Water heater data processing method, device and equipment and storage medium

Abstract

The invention relates to a water heater data processing method and device, equipment and a storage medium, and belongs to the field of intelligent household electrical appliances, a server side reliably and efficiently obtains running state data of a water heater by using a first distributed message queue, and then the server side distributes the data to a computing node for professional anomaly detection. According to the method, data acquisition and exception analysis links are separated, the processing specialty is improved, meanwhile, the second distributed message queue is specially used for bearing exceptional data screened out by the computing nodes, independent storage and circulation of exceptional information are achieved, and it is guaranteed that a server can accurately and rapidly obtain the exceptional data and generate a user prompt; according to the method, high throughput and low delay of data transmission are realized through the characteristics of the distributed message queue, specialization and parallelization of data processing are realized through node division, and the data processing efficiency is effectively improved.

Description

Technical Field

[0001] This application relates to the field of smart home appliances, and in particular to a data processing method, apparatus, device and storage medium for a water heater. Background Technology

[0002] With the deep penetration of IoT technology into the smart home field, the functional positioning of water heaters, as core household water appliances, has undergone significant changes. From initially only capable of simple water temperature adjustment, they have gradually upgraded towards intelligent control and refined management. In this process, user needs have also expanded from basic water supply assurance to diversified scenarios such as real-time monitoring of water heater operating status, precise analysis of daily energy consumption, and early warning of potential faults. This growth in demand has directly driven the continuous development of water heater data processing technology, making it a core support for improving the intelligence level of the device and the user experience.

[0003] In existing technologies, water heater data processing methods employ a centralized data processing architecture. First, in the data acquisition and transmission stage, the water heater uses an IoT module to upload its operational data to a cloud server. After being parsed by a gateway protocol, the operational data is stored in the corresponding database. Next, in the data processing and analysis stage, the server retrieves data from the database through periodic polling or message subscription mechanisms, and then uses a rule engine or scripts to analyze the data, such as determining whether the water usage time exceeds a set threshold. Finally, in the result push and alarm stage, the server pushes the analysis results to the user via SMS, App notifications, etc.

[0004] However, all data relies on centralized processing on the server side. Raw data is transmitted directly without preprocessing, and a large amount of invalid data (such as data on the normal operation status of the equipment) consumes too much bandwidth and storage resources. There are efficiency bottlenecks in the entire process from data transmission to processing, resulting in low overall data processing efficiency. Summary of the Invention

[0005] This application provides a water heater data processing method, apparatus, device, and storage medium to solve the problem of low data processing efficiency.

[0006] In a first aspect, embodiments of this application provide a water heater data processing method, applied to a water heater service system, the water heater service system including a server and computing nodes, the method comprising:

[0007] The server retrieves the water heater's operating status data from the first distributed message queue;

[0008] The server transmits the running status data to the computing node;

[0009] The computing node determines abnormal data based on the running status data and writes the abnormal data into the second distributed message queue;

[0010] The server retrieves the abnormal data from the second distributed message queue and generates user reminder information based on the abnormal data.

[0011] In one possible implementation, the operational status data includes water usage status data and equipment status data, and the computing node determines abnormal data based on the operational status data, including:

[0012] The computing node analyzes the water usage status data and the equipment status data based on preset rules in the rule engine to identify the abnormal data.

[0013] In one possible implementation, the water heater service system further includes a storage node and a data acquisition component; the method further includes:

[0014] The data acquisition component collects the running status data from the storage node and writes the running status data into the first distributed message queue.

[0015] In one possible implementation, writing the abnormal data into the second distributed message queue further includes:

[0016] The abnormal data is sorted according to a preset data priority rule, and the sorted abnormal data is written into a second distributed message queue.

[0017] In one possible implementation, generating user alert information based on the abnormal data includes:

[0018] Based on the abnormal data, the abnormal type and the time of occurrence of the abnormality are determined, and the user reminder information is generated based on the abnormal type and the time of occurrence of the abnormality.

[0019] The method further includes:

[0020] According to the priority of the abnormal data, the user reminder information will be pushed to the corresponding user terminals in sequence.

