An electromagnetic oven data acquisition and processing system

Abstract

This invention relates to the field of data processing technology, specifically to a data acquisition and processing system for an induction cooker. The system includes a power jump identification module, a response segment correction module, a trend sampling analysis module, a temperature control anomaly judgment module, and a working condition balance data calibration module. This invention calculates the rate change values ​​between power sampling points within the operating cycle and filters segments with obvious abrupt changes. It then corrects the power lag response by combining the voltage change slope, effectively correcting the time offset between the control response and the electrical signal change. Based on the corrected sequence, it analyzes the temperature fluctuation trend and the temperature control offset direction, and matches the symmetry characteristics of the current waveform to form a temperature control anomaly state label set. This provides a basis for adjusting the output power based on the temperature rise rate, improving the data analysis's adaptability to actual working conditions and the execution accuracy of the control strategy, thereby enhancing the stable operation capability of electromagnetic heating equipment under complex heat load changes.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a data acquisition and processing system for an induction cooker. Background Technology

[0002] The field of data processing technology primarily involves the entire process of storing, transforming, calculating, and outputting acquired data. This field includes several key technology modules, such as data acquisition interface design, communication protocol implementation, data cleaning and formatting, feature value calculation, time-series data analysis, algorithm-driven data optimization, and result visualization. Systems applying data processing typically encompass multiple stages, including sensor data reception, data flow control, edge and cloud processing architecture construction, and data consistency and integrity maintenance. The goal of data processing is to improve information utilization efficiency through the organization and analysis of structured or unstructured data, providing a data foundation for subsequent decision-making, control, or prediction. This technology is applicable to various scenarios such as manufacturing automation, intelligent control, energy management, and the Industrial Internet.

[0003] The induction cooker data acquisition and processing system is used for real-time monitoring, data acquisition, and analysis of the induction cooker's operating parameters. The system collects key operating data such as voltage, current, temperature, and power factor generated during the induction cooker's operation, and processes and analyzes this data to identify operating status, determine fault modes, or optimize control strategies. Its main applications include improving the accuracy of induction cooker energy efficiency control, achieving intelligent fault diagnosis, supporting remote monitoring and management, and is suitable for system integration of smart home appliances and operational optimization of industrial-grade electromagnetic heating equipment.

[0004] Traditional processing systems struggle to accurately capture sudden power fluctuations during operation and lack methods for locating power jumps. When power response is delayed or when electrical signal changes are out of sync with control execution time, data timing misalignment can lead to judgment errors. In temperature control, the system relies solely on average or fluctuation ranges to determine the status, failing to distinguish whether the temperature control trend is continuously deviating or whether there are structural anomalies in the waveform. For example, delayed temperature rise caused by load thermal inertia during heating can easily be misjudged as a normal system response. Furthermore, the system lacks a method for identifying cross-features based on standard deviation and rate of change in operational stability assessments, resulting in inaccurate identification of output anomalies caused by load instability. This affects the timely correction of control strategies and the energy efficiency of equipment operation. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose an induction cooker data acquisition and processing system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an induction cooker data acquisition and processing system, the system comprising:

[0007] The power jump identification module obtains the active power sampling sequence within the operating cycle of the induction cooker control motherboard, filters points with a rate greater than the power change rate threshold as abrupt change candidate points, calls the trend direction of power value change before and after the index position of the abrupt change candidate point, identifies the jump point, and generates power jump segment labeling results.

[0008] The response correction module compares the difference between the theoretical value and the collected power value point by point based on the segment index range in the power jump segment labeling result. If the difference is greater than the hysteresis response limit, the sampling time is adjusted forward to synchronize with the voltage change slope to obtain the power delay segment correction sampling sequence.

[0009] The trend sampling analysis module calculates the temperature fluctuation value based on the power delay segment correction sampling sequence corresponding to the time period, marks abnormal fluctuation windows, records the window segments in which the temperature mean change direction is consistent in three consecutive windows, and obtains the temperature control trend sampling status list.

[0010] The temperature control anomaly judgment module calls the temperature control trend sampling status list, and uses the presence of waveform asymmetry characteristics three or more times in the current peak sampling sequence within the segment as a judgment condition to mark the anomaly level and obtain the temperature control anomaly segment label set within the sampling period.

[0011] The present invention improves upon the following: the power jump segment labeling result specifically includes a jump point index group, segment start and end timestamps, peak amplitude of the rate of change, and trend direction turning point label; the power delay segment correction sampling sequence includes a reconstructed power time series, sampling point synchronization markers, difference mapping before and after correction, and a time axis offset value list; the temperature control trend sampling status list specifically includes a window number, temperature mean change direction label, fluctuation anomaly window marker, and continuous offset sequence index; and the temperature control anomaly segment label set within the sampling period includes the anomaly segment time range, anomaly level identifier, current distortion type code, and number of continuous anomaly windows.

[0012] The present invention is improved in that the power transition identification module includes:

[0013] The sampling sequence construction submodule obtains the active power sampling sequence within the operating cycle of the induction cooker control motherboard, calls the power difference and sampling time interval between any two adjacent sampling values, forms a continuous sequence of difference ratios in chronological order, performs unified conversion on the numerical units in the sequence, establishes sample data of power change rate corresponding to continuous time, and generates a sampling segment change rate sequence.

[0014] The rate mutation extraction submodule, based on the change rate sequence of the sampling segment, calls the value of each sampling point in the rate sample data, judges the relationship between the value and the set power change rate threshold, filters the sampling point index with a rate greater than the threshold and records the time position, extracts the positive and negative directions of the power value change before and after the corresponding point, calculates the average change direction value in five adjacent data groups and judges whether a reversal has occurred, obtains the sampling point position that satisfies both the direction reversal condition and the amplitude mutation condition, and obtains the power mutation candidate point index group.

[0015] The jump segment labeling submodule calls each pair of adjacent abrupt change points in the power abrupt change candidate point index group, sequentially obtains the power change amplitude before and after sampling, calculates the average fluctuation amplitude between the two points, compares it with the set step amplitude standard value, determines whether it belongs to the amplitude abrupt change segment, filters point pairs that meet the conditions and marks the start and end sampling positions and timestamps, establishes a jump labeling field and constructs a complete record according to the index order, and generates the power jump segment labeling result.

[0016] The present invention is improved in that the response correction module includes:

[0017] Based on the start and end indices in the power jump section labeling results, the parameter extraction submodule extracts the current RMS value sequence, voltage RMS value sequence, and active power sampled value sequence at the corresponding sampling positions of the section, organizes the voltage, current, and power sampled data in chronological order, establishes the synchronous sampling set of the section, and generates the power section synchronous parameter sequence.

