Induction cooker data acquisition and processing system

Abstract

The invention relates to the technical field of data processing, in particular to an induction cooker data acquisition and processing system which comprises a power jump identification module, a response section correction module, a trend sampling analysis module, a temperature control abnormity judgment module and a working condition balance data calibration module. According to the method, the rate change value between the power sampling points in the operation period is calculated, the sections with obvious abrupt change characteristics are screened, and the power lag response is corrected in combination with the voltage change slope, so that the time offset between the control response and the electric signal change is effectively corrected; the temperature fluctuation trend and the temperature control deviation direction are analyzed on the basis of the correction sequence, a temperature control abnormal state label set is formed by matching current waveform symmetry characteristics, a basis is provided for output power adjustment according to the temperature rise rate, the adaptation capacity of data analysis to actual working conditions and the execution precision of a control strategy are improved, and the accuracy of data analysis is improved. And the stable operation capability of the electromagnetic heating equipment under the complex thermal load change is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to an induction cooker data acquisition and processing system. BACKGROUND

[0002] The technical field of data processing mainly involves the whole process of storing, converting, calculating and outputting the obtained data. This field includes multiple key technology modules, such as data acquisition interface design, communication protocol implementation, data cleaning and formatting, characteristic value calculation, time series data analysis, algorithm-driven data optimization processing, result visualization, etc. The system applying data processing usually covers multiple links such as sensor data reception, data flow control, edge and cloud processing architecture construction, 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, and to provide a data basis for subsequent decision-making, control or prediction. This technical field is suitable for multiple application scenarios such as manufacturing automation, intelligent control, energy management, industrial internet, etc.

[0003] Among them, the induction cooker data acquisition and processing system is a system for real-time monitoring, data acquisition and analysis of induction cooker operating parameters. The system collects key operating data such as voltage, current, temperature, power factor generated by the induction cooker during operation, and processes and analyzes the collected data to identify operating status, judge fault mode or optimize control strategy. Its main uses include improving the accuracy of induction cooker energy efficiency control, realizing intelligent fault diagnosis, supporting remote monitoring and management, and are suitable for system integration of intelligent household appliances and operation optimization of industrial-grade electromagnetic heating equipment, The traditional processing system is difficult to accurately capture the sudden power fluctuation in the running process, lacks positioning means for power jump, and in the case of power response lag or time error between signal change and control execution time, it is easy to cause time sequence error of data, resulting in judgment deviation. In the temperature control link, only the temperature mean value or fluctuation range is used to judge the state, which cannot distinguish whether the temperature control trend direction is continuously deviated or the waveform has structural abnormalities, for example, the lagging temperature rise caused by load thermal inertia in the heating process is easy to be misjudged as a normal response of the system. In the running stability judgment, there is a lack of a way to identify the cross characteristics based on standard deviation and change rate, which leads to misidentification of output abnormalities caused by unstable load, affecting the timely correction of control strategy and the energy efficiency level of equipment operation. SUMMARY

[0004] The purpose of the present application is to solve the shortcomings in the prior art and to provide an induction cooker data acquisition and processing system.

[0005] In order to achieve the above purpose, the present application adopts the following technical scheme: an induction cooker data acquisition and processing system, the system comprises: 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. 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 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.

[0006] 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.

[0007] The present invention is improved 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 section marking submodule calls each pair of adjacent mutation points in the power mutation candidate point index set, sequentially obtains the power variation amplitude before and after sampling, and calculates the average fluctuation amplitude between the two points, compares the average fluctuation amplitude with a set step amplitude standard value, judges whether the average fluctuation amplitude belongs to an amplitude jump section, screens point pairs meeting the condition and marks the start and end sampling positions and time stamps, establishes a jump marking field and constructs a complete record in index order, and generates a power jump section marking result.