[0021] In one possible implementation, the preset rules include: anomaly thresholds for water usage status data and anomaly rules for equipment status data. The computing node analyzes the water usage status data and the equipment status data based on the preset rules in the rule engine to determine the anomaly data, including:

[0022] If the water usage status data exceeds the abnormal threshold for water usage status data, then the water usage status data is determined to be abnormal data;

[0023] If the device status data meets the device status data anomaly rules, then the device status data is determined to be abnormal data.

[0024] Secondly, embodiments of this application provide a water heater data processing device applied to a water heater service system, the water heater service system including a server and computing nodes, the method including:

[0025] The acquisition module is used by the server to obtain the operating status data of the water heater from the first distributed message queue;

[0026] The transmission module is used by the server to transmit the running status data to the computing node;

[0027] The storage module is used by the computing node to determine abnormal data based on the running status data and write the abnormal data into a second distributed message queue;

[0028] The generation module is used by the server to obtain the abnormal data from the second distributed message queue and generate user reminder information based on the abnormal data.

[0029] In one possible implementation, the water heater data processing device further includes: a determination module;

[0030] The determining module is used by the computing node to analyze the water usage status data and the equipment status data based on preset rules in the rule engine, and to determine the abnormal data.

[0031] In one possible implementation, the storage module is used by the data acquisition component to collect the running status data from the storage node and write the running status data into the first distributed message queue.

[0032] In one possible implementation, the storage module is further configured to sort the abnormal data according to a preset data priority rule and write the sorted abnormal data into a second distributed message queue.

[0033] In one possible implementation, the water heater data processing device further includes: a push module;

[0034] The generation module is also used to determine the anomaly type and the anomaly occurrence time based on the anomaly data, and to generate the user reminder information based on the anomaly type and the anomaly occurrence time;

[0035] The push module is also used to push the user reminder information to the corresponding user terminals in sequence according to the priority of the abnormal data.

[0036] In one possible implementation, the determining module is further configured to determine that the water usage status data is abnormal if the water usage status data exceeds the abnormal threshold of the water usage status data;

[0037] The determining module is further configured to determine the device status data as abnormal data if the device status data meets the device status data abnormality rules.

[0038] Thirdly, embodiments of this application provide a water heater data processing device, including: a sensor, a receiver, a transmitter, a memory, and a processor;

[0039] Sensors are used to detect the operating status data of the water heater;

[0040] Receiver, used to receive instructions and data;

[0041] A transmitter is used to send commands and data;

[0042] The memory stores computer-executed instructions;

[0043] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0044] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0045] The water heater data processing method provided in this application achieves a professional separation of data acquisition and anomaly analysis through the division of labor and cooperation between the server and computing nodes. The server is used for data transmission and reception and reminder generation, while the computing nodes are used for accurate identification of abnormal data. Two distributed message queues are used to carry operating status data and abnormal data respectively, giving full play to the high concurrency, high availability and low latency characteristics of distributed message queues. This ensures efficient transmission of massive operating status data and realizes independent and orderly flow of abnormal data, avoiding processing congestion caused by mixed data. This method not only significantly improves the overall data processing efficiency, ensures real-time detection of water heater operating status and rapid response to anomalies, but also enhances system stability through the buffering effect of message queues, reduces the impact of peak data on processing nodes, and finally improves the safety and user experience of water heater use through timely generated user reminder information. Attached Figure Description

[0046] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0047] Figure 1 Flowchart of the water heater data processing method provided in this application Figure 1 ;

[0048] Figure 2 Flowchart of the water heater data processing method provided in this application Figure 2 ;

[0049] Figure 3 A schematic diagram of the water heater data processing device provided in this application;

[0050] Figure 4 A schematic diagram of the structure of the water heater data processing device provided in this application.

[0051] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, other embodiments obtained by those skilled in the art without creative effort are all within the scope of protection of this application.

[0053] Currently, water heater data processing generally adopts a centralized data processing architecture, whose core process consists of three key stages. First, in the data acquisition and transmission stage, the water heater, through its built-in IoT modules such as Wi-Fi and Bluetooth, uploads data such as water temperature, water pressure, and water usage duration generated during operation to a cloud server in real time. This data, after being parsed by a gateway protocol, is uniformly stored in the corresponding database, completing the initial data aggregation. Second, in the data processing and analysis stage, the server retrieves the stored data from the database through either periodic polling or message subscription mechanisms. Then, it uses a rule engine or customized scripts to analyze and process the data, such as determining whether the water usage duration exceeds a preset threshold or whether the water temperature is within the normal range, thus judging the device's operating status. Finally, in the result push and alarm stage, the server pushes the analysis conclusions to the user via SMS, mobile app notifications, etc., allowing the user to keep track of the water heater's operating status. If any abnormality occurs, an alarm is triggered simultaneously.