[0018] The theoretical power calculation submodule calls the current effective value sequence and voltage effective value sequence in the power segment synchronization parameter sequence, extracts the corresponding sampled values ​​of each group in sequence, calculates and obtains the theoretical active power estimate of each sampling point, maps according to the position structure of the original power sampling sequence, establishes the corresponding group of theoretical values ​​and collected values, and generates the theoretical power sequence of sampling points.

[0019] The data timing correction submodule compares the theoretical power sequence of the sampling points with the original power value sequence in the power segment synchronization parameter sequence point by point, calculates the difference between the two, and filters the sampling points whose difference is greater than the set hysteresis response limit. It extracts the voltage change trend in the corresponding sampling points, and adjusts the power sampling time point index synchronously with the voltage slope as the reference. After correcting the points that meet the conditions, it reorders them and reconstructs the corrected power data column according to the time axis to obtain the power delay segment corrected sampling sequence.

[0020] The present invention is improved in that the trend sampling analysis module includes:

[0021] The temperature window segmentation submodule corrects the time period corresponding to the sampling sequence based on the power delay segment, extracts the temperature sampling value sequence output by the thermistor sensor within the time period, divides the sequence into multiple continuous and non-overlapping time windows according to the set sampling window length, and records the start and end time index positions and corresponding temperature values ​​of the time windows to generate multi-window temperature sampling group data.

[0022] The fluctuation degree judgment submodule calls the data of each window in the multi-window temperature sampling group data, extracts the maximum, minimum and average temperature values ​​within the window, calculates the fluctuation degree value of each window, and determines whether it exceeds the temperature fluctuation range threshold. Windows with fluctuation degrees greater than the threshold are marked as abnormal, and a temperature fluctuation abnormal window index set is obtained.

[0023] The mean trend identification submodule calls the temperature fluctuation anomaly window index set, extracts the temperature sampling mean of each corresponding window in sequence, judges the direction of change in time series order, records whether the direction of mean change is consistent in every three consecutive windows, and filters the window segments that meet the condition of consistent continuous change to obtain the temperature control trend sampling status list.

[0024] The present invention is improved in that the temperature control anomaly detection module includes:

[0025] The waveform distortion screening submodule calls all window sequences in the temperature control trend sampling status list, extracts continuous segments that satisfy the consistent direction of temperature mean change for three consecutive windows and whose corresponding window numbers have been marked as abnormal temperature fluctuations, extracts the current peak sampling sequence within the corresponding time interval, identifies the positive and negative half-wave extreme points in each cycle, compares whether their symmetry difference is greater than the current distortion identification threshold, accumulates the number of cycles with asymmetry, and if the count value is greater than or equal to three, marks the corresponding segment and obtains the waveform distortion matching segment number list;

[0026] The anomaly level labeling submodule calls each segment number in the waveform distortion conformal segment number list, matches the corresponding start and end time, sets the anomaly labeling level identifier code within the same time interval, sets the level score based on the weighted integral result of the current asymmetry cycle count and the number of continuous offset windows in the segment, calculates the level value of each segment and binds it to the corresponding segment, and obtains the temperature control anomaly segment label set within the sampling period.

[0027] The present invention has an improvement, wherein the system further includes:

[0028] The operating condition balance data calibration module divides the intervals based on the timestamp of each segment in the temperature control anomaly segment label set within the sampling period, according to the current standard deviation, matches the distribution interval of the power change rate, and determines whether the combination falls within the standard reference range in the load stability identification table. If it does not match, the target output power of the time period is adjusted according to the direction of temperature rise rate change to obtain the operating condition correction dataset of the sampling segment.

[0029] The sampled section working condition correction dataset specifically refers to the corrected target power setting sequence, stability matching level label, adjustment amplitude record, and original and corrected difference values.

[0030] The present invention is improved in that the working condition balance data calibration module includes:

[0031] The data extraction submodule, based on the start and end timestamps of each segment in the temperature control anomaly segment label set within the sampling period, collects the current effective value sequence, output power sequence, and coil temperature sampling sequence within the corresponding time range from the equipment operation record. It then calculates the standard deviation of the current effective value, the difference sequence of adjacent power sampling points, and the temperature rise slope curve within the time period, respectively, and generates a set of operating condition sampling fluctuation parameters.

[0032] The stability matching judgment submodule calls the standard deviation of the effective value of the current in the sampling fluctuation parameter set of the working condition, divides it into three levels of stable, overload and light load according to the set current fluctuation range, matches the power change rate corresponding interval segment and judges whether it is consistent with the combination in the stability identification table, filters out mismatched combinations and extracts the time period index range to obtain the stability deviation time interval group.

[0033] The power regulation execution submodule calls the segment index in the stability deviation time interval group, determines the adjustment direction according to the positive or negative direction of the temperature rise slope corresponding to the time period, adjusts the original output power value sequence according to the set amplitude, and merges the corrections within the time period to generate a continuous power data group, and obtains the sampled segment working condition correction dataset.

[0034] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0035] In this invention, by calculating the rate change values ​​between power sampling points within the operating cycle and screening segments with obvious abrupt change characteristics, the accurate identification of power jump locations can be achieved. The power lag response is corrected by combining the voltage change slope, effectively correcting the time offset between the control response and the electrical signal change. Based on the corrected sequence, the temperature fluctuation trend and temperature control offset direction are analyzed, and a temperature control anomaly state label set is formed by matching the current waveform symmetry characteristics. The load state stability is calibrated by combining the standard deviation and the power change rate distribution interval. The output power is adjusted based on the temperature rise rate, enabling parallel identification and closed-loop regulation of multiple operating conditions such as power response lag, abnormal temperature control fluctuations, and unstable operating states. This improves the data analysis's adaptability to actual operating conditions and the execution accuracy of the control strategy, enhancing the stable operation capability of electromagnetic heating equipment under complex thermal load changes. Attached Figure Description

[0036] Figure 1 This is a system flowchart of the present invention;

[0037] Figure 2 This is a flowchart of the power transition identification module of the present invention;

[0038] Figure 3 This is a flowchart of the response segment correction module of the present invention;

[0039] Figure 4 This is a flowchart of the trend sampling and analysis module of the present invention;

[0040] Figure 5 This is a flowchart of the temperature control anomaly detection module of the present invention;

[0041] Figure 6 This is a flowchart of the working condition balance data calibration module of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0043] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0044] Please see Figure 1 The present invention provides a technical solution: an induction cooker data acquisition and processing system, the system including a power jump identification module, a response segment correction module, a trend sampling analysis module, a temperature control anomaly judgment module and an operating condition balance data calibration module;

[0045] The power jump identification module acquires the active power sampling sequence within the operating cycle of the induction cooker control motherboard, sequentially calculates the ratio of the power difference between any two points to the sampling interval, constructs a change rate sequence, and filters points with a rate greater than the power change rate threshold as a mutation candidate point. It calls the change trend direction of the power value before and after the mutation candidate point index position, and combines the average rate change value of the five sets of sampling points before and after to determine whether there is a reversal of positive and negative directions. If there is a reversal and the fluctuation amplitude difference exceeds the step amplitude standard value, it is determined that there is a jump at the point, extracts the point pairs that meet the jump conditions, and generates the power jump segment labeling result.