[0008] The application improves that the response correction module comprises: The parameter extraction submodule extracts the current effective value sequence, the voltage effective value sequence and the active power sampling value sequence at the sampling position corresponding to the section based on the start and end indexes in the power jump section marking result, structurally organizes the voltage, current and power sampling data in time sequence, establishes a synchronous sampling set of the section, and generates a power section synchronous parameter sequence; The theoretical power calculation submodule sequentially extracts corresponding sampling values in each group by calling the current effective value sequence and the voltage effective value sequence in the power section synchronous parameter sequence, calculates the theoretical active power estimation value of each sampling point, maps the position structure of the original power sampling sequence, establishes a corresponding group of the theoretical value and the collected value, and generates a sampling point theoretical power sequence; The data time sequence correction submodule compares the sampling point theoretical power sequence with the original power value sequence in the power section synchronous parameter sequence point by point, calculates the difference value and screens the sampling points with a difference value greater than a set hysteresis response limit difference value, extracts the voltage change trend in the corresponding sampling points, and synchronously adjusts the power sampling time point index based on the voltage slope, reorders the points meeting the condition after correction, reconstructs the power data column after correction in the time axis, and obtains a power delay section correction sampling sequence.

[0009] The application improves that the trend sampling analysis module comprises: The temperature window division submodule extracts the temperature sampling value sequence output by the thermistor sensor in the time period based on the time period corresponding to the power delay section correction sampling sequence, divides the sequence into a plurality of continuous and non-overlapping time windows according to a set sampling window length, records the start and end time index positions and the corresponding temperature values of the time windows, and generates multi-window temperature sampling grouping data; The fluctuation degree judgment submodule calls each window data in the multi-window temperature sampling grouping data, extracts the maximum value, the minimum value and the average value of the temperature in the window, calculates the fluctuation degree value of each window, and determines whether the fluctuation degree value exceeds a temperature fluctuation range threshold value, marks the window with a fluctuation degree greater than the threshold value as abnormal, and obtains a temperature fluctuation abnormal window index set; The mean trend identification submodule calls the temperature fluctuation anomaly window index set, extracts the temperature sampling mean value of each corresponding window in turn, judges the change direction in time sequence order, records whether the mean value change direction is consistent in each three continuous windows, and filters the window paragraphs meeting the continuous change consistency condition to obtain a temperature control trend sampling state list.

[0010] The temperature control anomaly judgment module of the application comprises: The waveform distortion screening submodule calls all window sequences in the temperature control trend sampling state list, extracts continuous paragraphs meeting the conditions that the change direction of the temperature mean value of three continuous windows is consistent and the corresponding window number is marked as temperature fluctuation anomaly, extracts the current peak sampling sequence in the corresponding time interval, identifies the positive and negative half-wave extreme points in each period, compares whether the asymmetry difference is greater than the current distortion recognition threshold, accumulates the number of periods with asymmetry, and if the count value is greater than or equal to three, the corresponding paragraph is marked to obtain a waveform distortion compliance segment number list. The anomaly level labeling submodule calls each segment number in the waveform distortion compliance segment number list, matches the corresponding start and end time, sets an anomaly labeling level identification code in the same time interval, sets the level score according to the weighted integral result of the current asymmetry period count and the number of continuous offset windows, calculates the level value of each segment and binds it to the corresponding paragraph to obtain a temperature control anomaly segment label set in the sampling period.

[0011] The system further comprises: The working condition balance data calibration module divides the time interval according to the current standard deviation based on the time stamp of each paragraph in the temperature control anomaly segment label set in the sampling period, matches the distribution interval of the power change rate, judges whether the combination falls into the specification comparison range in the load stability identification table, and if not, adjusts the time period target output power according to the temperature rise rate change direction to obtain a sampling segment working condition correction data set. The sampling segment working condition correction data set specifically refers to the corrected target power setting sequence, the stability matching level label, the adjustment amplitude record and the original and corrected difference value.

[0012] The working condition balance data calibration module comprises: The data extraction submodule collects the current effective value sequence, the output power sequence and the coil temperature sampling sequence in the corresponding time range in the device operation record based on the start and end time stamps of each paragraph in the temperature control anomaly segment label set in the sampling period, calculates the current effective value standard deviation, the adjacent power sampling point difference sequence and the temperature rise slope curve in the time period respectively, and generates a working condition sampling fluctuation parameter set. The stable matching judgment submodule calls the current effective value standard deviation in the working condition sampling fluctuation parameter set, divides it into three levels of stable, overload and light load according to the set current fluctuation interval, judges whether the combination in the stability identification table is consistent after matching the corresponding interval segment of the power change rate, screens the unmatched combination and extracts the time interval index range to obtain the stability deviation time interval group; The power adjustment execution submodule calls the paragraph index in the stability deviation time interval group, judges the adjustment direction according to the positive and negative directions of the time interval corresponding temperature rise slope, adjusts the original output power value sequence by the set amplitude, respectively modifies and generates the continuous power data group after combination, and obtains the sampling section working condition correction data set.

