A method for intelligently generating a refrigerator use monthly report based on big data

Abstract

The application provides a method for intelligently generating a refrigerator use monthly report based on big data, comprising the following steps: a device reports data to an IoT platform through MQTT and device shadow, and the IoT platform stores the data into an HBASE database; device data is batched from the HBASE database, and after being parsed, encapsulated and statistically integrated, the data is stored into a data total table in batches according to a set maximum insertion load and a load factor of the database; all data of devices of the day is taken out from the data total table in batches in pages, the data is statistically integrated, a device use score is calculated in combination with a given rule, and a daily report and a door opening number reminding are generated by adapting to corresponding use and maintenance strategies; and at the beginning of each month, a device monthly report is intelligently generated based on all daily report data of devices of the month.

Description

Technical Field

[0001] This invention relates to the field of big data application technology in refrigerators, specifically to a method for intelligently generating monthly refrigerator usage reports based on big data. Background Technology

[0002] With technological advancements, the Internet of Things (IoT) has begun to take shape, and its development is maturing. Many home appliances, such as refrigerators, air conditioners, and washing machines, are gradually becoming intelligent. Consequently, users' need to monitor the real-time data and short-term usage of these smart devices is becoming increasingly urgent. However, users often lack knowledge about how to maintain and care for these smart devices, or what the optimal usage plan is to ensure they operate at their best. When problems arise, they frequently call customer service, only to find that service representatives lack understanding of the device's internal workings and cannot provide accurate maintenance solutions, leading to a crisis of trust in the product. Currently, there is no reasonable and efficient solution to these problems with smart home appliances on the market.

[0003] The existing patent, publication number CN105068978A, entitled "An Automated Audit Method and System for Daily and Monthly Reports Based on a Database," extracts the necessary data from the original data source to generate a usable report database. It then aggregates data from each user's data according to predetermined indicators to generate user-level daily and monthly reports. This patent has two main drawbacks: 1) It is not specifically designed for smart home appliances, thus failing to provide robust operational support and maintenance recommendations for appliances like refrigerators; 2) It lacks a push notification module, making it unable to promptly remind users to check monthly report data.

[0004] There is currently a patent, publication number: CN114546791A, titled: Data Processing Method, Apparatus, Readable Computer Storage Medium and Terminal. This patent solution collects behavioral log data of the target account on a first application and operation log data of the target account on the target refrigerator. Based on the behavioral log data and operation log data, indicator data is generated. Based on the indicator data and predetermined display conditions, display items are generated and displayed on the display interface of a second application. This solves the technical problem that users have difficulty systematically grasping the usage of the refrigerator and that the user experience is poor. This patent has three main drawbacks: 1. It only collects user operation logs for the refrigerator, without collecting and statistically analyzing the refrigerator's operating parameters. This makes it impossible to detect abnormal conditions in a timely manner or provide users with real-time operational updates. 2. The patent only generates indicator data from daily user operation logs, without monthly overall data statistics. Users cannot know the overall operation of the refrigerator over a period of time, and therefore cannot obtain universally applicable operating guidelines or usage instructions. 3. The patent does not include a push notification module, making it impossible to promptly remind users to check monthly report data. 4. The patent cannot dynamically provide users with different and novel monthly report covers on a monthly basis. Summary of the Invention

[0005] The purpose of this invention is to provide a method for intelligently generating monthly refrigerator usage reports based on big data, in order to solve the technical problems existing in the background art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for intelligently generating monthly refrigerator usage reports based on big data includes the following steps:

[0008] The device reports data to the IoT platform via MQTT and device shadow, and the IoT platform stores the data in the HBase database;

[0009] Device data is retrieved in batches from the HBase database, and after parsing, encapsulation, and data statistics integration, the data is stored in the master data table in batches according to the set maximum database insertion load and load factor.

[0010] The paginated batch process retrieves data from all devices for the day from the master data table, performs statistical integration on the data, calculates device usage scores based on given rules, and generates daily reports and door opening reminders by adapting to corresponding usage and maintenance strategies.

