Intelligent electric kettle remote control method and system based on internet of things

Abstract

The present application relates to the technical field of data acquisition and monitoring control, in particular to a smart electric kettle remote control method and system based on the Internet of Things, comprising the following steps: generating a water boiling instruction based on a comparison of a control serial number and a shadow serial number based on a water boiling request, generating a sampling message based on water temperature voltage wave calculation based on thermal information, generating a water temperature credible record by comparing water temperature changes and thermal variation boundaries, generating an execution instruction based on water temperature interval and target temperature calculation based on cloud time and placeholder record, and comparing current and power-off water temperature calibration results and residual heat record. In the present application, the bottom layer reference is constructed by comparing the request sequence and the mirror image label and calculating the voltage variation amplitude, the credible record is generated by verifying the water temperature trend and the thermal variation boundary to eliminate abnormal interference, the time axis and the thermal energy residual record are introduced to extract the residual heat for temperature compensation output, the temperature control command is compared with the real-time temperature and the power-off temperature calibration execution result, and the fine control is carried out and the redundant energy consumption is eliminated.

Description

Technical Field

[0001] This invention relates to the field of data acquisition and monitoring control technology, and in particular to a remote control method and system for smart electric kettles based on the Internet of Things. Background Technology

[0002] The field of data acquisition and monitoring control technology mainly involves the acquisition, transmission, display, recording, and control of field equipment operating status, environmental parameters, power parameters, switch status, and execution feedback information. Its core components include field data sources formed by temperature sensors, liquid level detectors, current detectors, voltage detectors, relay contacts, push-button switches, and equipment identification codes. The acquired data is sent to the monitoring terminal through wired communication lines, wireless communication links, and gateway devices. The monitoring terminal views, records, and issues control commands to the equipment status according to preset sampling periods, status thresholds, control permissions, and command formats. It also continuously monitors the power-on status, heating status, heat preservation status, water level status, and fault status returned by the execution terminal. Traditional remote control methods and systems for smart electric kettles refer to control methods for non-site start / stop, heating temperature setting, heat preservation time setting, working status viewing, and abnormal status prompts for smart electric kettles. The technical issues it addresses include the correspondence between water temperature, water level, kettle placement status, heating plate power status, and user remote commands. Typically, a temperature probe is used to collect the water temperature inside the kettle, a water level electrode and float switch are used to verify the water volume status, a kettle seat detection contact is used to verify whether the kettle is placed in place, and a current sampling resistor is used to verify whether the heating circuit is powered. The microcontroller reads the above data according to the pre-stored temperature value, heat preservation time, and dry-boil judgment threshold, and receives the boiling, stop, heat preservation, and reservation commands sent by the mobile phone through a Wi-Fi chip, Bluetooth chip, and home gateway. Then, a relay, a thyristor, and a heating plate power switch control the connection and disconnection of the heating circuit. At the same time, the water temperature value, heating indicator, heat preservation indicator, water shortage indicator, away from seat indicator, and fault prompt content are transmitted back to the mobile phone and monitoring terminal.

[0003] The existing control mechanism relies excessively on basic sensor nodes to obtain single-dimensional operational characteristics and combines them with fixed thresholds to determine heating conditions. During network interaction, it lacks synchronous verification of the timing of command issuance and the response status at the terminal side, which makes it easy to cause control timing misalignment and invalid request delays in the face of network fluctuations. In addition, it lacks accurate quantitative analysis of the residual heat energy after the equipment is disconnected from the power supply and after heating, making it difficult for the subsequent heating process to self-match and adjust the temperature control strategy, resulting in unnecessary energy loss and temperature control lag, which fails to meet the actual needs for accurate water temperature control and stable regulation throughout the entire cycle. Summary of the Invention

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a remote control method for a smart electric kettle based on the Internet of Things, comprising the following steps:

[0005] S1: Based on the smart home water heating request, collect remote control information and control timing information, compare the control sequence number with the shadow sequence number, filter the control sequence number, and generate a remote water heating command;

[0006] S2: Based on the remote water boiling command, calculate the water temperature and voltage fluctuations according to the thermoelectric operation information and sampling timing information of the networked electric kettle, and generate a kettle-end sampling message;

[0007] S3: Based on the sampling message at the kettle end, obtain the water temperature time sequence and thermal change verification basis, compare the water temperature change with the lower boundary and upper boundary of thermal change, and generate a reliable water temperature record.

[0008] S4: Based on the reliable water temperature record, collect cloud time and residual heat occupancy records, calculate water temperature interval, filter residual heat content according to water level scale, calculate target temperature and residual heat content, and generate temperature control execution command;

[0009] S5: Based on the temperature control execution command, according to the thermoelectric power failure detection information of the networked electric kettle, compare the current water temperature with the power failure water temperature, calibrate the power failure result and residual heat occupation record, and generate a remote control scheme for the smart electric kettle.

[0010] As a further aspect of the present invention, the remote water boiling command includes a heating start signal, a boiling mode selection, and a heat preservation time setting; the kettle-side sampling message includes sensor resistance and capacitance parameters, analog-to-digital conversion bit depth, and filter delay time; the reliable water temperature record includes a measurement deviation index, an abnormal jump flag, and data validity weight; the temperature control execution command includes a pulse width modulation parameter, a temperature control dead zone range, and a power attenuation slope; and the intelligent electric kettle remote control scheme includes a multi-stage heating curve, standby power consumption configuration, and a fault self-recovery mechanism.

[0011] As a further aspect of the present invention, the water temperature voltage fluctuation refers to the magnitude and offset of the temperature detection voltage over time caused by changes in water temperature during the heating and sampling process of the networked electric kettle.

[0012] As a further aspect of the present invention, the residual heat occupancy record refers to the record of the state in which the residual heat in the kettle can continue to affect the water temperature under different time, water level and temperature conditions after the electric kettle is powered off and heating stops.

