the computational efficiency of data parsing, and improves user experience.

[0204] Figure 6 This is a schematic diagram of the structure of the data analysis device provided in this application. Figure 6 As shown, the present application provides a data analysis device, the data analysis device 300 includes:

[0205] The acquisition module 301 is used to acquire the data to be processed, where the data to be processed is the operation data uploaded during the operation of the smart home appliance.

[0206] The processing module 302 is used to input the data to be processed into a data processing platform.

[0207] The processing module 302 is further used to control the data processing platform to parse the data to be processed according to the operation stage of the smart home appliance, so as to obtain parsed data corresponding to multiple operation stages.

[0208] The determination module 303 is used to determine the parsing result of the data to be processed according to the parsed data.

[0209] Optionally, the acquisition module 301 is also used to acquire parsing logic in historical data.

[0210] The determination module 303 is also used to determine multiple independent function logics and operation groups according to the parsing logic, and the independent function logic is used to indicate the logic corresponding to multiple operation stages in the parsing logic, and the operation group is determined according to the operation relationship of the multiple independent function logics.

[0211] The determination module 303 is further used to determine the data processing platform according to the independent function logic and the operation grouping.

[0212] Optionally, the processing module 302 is further used to process the parsing logic according to multiple operating stages of the smart home appliance.

[0213] The determination module 303 is further used to determine the independent function logic corresponding to each operation stage.

[0214] The acquisition module 301 is further used to acquire the operation relationship between the multiple independent function logics according to the parsing logic.

[0215] The processing module 302 is further configured to group the multiple independent function logics according to the operation relationship to obtain the operation grouping.

[0216] Optionally, the data analysis device further includes: a generation module 304.

[0217] The generating module 304 is configured to generate a plurality of function computing containers corresponding to the plurality of independent function logics according to the plurality of independent function logics.

[0218] The determining module 303 is further configured to determine the grouping of the multiple function computing containers according to the running grouping.

[0219] The determination module 303 is further configured to determine the data processing platform according to the grouping of the multiple function computing containers.

[0220] Optionally, the processing module 302 is further used to control the data processing platform to decompose the data to be processed into stages according to the operation stage of the smart home appliance.

[0221] The acquisition module 301 is further used to acquire a first trigger event in the decomposed data, where the first trigger event is used to indicate stage information of different operation stages of the smart home appliance.

[0222] The processing module 302 is further configured to match the decomposed data with a plurality of function computing containers according to the first triggering event.

[0223] The processing module 302 is further used to control the multiple function computing containers in the data processing platform to parse the decomposed data according to the matching result.

[0224] The acquisition module 301 is also used to obtain parsing data corresponding to multiple operation stages.

[0225] Optionally, the data analysis device further includes: a judgment module 305.

[0226] The judgment module 305 is used to judge, according to the matching result, whether the matched multiple function computing containers belong to a first running group, where the first running group is used to indicate a group that does not receive parsing results output by other function computing containers.

[0227] The processing module 302 is further configured to parse in parallel the data in the group that matches the first trigger event.

[0228] The acquisition module 301 is further used to acquire a second trigger event, where the second trigger event is used to indicate the data parsing result output by the previous grouping.

[0229] The determining module 303 is further configured to determine trigger information according to the second trigger event, where the trigger information is used to indicate address information in the data. The processing module 302 is further configured to parse the decomposed data of the received trigger information.

[0230] Optionally, the processing module 302 is further configured to aggregate the parsed data in real time through a parsing aggregation function on a data processing platform when a start event is received.

[0231] The determination module 305 is further configured to determine whether the function computing container has completed data parsing when receiving an end event.

[0232] The determination module 303 is further used to determine the parsing result of the data to be processed.

[0233] The processing module 302 is also used to call the resource coordinator to expand resources.

[0234] Figure 7 This is a schematic diagram of the structure of the data analysis device provided in this application. Figure 7 As shown, the present application provides a data analysis device, which includes a receiver 401 , a transmitter 402 , a processor 403 and a memory 404 .

[0235] Receiver 401, for receiving instructions and data;

[0236] A transmitter 402, used for sending instructions and data;

[0237] Memory 404, for storing computer-executable instructions;

[0238] The processor 403 is used to execute the computer-executable instructions stored in the memory 404 to implement the various steps performed by the data analysis method in the above embodiment. For details, please refer to the relevant description in the above data analysis method embodiment.