[0046] Active power refers to the energy actually consumed or output by the induction cooker per unit time, which can be obtained through a power sampling chip; the power change rate threshold refers to the limit of the rate of power change per unit time, usually measured in watts per second, and the reference is set as the maximum allowable change response of the equipment load; the step amplitude standard value refers to the minimum difference limit of the average value of power before and after a sudden change event, which is often set as 5%-10% of the rated power as the identification line.

[0047] The response correction module extracts the effective values ​​of current, voltage, and active power of the segment based on the segment index range in the power jump segment labeling results. It then uses the current and voltage values ​​to calculate the theoretical power value sequence of the segment and compares the difference between the theoretical value and the collected power value point by point. If the difference is greater than the hysteresis response limit, the sampling time is adjusted forward to synchronize with the voltage change slope. The corrected power sequence is then reassembled in the original time order to obtain the power delay segment corrected sampling sequence.

[0048] The hysteresis response limit is set as the reference for delay recognition sensitivity and is generally adjusted within the range of 3% to 5% of the rated power; the voltage change slope is the amount of voltage change per unit time, which represents the controller input response speed;

[0049] The trend sampling analysis module corrects the sampling sequence based on the power delay segment and the corresponding time period. It simultaneously extracts the temperature sampling value sequence output by the thermistor sensor within the time period, divides multiple time windows with a fixed sampling window length, calculates the temperature fluctuation degree value based on the temperature mean and the maximum and minimum values ​​of the interval in each window, and determines whether it exceeds the temperature fluctuation range threshold. It marks abnormal fluctuation windows, records window segments where the temperature mean changes in the same direction in three consecutive windows, and obtains a temperature control trend sampling status list.

[0050] Thermistor sensors are commonly used NTC type components, and their output resistance value changes with temperature; the temperature fluctuation range threshold is generally defined as fluctuation within ±1℃, which is judged as a relatively stable window.

[0051] The temperature control anomaly judgment module calls the temperature control trend sampling status list, filters out window segments marked as having a consistent continuous offset direction and abnormal fluctuation amplitude, and uses the presence of three or more waveform asymmetry features in the current peak sampling sequence within the segment as a judgment condition. If the condition is met, the start and end times of the corresponding segment are marked with an abnormality level label, and the temperature control anomaly segment label set within the sampling period is obtained.

[0052] Waveform asymmetry refers to the deviation of the peak amplitude of the positive and negative half-cycles in the current period waveform exceeding 10%, which is used to identify nonlinear responses caused by uneven load or overheating; the current peak sampling sequence is extracted from the whole cycle peak hold module or calculated by ADC peak;

[0053] The operating condition balance data calibration module extracts the current effective value standard deviation sequence, output power change rate sequence, and temperature rise rate curve from the equipment data record based on the timestamp of each segment in the temperature control anomaly segment label set within the sampling period. It divides the interval according to the current standard deviation, matches the distribution interval of the power change rate, and determines whether the combination falls within the specification comparison range in the load stability identification table. If it does not match, it adjusts the target output power of the time period according to the direction of temperature rise rate change to obtain the sampling segment operating condition correction dataset.

[0054] The standard deviation of the effective current reflects whether the load is stable; the temperature rise rate is calculated as the temperature rise of the coil temperature measuring point per unit time; the load stability identification table is preset by the manufacturer based on experimental data, defining the allowable combination range between the power change rate and current fluctuation.

[0055] The power jump segment labeling results specifically include jump point index group, segment start and end timestamps, peak amplitude of change rate, and trend direction turning point label. The power delay segment correction sampling sequence includes reconstructed power time series, sampling point synchronization mark, difference mapping before and after correction, and time axis offset value list. The temperature control trend sampling status list specifically includes window number, temperature mean change direction label, fluctuation abnormal window mark, and continuous offset sequence index. The temperature control abnormal segment label set within the sampling period includes abnormal segment time range, abnormal level identifier, current distortion type code, and number of continuous abnormal windows. The sampling segment operating condition correction dataset specifically refers to the corrected target power setting sequence, stability matching level label, adjustment amplitude record, and original and corrected difference value.

[0056] Please see Figure 2 The power transition recognition module includes:

[0057] The sampling sequence construction submodule obtains the active power sampling sequence within the operating cycle of the induction cooker control motherboard, calls the power difference and sampling time interval between any two adjacent sampling values, forms a continuous sequence of difference ratios in chronological order, performs unified conversion on the numerical units in the sequence, establishes sample data of power change rate corresponding to continuous time, and generates a sampling segment change rate sequence.

[0058] The sampling sequence construction submodule acquires the active power sampling sequence within the operating cycle of the induction cooker control motherboard. During a complete heating cycle of the induction cooker, the microcontroller of the control motherboard samples the power at a fixed frequency every [number of sampling intervals]. The active power is sampled once per second to obtain a series of power values, for example, in... Measured in seconds W, in Measured in seconds W, in Measured in seconds W, in Measured in seconds W arranges these acquired power values ​​in chronological order to form an active power sampling sequence, and calls the power difference and sampling time interval between any two adjacent sample values ​​in the sequence, for example, calling... and The difference W, call and The difference W, call and The difference W, divide these differences by the fixed sampling time interval. Seconds, to obtain the rate of change of power per second, for example W / s, W / s, W / s, these differences are arranged into a continuous series in chronological order. The units of the values ​​in the series are then uniformly converted, since all values ​​are in the International System of Units (SI). As a unit, this step allows the numerical values ​​to be directly applied to subsequent processing without additional conversion, establishing sample data of the power change rate corresponding to continuous time, and generating a sequence of change rates for the sampled segments. The values ​​in this sequence are... This is used for subsequent rate mutation extraction.