[0013] As a further aspect of the present invention, the specific steps of S1 are as follows:

[0014] S101: Based on the smart home network water heating request, collect the control identity, water heating parameters and trigger timing fields in the remote water heating request, perform null value judgment and format verification, establish key value association according to the request session identifier, and generate a remote control parameter set;

[0015] S102: Based on the remote control parameter set, arrange the request records in ascending order according to the trigger timestamp, assign incremental numbers to the arrangement positions, associate the request session identifier with the number, and generate a control sequence number mapping table;

[0016] S103: Based on the control sequence number mapping table, collect the shadow control sequence number within the device shadow state, filter valid control records with control sequence numbers later than the shadow control sequence number, establish a control field mapping relationship, and generate a remote water boiling command.

[0017] As a further aspect of the present invention, the specific steps of S2 are as follows:

[0018] S201: Based on the remote water boiling command, collect the kettle end temperature control, electrical status and sampling time sequence fields, establish field value mapping according to the sampling time, and generate kettle end sampling parameter matrix;

[0019] S202: Based on the sampling parameter matrix at the kettle end, arrange the water temperature and voltage according to the sampling time, calculate the difference between adjacent water temperature and voltage, compare the absolute value of the difference with the voltage fluctuation reference value, and generate a voltage fluctuation sampling sequence.

[0020] S203: Based on the voltage fluctuation sampling sequence, associate the kettle end water level, drive, voltage, current and power supply status fields, establish message field mapping according to the sampling time, and generate kettle end sampling message.

[0021] As a further aspect of the present invention, the specific steps of S3 are as follows:

[0022] S301: Based on the kettle end sampling message, calculate the water temperature value in the sampling message and associate it with the sampling status field, associate the water temperature update time with the thermal change boundary range, and generate a reliable water temperature verification basis;

[0023] S302: Based on the water temperature reliable verification basis, perform a sequential determination on the sampling time and the water temperature update time, retain records where the sampling time is not earlier than the water temperature update time, establish record markers according to the time difference, and generate a water temperature time sequence determination table.

[0024] S303: Based on the water temperature time series determination table, calculate the difference between adjacent water temperature values ​​and compare it with the thermal change boundary range, retain the records within the thermal change boundary range, map the sampling time, water temperature value and reliable record mark, and generate a reliable water temperature record.

[0025] As a further aspect of the present invention, the specific steps of S4 are as follows:

[0026] S401: Based on the reliable water temperature record, collect cloud time and residual heat occupancy record, perform difference conversion between sampling time and cloud time, associate target temperature with water temperature value, and generate water temperature time parameter set;

[0027] S402: Based on the water temperature aging parameter set, match the residual heat occupation records according to the water temperature aging, verify the relationship between the occupation start and end times and the sampling time, filter the hit occupation content, and generate a residual heat occupation matching table.

[0028] S403: Based on the waste heat occupancy matching table, call the target temperature, calculate the difference between the target temperature and the waste heat occupancy content, establish a heating control field mapping according to the difference, and generate a temperature control execution instruction.

[0029] As a further aspect of the present invention, the specific steps of S5 are as follows:

[0030] S501: Based on the temperature control execution command, detect the current water temperature, contact voltage, heating plate current, power supply voltage, and peak water temperature after power failure, establish field value mapping according to the sampling time, and generate a temperature control status sampling set;

[0031] S502: Based on the temperature control status sampling set, compare the power-off determination status of the contact voltage and the heating plate current, and generate a power-off status determination table by associating the current water temperature with the peak water temperature during power-off according to the comparison result.

[0032] S503: Based on the power outage status determination table, match the residual heat occupancy record, calculate the difference between the peak water temperature during power outage and the current water temperature, correct the occupancy temperature field and occupancy time period field according to the difference, and generate a remote control scheme for the smart electric kettle.

[0033] The IoT-based smart electric kettle remote control system includes:

[0034] The remote control serial number comparison and filtering module is used to implement S1: based on the smart home water heating request, collect remote control information and control timing information, compare the control serial number with the shadow serial number, filter the control serial number, and generate a remote water heating command.

[0035] The kettle-end thermoelectric operation sampling message generation module is used to implement S2: based on the remote water boiling command, according to the thermoelectric operation information and sampling timing information of the networked electric kettle, calculate the water temperature and voltage fluctuations, and generate a kettle-end sampling message;

[0036] The water temperature time-series thermal change reliable verification module is used to implement S3: based on the kettle end sampling message, obtain the water temperature time sequence and thermal change verification basis, compare the water temperature change with the lower boundary and upper boundary of thermal change, and generate a reliable water temperature record.

[0037] The waste heat occupancy compensation temperature control execution module is used to implement S4: based on the reliable water temperature record, collect cloud time and waste heat occupancy record, calculate water temperature interval, filter waste heat content according to water level scale, calculate target temperature and waste heat content, and generate temperature control execution command;

[0038] The power failure detection calibration and control scheme output module is used to implement S5: based on the temperature control execution command, according to the thermoelectric power failure detection information of the networked electric kettle, compare the current water temperature with the power failure water temperature, calibrate the power failure result and residual heat occupation record, and generate a remote control scheme for the smart electric kettle.

[0039] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0040] In this invention, a bottom-level verification benchmark is constructed by comparing the remote request sequence with the preset mirror number and combining the operating characteristics and sampling cycle to calculate the water temperature and voltage variation amplitude. Based on the extracted water temperature variation trend and thermal change boundary range, bidirectional verification is carried out to generate a reliable temperature record, thereby eliminating abnormal time sequence interference and distortion state interference. Based on this, the cloud time axis and residual heat energy record are introduced to deduce the temperature decay span, and the corresponding residual heat value is extracted according to the liquid level standard to perform target temperature deviation compensation and output precise temperature control command. Based on the thermoelectric disconnection feedback characteristics, the current real-time temperature and the power failure benchmark temperature are compared with the bidirectional calibration execution results and heat energy distribution records to achieve fine temperature control at all times and eliminate the phenomenon of excessive energy consumption. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of the steps of the present invention;

[0043] Figure 2 This is a detailed schematic diagram of S1 of the present invention;

[0044] Figure 3 This is a detailed schematic diagram of S2 of the present invention;

[0045] Figure 4 This is a detailed schematic diagram of S3 of the present invention;

[0046] Figure 5 This is a detailed schematic diagram of S4 of the present invention;

[0047] Figure 6 This is a detailed schematic diagram of S5 of the present invention. Detailed Implementation

[0048] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0049] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0050] This embodiment provides a remote control method for a smart electric kettle based on the Internet of Things (IoT). In practical applications, such as in the actual operation of an IoT smart home cloud control system and a kettle-end microcontroller that continuously maintain long-term communication and perform remote high-concurrency temperature control tasks.