[0011] At the beginning of each month, a monthly device report is intelligently generated based on all daily data from all devices for that month.

[0012] In some embodiments, the step of intelligently generating a monthly device report at the beginning of each month based on all daily data from all devices for that month includes the following steps:

[0013] S401: Obtain the unique identifier (sn) of all devices in the current month for batch processing of big data pagination, and create a buffer to cache all device data; obtain the daily report data of all devices in the previous month by pagination based on sn, and remotely obtain other device information from other services in batches for preparing to generate monthly reports;

[0014] S402: Generate a unique ID monthly report based on the SN and family, and initialize the basic data in the monthly report based on all daily data from the device last month and established rules; batch retrieve other device data from the previous month to prepare data for generating comparison items and exclusive tags in the monthly report; based on the prepared data, batch set the data for the monthly report using the SN as the identifier to build a complete monthly report entity; after generating the monthly report entity, batch preload the device monthly report message notifications and build the message body; persist the monthly report data and monthly report message reminder data to the corresponding monthly report and message tables in the database, and store them in a buffer;

[0015] S403: If not all device monthly reports are generated, repeat steps S401-S402 until all device monthly report information from the previous month is persisted; based on the monthly report data of all devices from the previous month cached in the buffer, perform national percentage processing and page-permanently persist all percentage data of the device monthly report.

[0016] In some embodiments, the method further includes step S404: at the beginning of each month, a monthly report for the previous month is pushed to the user on a regular basis, and a corresponding usage and maintenance plan is adapted based on the average data of the previous month and the refrigerator usage and maintenance rules. At the same time as pushing the monthly report, a monthly report message is sent to the user to remind the user to check the refrigerator's monthly report for the previous month in a timely manner; the process of generating a smart refrigerator monthly report based on big data ends.

[0017] In some embodiments, the pagination batch retrieves data from all devices for the day from the master data table, performs statistical integration on the data, calculates device usage scores based on given rules, and generates daily reports and door opening reminders according to corresponding usage and maintenance strategies; including the following steps:

[0018] S201: Create a message cache buffer1 for excessive door openings and a daily report data cache buffer2, which are used to cache the message body and the daily report data entity, respectively.

[0019] S202: The big data service retrieves device data in batches from the master data table in pages using scheduled tasks, using the ID as the page offset; it also retrieves all device data from yesterday and caches it in buffer2; and it retrieves all refrigerator usage index rules and calculates the refrigerator usage score based on the given rules.

[0020] S203: Initialize Daily Report Information: Generate a unique ID daily report based on the SN, and initialize the basic data in the daily report based on all data of the device yesterday and given rules; format the door opening data of all devices, and obtain the number of days the device has continuously turned on the constant temperature micro-freezing function; remotely obtain other device information from other services based on the SN, and batch obtain the average number of door openings and device function usage score of the device last month; prepare data, batch set the data of the daily report using the SN as the identifier, and build a complete daily report entity;

[0021] S204: After generating the daily report entity, construct the daily report door opening count reminder message body based on the door opening count rule and cache it in buffer1;

[0022] S205: Persistently store daily report data and excessive door opening reminder data into the corresponding daily report and message table in the database;

[0023] S206: Clear buffer1 and buffer2 data caches, repeat steps S202-S205 until all device daily report information from yesterday is persisted; the process of intelligently generating refrigerator usage daily reports ends.

[0024] The method for intelligently generating monthly refrigerator usage reports based on big data provided in this application has beneficial effects including, but not limited to:

[0025] The purpose of this invention is to provide a method for generating daily and monthly refrigerator usage reports based on big data intelligence. Based on the frequent daily reports from the refrigerator, and by collecting and storing device data through the HBase big data platform, the method can comprehensively and in real-time monitor the user's refrigerator usage, providing reasonable usage suggestions. It can also monitor the refrigerator's health status in real-time, providing users with specialized refrigerator maintenance plans. By integrating all refrigerator data for a month, the method automatically generates monthly refrigerator usage reports for users and pushes the reports to users at the beginning of each month. Users can use the Smart Home app to periodically monitor their refrigerator's usage and maintenance plans. Simultaneously, a message reminder is sent to the user when the monthly report is generated, ensuring that the user does not miss the monthly refrigerator usage information and can maintain the refrigerator more rationally and systematically. Furthermore, the method can provide users with novel monthly reports each month, featuring different background colors and animations. Attached Figure Description

[0026] Figure 1Here is a simplified flowchart of the process of uploading refrigerator data to the cloud. The refrigerator reports and transmits data to the cloud IoT platform through MQTT service and device shadow. The cloud IoT platform parses and encapsulates the data and stores it in the HBase database of the big data platform.