[0059] The rate mutation extraction submodule, based on the change rate sequence of the sampling segment, calls the value of each sampling point in the rate sample data, judges the relationship between the value and the set power change rate threshold, filters the sampling point index with a rate greater than the threshold and records the time position, extracts the positive and negative directions of the power value change before and after the corresponding point, calculates the average change direction value in five adjacent data groups and judges whether a reversal has occurred, obtains the sampling point position that satisfies both the direction reversal condition and the amplitude mutation condition, and obtains the power mutation candidate point index group.

[0060] The rate mutation extraction submodule, based on the rate change sequence of the sampling segment, calls the value of each sampling point in the rate sample data and compares it with a set power change rate threshold to determine the relationship between the values. This power change rate threshold must be set based on historical power change rate data of the induction cooker under stable operating conditions; for example, by observing the power change rate under stable heating conditions of the induction cooker. In each cycle, data is collected. These power change rate data points are for this The absolute values ​​of each data point are statistically analyzed, and their mean is calculated. and standard deviation The threshold is set to For example, when the mean of the absolute value of the power change rate is statistically obtained, W / s, standard deviation is W / s, then the power change rate threshold is set to W / s, filtering rate greater than The sampling point index (W / s) is recorded and the time position is recorded, for example, in the sampling rate sequence. In the middle, the rate is greater than The sampling point index of W / s is (speed is) W / s) (speed is) W / s) and (speed is) W / s), the recorded time positions are respectively s、 s and s, extract the positive and negative directions of the power value change before and after the corresponding point, in the index At that point, the power comes from W becomes W, the direction of change is positive, in the index At that point, the power comes from W becomes W, the direction of change is positive, in the index At that point, the power comes from W becomes W represents a negative direction of change. The average direction of change is calculated across five adjacent data sets, and a reversal is determined. For example, for an index... At the sampling point, analyze the five sets of data before and after it, i.e., the index. The corresponding power change direction is set as Calculate its average direction of change value. Because this value is derived from the average of the previous five groups (set as...). When the value becomes negative, it can be determined that a direction reversal has occurred. The sampling point location that satisfies both the direction reversal condition and the amplitude change condition can be obtained, thus obtaining the power change candidate point index group.

[0061] The jump section labeling submodule calls each pair of adjacent abrupt change points in the power abrupt change candidate point index group, sequentially obtains the power change amplitude before and after sampling, calculates the average fluctuation amplitude between the two points, compares it with the set step amplitude standard value, determines whether it belongs to the amplitude abrupt change section, filters the point pairs that meet the conditions and marks the start and end sampling positions and timestamps, establishes the jump labeling field and constructs a complete record according to the index order, and generates the power jump section labeling result;

[0062] The jump segment labeling submodule calls each pair of adjacent jump points in the power jump candidate point index group; for example, it calls the index... and index The points formed are used to sequentially obtain the power change amplitude before and after sampling, for the index At that point, the power change before and after sampling was... W, for indexes At that point, the power change before and after sampling was... W, and calculate the average fluctuation range between the two points. W is compared with a set step amplitude standard value, which is a statistical measure of the power change amplitude caused by load fluctuations in the induction cooker during stable heating mode. Over a period of time, the average value of all power changes is calculated, and this average value is set as the standard value for the step amplitude. For example, the average power change amplitude is calculated as follows: W, then the standard value for the step amplitude is set to W, determining the average fluctuation range Whether W belongs to the amplitude abrupt change segment depends on... W is much larger than W determines that the point pair belongs to the amplitude change segment, filters point pairs that meet the conditions, and marks the start and end sampling positions and timestamps, such as marking the index. (timestamp) s) and index (timestamp) For a valid point pair, establish a jump label field and construct a complete record according to the index order to generate the power jump segment labeling result.

[0063] Please see Figure 3 The response correction module includes:

[0064] The parameter extraction submodule extracts the current RMS value sequence, voltage RMS value sequence, and active power sample value sequence at the corresponding sampling position of the power jump section based on the start and end index in the power jump section labeling result. It then organizes the voltage, current, and power sample data in chronological order to establish the synchronous sampling set of the section and generate the power section synchronous parameter sequence.

[0065] The parameter extraction submodule extracts the start and end indices from the power transition segment annotation results. For example, based on the annotation result {start_index: 1, end_index: 5, start_time: 0.05s, end_time: 0.25s}, it extracts the current RMS value sequence, voltage RMS value sequence, and active power sample value sequence at the corresponding sampling positions of the segment. s, s, s, s, At these five time points, the effective value of the current was extracted from the equipment operation record. RMS voltage and original power sample value The voltage, current and power sampling data are organized in chronological order, for example, into a three-column data table, and a synchronous sampling set for the section is established, as shown in Table 1, to generate the power section synchronous parameter sequence.

[0066] Table 1 Synchronization Parameter Sequence for Power Jump Section

[0067]

[0068] The theoretical power calculation submodule calls the current RMS value sequence and voltage RMS value sequence from the power segment synchronization parameter sequence, extracts the corresponding sampled values ​​for each group in sequence, and uses the formula:

[0069] ;

[0070] The theoretical active power estimate of each sampling point is obtained by calculation. Based on the positional structure of the original power sampling sequence, a mapping is performed to establish a corresponding group of theoretical values ​​and collected values, and a theoretical power sequence of sampling points is generated.

[0071] in, Indicates the first Theoretical active power estimate for each sampling point Indicates the first The effective voltage value at each time point is derived from the sampled values ​​of the voltage sampling chip. Indicates the first The effective current value at each time point is derived from the converted sampled value of the current transformer output. Indicates the first The effective voltage value at each time point Indicates the first The effective value of the current at each time point is the base of the natural logarithm. Indicates the first The normalized value of the load dynamic change at each time point is calculated as the ratio of the coil temperature change rate to the current current value. The data source is the NTC sensor temperature rise sampling sequence and the current current sample value. The hysteresis response limit is set as the error tolerance set by the system load control strategy. It is derived from the minimum response offset statistics between voltage, current and power under stable operating conditions of the equipment. Usually, the average power change rate of the equilibrium interval of the three under this state is multiplied by the response delay time window constant.