[0051] Please see Figure 1 and Figure 2 The specific steps of S1 are as follows: Based on the smart home network water boiling request, remote control information and control timing information are collected. The control sequence number and shadow control sequence number are compared sequentially, and the control sequence number later than the shadow control sequence number is selected to generate a remote water boiling command. In this execution phase, in order to ensure the timeliness and uniqueness of the commands issued by the cloud control system to the kettle device and to prevent command disorder caused by network delays or retransmission mechanisms, after receiving the smart home network water boiling request transmitted by the mobile terminal or smart voice hub and other gateway nodes, the cloud server extracts the remote control information and control timing information with time tags from the load message through the communication protocol parsing engine. The remote control information includes the heating start signal representing the execution action, the boiling mode selection representing the target temperature mode, and the heat preservation time setting for maintaining a specific temperature range. The control sequence number represents the globally incrementing sequence identifier of each control event, while the shadow control sequence number is the instruction sequence identifier of the last successfully received and confirmed execution by the kettle recorded by the device shadow node in the IoT cloud platform. By performing a strict numerical comparison between the control sequence number and the shadow control sequence number, the system can identify and directly discard historical delayed commands or replay attack packets that are earlier than or equal to the shadow control sequence number. It only filters out the latest valid control sequence number generated later than the shadow control sequence number, and then packages and encapsulates the extracted control information payload to generate a remote water-boiling command for sending to the microcontroller, thereby completing the debouncing and deduplication of downlink commands.

[0052] S101: Based on the smart home network water heating request, collect the control identity, water heating parameters, and trigger timing fields from the remote water heating request. Perform null value judgment and format verification, establish key-value associations according to the request session identifier, and generate a remote control parameter set. In this sub-step, the cloud access node first reads each business field of the smart home network water heating request through the message unpacker. It collects in detail the control identity field representing user permissions and device binding relationships, the water heating parameter field defining specific heating targets and heat preservation rules, and the trigger timing field recording the moment the request is generated. Subsequently, the data cleaning engine performs strict null value judgment and data type format verification on the above-collected fields. Once it finds that key fields are missing, the format does not conform to the preset protocol specifications, or the field value exceeds the physical limit, it directly triggers the discard mechanism and records the security audit log, while returning an error response message to the request initiator. For valid requests that pass verification, the system calls the in-memory database component, using the request session identifier that uniquely identifies each communication interaction process as the primary key, and binds and maps the control identity, water boiling parameters, and trigger timing as key-value pairs. This generates a structured and validated set of remote control parameters in the memory cache, providing a reliable data source for subsequent timing sorting.

[0053] S102: Based on the remote control parameter set, request records are sorted in ascending order by trigger timestamp, and incremental numbers are assigned to the sorted positions. The request session identifier is associated with the number, generating a control sequence number mapping table. In this processing stage, the sorting and scheduling engine reads multiple concurrent remote control parameter sets from the cache and extracts the trigger time sequence field as the time determination benchmark. For multiple control operations that may flood into the same device within the same time period, the system reorganizes all request records in ascending order according to the order of their trigger timestamps. After sorting, to ensure the absolute causal relationship of the control chain, the sequence generator assigns consecutive and strictly incremental numbers starting from the initial count value based on the current sorted position of the request record. Subsequently, the mapping and association module performs a database table-level association lock between this incremental number and the original request session identifier, transforming the time dimension feature into a deterministic logical execution sequence feature. Finally, a control sequence number mapping table with high query efficiency is generated in the persistent storage node, effectively solving the problem of subtle deviations and out-of-order concurrency that may exist in timestamps in a distributed network environment.

[0054] S103: Based on the control sequence number mapping table, the system collects shadow control sequences within the device's shadow state, filters valid control records with control sequences later than the shadow control sequences, establishes control field mapping relationships, and generates remote water boiling instructions. In the final stage of this process, the status synchronization verification module, based on the device identification information in the control sequence number mapping table, initiates a status retrieval request to the device shadow database, collecting the last reported and confirmed shadow control sequence number of the specific kettle within the digital twin mapping state. The comparison engine traverses the pending numbers in the mapping table, rigorously filtering out valid control records with control sequences later than the shadow control sequences. For these filtered valid records, the instruction assembly module establishes a corresponding control field mapping relationship between the water boiling parameter fields it carries and the underlying hardware control register mapping template of the target device. Through this mapping mechanism, abstract business parameters are translated into control machine code that the underlying microcontroller can recognize. Finally, an instruction frame header, checksum, and frame trailer are added to generate a complete remote water boiling instruction, which is then pushed into the downlink message queue to await publication to the message middleware, thus ensuring that every instruction ultimately transmitted to the kettle is the latest valid control requirement.

[0055] Please see Figure 1 and Figure 3 The specific steps of S2 are as follows: Based on the remote water boiling command, the output voltage of the water temperature sensor is sampled and fluctuated according to the water temperature voltage, water level scale, relay drive level, contact voltage, heating plate current, power supply voltage, and sampling time, generating a kettle-end sampling message. After the electric kettle microcontroller receives, parses, and executes the remote water boiling command sent from the cloud, the underlying real-time operating system triggers a multi-channel high-frequency sampling task. The system continuously captures the water temperature voltage signal fed back by the negative temperature coefficient thermistor through the analog-to-digital conversion interface. At the same time, the water level scale sensor signal representing the water volume boundary in the kettle is synchronously acquired, the relay drive level signal output by the microcontroller to the power control component, the contact voltage signal in the open or closed state of the main circuit, the heating plate current signal actually consumed by the heating element, the power supply voltage signal after step-down rectification at the mains input, and the precise sampling time calibrated by the hardware timer. Based on these multi-dimensional physical quantities and timestamps, the microprocessor first performs differential filtering and fluctuation severity calculation based on time windows on the output voltage of the water temperature sensor to filter out transient noise spikes caused by power grid surges or electromagnetic interference. While ensuring data smoothness and accurately reflecting the thermal state of the physical world, various resistance and capacitance sensing parameters and electrical operating states are encapsulated according to a predetermined protocol format. Combined with timestamp verification at the sampling time, a kettle-side sampling message that can be stably reported by the wireless communication module is generated, providing comprehensive edge-side operating data support for the cloud.