[0054] The current centralized data processing system results in low overall data processing efficiency. On the one hand, all data relies on centralized processing on the server side, without a front-end preprocessing mechanism. This leads to a large amount of invalid raw data, such as the normal operating status of devices, being directly transmitted to the cloud, which not only consumes excessive network bandwidth but also consumes a large amount of server storage resources, resulting in resource waste. On the other hand, the redundancy of data transmission and the centralization of server processing create a dual efficiency bottleneck throughout the entire process from data transmission to server-side analysis and processing. This affects the timeliness of data transmission and increases the processing burden on the server, ultimately making it difficult for data processing efficiency to meet users' demands for real-time and accurate data.

[0055] To address the aforementioned issues, the water heater data processing method provided in this application utilizes a first distributed message queue to enable the server to reliably and efficiently acquire water heater operating status data. The server then distributes the data to computing nodes for professional anomaly detection, separating the data acquisition and anomaly analysis stages and improving processing professionalism. Simultaneously, a second distributed message queue is used to specifically carry the anomaly data filtered by the computing nodes, achieving independent storage and flow of anomaly information. This ensures that the server can accurately and quickly acquire anomaly data and generate user alerts. This method achieves high throughput and low latency in data transmission through the characteristics of distributed message queues, and achieves professional and parallel data processing through node division of labor, effectively improving data processing efficiency.

[0056] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0057] Figure 1 Flowchart of the water heater data processing method provided in this application Figure 1 The execution entity in this embodiment is a water heater service system, which includes a server and computing nodes. For example... Figure 1 As shown, the method includes:

[0058] S101: The server retrieves the water heater's operating status data from the first distributed message queue.

[0059] The process of the server obtaining the water heater's operating status data begins with the first distributed message queue (such as TopicA of the Kafka message queue).

[0060] Specifically, when the server needs to obtain water heater operating status data, it will directly retrieve the data from the first distributed message queue. At this time, the first distributed message queue does not store the raw data; the operating status data already has a structured format and integrity guarantee, which can directly meet the server's needs for water heater operating status data. This avoids the problems of format chaos and incomplete data that the server may encounter when directly processing raw data, thus improving the efficiency and reliability of data acquisition.

[0061] By connecting to the first distributed message queue, the server does not need to be directly associated with the complex data collection and processing process of the front end. It only needs to efficiently extract the required operating status data from the first distributed message queue, which reduces the coupling between the server and the original data link of the water heater and ensures the stability and real-time performance of data acquisition.

[0062] S102: The server transmits the running status data to the compute node.

[0063] After obtaining the water heater's operating status data, the server will transmit this data to computing nodes (such as Flink).

[0064] As the intermediate hub for data flow, the server is responsible for sending the structured runtime status data obtained from the first distributed message queue (such as Kafka's TopicA) to the computing nodes in a targeted manner according to the preset transmission protocol and format.

[0065] Specifically, when the server transmits operational status data to the computing node, a stable connection is established based on the communication mechanism between the two. Since the computing node, as a stream processing framework, has the ability to efficiently process real-time data, the server prioritizes timeliness and continuity in data transmission, ensuring that the water heater's real-time operational status data continuously flows into the computing node.

[0066] S103: The compute node determines the abnormal data based on the running status data and writes the abnormal data into the second distributed message queue.

[0067] After receiving the water heater's operating status data from the server, the computing node will perform real-time analysis and processing of the operating status data to identify any abnormal data.

[0068] For example, when the compute node is Flink, it continuously computes and verifies the incoming real-time data based on preset anomaly detection rules. Once it detects runtime status data that matches the abnormal characteristics, Flink marks it as abnormal data and triggers the operation of writing it to a second distributed message queue (such as Topic B of the Kafka message queue).