[0013] Compared with the prior art, the advantages and positive effects of the present application are that: In the present application, by calculating the rate change value between power sampling points in the running period and screening the sections with obvious mutation characteristics, the power jump position can be accurately identified. The power hysteresis response is corrected in combination with the voltage change slope, the time offset between the control response and the electrical signal change is effectively corrected, the temperature fluctuation trend and the temperature control deviation direction are analyzed on the basis of the corrected sequence, the temperature control abnormal state label set is formed by matching the current waveform symmetry characteristics, the load state stability is calibrated in combination with the standard deviation and the power change rate distribution interval, the output power adjustment is provided according to the temperature rise rate, the parallel identification and closed-loop adjustment of multiple working conditions such as power response hysteresis, temperature control fluctuation abnormality and unstable running state are realized, the adaptation ability of data analysis to actual working conditions and the execution precision of control strategy are improved, and the stable running ability of electromagnetic heating equipment under complex thermal load changes is enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 The system flowchart of the present application is shown in the figure; Figure 2 The flowchart of the power jump identification module of the present application is shown in the figure; Figure 3 The flowchart of the response section correction module of the present application is shown in the figure; Figure 4 The flowchart of the trend sampling analysis module of the present application is shown in the figure; Figure 5 The flowchart of the temperature control abnormality judgment module of the present application is shown in the figure; Figure 6 The flowchart of the working condition balance data calibration module of the present application is shown in the figure. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical scheme and advantages of the present application clearer and more apparent, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0016] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0017] Referring to Figure 1 The present application provides a technical solution: an electromagnetic oven data acquisition and processing system, the system comprising a power jump identification module, a response segment correction module, a trend sampling analysis module, a temperature control abnormality judgment module and a working condition balance data calibration module; The power jump identification module obtains the active power sampling sequence of the electromagnetic oven control mainboard in the running period, sequentially calculates the power difference value between any two points and the sampling interval ratio, constructs the change rate sequence, selects the points with a rate greater than the power change rate threshold as the mutation candidate points, calls the change trend direction of the power value before and after the index position of the mutation candidate points, judges whether there is a positive and negative direction reversal combined with the average rate change value of the previous five groups of sampling points, if the reversal meets the fluctuation amplitude difference exceeding the step amplitude standard value, it is determined that there is a jump at the point, the point pair meeting the jump condition is extracted, and the power jump section annotation result is generated; The active power refers to the energy actually consumed or output by the electromagnetic oven per unit time, which can be obtained through the power sampling chip; the power change rate threshold refers to the limit of the power change rate per unit time, which is usually measured in watts per second, and the reference is set as the maximum allowed load change response of the device; the step amplitude standard value refers to the minimum difference limit of the average value of the power before and after the sampling, which is usually set as 5%-10% of the rated power as the identification boundary; The response correction module extracts the segment current effective value, voltage effective value and active power sampling value based on the segment index range in the power jump section annotation result, calls the current and voltage value to calculate the segment theoretical power value sequence, compares the difference between the theoretical value and the collected power value point by point, if the difference is greater than the lag response limit difference value, the sampling time is adjusted forward synchronously with the voltage change slope, the corrected power sequence is reorganized in the original time sequence, and the power delay segment correction sampling sequence is obtained; The lag response limit difference value is set as the delay identification sensitivity reference, which is generally adjusted within 3%-5% of the rated power; the voltage change slope is the voltage change per unit time, indicating the input response speed of the controller; The trend sampling analysis module corrects the sampling sequence corresponding to the time period based on the power delay section, synchronously extracts the temperature sampling value sequence output by the thermistor sensor in the time period, divides a plurality of time windows with a fixed sampling window length, calculates the temperature fluctuation degree value according to the temperature mean value of each window and the maximum and minimum values in the interval, judges whether it is greater than the temperature fluctuation range threshold, marks the fluctuation amplitude abnormal window, records the window paragraph with the same change direction of the temperature mean value in the continuous three windows, and obtains a temperature control trend sampling state list; The thermistor sensor is a commonly used NTC type element, and the output resistance value changes with temperature; the temperature fluctuation range threshold is generally defined as fluctuation within ±1℃ range, that is, it is determined as a relatively stable window; The temperature control abnormality judgment module calls the temperature control trend sampling state list, filters the window paragraph marked as continuous offset direction consistent and fluctuation amplitude abnormal, combines whether the current peak sampling sequence in the paragraph appears three times or more waveform asymmetry characteristics as a judgment condition, and if the condition is met, marks the abnormality level label of the start and end time corresponding to the paragraph, and obtains a temperature control abnormality segment label set in the sampling period; The waveform asymmetry feature refers to the deviation of the positive and negative half cycle peak amplitude in the current cycle waveform exceeding 10%, which is used to identify the nonlinear response caused by uneven load or overheating; the current peak sampling sequence is extracted from the integral period peak value retention module or obtained by ADC peak value calculation; The working condition balance data calibration module extracts the time period current effective value standard deviation sequence, the output power change rate sequence and the temperature rise rate curve in the device data record based on the timestamp of each paragraph in the temperature control abnormality segment label set in the sampling period, divides the interval according to the current standard deviation, matches the distribution interval where the power change rate is located, judges whether the combination falls into the specification comparison range in the load stability identification table, and if not, adjusts the target output power in the time period according to the temperature rise rate change direction, and obtains a sampling segment working condition correction data set; The current effective value standard deviation reflects whether the load is stable; the temperature rise rate is calculated as the temperature rise value of the coil temperature measurement point per unit time; the load stability identification table is preset by the manufacturer based on experimental data, and defines the allowable combination range between power change rate and current fluctuation; The power jump section annotation result is specifically a jump point index group, a section start and end timestamp, a change rate peak amplitude, and a trend direction turning label. The power delay section correction sample sequence includes a reconstructed power time sequence, a sample point synchronization label, a difference value mapping before and after correction, and a time axis offset value list. The temperature control trend sampling state list is specifically a window number, a temperature mean value change direction label, a fluctuation abnormal window label, and a continuous offset sequence index. The sampling period temperature control abnormal section label set includes an abnormal section time range, an abnormal level identification, a current distortion type code, and a continuous abnormal window number. The sampling section working condition correction data set is specifically a corrected target power setting sequence, a stability matching level label, an adjustment amplitude record, and an original and correction difference value.