[0056] S201: Based on remote water boiling commands, the system collects kettle-end temperature control, electrical status, and sampling timing fields. It establishes a field value mapping according to the sampling time, generating a kettle-end sampling parameter matrix. During the actual edge-side data acquisition process, when the heating start signal in the remote water boiling command activates the system interrupt service routine, the microcontroller's multiplexed analog-to-digital converter cyclically collects kettle-end data at a preset fixed scan cycle. The acquisition module comprehensively captures kettle-end temperature control information fields, including sensor resistance and capacitance parameters; electrical status fields, including drive level and heating power consumption; and sampling timing fields generated by the internal real-time clock chip. Due to slight differences in response delays among various sensors, the alignment and synchronization processing unit needs to align the data captured by different channels within the same scan cycle to a unified reference timeline. Subsequently, the matrix reconstruction module uses the sampling time as the row index and various temperature control and electrical status parameters as column vectors. It dynamically allocates memory space within the random access memory and establishes a two-dimensional field value mapping structure according to the sampling time, thereby generating a continuous and structured kettle-end sampling parameter matrix. This transforms the dispersed physical quantities into a comprehensive monitoring dataset with time correlation.

[0057] S202: Based on the kettle-end sampling parameter matrix, water temperature and voltage are arranged according to sampling time, and the difference between adjacent water temperature and voltage is calculated. The absolute value of the difference is compared with the voltage fluctuation reference value to generate a voltage fluctuation sampling sequence. After the data matrix is ​​generated, the digital signal processing unit intervenes to perform preliminary anomaly cleaning. The program pointer sequentially traverses the row data in the kettle-end sampling parameter matrix, extracting and arranging water temperature and voltage elements individually according to the order of sampling time. Then, the difference calculation module continuously subtracts the water temperature and voltage values ​​of adjacent sampling times to obtain the absolute value of the difference representing the rate of temperature change. To eliminate non-physical abrupt changes caused by poor sensor contact or strong electromagnetic interference, the comparator module compares the obtained absolute value of the difference with the system's preset physical thermal voltage fluctuation reference value. If the absolute value of the difference exceeds the reference limit range, it is judged as a glitch anomaly and smoothed using a median filtering algorithm or a linear interpolation algorithm. If it is within a reasonable reference range, the original value is maintained. After this series of dynamic judgments and filtering replacements, a voltage fluctuation sampling sequence that eliminates noise interference and reflects the true temperature rise trend is output.

[0058] S203: Based on the voltage fluctuation sampling sequence, the system associates the kettle-end water level, drive, voltage, current, and power supply status fields, establishes a message field mapping according to the sampling time, and generates a kettle-end sampling message. After obtaining the purified voltage fluctuation sampling sequence, the message encapsulation unit uses this sequence as the core thermal parameters and re-associates and fuses it with the kettle-end water level status, relay drive command, mains input voltage, heating element circuit current, and overall power supply status fields corresponding to the same sampling time in the kettle-end sampling parameter matrix. The encapsulation program establishes a mapping and binding relationship between message fields according to the device reporting protocol specifications defined in the cloud, and configures corresponding analog-to-digital conversion bit width declarations and filter delay duration parameter identifiers for each data item. After completing the data body filling, a cyclic redundancy check code and the device's unique media access control address identifier are appended, and the data is serialized to generate a structurally complete kettle-end sampling message. If the current network connection is normal, the message is pushed to the cloud platform through the IoT communication module. If a network outage occurs, the message is temporarily stored in local non-volatile flash memory and marked as pending transmission, waiting for the network to recover before triggering the breakpoint resumption mechanism in sequence.

[0059] Please see Figure 1 and Figure 4 The specific steps of S3 are as follows: Based on the kettle-side sampling message, obtain reliable water temperature verification basis; determine the order of sampling time and water temperature update time to verify whether the water temperature change is within the thermal change boundary range, and generate a reliable water temperature record; after receiving the kettle-side sampling message uploaded from the edge side, the cloud platform IoT access gateway needs to perform a secondary cloud-based verification of the absolute reliability of the temperature data carried in the message due to the instability of the wireless transmission environment and the possibility of hardware aging. The data verification engine first combines the native voltage information in the message with the pre-stored resistance-temperature characteristic curve for conversion and analysis to obtain the real physical water temperature and boundary conditions that can be used for comparison and judgment, forming a reliable water temperature verification basis. Subsequently, the timing logic verifier compares the sampling time carried in the message with the water temperature update time of the previous frame recorded by the cloud digital twin node, performs strict order judgment, intercepts and discards out-of-order or delayed retransmission messages caused by network congestion, and ensures the monotonically increasing nature of data processing. After the timing verification is passed, the thermodynamic verifier further verifies whether the water temperature change range between adjacent valid messages is within a reasonable thermal change boundary range, based on physical constraints such as the specific heat capacity of water and the maximum output power of the heating plate. Only data records that pass both timing consistency and physical rationality verification are assigned a high data validity weight and are persistently stored as reliable water temperature records for subsequent precise temperature control algorithms to use.