[0069] During the process of writing abnormal data to a TopicB in the Kafka message queue, the compute nodes follow the Kafka message queue mechanism to ensure the reliability and orderliness of data transmission. Flink, as a stream processing framework, can efficiently send identified abnormal data to the TopicB via the connection protocol with the Kafka message queue. This writing method ensures that abnormal data is stored promptly and leverages Kafka's high throughput to avoid data backlog, allowing subsequent processing stages to retrieve abnormal data from the TopicB for further processing at any time.

[0070] The compute nodes write abnormal data to a second distributed message queue (such as Kafka's TopicB), forming an independent flow path for abnormal data. Separated from the processing path of the original runtime status data, TopicB is used to store and transmit abnormal data, which facilitates subsequent anomaly analysis and reduces the interference of abnormal data processing on the normal business data path. At the same time, Kafka's persistent storage feature ensures the traceability of abnormal data.

[0071] S104: The server retrieves abnormal data from the second distributed message queue and generates user reminder information based on the abnormal data.

[0072] The server monitors a second distributed message queue (such as Kafka's TopicB) in real time to obtain abnormal data written by the compute nodes. During this process, the server, acting as a message consumer, pulls data from the second distributed message queue according to a preset frequency or triggering mechanism to ensure that abnormal data in the water heater's operating status can be captured in a timely manner.

[0073] After obtaining abnormal data, the server will parse and process it, and then generate user reminder information.

[0074] The water heater data processing method provided in this application involves the server obtaining the water heater's operating status data from a first distributed message queue, transmitting the operating status data to a computing node, the computing node identifying abnormal data based on the operating status data, and writing the abnormal data into a second distributed message queue. The server then obtains the abnormal data from the second distributed message queue and generates user reminder information based on the abnormal data. This method, through the division of labor and cooperation between the server and the computing node, utilizes two distributed message queues to handle the acquisition and transmission of operating status data and the filtering and storage of abnormal data respectively, ultimately generating user reminder information, effectively improving data processing efficiency.

[0075] Figure 2 Flowchart of the water heater data processing method provided in this application Figure 2 ,like Figure 2 As shown, in this embodiment... Figure 1Based on the embodiments, the data processing method for water heaters is described in detail, and the method includes:

[0076] S201: The data acquisition component collects runtime status data from the storage node and writes the runtime status data into the first distributed message queue.

[0077] The water heater service system also includes storage nodes and data acquisition components.

[0078] Data acquisition components (such as log collection middleware) collect operational status data from storage nodes (such as ElasticSearch). Previously, the water heater's raw operational data had undergone IoT uploading, gateway protocol parsing, and integrity verification, ultimately being stored in the storage node in structured data form, providing the data acquisition components with a standardized and reliable data source. The data acquisition components will proactively establish connections with the storage node according to preset acquisition strategies (such as scheduled acquisition) to accurately extract the water heater's operational status data that has been persistently stored.

[0079] After successfully collecting runtime status data, the data acquisition component adapts the collected data according to the message format requirements of the first distributed message queue (such as Kafka's TopicA). Both the mapping and adjustment of data fields and the encapsulation of message structures are aimed at ensuring that the data can be correctly received and stored by the first distributed message queue. Subsequently, the data acquisition component stably writes the processed runtime status data into the first distributed message queue through its communication interface. This process fully utilizes the high throughput and low latency characteristics of the data acquisition component, avoiding data loss or delay during transmission and ensuring that runtime status data enters the first distributed message queue efficiently and orderly.

[0080] S202: The server retrieves the water heater's operating status data from the first distributed message queue.

[0081] Step S202 is similar to step S101, and will not be described again here.

[0082] S203: The server transmits the running status data to the compute node.

[0083] Step S203 is similar to step S102, and will not be described again here.

[0084] S204: The computing node analyzes water usage status data and equipment status data based on preset rules in the rule engine to identify abnormal data.

[0085] The operational status data includes water usage data (such as water consumption, water temperature adjustment records, and water usage time distribution) and equipment status data (such as heating element operating current, inner tank pressure, and sensor operating parameters). The computing node then imports the water usage data and equipment status data into the rule engine, which performs systematic analysis based on preset rules to accurately identify abnormal data.

[0086] Optionally, the preset rules include: water usage status data anomaly thresholds and equipment status data anomaly rules. The computing nodes analyze the water usage status data and equipment status data based on the preset rules in the rule engine to identify anomalies, including:

[0087] If the water usage status data exceeds the abnormal threshold for water usage status data, then the water usage status data is determined to be abnormal data.