[0018] Referring to Figure 2 , the power jump identification module includes: The sampling sequence construction submodule obtains the active power sampling sequence in the running period of the induction cooker control mainboard, calls the power difference value between any two adjacent sampling values and the sampling time interval, arranges the difference value ratio in a continuous sequence according to time sequence, uniformly converts the numerical units in the sequence, establishes the power change rate sample data corresponding to continuous time, and generates the sampling section change rate sequence. The sampling sequence construction submodule obtains the active power sampling sequence in the running period of the induction cooker control mainboard. In a complete heating period of the induction cooker continuous work, the microcontroller of the control mainboard samples the active power once every seconds at a fixed sampling frequency. A series of power values are obtained, for example, 1000 W is measured at seconds, 1200 W is measured at seconds, 1100 W is measured at seconds, 1300 W is measured at seconds, and 1400 W is measured at seconds. These power values are arranged in time sequence to form the active power sampling sequence, and the power difference value between any two adjacent sampling values in the sequence is called, for example, the difference W between and is called, the difference W between and is called, and the difference W between and is called. These differences are divided by the fixed sampling time interval of seconds to obtain the power change rate per second, for example, W / s, W / s, W / s, W / s, W / s, W / s, these difference ratios are composed into a continuous sequence in time sequence, and the numerical units in the sequence are uniformly converted, since all the values have adopted the International System of Units As a unit, this step can directly apply the values to subsequent processing without additional conversion, establish the power change rate sample data corresponding to continuous time, and generate the sample segment change rate sequence, the values in the sequence are , for subsequent rate mutation extraction.