[0060] S301: Based on the kettle-side sampling message, the system converts the water temperature value in the message and associates it with the sampling status field, as well as the water temperature update time and thermal change boundary range, to generate a reliable water temperature verification basis. In the first stage of cloud verification, the decoding service parses the kettle-side sampling message, extracts the voltage fluctuation sampling sequence data after anti-jitter processing, and calls the thermistor nonlinear mapping piecewise fitting table pre-stored in the cloud configuration center to accurately convert the analog voltage value into a water temperature value in degrees Celsius. After conversion, the system strongly associates and binds the water temperature value with the sensing resistance and capacitance parameters and sampling status field in the message. Subsequently, the status management service queries the latest valid water temperature update timestamp stored in the cloud cache database. Simultaneously, based on the currently detected water level and power supply voltage, it dynamically calls the fluid thermodynamics model to calculate the upper limit of the maximum heating rate and the lower limit of the maximum cooling rate that may exist under the current operating state, thus jointly constructing a thermal change boundary range constrained by physical laws. The system combines and packages the current water temperature value, update timestamp, and dynamically calculated boundary range to generate a reliable water temperature verification basis, providing a complete comparison reference standard for subsequent dual verification.

[0061] S302: Based on the reliable water temperature verification criteria, the sampling time and water temperature update time are judged sequentially. Records with sampling times no earlier than the water temperature update time are retained, and records are marked according to the time difference to generate a water temperature time sequence judgment table. After establishing the verification criteria, the time sequence verification module extracts the current message sampling time and historical water temperature update time from the reliable water temperature verification criteria. The comparison engine executes strict timestamp sequence judgment logic. If the current sampling time is earlier than the latest water temperature update time recorded in the cloud, it indicates that the message has been severely delayed in the network transmission link. At this time, the system will trigger the abnormal discard mechanism and write an abnormal jump flag to the log center, abandoning the processing of the delayed record. Conversely, if the sampling time is no earlier than the water temperature update time, the record of the normal sequence is retained. Subsequently, the difference calculation component calculates the time difference between the current sampling time and the historical water temperature update time, and uses this time difference as a key index indicator for marking and mapping, forming a water temperature time sequence judgment table arranged in ascending order of time and containing time interval information. This process effectively filters dirty data caused by network jitter and ensures the temporal causal consistency of the control system input.

[0062] S303: Based on the water temperature time series judgment table, the difference between adjacent water temperature values ​​is calculated and compared with the thermal change boundary range. Records within the thermal change boundary range are retained, and the sampling time, water temperature value, and reliable record marker are mapped to generate a reliable water temperature record. Based on the cleaned water temperature time series judgment table, the thermal mutation verification module performs a final review of the physical authenticity of the temperature change. The calculation unit extracts the water temperature values ​​from two adjacent valid records, calculates the difference between their water temperature values, and converts the actual temperature change rate by combining it with the previously calculated time difference. Subsequently, the judge compares the actual change rate with the dynamically generated thermal change boundary range in the reliable water temperature verification basis. Once it is found that the water temperature difference exceeds the extreme value boundary allowed by the physical laws under the current environmental conditions, such as a sudden temperature rise of tens of degrees within a few seconds (dry burning jump) or sensor detachment and short circuit, the system immediately triggers the alarm mechanism, records the measurement deviation index, and intercepts the abnormal record from flowing to the subsequent control link. For valid records whose comparison results fall entirely within the thermal change boundary range, the system recognizes them as having real physical meaning. Then, it maps and solidifies the sampling time, water temperature value, and the reliable record marker representing the verified record into the database, ultimately generating a high-confidence reliable water temperature record. This ensures that the subsequent feedforward compensation temperature control algorithm receives absolutely safe and reliable input sources.

[0063] Please see Figure 1 and Figure 5 The specific steps of S4 are as follows: Based on the reliable water temperature record, collect cloud time and residual heat occupancy records, calculate the water temperature timeliness and match the residual heat occupancy content, calculate the target temperature with the residual heat occupancy content, and generate a temperature control execution command. After ensuring the absolute reliability of the data reported by the kettle, the cloud temperature control decision center enters the most crucial feedforward compensation calculation stage. Due to the significant thermal inertia of the electric kettle's heating component, after the relay disconnects the physical power supply, a large amount of residual heat accumulated inside the heating plate will continue to be conducted to the water, causing the water temperature to continue to rise after the power is cut off, resulting in an overshoot phenomenon. To eliminate this error, the decision engine, based on the newly generated reliable water temperature record, synchronously collects the current server system's cloud time and retrieves the residual heat occupancy records trained by the electric kettle under different water levels and initial water temperatures from the historical feature library. The system first converts the collection delay to clarify the true timeliness of the water temperature data, and then accurately matches the residual heat occupancy compensation content that conforms to the current physical conditions from the historical feature library. By subtracting the user-set target temperature from the inevitable temperature rise deviation represented by the residual heat storage space, the actual trigger threshold temperature at which power-off control should be issued in advance is derived. Finally, combining the pulse width modulation parameters, temperature control dead zone range, and power attenuation slope, a temperature control execution command containing pre-judgment electrical logic is generated and sent to the device for execution.

[0064] S401: Based on reliable water temperature records, the system collects cloud time and residual heat occupancy records, performs difference calculations between the sampling time and cloud time, associates the target temperature with the water temperature value, and generates a water temperature timeliness parameter set. In the specific operation of preparing compensation parameters, the time alignment service first takes over the processing flow, extracts reliable sampling time markers from the reliable water temperature records, and requests absolutely accurate cloud time from the cloud network time protocol server. By performing difference calculations between the sampling time and cloud time, the system can accurately quantify the communication latency consumed in the entire process of packet packaging at the edge, wireless network transmission, and cloud gateway parsing. Next, the system calls the residual heat occupancy record table stored in the cloud platform database through the pre-read interface. To provide sufficient basis for subsequent timeliness compensation, the parameter aggregation component combines this communication latency feature with the target temperature pre-set by the user on the smart home terminal and the current reliable water temperature value for multi-dimensional correlation mapping. All this data related to time lag and target deviation is uniformly sealed in the internal cache structure, generating a water temperature timeliness parameter set specifically designed for dynamic timeliness compensation.