[0239] Optionally, the memory 404 may be independent or integrated with the processor 403 .

[0240] When the memory 404 is independently provided, the electronic device further includes a bus for connecting the memory 404 and the processor 403 .

[0241] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the data analysis method performed by the above-mentioned data analysis device is implemented.

[0242] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0243] So far, the technical solution of the present application has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments, and the above embodiments are only used to illustrate the technical solution of the present application rather than to limit it. Although the present application 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 by 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 application.

Claims

1. A data analysis method, characterized in that: The method comprises: Acquire data to be processed, and input the data to be processed into a data processing platform, wherein the data to be processed is operation data uploaded during the operation of the smart home appliance; Controlling the data processing platform to parse the data to be processed according to the operation stage of the smart home appliance to obtain parsed data corresponding to multiple operation stages; According to the parsed data, a parsing result of the data to be processed is determined.

2. The method according to claim 1, characterized in that Before obtaining the data to be processed, the method further includes: Obtain the parsing logic in historical data; According to the parsing logic, a plurality of independent function logics and operation groups are determined, wherein the independent function logic is used to indicate logic corresponding to a plurality of operation stages in the parsing logic, and the operation group is determined according to the operation relationship of the plurality of independent function logics; The data processing platform is determined according to the independent function logic and the operation grouping.

3. The method according to claim 2, characterized in that Determining a plurality of independent function logics and operation groups according to the parsing logic includes: According to the multiple operation stages of the smart home appliance, the analysis logic is processed to determine the independent function logic corresponding to each operation stage; According to the parsing logic, obtaining the operation relationship between the multiple independent function logics; The plurality of independent function logics are grouped according to the operation relationship to obtain the operation grouping.

4. The method according to claim 3, characterized in that The determining the data processing platform according to the independent function logic and the operation grouping includes: According to the multiple independent function logics, generate multiple function computing containers corresponding to the multiple independent function logics; Determining a grouping of the plurality of function computing containers according to the running grouping; The data processing platform is determined according to the grouping of the multiple function computing containers.

5. The method according to claim 4, characterized in that The controlling the data processing platform to parse the data to be processed according to the operation stage of the smart home appliance to obtain parsed data corresponding to multiple operation stages, including: Controlling the data processing platform to decompose the data to be processed into stages according to the operation stage of the smart home appliance; Acquire a first trigger event in the decomposed data, where the first trigger event is used to indicate stage information of different operation stages of the smart home appliance; According to the first trigger event, matching the decomposed data with a plurality of function computing containers; According to the matching results, the multiple function computing containers in the data processing platform are controlled to parse the decomposed data to obtain parsed data corresponding to multiple running stages.

6. The method according to claim 5, characterized in that According to the matching result, controlling the multiple function computing containers in the data processing platform to parse the decomposed data includes: According to the matching result, respectively determine whether the matched multiple function computing containers belong to a first running group, where the first running group is used to indicate a group that does not receive parsing results output by other function computing containers; If yes, then parse the data in the group that matches the first trigger event in parallel; If not, obtaining a second trigger event, where the second trigger event is used to indicate the data parsing result output by the previous grouping; Determine trigger information according to the second trigger event, where the trigger information is used to indicate address information in the data; The decomposed data receiving the trigger information is parsed.

7. The method according to claim 6, characterized in that Determining the parsing result of the data to be processed according to the parsed data includes: When a starting event is received, the parsed data is aggregated in real time through a parsing aggregation function on a data processing platform; When receiving the end event, determining whether the function computing container has completed data parsing; If yes, determining the parsing result of the data to be processed; If not, the resource coordinator is called to expand the resources, and after the expanded function computing container completes data parsing, the parsing result of the data to be processed is determined.

8. A data analysis device, characterized in that: include: An acquisition module is used to acquire data to be processed, where the data to be processed is operation data uploaded during the operation of the smart home appliance; A processing module, used for inputting the data to be processed into a data processing platform; The processing module is further used to control the data processing platform to parse the data to be processed according to the operation stage of the smart home appliance to obtain parsed data corresponding to multiple operation stages; A determination module is used to determine the parsing result of the data to be processed according to the parsed data.

9. A data analysis device, characterized in that: include: Memory; processor; Wherein, the memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the data parsing method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The computer storage medium stores computer executable instructions, which, when executed by a processor, are used to implement the data parsing method according to any one of claims 1 to 7.

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