[0019] The rate mutation extraction submodule calls the value of each sampling point in the rate sample data based on the sample segment change rate sequence, and performs point-by-point size relationship judgment with the set power change rate threshold, filters the sampling point index whose rate is 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 the adjacent five groups of data and judges whether the inversion occurs, obtains the sampling point position that meets the direction inversion condition and the amplitude mutation at the same time, and obtains the power mutation candidate point index group; The rate mutation extraction submodule calls the value of each sampling point in the rate sample data based on the sample segment change rate sequence, and performs point-by-point size relationship judgment with the set power change rate threshold, which needs to be set based on the historical data of the power change rate of the induction cooker in the stable running state, for example, by collecting power change rate data points in each period during the stable heating of the induction cooker, and statistically analyzing the absolute values of these data points, calculating the mean value and the standard deviation , the threshold is set to , for example, when the mean value of the absolute value of the power change rate is W / s and the standard deviation is W / s, the power change rate threshold is set to W / s, the sampling point index whose rate is greater than W / s is filtered and the time position is recorded, for example, in the sample segment change rate sequence , the sampling point indexes whose rates are greater than W / s are (rate is W / s), (rate is W / s), and (rate is W / s), the recorded time positions are s, s, and s, the positive and negative directions of the power value change before and after the corresponding point are extracted, at the index , the power changes from W to W, the change direction is positive, at index , the power changes from W to W, the change direction is positive, at index , the power changes from W to W, the change direction is negative, calculate the average change direction value in the adjacent five groups of data and determine whether a reversal occurs, for example, for the sampling point at index , analyze the five groups of data before and after it, i.e. indexes , the corresponding power change direction is set to , and the average change direction value is calculated as , since the value changes from the average value of the previous five groups (set as ) to a negative value, it can be determined that a direction reversal occurs, the sampling point position that meets the direction reversal condition and the amplitude mutation at the same time is obtained, and the power mutation candidate point index group is obtained.

[0020] The jump section labeling submodule calls each pair of adjacent mutation points in the power mutation candidate point index group, sequentially obtains the power change amplitude before and after sampling, and 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 mutation section, filters the point pairs that meet the conditions and marks the start and end sampling positions and time stamps, establishes the jump labeling field and constructs the complete record in index order, generates the power jump section labeling result; The jump section labeling submodule calls each pair of adjacent mutation points in the power mutation candidate point index group, for example, calls the point pair composed of index and index , sequentially obtains the power change amplitude before and after sampling, for index , the power change amplitude before and after sampling is W, for index , the power change amplitude before and after sampling is W, and the average fluctuation amplitude between the two points is calculated, W, which is compared with the set step amplitude standard value. The step amplitude standard value is a statistical quantity of the power change amplitude caused by load fluctuation of the electromagnetic oven in the stable heating mode, for example, by statistically calculating the average value of all power change amplitudes in the stable heating period, the average value is set as the step amplitude standard value, for example, if the average power change amplitude is W, the step amplitude standard value is set as W, and it is determined whether the average fluctuation amplitude W belongs to the amplitude mutation section, since W is much larger than W, determine that the point pair belongs to the amplitude mutation section, screen the qualified point pairs and mark the start and end sampling positions and timestamps, for example, mark the index (time stamp s) and index (time stamp s) as a qualified point pair, establish a jump mark field and construct a complete record in index order to generate a power jump section mark result.