[0072] The theoretical power calculation submodule calls the current RMS value sequence and voltage RMS value sequence from the power segment synchronization parameter sequence, and extracts the corresponding sample values ​​for each group in sequence, such as extracting the sampling point index. time V, A, and the previous sampling point time V, A, using the formula:

[0073] ;

[0074] The theoretical active power estimate for each sampling point is obtained through calculation, where, Indicates the first The theoretical active power estimate for each sampling point represents the theoretical active power that the induction cooker should output at the current moment, after considering the dynamic changes in voltage and current and the influence of load characteristics. This value serves as a benchmark for subsequent comparison with the original collected power values. Indicates the first The effective voltage value at each time point, for example V is derived from the sampled value of the voltage sampling chip, obtained by sampling the mains voltage waveform and calculating its effective value. Indicates the first The effective value of the current at each time point, for example A is derived from the converted sampled value of the current transformer output, obtained by sampling the coil current waveform and calculating its effective value. Indicates the first The effective voltage value at each time point, for example V, Indicates the first The effective value of the current at each time point, for example A, Let be the base of the natural logarithm, and let its value be . , Indicates the first The normalized value of load dynamic change at each time point is calculated as the ratio of the coil temperature change rate to the current value. For example, at... At that time, the thermistor sensor is s and The coil temperatures measured by s were respectively and The rate of change of coil temperature is Current current value A, then The calculated value is This value reflects the rate of change of load temperature at the current power. The advantage of the formula is that it uses the product of the voltage change rate and the RMS current with the voltage change rate as a power correction term, and introduces a normalized value for load dynamic changes. As the independent variable of the logarithmic function, it can finely evaluate the theoretical power of the induction cooker under dynamic load response. This allows the theoretical value to more accurately reflect the instantaneous behavior of the device in the power jump range, thus providing a reliable benchmark for subsequent data timing correction. Mapping is performed based on the positional structure of the original power sampling sequence; for example, the calculated... Values ​​mapped to indices of the original sequence At this point, establish a correspondence between theoretical values ​​and collected values, and generate a theoretical power sequence of sampling points for power jump segments. For example, for the index... Theoretical power at time Perform the calculation:

[0075] W, repeat this process to obtain the complete theoretical power sequence of the sampling points.

[0076] The data timing correction submodule compares the theoretical power sequence of the sampling points with the original power value sequence in the power segment synchronization parameter sequence point by point, calculates the difference between the two, and filters the sampling points whose difference is greater than the set hysteresis response limit. It extracts the voltage change trend in the corresponding sampling points and adjusts the power sampling time point index synchronously with the voltage slope as the reference. After correcting the points that meet the conditions, it reorders them and reconstructs the corrected power data column according to the time axis to obtain the power delay segment corrected sampling sequence.

[0077] The data timing correction submodule compares the theoretical power sequence at each sampling point with the original power value sequence in the power segment synchronization parameter sequence point by point. For example, it compares the theoretical power sequence calculated in the previous step with the original power value sequence at each sampling point. W and the original power sequence W is compared, the difference between the two is calculated, and sampling points with a difference greater than a set hysteresis response limit are selected. This hysteresis response limit is calculated by statistically analyzing the minimum response offset between voltage, current, and power under stable operating conditions, and calculating the average power change rate within the equilibrium range of the three under that condition. For example, it is... W / s, then multiplied by the response delay time window constant, for example, this constant is W / s. If s, then the hysteresis limit value is W extracts the voltage change trend at the corresponding sampling points, for example, at the index. At the original power W and theoretical power The difference of W is W, this value is greater than the limit of error. W meets the screening criteria, and the voltage change trend at the corresponding point is extracted, i.e., the voltage slope. The voltage changes from... V becomes V, the voltage slope is negative, and the power sampling time point index is adjusted synchronously based on the voltage slope. For sampling points where the difference is greater than the limit and the voltage slope is negative, the power sampling time point index is adjusted forward, for example, index... The corresponding power sampling time point is determined by s adjusted to s, after correcting the points that meet the conditions, reorder them, reconstruct the corrected power data column according to the time axis, and obtain the corrected sampling sequence of the power delay segment.

[0078] Please see Figure 4 The trend sampling analysis module includes:

[0079] The temperature window segmentation submodule corrects the time period corresponding to the sampling sequence based on the power delay segment, extracts the temperature sampling value sequence output by the thermistor sensor within the time period, divides the sequence into multiple continuous and non-overlapping time windows according to the set sampling window length, and records the start and end time index positions and corresponding temperature values ​​of the time windows to generate multi-window temperature sampling group data.

[0080] The temperature window partitioning submodule corrects the time period corresponding to the sampling sequence based on the power delay segment, for example, the time period starts from... s to s, extract the temperature sampling value sequence output by the thermistor sensor within a time period, for example, obtain the temperature sequence. The sequence is divided into multiple consecutive, non-overlapping time windows according to a set sampling window length. The sampling window length is set based on the minimum operating cycle and temperature control response time of the induction cooker, for example, set to... seconds, to cover at least two power sampling points, and divide the sequence into multiple windows, for example, the first window is s to s, including temperature value The second window is s to s, including temperature value It records the start and end time indices of the time window and their corresponding temperature values. For example, the index of window 1 is recorded as follows: Temperature value Generate multi-window temperature sampling group data.

[0081] The fluctuation level assessment submodule calls data from each window in the multi-window temperature sampling group data, extracts the maximum, minimum, and average temperature values ​​within each window, and uses the following formula:

[0082] ;

[0083] The algorithm calculates the fluctuation level of each window and determines whether it exceeds the temperature fluctuation range threshold. Windows with fluctuation levels greater than the threshold are marked as abnormal, resulting in a set of temperature fluctuation abnormal window indexes.

[0084] in, Indicates the first The temperature fluctuation value of each window is used to measure the temperature stability inside the window. Indicates the first The ratio of the maximum value to the mean value of the temperature samples within a window. Indicates the first The ratio of the minimum temperature sample value to the mean temperature sample value within a window. Indicates the first The normalized value is the average temperature sampled from each window, and the normalization method is to divide the average temperature of the current window by the average temperature of the entire window set. This represents the normalized value of the temperature reference, calculated by dividing the median of the set of mean temperatures across all windows by the maximum value of the temperature sample. Indicates the first The variance of temperature sampling in each window is calculated as the average of the squared differences between the mean and the mean of all sampling points in that window. Indicates the first Number of temperature sampling points in each window Indicates the first The normalized average value of the rate of temperature change of the sampling points within a window is calculated by dividing the absolute value of the temperature difference of consecutive sampling points by the standard deviation of the time interval. The temperature fluctuation range threshold is set by extracting the temperature window fluctuation value under stable power conditions in multiple consecutive operating cycles, calculating its mean and standard deviation, and using the mean plus 1.5 times the standard deviation as the temperature fluctuation range threshold.