[0027] Figure 2 The system framework diagram for intelligently generating monthly reports for refrigerators shows that data is obtained from the big data center HBase through scheduled tasks, and daily reports are generated by combining device data from other services. Then, monthly reports and message pushes are intelligently generated based on the daily report data, other data, and rules.

[0028] Figure 3 A flowchart for generating monthly refrigerator usage reports using smart technology;

[0029] Figure 4 A detailed flowchart for generating monthly refrigerator usage reports using intelligent technology. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0031] Conversely, this application covers any substitutions, modifications, equivalent methods, and schemes made within the spirit and scope of this application as defined in the claims. Furthermore, to provide the public with a better understanding of this application, certain specific details are described in detail below. However, this application can be fully understood by those skilled in the art even without these detailed descriptions.

[0032] The following will describe in detail, with reference to the accompanying drawings, a method for intelligently generating monthly refrigerator usage reports based on big data, according to embodiments of this application. It is worth noting that the following embodiments are merely illustrative of this application and do not constitute a limitation thereof.

[0033] To provide users with real-time information on refrigerator usage and to generate monthly reports that are intelligently pushed to users, allowing users to monitor their refrigerator's usage and maintenance plans in real-time and periodically through the Smart Home app.

[0034] like Figure 1-4As shown, this solution provides a method for intelligently generating daily and monthly refrigerator usage reports based on big data. The system and control method for intelligently generating monthly refrigerator usage reports based on big data include: the device transmits device data, including device operation data and device self-data, to a cloud-based IoT platform via MQTT service and device shadow service using sensors; the IoT platform parses and encapsulates the data and stores it in an HBase database; a scheduled task retrieves device data from the HBase database, and after parsing, encapsulating, and statistically integrating the data, the analyzed and statistically analyzed data is stored in a master data table containing the device's daily operation data and basic data; based on the data in the master data table, the data is statistically integrated, and a device usage score is calculated using given rules. This is then used to generate daily reports based on corresponding usage and maintenance strategies and returned to the user in real time, thereby extending product lifespan and indirectly improving product quality, thus increasing user satisfaction.

[0035] Meanwhile, at the beginning of each month, a monthly equipment report is intelligently generated based on all daily data from the equipment for that month. The monthly report data, after data statistics and integration, and given rules, calculates the refrigerator's usage status for the month and adapts the corresponding usage and maintenance strategies. The report is then returned to the user with a specific master page and is sent to the user via push notifications to remind them to check the monthly equipment report and make adjustments to the refrigerator's usage, ensuring that the refrigerator is always in the most comfortable operating state.

[0036] For you, the given rules are as follows (hereinafter referred to as the four-dimensional formula):

[0037] 1) Number of times the door is opened (including refrigeration and freezing): Full score 40 points, minimum score 10 points

[0038] Number of times the door is opened <= 10 times, 40 points.

[0039] 10 times < number of times the door is opened <= 20 times, 30 minutes.

[0040] 21 times < number of times the door was opened <= 30 times, 20 points.

[0041] 10 points if the door is opened more than 30 times.

[0042] 2) Average door opening time (including refrigeration and freezing) score: 40 points maximum, 30 points minimum.

[0043] (1) The average opening time of the previous day was ≤10s, 40 minutes;

[0044] (2) 11s≤the average opening time of the previous day≤15s, 38 minutes;

[0045] (3) 16s≤the average opening time of the previous day≤20s, 36 minutes;

[0046] (4) 21s≤the average opening time of the previous day≤30s, 34 minutes;

[0047] (5) The average opening time of the previous day was >30 seconds or 30 minutes;

[0048] 3) Refrigeration temperature setting score: 10 points maximum, 0 points minimum.