[0065] S402: Based on the water temperature aging parameter set, the system matches residual heat occupancy records according to water temperature aging, verifies the relationship between the start and end times of occupancy and the sampling time, filters the matched occupancy content, and generates a residual heat occupancy matching table. Subsequently, the residual heat feature addressing module calls the communication lag delay data and current water level and temperature status parameters in the water temperature aging parameter set to perform a multi-condition joint query mechanism in the large residual heat occupancy record database. To ensure the accuracy and usability of the matched compensation records, the system not only compares the temperature drift estimate caused by water temperature aging, but also strictly verifies whether the historical occupancy start and end time characteristics of the matched residual heat occupancy records match the trend of sampling time changes in the current acquisition process, thereby eliminating outdated records that do not conform to the dynamic characteristics of the current heating stage. After strict filtering and comparison under multiple constraints, the system successfully locks and matches the most suitable residual heat occupancy content under the current specific operating conditions. The mapping and matching unit establishes a mapping connection between the current operating condition identifier and the expected value of waste heat temperature rise compensation that has been selected, and finally generates a waste heat occupancy matching table specifically for the current heating cycle.

[0066] S403: Based on the waste heat occupancy matching table, the target temperature is called, the difference between the target temperature and the waste heat occupancy content is calculated, a heating control field mapping is established according to the difference, and a temperature control execution instruction is generated. After obtaining the accurate expected value of waste heat temperature rise compensation, the core computing unit calls the target temperature parameter initially set by the user from the session memory again according to the business logic. The computing component uses the target temperature as the subtrahend and the waste heat occupancy content in the waste heat occupancy matching table, which represents the inertial temperature rise after power failure, as the minuend to perform the difference calculation. The difference obtained by this calculation is the actual compensation trigger temperature at which the heating plate should disconnect the power supply in advance after considering the thermal inertia of the system. The instruction refactoring module establishes a heating control field association mapping based on this corrected trigger difference and integrates the pulse width modulation control duty cycle, the temperature control dead zone range used to suppress small temperature fluctuations, and the power attenuation slope to reduce the intensity of heating when approaching the target temperature, and other control strategy parameters. Finally, the above control parameters are encapsulated by protocol encoding to generate a temperature control execution instruction with forward prediction capability, and pushed down to the underlying microcontroller of the smart kettle through a message queue mechanism.

[0067] Please see Figure 1 and Figure 6 The specific steps of S5 are as follows: Based on the temperature control execution command, the current water temperature, contact voltage, heating plate current, power supply voltage, and peak water temperature during power outage are detected. The power outage status is determined, and the residual heat occupation record is corrected to generate a remote control scheme for the smart electric kettle. When the kettle-side microcontroller executes the received temperature control execution command and cuts off the power supply to the heating plate when the water temperature reaches the early power outage compensation trigger value, the closed-loop control mechanism does not stop working but enters the power outage status monitoring and adaptive learning evaluation stage. The microcontroller continues to maintain high-frequency monitoring of various electrical parameters at the kettle end, continuously detects real-time sensor data reflecting the current water temperature change, verifies the contact voltage signal of the relay main circuit being completely cut off, confirms the heating plate current signal of the heating element having no leakage current consumption, monitors the power supply voltage status of the power grid fluctuation environment, and locks the peak water temperature during power outage at the historical moment when the temperature exceeds the highest point and begins to drop. By comprehensively analyzing the logical relationship of these parameter signals, the system determines the implementation of the actual hardware power outage status. Subsequently, the cloud system compares the actual peak water temperature reached after the power outage with the expected target. Based on the temperature difference obtained from the comparison, it dynamically corrects the historically saved residual heat occupancy records, realizes the self-evolution and parameter iteration of the control algorithm, and generates a smart electric kettle remote control scheme based on the correction results, including multi-stage heating curve optimization and standby power consumption configuration, to ensure that the accuracy of the next water boiling task continues to improve.

[0068] S501: Based on temperature control execution commands, it detects the current water temperature, contact voltage, heating plate current, power supply voltage, and peak water temperature after power failure. It establishes a field value mapping according to the sampling time, generating a temperature control status sampling set. At the moment the power failure control command is issued, the edge acquisition program starts a high-frequency monitoring and listening mode. The system continuously monitors the current water temperature trend after power failure through the hardware interrupt interface, simultaneously capturing the logic level of the contact voltage at the control relay's action execution end, detecting whether the current signal flowing through the heating plate circuit has indeed dropped to zero amperes, and synchronously recording the mains input power supply voltage that may affect heat dissipation or leakage current characteristics. During this continuous monitoring process, the microprocessor's built-in peak-hold comparison algorithm continuously compares adjacent water temperature sampling values. Once it detects the inflection point where the derivative of the water temperature curve changes from positive to negative (i.e., the water temperature stops rising and begins to fall), it immediately locks the temperature record at that inflection point as the true peak water temperature after power failure. The data aggregation service establishes a continuous field value mapping relationship for all the tracked and detected physical quantities after the power outage according to the time axis order of the sampling time, and constructs a complete temperature control state sampling set in the cache that records the thermodynamic inertial performance after the power outage.

[0069] S502: Based on the temperature control status sampling set, the power-off determination status is compared with the contact voltage and heating plate current. The comparison results are then correlated with the current water temperature and the peak water temperature during power-off to generate a power-off status determination table. Subsequently, a safety assessment mechanism intervenes to verify the physical and logical consistency of the data within the temperature control status sampling set. The logic comparison unit extracts the contact voltage status identifier and heating plate current value within the same time slice, comparing whether the control level command for relay disconnection completely matches the actual detected zero-current state. This cross-comparison confirms that the hardware actuator has not experienced contact adhesion or breakdown failure, verifying the absolute physical effectiveness of the power-off action. With the power-off determination status confirmed as correct, the feature extractor filters the current water temperature data triggered at the moment of power-off from the sampling set and causally correlates it with the subsequent peak water temperature during power-off. By mapping the execution time and temperature state extremes of the action, a power-off status determination table containing the identified hardware action identifier and corresponding thermal results is generated, eliminating hardware fault interference factors for subsequent error calculations.