[0085] The fluctuation level judgment submodule calls the data from each window in the multi-window temperature sampling group data, extracts the maximum and minimum temperature values ​​and the average of all values ​​within the window. For example, for the first window... maximum value Minimum value ,average value The formula used is:

[0086] ;

[0087] The calculation obtains the fluctuation level value for each window, where, Indicates the first The temperature fluctuation value for each window represents a quantitative assessment of the temperature stability within that window. This value is compared with a set threshold to determine whether the temperature is abnormal. Indicates the first The ratio of the maximum value to the mean value of the temperature samples within a window. For example, for window 1, the mean value of the temperature samples is... , , Indicates the first The ratio of the minimum temperature sample value to the mean temperature sample value within a window, for example , Indicates the first The normalized value of the temperature sample mean for each window is calculated by dividing the current window's mean by the average temperature of the entire set of windows. For example, the average of the set of temperature mean values ​​for all windows is... ,but , This represents the normalized value of the temperature reference, calculated by dividing the median of the set of temperature means across all windows by the maximum value of the temperature samples. For example, the median of the set of temperature means across all windows is... The maximum value of the temperature sample is ,but , Indicates the first The variance of temperature sampling in each window is calculated as the average of the squared differences between the mean and the mean of all sampling points in that window. For example... , Indicates the first The number of temperature sampling points in each window, for example , Indicates the first The normalized average rate of temperature change at each sampling point within a window is calculated by dividing the absolute average of the temperature differences between consecutive sampling points by the standard deviation of the time interval. For example, for window 1, the absolute average of the temperature differences between consecutive sampling points is... The standard deviation of the time interval is (Because the time interval is fixed) s), the standard deviation can be set to ,but The advantage of this formula lies in its ability to comprehensively assess the amplitude and frequency of temperature fluctuations by weighting multiple parameters, including the ratio of maximum to minimum values, the deviation of the mean from the benchmark, variance, and the number and rate of change of sampling points. This allows for more accurate identification of subtle temperature anomalies. The temperature fluctuation range threshold is set by extracting the temperature window fluctuation values ​​during multiple consecutive stable operating cycles of the induction cooker, calculating their mean and standard deviation, and then using the mean plus... Using multiples of the standard deviation as a threshold, for example, the statistical mean is... The standard deviation is The threshold is For fluctuations greater than The window is marked as abnormal, for example, the fluctuation value of window 1 is calculated:

[0088] ;

[0089] because Less than the threshold The window was not marked as abnormal, and the temperature fluctuation abnormal window index set was obtained.

[0090] The mean trend identification submodule calls the temperature fluctuation anomaly window index set, extracts the temperature sampling mean of each corresponding window in turn, judges the direction of change in the order of time series, records whether the direction of mean change is consistent in every three consecutive windows, and filters the window segments that meet the condition of continuous and consistent change to obtain the temperature control trend sampling status list.

[0091] The mean trend identification submodule calls the temperature fluctuation anomaly window index set, for example, calling the index as For abnormal windows, extract the average temperature sample value of each corresponding window in sequence, for example, window The mean is ,window The mean is ,window The mean is Determine the direction of change in chronological order within the window. To the window The mean from Rise to The direction of change is positive, in the window. To the window The mean from Rise to The direction of change is positive. Within every three consecutive windows, it is recorded whether the direction of change of the mean is consistent. For example, for a window... The mean changes in the direction of the changes are all positive. We determine that their directions are consistent and filter out window segments that meet the condition of continuous and consistent changes. For example, we filter out windows... As a paragraph that meets the conditions, obtain the list of temperature control trend sampling states.

[0092] Please see Figure 5 The temperature control anomaly detection module includes:

[0093] The waveform distortion filtering submodule calls all window sequences in the temperature control trend sampling status list, extracts continuous segments that satisfy the consistent direction of the average temperature change of three consecutive windows and whose corresponding window numbers have been marked as abnormal temperature fluctuations, extracts the current peak sampling sequence within the corresponding time interval, identifies the positive and negative half-wave extreme points in each cycle, compares whether their symmetry difference is greater than the current distortion identification threshold, accumulates the number of cycles with asymmetry, and if the count value is greater than or equal to three, marks the corresponding segment and obtains the waveform distortion matching segment number list;

[0094] The waveform distortion filtering submodule calls all window sequences in the temperature control trend sampling status list, for example, calling {start_window: 5, end_window: 7, direction: positive}, to extract continuous segments that meet the condition that the average temperature change direction is consistent for three consecutive windows and the corresponding window number has been marked as abnormal temperature fluctuation. The list itself already meets this condition. It then extracts the current peak sampling sequence within the corresponding time interval, for example, within the window... corresponding Within seconds, each The current peak value is sampled every second to obtain the current peak value sequence, and the positive and negative half-wave extreme points within each cycle are identified, for example, in Within a 1-second AC cycle, the positive and negative peak points are identified, and their symmetry difference is compared to determine if it exceeds the current distortion detection threshold. This threshold is set under stable operating conditions of the induction cooker. The peak differences between the positive and negative half-waves of each AC cycle are statistically analyzed, and their mean and standard deviation are calculated. A threshold is set as the mean plus... Multiple standard deviations, for example, the statistical mean is 1 / 2. A, standard deviation is A, then the threshold is A, the cumulative number of asymmetric cycles, for example in Within a time period of seconds, there are a total of One exchange cycle, of which The difference between the peak values ​​of the positive and negative half-waves within a period exceeded A, if the count value is greater than or equal to three, then mark the corresponding paragraph, because... Greater than or equal to The paragraph is marked, and a list of waveform distortion matching segment numbers is obtained.

[0095] The anomaly level labeling submodule calls each segment number in the waveform distortion conformal segment number list, matches the corresponding start and end time, sets the anomaly labeling level identifier code within the same time interval, sets the level score based on the weighted integral result of the current asymmetry cycle count and the number of continuous offset windows in the segment, calculates the level value of each segment and binds it to the corresponding segment, and obtains the temperature control anomaly segment label set within the sampling period.

[0096] The anomaly level labeling submodule calls each segment number in the waveform distortion conformation segment number list, for example, calling segment number 1. For each paragraph, a corresponding start and end time is matched, and an anomaly level identifier code is set within the same time interval. For example, the identifier code can be set to LEVEL_1. The level score is set based on the weighted integral result of the current asymmetry cycle count and the number of continuous offset windows in the paragraph. The weighted integral is calculated as follows: Level Score = The weighting coefficient and These reflect the relative importance of current distortion and the duration of temperature control trends, respectively. For example, based on historical fault data analysis, settings can be configured... , Calculate the rating value for each segment and bind it to the corresponding segment. For example, the asymmetric cycle count is... The number of consecutive offset windows is Then the grade score = Rank scores Bind to paragraph Obtain the label set of temperature control anomaly segments within the sampling period.