[0049] Refrigeration temperature setting score: 10 points maximum. 0 points if the temperature the previous day was below 3℃ (≤3℃) or above 7℃ (≥7℃).

[0050] 4) Freezing temperature setting score: 10 points maximum, 0 points minimum.

[0051] Freezing temperature setting score: 10 points maximum. 0 points if the previous day's temperature was below -21℃ (≤-21℃) or above -17℃ (≥-17℃).

[0052] This shows that the refrigerator's usage index yesterday was out of 100 points, with a minimum score of 40 points.

[0053] A method for intelligently generating monthly refrigerator usage reports based on big data includes the following steps:

[0054] S1. The device reports data to the IoT platform via MQTT and device shadow. The IoT platform parses and encapsulates the data and stores it in the HBase database.

[0055] S2. The big data service retrieves device data in batches from the HBase database through scheduled tasks, and after parsing, encapsulating and statistically integrating the data, it stores the data in batches into the data master table according to the set maximum database insertion load (5000) and load factor (0.7).

[0056] The big data service is based on device data in the HBase library, combined with device information from other services such as household information and cleaning information, and statistically integrates the data. Then, it calculates the device usage score according to given rules, and generates daily reports based on the corresponding usage and maintenance strategies, so that users can keep track of the refrigerator's status in real time.

[0057] S3. The big data service retrieves data from all devices for the day in batches from the master data table through scheduled tasks, performs statistical integration on the data, calculates device usage scores based on given rules, and generates daily reports and door opening reminders according to the corresponding usage and maintenance strategies. For detailed process, please refer to the process of generating daily refrigerator usage reports.

[0058] S4. The big data service intelligently generates a monthly device report at the beginning of each month based on all daily data from all devices for that month through scheduled tasks.

[0059] S401: Obtain the unique identifiers (sn) of all devices for the current month (hereinafter referred to as sn) for batch processing of big data pagination, and create a buffer (hereinafter referred to as buffer) to cache all device data; obtain the daily report data of all devices from the previous month by pagination based on sn, and remotely obtain other device information from other services such as device cleaning information, household information, power outage information, and smart preservation information in batches for preparing to generate monthly reports;

[0060] S402 generates a unique ID monthly report based on the SN and family, and initializes the basic data in the monthly report based on all daily data of the device last month and established rules; it batch retrieves other data of the device from the month before last to prepare comparison items and exclusive tags for the monthly report; based on the prepared data, it batch sets the data of the monthly report using the SN as the identifier to build a complete monthly report entity; after generating the monthly report entity, it batch preloads the device monthly report message notifications and builds the message body; it persists the monthly report data and monthly report message reminder data to the corresponding monthly report and message tables in the database, and stores them in the buffer.

[0061] S403, if not all device monthly reports are generated, repeat steps S401-S402 until all device monthly report information from the previous month is persisted; based on the monthly report data of all devices from the previous month cached in the buffer, perform national percentage processing and page-permanently persist all percentage data of the device monthly report;

[0062] S404: At the beginning of each month, a monthly report for the previous month is pushed to the user on a regular schedule. Based on the average data of the previous month and the refrigerator's usage and maintenance rules, a corresponding usage and maintenance plan is adapted. At the same time as pushing the monthly report, a monthly report message is sent to the user to remind them to check the refrigerator's monthly report for the previous month. The smart refrigerator monthly report generation process based on big data is now complete.

[0063] The process for intelligently generating daily refrigerator usage reports is as follows:

[0064] S201, create a message cache buffer1 (hereinafter referred to as buffer1) for excessive door openings and a daily report data cache buffer2 (hereinafter referred to as buffer2) to cache the message body and the daily report data entity, respectively;

[0065] S202, the big data service retrieves device data in batches from the master data table in pages through scheduled tasks, using the ID as the page offset to avoid full table scans; it also retrieves all device data from yesterday in batches and caches it in buffer2; and it retrieves all refrigerator usage index rules and calculates the refrigerator usage score according to the established rules.