[0070] S503: Based on the power outage status determination table, it matches the residual heat occupancy record, calculates the difference between the peak water temperature during the power outage and the current water temperature, and corrects the occupancy temperature field and occupancy time period field according to the difference, generating a remote control scheme for the smart electric kettle. In the final calibration stage of closed-loop control, the model update engine extracts the feature elements from the power outage status determination table, queries and matches the residual heat occupancy record initially called in this control cycle through the database interface. Subsequently, the error calculation component retrieves the peak water temperature during the power outage and the current water temperature parameters at the moment of power outage, and calculates the actual difference between the two. This difference represents the absolute temperature rise caused by thermal inertia under the actual working conditions. The system cross-compares this actual temperature rise difference with the original expected temperature rise compensation content in the residual heat occupancy record. If there is a deviation, a smoothed weighted moving average algorithm is used to correct the numerical value of the occupancy temperature field in the cloud database, and the occupancy time period field parameter affecting the compensation timing is adjusted simultaneously. Through the aforementioned dynamic closed-loop parameter self-learning and error calibration mechanism, the system continuously updates the overall execution logic, including multi-stage heating curve approximation strategies, fault self-recovery mechanisms, and standby power consumption configuration after heating. Ultimately, it generates the globally optimal remote control solution for the smart electric kettle, ensuring the long-term stability and adaptive convergence of the device's temperature control accuracy.

[0071] The IoT-based smart electric kettle remote control system includes:

[0072] Remote control sequence number comparison and filtering module: After the smart home gateway transmits the request message, this module collects and receives the remote control information and control timing information including timestamps. The data cleaning subunit performs format specification validation and null value interception on the request fields, generating a verified remote control parameter set. Subsequently, the sequence allocation component reorganizes and arranges the parameters based on the trigger timestamp and assigns incremental numbers, mapping and generating a control sequence number mapping table. Finally, the comparison and filtering component compares and filters the control sequence numbers in the mapping table with the shadow control sequence numbers pulled from the device shadow status, discarding expired and delayed operations, and generating the latest valid remote water boiling command to be sent to the edge device.

[0073] Kettle-side thermoelectric operation sampling message generation module: When a remote water boiling command triggers the microcontroller's underlying execution, relevant physical state information is input into this module. The multi-channel acquisition unit within this module acquires water temperature and voltage at continuous intervals, simultaneously capturing water level scale, relay drive level, contact voltage, heating plate current, and power supply voltage, and maps these data to generate a structured kettle-side sampling parameter matrix. The processing unit arranges the water temperature and voltage matrix in time sequence and performs differential calculations, comparing the resulting fluctuation difference with a preset fluctuation benchmark constraint range to generate a voltage fluctuation sampling sequence after anti-jitter filtering. Finally, the encapsulation unit assembles this sequence with various electrical state fields according to a predetermined protocol, generating a kettle-side sampling message containing multi-dimensional sensor status, which is then pushed to the cloud platform.

[0074] Water Temperature Time-Sequence Thermal Change Reliability Verification Module: Upon the transmission of an uplink message to the cloud gateway, the parsing and verification mechanism is activated. The decoding unit converts the message voltage value into a water temperature value based on the built-in RC characteristic mapping table, and generates a reliable water temperature verification basis by combining it with physical model constraints. Based on this, the time-series verification component makes an absolute chronological judgment on the message sampling time and the historical water temperature update time, removes out-of-order records from the network, and generates a marked water temperature time-series judgment table. Next, the mutation verification component calculates the difference between adjacent water temperatures, compares it with the maximum allowable thermal change boundary range of the system, maps the status identifier for normal records that conform to the physical change law, generates a highly confident reliable water temperature record, and saves it to the cloud cache.

[0075] The residual heat occupancy compensation temperature control execution module: Based on highly reliable water temperature data input, the compensation calculation process is initiated within this module. The time synchronization component collects the current absolute time from the cloud, compares it with the sampling time, performs network difference conversion, and generates a water temperature timeliness parameter set including communication latency parameters. The addressing retrieval component, based on this timeliness parameter and the current water level and temperature characteristics, matches residual heat occupancy records in the historical database, verifies the start and end times of the occupancy logic, and generates a residual heat occupancy matching table. The feedforward control component calls the preset target temperature, performs difference deduction calculation with the expected value of the residual heat occupancy content, uses this difference value to construct the underlying control field, and finally generates a temperature control execution command that takes into account thermal inertia compensation characteristics, which is then sent back to the kettle for execution.

[0076] Power Outage Detection, Calibration, and Control Scheme Output Module: After the hardware executes early power-off control, this feedback learning module intervenes in the continuous monitoring task. The edge tracking component collects and correlates the current water temperature, contact voltage, heating plate current, power supply voltage, and the peak water temperature at the inflection point after power outage, generating a temperature control status sampling set that tracks the inertial performance after power outage. The logic judgment component confirms the complete power outage is effective and correlates and locks the peak temperature by cross-comparing the contact and current states, generating a power outage status judgment table. Finally, the iterative calibration component matches the corresponding residual heat occupancy record, calculates the error between the actual peak water temperature and the power-off water temperature, and completes the adaptive closed-loop learning of the feedforward compensation algorithm by dynamically correcting the occupancy feature parameters, outputting the globally optimal remote control scheme for the smart electric kettle.

[0077] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the technical solution.

Claims

1. A remote control method for a smart electric kettle based on the Internet of Things, characterized in that, Includes the following steps: S1: Based on the smart home water heating request, collect remote control information and control timing information, compare the control sequence number with the shadow sequence number, filter the control sequence number, and generate a remote water heating command; S2: Based on the remote water boiling command, calculate the water temperature and voltage fluctuations according to the thermoelectric operation information and sampling timing information of the networked electric kettle, and generate a kettle-end sampling message; S3: Based on the sampling message at the kettle end, obtain the water temperature time sequence and thermal change verification basis, compare the water temperature change with the lower boundary and upper boundary of thermal change, and generate a reliable water temperature record. S4: Based on the reliable water temperature record, collect cloud time and residual heat occupancy records, calculate water temperature interval, filter residual heat content according to water level scale, calculate target temperature and residual heat content, and generate temperature control execution command; S5: Based on the temperature control execution command, according to the thermoelectric power failure detection information of the networked electric kettle, compare the current water temperature with the power failure water temperature, calibrate the power failure result and residual heat occupation record, and generate a remote control scheme for the smart electric kettle.