[0021] Please refer to Figure 3 , the response correction module includes: The parameter extraction submodule extracts the current effective value sequence, voltage effective value sequence and active power sampling value sequence at the sampling position corresponding to the section based on the start and end indexes in the power jump section mark result, and arranges the voltage, current and power sampling data in time order to establish a synchronous sampling set of the section and generate a power section synchronous parameter sequence; The parameter extraction submodule extracts the current effective value sequence, voltage effective value sequence and active power sampling value sequence at the sampling position corresponding to the section based on the start and end indexes in the power jump section mark result, for example, based on the mark result {start_index: 1, end_index: 5, tart_time: 0.05s, end_time: 0.25s}, extracts the current effective value sequence, voltage effective value sequence and active power sampling value sequence at the sampling position corresponding to the section, for example, at s, s, s, s, s, the current effective value , voltage effective value and original power sampling value are extracted from the device operation record respectively, the voltage, current and power sampling data are arranged in time order, for example, arranged into a three-column data table, a synchronous sampling set of the section is established, as shown in Table 1, and a power section synchronous parameter sequence is generated.

[0022] Table 1 Power jump section synchronous parameter sequence

[0023] The theoretical power calculation submodule calls the current effective value sequence and voltage effective value sequence in the power section synchronous parameter sequence, sequentially extracts each corresponding sampling value, and uses the formula: ; to obtain the theoretical active power estimate value of each sampling point, maps according to the position structure of the original power sampling sequence, establishes a corresponding group of theoretical values and collected values, and generates a sampling point theoretical power sequence; wherein, represents the theoretical active power estimation value of the i-th sampling point, represents the voltage effective value at the i-th time point, represents the current effective value at the i-th time point, represents the voltage effective value at the i-th time point, represents the current effective value at the i-th time point, is the base of natural logarithm, represents the load dynamic change normalized value at the i-th time point, the theoretical power calculation submodule calls the current effective value sequence and the voltage effective value sequence in the power segment synchronization parameter sequence, sequentially extracts each group of corresponding sampling values, for example, extracts the current effective value V and the current A at the sampling point index i, and the current effective value V and the current A at the previous sampling point i-1, uses the formula: represents the theoretical active power estimation value of the i-th sampling point, the calculation result represents the theoretical active power value that the electromagnetic oven should output at the current time after considering the influence of voltage, current dynamic change and load characteristics, which is used as a reference for subsequent comparison with the original collected power value, ​​​​​​​​​​​​​​​​​​​​​the voltage effective value at the time point, for example V, the current effective value at the time point, for example A, the natural logarithm base, whose value is , the load dynamic change normalized value at the time point, calculated as the ratio of the coil temperature change rate to the current value, for example, when , the thermistor sensor measures the coil temperature at s and s as and respectively, then the coil temperature change rate is , the current value is A, and the calculated value of is , which reflects the speed of load temperature change under the current power. The benefit of the formula is that by multiplying the voltage change rate and the current effective value with the voltage change rate as the power correction term, and introducing the load dynamic change normalized value as the independent variable of the logarithmic function, the theoretical power of the induction cooker under load dynamic response can be evaluated in detail, which makes the theoretical value more accurately reflect the instantaneous behavior of the device in the power jump section, thereby providing a reliable benchmark for subsequent data timing correction. According to the position structure of the original power sampling sequence, for example, the calculated value of is mapped to the index of the original sequence, a corresponding group of theoretical values and collected values is established, and a sampling point theoretical power sequence of the power jump section is generated, for example, the theoretical power at index is calculated as W, and the process is repeated to obtain the complete sampling point theoretical power sequence. The data timing correction submodule compares the sampling point theoretical power sequence with the original power value sequence in the power segment synchronization parameter sequence point by point, calculates the difference between them, and selects the sampling points whose difference is greater than the set hysteresis response limit value. The voltage change trend is extracted in the corresponding sampling points, and the power sampling time point index is adjusted synchronously based on the voltage slope. The points that meet the conditions are reordered after correction, and the corrected power data column is reconstructed along the time axis to obtain the power delay section correction sampling sequence. The data timing correction submodule compares the sampling point theoretical power sequence with the original power value sequence in the power segment synchronization parameter sequence point by point, for example, the theoretical power sequence calculated in the previous step is compared with the original power value sequence in the power segment synchronization parameter sequence point by point.