[0066] S203, Initialize Daily Report Information: Generate a unique ID daily report based on the SN, and initialize the basic data in the daily report based on all data of the device yesterday and the established rules, such as the number of times the door was opened during the day and the time of opening the door, the number of times the door was opened at night and the time of opening the door, the total food freshness score, the total number of times food freshness was reported, the refrigerator set temperature, the refrigerator set temperature, and the usage score of each refrigerator operation, etc.; format the door opening data of all devices, and obtain the number of days the constant temperature micro-freezing function of the device was continuously turned on; remotely obtain other device information from other services, such as device cleaning information, household information, etc., based on the SN, and batch obtain the average number of times the device was opened last month and the device function usage score; prepare data, use the SN as an identifier to batch set the data of the daily report, and build a complete daily report entity;

[0067] S204. After generating the daily report entity, construct the daily report door opening reminder message body based on the door opening count rule (door opening count exceeds the baseline value). Since the message is pushed on a household basis, it is necessary to filter out devices in empty households and cache them in buffer1.

[0068] S205, persist the daily report data and the message reminder data for too many door openings into the corresponding daily report and message table in the database;

[0069] S206, clear the data cache of buffer1 and buffer2, repeat steps S202-S205 until all device daily report information from yesterday is persisted; the process of intelligently generating the refrigerator usage daily report ends.

[0070] To illustrate the technical content of this invention in detail, the following example illustrates a scenario of a system and control method for intelligently generating monthly refrigerator usage reports based on big data.

[0071] When a refrigerator reports device data to the cloud-based IoT platform, the IoT platform collects and stores the data in the HBase database in real time. The big data service retrieves data from all devices in HBase daily, performs statistical analysis, and generates daily reports based on predetermined rules. At the beginning of each month, the big data service automatically starts a scheduled task to intelligently generate monthly reports for all refrigerators from the previous month and push them to the user. The scenario is as follows:

[0072] 1. At the beginning of each month, a monthly device report is intelligently generated based on all daily data of the devices for that month. First, the unique identifier (sn) of all devices for that month is obtained for batch processing of big data pagination, and a cache buffer is created to cache all device data.

[0073] 2. Retrieve daily data of all devices from the previous month by pagination based on SN, and remotely batch retrieve other device information from other services, such as device cleaning information, household information, power outage information, and smart preservation information, for use in preparing monthly reports;

[0074] 3. Initialize monthly report information: Generate a unique ID monthly report based on the SN and family, and initialize the basic data in the monthly report based on all daily reports from the device last month and established rules;

[0075] 4. Batch acquire data such as the number of times the equipment door was left open for an extended period of time, the number of days the constant temperature micro-freezing system was used, and obtain labels from the month before last. This data will be used to generate comparison items with the previous month's data in the monthly report and to prepare exclusive labels.

[0076] 5. Based on the data preparation in steps 6, 7, and 8, batch set the data for the monthly report using sn as the identifier to construct a complete monthly report entity;

[0077] 6. After generating the monthly report entity, preload the device monthly report message notifications in batches, construct the message body, and filter out devices with empty household registrations;

[0078] 7. Finally, persist the monthly report data and monthly report message reminder data into the corresponding monthly report and message tables in the database, and store them in the buffer;

[0079] 8. If not all device monthly reports are generated, repeat steps 5-11 until all device monthly report information from the previous month is persisted;

[0080] 9. Based on the monthly report data of all devices cached in the buffer from the previous month, perform national percentage processing and page-based persistence of all percentage data of the monthly report of devices;

[0081] 11. At the beginning of each month, a monthly report for the previous month will be pushed to users on a regular basis. Based on the average data of the previous month and the refrigerator's usage and maintenance rules, a corresponding usage and maintenance plan will be adapted. At the same time as pushing the monthly report, a monthly report message will be sent to users to remind them to check the refrigerator's monthly report for the previous month in a timely manner.