[0088] If the device status data meets the device status data anomaly rules, then the device status data is determined to be abnormal data.

[0089] The preset rules provide clear criteria for identifying abnormal data in computing nodes, mainly including thresholds for abnormal water usage data and rules for abnormal equipment status data. Thresholds for abnormal water usage data are quantitative standards set for water-related indicators such as water consumption, water temperature changes, and water usage duration, such as the maximum hourly water consumption limit and the allowable range for sudden rises and falls in water temperature. Rules for abnormal equipment status data are conditional criteria established for the operating parameters of the water heater hardware, such as the heating element's operating temperature not exceeding a certain critical value and the sensor response time needing to be within a specified range.

[0090] Specifically, for water usage status data, the computing nodes directly compare the real-time collected values ​​with preset anomaly thresholds. If a certain indicator (such as instantaneous water consumption) exceeds or falls below the set threshold range, the system will immediately determine that the water usage status data is abnormal. For example, when it is detected that the water consumption in a certain minute exceeds 150% of the historical maximum for the same period, an anomaly judgment is triggered.

[0091] For equipment status data, the computing nodes perform condition matching based on preset anomaly rules. These rules may include range restrictions for single parameters (such as maintaining the inner tank pressure between 0.6-0.8 MPa) or may involve the linkage logic of multiple parameters (such as when the inlet water temperature is below 5°C and the heating power does not automatically increase). Once the equipment status data meets the anomaly conditions defined in the rules, the computing node will mark it as an anomaly.

[0092] S205: Sort the abnormal data according to the preset data priority rules, and write the sorted abnormal data into the second distributed message queue.

[0093] Before writing abnormal data to a second distributed message queue (such as Kafka's TopicB), the compute nodes first sort the identified abnormal data according to preset data priority rules. These priority rules are typically set based on the severity, scope of impact, and urgent handling needs of the abnormality. For example, failures of core equipment components (such as short circuits in heating elements) have higher priority than minor fluctuations in water usage data, and abnormalities that may pose safety hazards (such as excessive pressure) have higher priority than abnormalities that only affect the user experience (such as minor deviations in water temperature). Through these rules, the compute nodes can hierarchically sort different types and levels of abnormal data, ensuring that important abnormal information is processed first.

[0094] During the sorting process, the computing nodes assign a corresponding priority identifier to each piece of abnormal data. For example, anomalies that may cause equipment downtime or security risks are marked as high priority, while minor anomalies that only require user attention are marked as low priority, and the abnormal data is arranged in the order of "high → medium → low". This sorting is not only reflected in the logical order of the data, but may also be reinforced through message queue partitioning strategies or message attributes to ensure that when the server subsequently retrieves data from the second distributed message queue, it can prioritize reading and processing high-priority abnormal information.

[0095] S206: The server retrieves abnormal data from the second distributed message queue.

[0096] Step S206 is similar to step S104, and will not be described again here.

[0097] S207: Determine the anomaly type and occurrence time based on the anomaly data, and generate user reminder information based on the anomaly type and occurrence time.

[0098] After receiving abnormal data, the server first parses the data to extract key information to determine the anomaly type and the time of occurrence. The anomaly type is determined based on the specific abnormal characteristics contained in the data. For example, "sudden increase in water consumption" in water usage status data corresponds to a water usage anomaly, and "excessive heating pipe current" in equipment status data corresponds to a hardware malfunction. The time of occurrence is directly extracted from the timestamp or record field of the data to ensure the accuracy of the time information.

[0099] After identifying the anomaly type and occurrence time, the server combines this information to construct a user alert. For different anomaly types, the system uses corresponding alert templates. For example, a hardware anomaly template includes information about faulty components and potential risks, while a water usage anomaly template focuses on changes in water consumption and possible causes. The anomaly occurrence time is precisely embedded in the information, allowing users to clearly understand the exact moment the anomaly occurred. For instance, when an "abnormal heating element temperature" is detected and the occurrence time is "September 10, 2025, 08:30," the generated alert might be: "Your water heater experienced an abnormally high heating element temperature at 08:30. It is recommended to immediately stop using it and contact repair services."

[0100] Users can quickly understand the abnormal condition of the water heater, the time of occurrence, and its potential impact through user alerts, and thus take appropriate measures, effectively improving the practicality of the alerts and the user's response efficiency to abnormal situations.