[0024] The data timing correction submodule compares the sampling point theoretical power sequence with the original power value sequence in the power segment synchronization parameter sequence point by point, calculates the difference between them, and selects the sampling points whose difference is greater than the set hysteresis response limit value. The voltage change trend is extracted in the corresponding sampling points, and the power sampling time point index is adjusted synchronously based on the voltage slope. The points that meet the conditions are reordered after correction, and the corrected power data column is reconstructed along the time axis to obtain the power delay section correction sampling sequence. The data timing correction submodule compares the sampling point theoretical power sequence with the original power value sequence in the power segment synchronization parameter sequence point by point, for example, the theoretical power sequence calculated in the previous step is compared with the original power value sequence in the power segment synchronization parameter sequence point by 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.

[0025] Please see Figure 4 The trend sampling 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 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, containing temperature value , the second window is s to s, containing temperature value , and recording the start and end time index positions of the time window and the corresponding temperature value, for example, the index of window 1 is , and the temperature value is , generating multi-window temperature sampling grouping data, The fluctuation degree judgment submodule calls each window data in the multi-window temperature sampling grouping data, extracts the maximum, minimum and average values of the temperature in the window, and uses the formula: ; to obtain the fluctuation degree value of each window, and determine whether it exceeds the temperature fluctuation range threshold value, and mark the window with abnormal fluctuation degree greater than the threshold value to obtain the temperature fluctuation abnormal window index set; wherein, represents the temperature fluctuation degree value of the first window, which is used to measure the temperature stability inside the window, represents the ratio of the maximum value in the temperature sampling value in the first window to the temperature sample mean value, represents the ratio of the minimum value in the temperature sampling value in the first window to the temperature sample mean value, represents the temperature sampling mean value normalization value of the first window, and the normalization method is to divide the current window mean value by the temperature mean value of the whole window set, represents the temperature reference value normalization value, which is the median value of the temperature mean value set of all windows divided by the maximum temperature sample value, represents the temperature sampling variance value in the first window, which is calculated by averaging the square of the difference between all sampling points in the window and the mean value, represents the number of temperature sampling points in the first window, represents the sampling point temperature change rate average value normalization value in the first window, which is calculated by dividing the average of the absolute values of the temperature differences of the consecutive sampling points by the standard deviation of the time interval, and the temperature fluctuation range threshold value is set by extracting the temperature window fluctuation degree value in the power stable state in a plurality of consecutive operation periods, calculating the mean value and the standard deviation, and taking the mean value plus 1.5 times the standard deviation as the temperature fluctuation range threshold value; The fluctuation degree judgment submodule calls each window data in the multi-window temperature sampling grouping data, extracts the maximum, minimum and average values of the temperature in the window, for example, for the first window , the maximum value is , minimum value , average value , using the formula: ; The fluctuation degree value of each window is obtained by operation, wherein, represents the temperature fluctuation degree value of the th window, and the calculation result represents the quantitative evaluation of the temperature stability in the window. The value is compared with the set threshold value to determine whether the temperature is abnormal, represents the ratio of the maximum value in the th window to the temperature sample mean value. For example, for window 1, the temperature sample mean value is , , represents the ratio of the minimum value in the th window to the temperature sample mean value. For example, , represents the temperature sample mean value of the th window, and the normalization method is to divide the current window mean value by the temperature mean value of the whole window set. For example, the mean value of the temperature mean value set of all windows is , , represents the temperature reference value normalized value, which is the median value of the temperature mean value set of all windows divided by the maximum value of the temperature sample. For example, the median value of the temperature mean value set of all windows is , and the maximum value of the temperature sample is , , represents the temperature sample variance value in the th window, and the calculation method is the average of the square of the difference between all sample points in the window and the mean value. For example, , represents the number of temperature sampling points in the th window, for example, , represents the temperature change rate average value normalized value in the th window, and the calculation method is to divide the average of the absolute value of the temperature difference of the continuous sampling points by the standard deviation of the time interval. For example, for window 1, the average of the absolute value of the temperature difference of the continuous sampling points is , and the standard deviation of the time interval is (the time interval is fixed at s), and the standard deviation can be set to , 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 Then the threshold is For fluctuations greater than The window is marked as abnormal, for example, the fluctuation value of window 1 is calculated: ; because Less than the threshold The window was not marked as abnormal, and the temperature fluctuation abnormal window index set was obtained.