[0082] 12. When users open the Smart Home app, they will receive a monthly report notification on the homepage. Users can directly click to enter the monthly report page to view the refrigerator's usage and basic data for the previous month, and make improvements based on the usage and maintenance plans given in the monthly report to improve product performance and lifespan, providing users with a better product experience and after-sales service.

[0083] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligently generating monthly refrigerator usage reports based on big data, characterized in that, Includes the following steps: The device reports data to the IoT platform via MQTT and device shadow, and the IoT platform stores the data in the HBase database; Device data is retrieved in batches from the HBase database, and after parsing, encapsulation, and data statistics integration, the data is stored in the master data table in batches according to the set maximum database insertion load and load factor. The paginated batch process retrieves data from all devices for the day from the master data table, performs statistical integration on the data, calculates device usage scores based on given rules, and generates daily reports and door opening reminders by adapting to corresponding usage and maintenance strategies. At the beginning of each month, a monthly device report is intelligently generated based on all daily data from all devices for that month, including the following steps: S401: Obtain the unique identifier (sn) of all devices in the current month for batch processing of big data pagination, and create a buffer to cache all device data; obtain the daily report data of all devices in the previous month by pagination based on sn, and remotely obtain other device information from other services in batches for preparing to generate monthly reports; S402: Generate a unique ID monthly report based on the SN and family, and initialize the basic data in the monthly report based on all daily data from the device last month and established rules; batch retrieve other device data from the previous month to prepare data for generating comparison items and exclusive tags in the monthly report; based on the prepared data, batch set the data for the monthly report using the SN as the identifier to build a complete monthly report entity; after generating the monthly report entity, batch preload the device monthly report message notifications and build the message body; persist the monthly report data and monthly report message reminder data to the corresponding monthly report and message tables in the database, and store them in a buffer; S403: If not all device monthly reports are generated, repeat steps S401-S402 until all device monthly report information from the previous month is persisted; based on the monthly report data of all devices from the previous month cached in the buffer, perform national percentage processing and page-permanently persist all percentage data of the device monthly report.

2. The method for intelligently generating monthly refrigerator usage reports based on big data according to claim 1, characterized in that, It also includes step S404: At the beginning of each month, a monthly report for the previous month is pushed to the user on a regular basis, and a corresponding usage and maintenance plan is adapted based on the average data of the previous month and the refrigerator usage and maintenance rules. At the same time as pushing the monthly report, a monthly report message is sent to the user to remind the user to check the refrigerator's monthly report for the previous month in a timely manner; the smart refrigerator monthly report generation process based on big data ends.

3. The method for intelligently generating monthly refrigerator usage reports based on big data according to claim 1, characterized in that, The pagination batch process retrieves data from all devices for the day from the master data table, performs statistical integration on the data, calculates device usage scores based on given rules, and generates daily reports and door opening reminders according to corresponding usage and maintenance strategies; including the following steps: S201: Create a message cache buffer1 for excessive door openings and a daily report data cache buffer2, which are used to cache the message body and the daily report data entity, respectively. S202: The big data service retrieves device data in batches from the master data table in pages using scheduled tasks, using the ID as the page offset; it also retrieves all device data from yesterday and caches it in buffer2; and it retrieves all refrigerator usage index rules and calculates the refrigerator usage score based on the given rules. S203: Initialize Daily Report Information: Generate a unique ID daily report based on the SN, and initialize the basic data in the daily report based on all data of the device yesterday and given rules; format the door opening data of all devices, and obtain the number of days the device has continuously turned on the constant temperature micro-freezing function; remotely obtain other device information from other services based on the SN, and batch obtain the average number of door openings and device function usage score of the device last month; prepare data, batch set the data of the daily report using the SN as the identifier, and build a complete daily report entity; S204: After generating the daily report entity, construct the daily report door opening count reminder message body based on the door opening count rule and cache it in buffer1; S205: Persistently store daily report data and excessive door opening reminder data into the corresponding daily report and message table in the database; S206: Clear buffer1 and buffer2 data caches, repeat steps S202-S205 until all device daily report information from yesterday is persisted; the process of intelligently generating refrigerator usage daily reports ends.

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