[0101] S208: Based on the priority of abnormal data, push user reminder information to the corresponding user terminals in sequence.

[0102] After generating user alerts, the server will push the alerts to the corresponding user terminals sequentially according to the priority determined during the previous sorting of abnormal data, following the principle of "high priority first push". The priority here is related to the urgency of the abnormal data. For example, device safety fault alerts marked as "high priority" (such as excessive inner tank pressure) will take precedence over "medium priority" water experience abnormality alerts (such as water temperature fluctuations), while "low priority" routine water data abnormality alerts (such as a slight increase in short-term water consumption) will be placed last, ensuring that the information most urgently needed by the user reaches them first.

[0103] During the actual push notification process, the server employs differentiated push strategies for reminders of different priorities to enhance their effectiveness. For high-priority reminders, in addition to basic app push notifications, simultaneous notifications via SMS, voice calls, and other channels may be triggered to prevent users from missing crucial information due to not checking the app. Medium-priority reminders are typically delivered via app push notifications combined with pinning them to the top of the message center, ensuring users see them first when they open the relevant application. Low-priority reminders are primarily displayed regularly in the app's message center, delivering information without interfering with the user's core operations. This tiered push approach ensures that reminders of varying urgency receive appropriate reach.

[0104] From the perspective of user experience and problem-solving efficiency, prioritizing notifications based on abnormal data effectively avoids overwhelming users with a large amount of disorganized information. Users no longer need to sift through numerous notifications for key information; high-priority emergency notifications are proactively prioritized, helping users focus on water heater malfunctions that require immediate attention (such as safety hazards), allowing time for troubleshooting or repair. Meanwhile, medium- and low-priority notifications inform users of non-urgent issues without occupying too much of their immediate attention, ensuring users have a comprehensive understanding of the device's status and achieving efficient delivery of notification information, further improving the user feedback loop of the water heater service system.

[0105] The water heater data processing method provided in this application embodiment collects operating status data from a storage node through a data acquisition component, writes the operating status data into a first distributed message queue, the server obtains the water heater's operating status data from the first distributed message queue, and transmits the operating status data to a computing node. The computing node analyzes the water usage status data and equipment status data based on preset rules in a rule engine, identifies abnormal data, sorts the abnormal data according to preset data priority rules, and writes the sorted abnormal data into a second distributed message queue. The server obtains the abnormal data from the second distributed message queue, determines the abnormal type and occurrence time based on the abnormal data, generates user reminder information based on the abnormal type and occurrence time, and pushes the user reminder information to the corresponding user terminals in sequence according to the priority of the abnormal data. This method achieves asynchronous decoupling of data acquisition and processing through a distributed message queue, and combines rule engine analysis and data priority sorting, enabling the server to efficiently acquire and process abnormal data and push reminder information according to priority, thereby improving the overall efficiency of water heater data processing.

[0106] Figure 3 This is a schematic diagram of the structure of the water heater data processing device provided in this application, as shown below. Figure 3 As shown, the water heater data processing device 300 provided in this embodiment includes:

[0107] The acquisition module 301 is used by the server to obtain the operating status data of the water heater from the first distributed message queue;

[0108] Transmission module 302 is used for the server to transmit running status data to the computing node;

[0109] Storage module 303 is used by computing nodes to determine abnormal data based on running status data and write the abnormal data into the second distributed message queue;

[0110] The generation module 304 is used by the server to obtain abnormal data from the second distributed message queue and generate user reminder information based on the abnormal data.

[0111] In one possible implementation, the water heater data processing device 300 further includes: a determination module 305;

[0112] The determination module 305 is used to calculate the analysis of water usage status data and equipment status data by the node based on the preset rules in the rule engine, and to determine abnormal data.

[0113] In one possible implementation, the storage module 303 is used by the data acquisition component to collect running status data from the storage node and write the running status data into the first distributed message queue.

[0114] In one possible implementation, the storage module 303 is further configured to sort the abnormal data according to a preset data priority rule and write the sorted abnormal data into a second distributed message queue.

[0115] In one possible implementation, the water heater data processing device further includes: a push module 306;

[0116] The generation module 304 is also used to determine the anomaly type and the time of anomaly occurrence based on the anomaly data, and to generate user reminder information based on the anomaly type and the time of anomaly occurrence.