2. The method for remote control of a smart electric kettle based on the Internet of Things according to claim 1, characterized in that, The remote water boiling command includes a heating start signal, boiling mode selection, and heat preservation time setting. The kettle-side sampling message includes sensor resistance and capacitance parameters, analog-to-digital conversion bit depth, and filtering delay time. The reliable water temperature record includes measurement deviation index, abnormal jump flag, and data validity weight. The temperature control execution command includes pulse width modulation parameters, temperature control dead zone range, and power attenuation slope. The intelligent electric kettle remote control scheme includes multi-stage heating curves, standby power consumption configuration, and fault self-recovery mechanism.

3. The method for remote control of a smart electric kettle based on the Internet of Things according to claim 1, characterized in that, The water temperature voltage fluctuation refers to the magnitude and deviation of the temperature detection voltage over time caused by changes in water temperature during the heating and sampling process of the networked electric kettle.

4. The method for remote control of a smart electric kettle based on the Internet of Things according to claim 1, characterized in that, The residual heat occupancy record refers to the record of the state in which the residual heat in the kettle can continue to affect the water temperature under different time, water level and temperature conditions after the electric kettle is powered off and heating stops.

5. The method for remote control of a smart electric kettle based on the Internet of Things according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Based on the smart home network water heating request, collect the control identity, water heating parameters and trigger timing fields in the remote water heating request, perform null value judgment and format verification, establish key value association according to the request session identifier, and generate a remote control parameter set; S102: Based on the remote control parameter set, arrange the request records in ascending order according to the trigger timestamp, assign incremental numbers to the arrangement positions, associate the request session identifier with the number, and generate a control sequence number mapping table; S103: Based on the control sequence number mapping table, collect the shadow control sequence number within the device shadow state, filter valid control records with control sequence numbers later than the shadow control sequence number, establish a control field mapping relationship, and generate a remote water boiling command.

6. The method for remote control of a smart electric kettle based on the Internet of Things according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Based on the remote water boiling command, collect the kettle end temperature control, electrical status and sampling time sequence fields, establish field value mapping according to the sampling time, and generate kettle end sampling parameter matrix; S202: Based on the sampling parameter matrix at the kettle end, arrange the water temperature and voltage according to the sampling time, calculate the difference between adjacent water temperature and voltage, compare the absolute value of the difference with the voltage fluctuation reference value, and generate a voltage fluctuation sampling sequence. S203: Based on the voltage fluctuation sampling sequence, associate the kettle end water level, drive, voltage, current and power supply status fields, establish message field mapping according to the sampling time, and generate kettle end sampling message.

7. The method for remote control of a smart electric kettle based on the Internet of Things according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Based on the kettle end sampling message, calculate the water temperature value in the sampling message and associate it with the sampling status field, associate the water temperature update time with the thermal change boundary range, and generate a reliable water temperature verification basis; S302: Based on the water temperature reliable verification basis, perform a sequential determination on the sampling time and the water temperature update time, retain records where the sampling time is not earlier than the water temperature update time, establish record markers according to the time difference, and generate a water temperature time sequence determination table. S303: Based on the water temperature time series determination table, calculate the difference between adjacent water temperature values ​​and compare it with the thermal change boundary range, retain the records within the thermal change boundary range, map the sampling time, water temperature value and reliable record mark, and generate a reliable water temperature record.

8. The method for remote control of a smart electric kettle based on the Internet of Things according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Based on the reliable water temperature record, collect cloud time and residual heat occupancy record, perform difference conversion between sampling time and cloud time, associate target temperature with water temperature value, and generate water temperature time parameter set; S402: Based on the water temperature aging parameter set, match the residual heat occupation records according to the water temperature aging, verify the relationship between the occupation start and end times and the sampling time, filter the hit occupation content, and generate a residual heat occupation matching table. S403: Based on the waste heat occupancy matching table, call the target temperature, calculate the difference between the target temperature and the waste heat occupancy content, establish a heating control field mapping according to the difference, and generate a temperature control execution instruction.

9. The method for remote control of a smart electric kettle based on the Internet of Things according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Based on the temperature control execution command, detect the current water temperature, contact voltage, heating plate current, power supply voltage, and peak water temperature after power failure, establish field value mapping according to the sampling time, and generate a temperature control status sampling set; S502: Based on the temperature control status sampling set, compare the power-off determination status of the contact voltage and the heating plate current, and generate a power-off status determination table by associating the current water temperature with the peak water temperature during power-off according to the comparison result. S503: Based on the power outage status determination table, match the residual heat occupancy record, calculate the difference between the peak water temperature during power outage and the current water temperature, correct the occupancy temperature field and occupancy time period field according to the difference, and generate a remote control scheme for the smart electric kettle.

10. A remote control system for an intelligent electric kettle based on the Internet of Things, characterized in that: The system is used to implement the IoT-based remote control method for smart electric kettles as described in any one of claims 1-9, comprising: The remote control serial number comparison and filtering module is used to implement S1: based on the smart home water heating request, collect remote control information and control timing information, compare the control serial number with the shadow serial number, filter the control serial number, and generate a remote water heating command. The kettle-end thermoelectric operation sampling message generation module is used to implement S2: based on the remote water boiling command, according to the thermoelectric operation information and sampling timing information of the networked electric kettle, calculate the water temperature and voltage fluctuations, and generate a kettle-end sampling message; The water temperature time-series thermal change reliable verification module is used to implement S3: based on the kettle end sampling message, obtain the water temperature time sequence and thermal change verification basis, compare the water temperature change with the lower boundary and upper boundary of thermal change, and generate a reliable water temperature record. The waste heat occupancy compensation temperature control execution module is used to implement S4: based on the reliable water temperature record, collect cloud time and waste heat occupancy record, calculate water temperature interval, filter waste heat content according to water level scale, calculate target temperature and waste heat content, and generate temperature control execution command; The power failure detection calibration and control scheme output module is used to implement S5: based on the temperature control execution command, according to the thermoelectric power failure detection information of the networked electric kettle, compare the current water temperature with the power failure water temperature, calibrate the power failure result and residual heat occupation record, and generate a remote control scheme for the smart electric kettle.

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