[0097] Please see Figure 6 The operating condition balance data calibration module includes:

[0098] The data extraction submodule is based on the start and end timestamps of each segment in the temperature control anomaly segment label set within the sampling period. It collects the current effective value sequence, output power sequence and coil temperature sampling sequence within the corresponding time range from the equipment operation record. It calculates the standard deviation of the current effective value, the difference sequence of adjacent power sampling points and the temperature rise slope curve within the time period, respectively, and generates a set of operating condition sampling fluctuation parameters.

[0099] The operating condition balance data calibration module is based on the start and end timestamps of each segment in the temperature control anomaly segment label set within the sampling period. For example, based on the timestamps corresponding to the label set {start_window: 5, end_window: 7}, it collects the current effective value sequence, output power sequence and coil temperature sampling sequence within the corresponding time range in the equipment operation record. It calculates the standard deviation of the current effective value, the difference sequence of adjacent power sampling points and the temperature rise slope curve within the time period, respectively, and generates the operating condition sampling fluctuation parameter set, as shown in Table 2.

[0100] Table 2 Set of Sampling Fluctuation Parameters for Abnormal Section Operating Conditions

[0101]

[0102] The stability matching judgment submodule calls the standard deviation of the effective value of the current in the sampling fluctuation parameter set of the working condition, divides it into three levels of stable, overload and light load according to the set current fluctuation range, matches the power change rate corresponding interval segment and judges whether it is consistent with the combination in the stability identification table, filters out mismatched combinations and extracts the time period index range to obtain the stability deviation time interval group.

[0103] The stability matching judgment submodule calls the standard deviation of the effective value of the current in the operating condition sampling fluctuation parameter set, for example, by calling... A. Based on the set current fluctuation range, it is divided into three levels: stable, overload, and light load. This current fluctuation range is obtained by conducting experiments under different load conditions and statistically analyzing the current standard deviation. For example, when the induction cooker is heating normally and stably, the current standard deviation is usually within a certain range. Between A and B, this is classified as a stability level. When the load is too large (such as an uneven pot bottom or coil malfunction), the current standard deviation will increase significantly. Classified as overload level A, when the load is too small (such as dry heating or pot removal), the standard deviation of the current is usually less than [value missing]. Classified as light load, A After matching the overload level, the corresponding power change rate interval is checked to see if it matches the combination in the stability identification table. For example, under the overload level, the corresponding power change rate interval is usually characterized by large fluctuations, such as greater than [a certain value]. W / s, but at this overload level, the rate of power change is actually very small, for example less than If the power change rate is within a certain range (W / s), the combination is considered mismatched. The stability identification table, for example, defines "standard deviation of stable current - small power fluctuation," "standard deviation of overload current - large power fluctuation," and "standard deviation of light load current - small power fluctuation" as consistent combinations. Mismatched combinations are filtered out, and the time period index range is extracted. For example, if a mismatched combination exists with both "standard deviation of overload current" and "small fluctuation in power change rate," the corresponding time period index is extracted. Obtain the stability deviation time interval group.

[0104] The power regulation execution submodule calls the segment index in the stability deviation time interval group, determines the adjustment direction based on the positive or negative direction of the temperature rise slope corresponding to the time period, adjusts the original output power value sequence according to the set amplitude, corrects them separately within the time period, merges them to generate a continuous power data group, and obtains the sampled segment working condition correction dataset.

[0105] The power regulation execution submodule calls the segment index in the stability deviation time interval group, for example, calling the index as... For a given time period, the adjustment direction is determined by the positive or negative direction of the temperature rise slope corresponding to that time period. For example, in the index... The slope curve of the temperature rise during the corresponding time period is as follows: A positive slope indicates a continuously rising temperature, suggesting a negative adjustment direction, i.e., reducing power. The original output power value is adjusted according to a set increment, which is a preset adjustment step size by the induction cooker control system based on the temperature change rate and power level. For example, it might be set to the current power value. ,Right now At W power, the adjustment range is W, after being corrected separately within a time period, is merged to generate a continuous power data set. For example, the time period... Original power value within according to Make corrections, among which The adjusted power data is as follows, based on the set adjustment range. W, W, W merges these corrected data into the original data column to obtain the sampled section working condition correction dataset.

[0106] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A data acquisition and processing system for an induction cooker, characterized in that, The system includes: The power jump identification module acquires the active power sampling sequence within the operating cycle of the induction cooker control motherboard, filters points whose power change rate is greater than the power change rate threshold as abrupt change candidate points, calls the trend direction of power value change before and after the index position of the abrupt change candidate point, identifies the jump point, and generates power jump segment labeling results. The response correction module compares the difference between the theoretical value and the collected power value point by point based on the segment index range in the power jump segment labeling result. If the difference is greater than the hysteresis response limit, the sampling time is adjusted forward to synchronize with the voltage change slope to obtain the power delay segment correction sampling sequence. The trend sampling analysis module calculates the temperature fluctuation value based on the power delay segment correction sampling sequence corresponding to the time period, marks abnormal fluctuation windows, records the window segments in which the temperature mean change direction is consistent in three consecutive windows, and obtains the temperature control trend sampling status list. The temperature control anomaly judgment module calls the temperature control trend sampling status list, and uses the presence of waveform asymmetry three or more times in the current peak sampling sequence within the segment as a judgment condition to mark the anomaly level and obtain the temperature control anomaly segment label set within the sampling period. The power jump segment labeling results specifically include jump point index group, segment start and end timestamps, peak amplitude of change rate, and trend direction turning point label. The power delay segment correction sampling sequence includes reconstructed power time series, sampling point synchronization mark, difference mapping before and after correction, and time axis offset value list. The temperature control trend sampling status list specifically includes window number, temperature mean change direction label, fluctuation abnormal window mark, and continuous offset sequence index. The temperature control abnormal segment label set within the sampling period includes abnormal segment time range, abnormal level identifier, current distortion type code, and number of continuous abnormal windows.