[0117] The push module 306 is also used to push user reminder information to the corresponding user terminals in sequence according to the priority of abnormal data.

[0118] In one possible implementation, the determining module 305 is further configured to determine that the water usage status data is abnormal if the water usage status data exceeds the abnormal threshold of the water usage status data.

[0119] The determination module 305 is also used to determine that the device status data is abnormal if the device status data meets the device status data abnormality rules.

[0120] Figure 4 This is a schematic diagram of the structure of the water heater data processing device provided in this application. Figure 4 As shown, this application provides a water heater data processing device 400, which includes: a sensor 401, a receiver 402, a transmitter 403, a processor 404, and a memory 405.

[0121] Sensor 401 is used to detect the operating status data of the water heater;

[0122] Receiver 402 is used to receive instructions and data;

[0123] Transmitter 403 is used to send commands and data;

[0124] Memory 405 is used to store instructions executed by the computer;

[0125] The processor 404 is used to execute computer execution instructions stored in the memory 405 to implement the various steps of the water heater data processing method in the above embodiments. For details, please refer to the relevant descriptions in the foregoing embodiments of the water heater data processing method.

[0126] Alternatively, the memory 405 can be either standalone or integrated with the processor 404.

[0127] When the memory 405 is set up independently, the electronic device also includes a bus for connecting the memory 405 and the processor 404.

[0128] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the water heater data processing method performed by the aforementioned water heater data processing device.

[0129] It will be understood by those skilled in the art that all or some of the steps, systems, or apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above 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 collaboratively by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media 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 technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0130] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it is readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. The above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A water heater data processing method, characterized in that, Applied to a water heater service system, the water heater service system including a server and computing nodes, the method includes: The server retrieves the water heater's operating status data from the first distributed message queue; The server transmits the running status data to the computing node; The computing node determines abnormal data based on the running status data and writes the abnormal data into the second distributed message queue; The server retrieves the abnormal data from the second distributed message queue and generates user reminder information based on the abnormal data.

2. The method according to claim 1, characterized in that, The operational status data includes water usage status data and equipment status data. The computing node determines abnormal data based on the operational status data, including: The computing node analyzes the water usage status data and the equipment status data based on preset rules in the rule engine to identify the abnormal data.

3. The method according to claim 1, characterized in that, The water heater service system also includes a storage node and a data acquisition component, and the method further includes: The data acquisition component collects the running status data from the storage node and writes the running status data into the first distributed message queue.

4. The method according to claim 1, characterized in that, The step of writing the abnormal data into the second distributed message queue further includes: The abnormal data is sorted according to a preset data priority rule, and the sorted abnormal data is written into a second distributed message queue.

5. The method according to claim 4, characterized in that, The process of generating user alert information based on the abnormal data includes: Based on the abnormal data, the abnormal type and the time of occurrence of the abnormality are determined, and the user reminder information is generated based on the abnormal type and the time of occurrence of the abnormality. The method further includes: According to the priority of the abnormal data, the user reminder information will be pushed to the corresponding user terminals in sequence.

6. The method according to claim 2, characterized in that, The preset rules include: anomaly thresholds for water usage status data and anomaly rules for equipment status data. The computing node analyzes the water usage status data and the equipment status data based on the preset rules in the rule engine to determine the anomaly data, including: If the water usage status data exceeds the abnormal threshold for water usage status data, then the water usage status data is determined to be abnormal data; If the device status data meets the device status data anomaly rules, then the device status data is determined to be abnormal data.

7. A water heater data processing device, characterized in that, include: The acquisition module is used by the server to obtain the operating status data of the water heater from the first distributed message queue; The transmission module is used by the server to transmit the running status data to the computing node; The storage module is used by the computing node to determine abnormal data based on the running status data and write the abnormal data into a second distributed message queue; The generation module is used by the server to obtain the abnormal data from the second distributed message queue and generate user reminder information based on the abnormal data.

8. The apparatus according to claim 7, characterized in that, Also includes: The determination module is used by the computing node to analyze water usage status data and equipment status data based on preset rules in the rule engine, and to determine the abnormal data.

9. A water heater data processing device, characterized in that, include: Sensors, memory, processors; The sensor is used to detect the operating status data of the water heater; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

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