2. The induction cooker data acquisition and processing system according to claim 1, characterized in that, The power transition identification module includes: The sampling sequence construction submodule obtains the active power sampling sequence within the operating cycle of the induction cooker control motherboard, calls the power difference and sampling time interval between any two adjacent sampling values, forms a continuous sequence of difference ratios in chronological order, performs unified conversion on the numerical units in the sequence, establishes sample data of power change rate corresponding to continuous time, and generates a sampling segment change rate sequence. The rate mutation extraction submodule, based on the change rate sequence of the sampling segment, calls the value of each sampling point in the rate sample data, judges the relationship between the value and the set power change rate threshold, filters the sampling point index with a rate greater than the threshold and records the time position, extracts the positive and negative directions of the power value change before and after the corresponding point, calculates the average change direction value in five adjacent data groups and judges whether a reversal has occurred, obtains the sampling point position that satisfies both the direction reversal condition and the amplitude mutation condition, and obtains the power mutation candidate point index group. The jump segment labeling submodule calls each pair of adjacent abrupt change points in the power abrupt change candidate point index group, sequentially obtains the power change amplitude before and after sampling, calculates the average fluctuation amplitude between the two points, compares it with the set step amplitude standard value, determines whether it belongs to the amplitude abrupt change segment, filters point pairs that meet the conditions and marks the start and end sampling positions and timestamps, establishes a jump labeling field and constructs a complete record according to the index order, and generates the power jump segment labeling result.

3. The induction cooker data acquisition and processing system according to claim 2, characterized in that, The response correction module includes: Based on the start and end indices in the power jump section labeling results, the parameter extraction submodule extracts the current RMS value sequence, voltage RMS value sequence, and active power sampled value sequence at the corresponding sampling positions of the section, organizes the voltage, current, and power sampled data in chronological order, establishes the synchronous sampling set of the section, and generates the power section synchronous parameter sequence. The theoretical power calculation submodule calls the current effective value sequence and voltage effective value sequence in the power segment synchronization parameter sequence, extracts the corresponding sampled values ​​of each group in sequence, calculates and obtains the theoretical active power estimate of each sampling point, maps according to the position structure of the original power sampling sequence, establishes the corresponding group of theoretical values ​​and collected values, and generates the theoretical power sequence of sampling points. The data timing correction submodule compares the theoretical power sequence of the sampling points with the original power value sequence in the power segment synchronization parameter sequence point by point, calculates the difference between the two, and filters the sampling points whose difference is greater than the set hysteresis response limit. It extracts the voltage change trend in the corresponding sampling points, and adjusts the power sampling time point index synchronously with the voltage slope as the reference. After correcting the points that meet the conditions, it reorders them and reconstructs the corrected power data column according to the time axis to obtain the power delay segment corrected sampling sequence.

4. The induction cooker data acquisition and processing system according to claim 3, characterized in that, The trend sampling and analysis module includes: The temperature window segmentation submodule corrects the time period corresponding to the sampling sequence based on the power delay segment, extracts the temperature sampling value sequence output by the thermistor sensor within the time period, divides the sequence into multiple continuous and non-overlapping time windows according to the set sampling window length, and records the start and end time index positions and corresponding temperature values ​​of the time windows to generate multi-window temperature sampling group data. The fluctuation degree judgment submodule calls the data of each window in the multi-window temperature sampling group data, extracts the maximum, minimum and average temperature values ​​within the window, calculates the fluctuation degree value of each window, and determines whether it exceeds the temperature fluctuation range threshold. Windows with fluctuation degrees greater than the threshold are marked as abnormal, and a temperature fluctuation abnormal window index set is obtained. The mean trend identification submodule calls the temperature fluctuation anomaly window index set, extracts the temperature sampling mean of each corresponding window in sequence, judges the direction of change in time series order, records whether the direction of mean change is consistent in every three consecutive windows, and filters the window segments that meet the condition of consistent continuous change to obtain the temperature control trend sampling status list.

5. The induction cooker data acquisition and processing system according to claim 4, characterized in that, The temperature control anomaly detection module includes: The waveform distortion screening submodule calls all window sequences in the temperature control trend sampling status list, extracts continuous segments that satisfy the consistent direction of temperature mean change for three consecutive windows and whose corresponding window numbers have been marked as abnormal temperature fluctuations, extracts the current peak sampling sequence within the corresponding time interval, identifies the positive and negative half-wave extreme points in each cycle, compares whether their symmetry difference is greater than the current distortion identification threshold, accumulates the number of cycles with asymmetry, and if the count value is greater than or equal to three, marks the corresponding segment and obtains the waveform distortion matching segment number list; The anomaly level labeling submodule calls each segment number in the waveform distortion conformal segment number list, matches the corresponding start and end time, sets the anomaly labeling level identifier code within the same time interval, sets the level score based on the weighted integral result of the current asymmetry cycle count and the number of continuous offset windows in the segment, calculates the level value of each segment and binds it to the corresponding segment, and obtains the temperature control anomaly segment label set within the sampling period.

6. The induction cooker data acquisition and processing system according to claim 1, characterized in that, The system also includes: The operating condition balance data calibration module divides the intervals based on the timestamp of each segment in the temperature control anomaly segment label set within the sampling period, according to the current standard deviation, matches the distribution interval of the power change rate, and determines whether the combination falls within the standard reference range in the load stability identification table. If it does not match, the target output power of the time period is adjusted according to the direction of temperature rise rate change to obtain the operating condition correction dataset of the sampling segment. The sampled section working condition correction dataset specifically refers to the corrected target power setting sequence, stability matching level label, adjustment amplitude record, and original and corrected difference values.

7. The induction cooker data acquisition and processing system according to claim 6, characterized in that, The operating condition balance data calibration module includes: The data extraction submodule, based on the start and end timestamps of each segment in the temperature control anomaly segment label set within the sampling period, collects the current effective value sequence, output power sequence, and coil temperature sampling sequence within the corresponding time range from the equipment operation record. It then calculates the standard deviation of the current effective value, the difference sequence of adjacent power sampling points, and the temperature rise slope curve within the time period, respectively, and generates a set of operating condition sampling fluctuation parameters. The stability matching judgment submodule calls the standard deviation of the effective value of the current in the sampling fluctuation parameter set of the working condition, divides it into three levels of stable, overload and light load according to the set current fluctuation range, matches the power change rate corresponding interval segment and judges whether it is consistent with the combination in the stability identification table, filters out mismatched combinations and extracts the time period index range to obtain the stability deviation time interval group. The power regulation execution submodule calls the segment index in the stability deviation time interval group, determines the adjustment direction according to the positive or negative direction of the temperature rise slope corresponding to the time period, adjusts the original output power value sequence according to the set amplitude, and merges the corrections within the time period to generate a continuous power data group, and obtains the sampled segment working condition correction dataset.

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