[0026] 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. 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 change are all positive. We determine that their directions are consistent and filter out window segments that meet the condition of continuous and consistent change. For example, we filter out windows... As a paragraph that meets the conditions, obtain the list of temperature control trend sampling states.

[0027] Please refer to Figure 5 , the temperature control abnormality judgment module comprises: The waveform distortion screening submodule calls all window sequences in the temperature control trend sampling state list, extracts a continuous paragraph that meets the condition that the change direction of the average temperature of three consecutive windows is consistent and the corresponding window number has been marked as temperature fluctuation abnormality, extracts the current peak sampling sequence in the corresponding time interval, and identifies the positive and negative half-wave extreme points in each cycle. If the difference between the symmetry values is greater than the current distortion recognition threshold, the number of cycles with asymmetry is accumulated. If the count value is greater than or equal to three, the corresponding paragraph is marked, and the waveform distortion paragraph number list is obtained. The waveform distortion screening submodule calls all window sequences in the temperature control trend sampling state list, for example, calls {start_window: 5, end_window: 7, direction: positive}, extracts a continuous paragraph that meets the condition that the change direction of the average temperature of three consecutive windows is consistent and the corresponding window number has been marked as temperature fluctuation abnormality. This list itself meets this condition. Extract the current peak sampling sequence in the corresponding time interval, for example, in the window The corresponding seconds, the current peak is sampled every seconds to obtain a current peak sequence, and the positive and negative half-wave extreme points in each cycle are identified, for example, in an alternating current cycle of seconds, the positive and negative peak points are found. If the difference between the symmetry values is greater than the current distortion recognition threshold, the threshold is set to be the average value plus times the standard deviation of the positive and negative half-wave peak value difference of alternating current cycles in the stable operation state of the induction cooker, for example, the average value is A, and the standard deviation is A, then the threshold is A, the number of cycles with asymmetry is accumulated, for example, in a second time period, there are alternating current cycles, of which cycles have a positive and negative half-wave peak value difference greater than A, if the count value is greater than or equal to three, the corresponding paragraph is marked, and since is greater than or equal to , the paragraph is marked, and the waveform distortion paragraph number list is obtained.

[0028] The abnormal level labeling submodule calls each segment number in the waveform distortion compliance segment number list, matches the corresponding start and end time, sets the abnormal labeling level identification code within the same time interval, sets the level score according to the weighted integral result of the current asymmetric period count and the number of continuous offset windows in the paragraph, calculates the level value of each paragraph and binds it to the corresponding paragraph, and obtains the temperature control abnormal segment label set in the sampling period. The abnormal level labeling submodule calls each segment number in the waveform distortion compliance segment number list, for example, calls the paragraph with the number , matches the corresponding start and end time, sets the abnormal labeling level identification code within the same time interval, for example, sets the identification code to LEVEL_1, sets the level score according to the weighted integral result of the current asymmetric period count and the number of continuous offset windows in the paragraph, and the calculation method of the weighted integral is: level score = , where the weight coefficient and respectively reflect the relative importance of current distortion and temperature control trend duration, for example, according to historical fault data analysis, set , , calculate the level value of each paragraph and bind it to the corresponding paragraph, for example, the asymmetric period count is , the number of continuous offset windows is , then the level score is = , and the level score is bound to the paragraph , and the temperature control abnormal segment label set in the sampling period is obtained.

[0029] Please refer to Figure 6 , the working condition balance data calibration module includes: The data extraction submodule collects the current effective value sequence, output power sequence and coil temperature sampling sequence within the corresponding time range in the device operation record based on the start and end time stamps of each paragraph in the temperature control abnormal segment label set in the sampling period, calculates the current effective value standard deviation, adjacent power sampling point difference sequence and temperature rise slope curve in the time period, and generates the working condition sampling fluctuation parameter set; The working condition balance data calibration module collects the current effective value sequence, output power sequence and coil temperature sampling sequence within the corresponding time range in the device operation record based on the start and end time stamps of each paragraph in the temperature control abnormal segment label set in the sampling period, for example, based on the time stamps corresponding to the label set {start_window: 5, end_window: 7}, calculates the current effective value standard deviation, adjacent power sampling point difference sequence and temperature rise slope curve in the time period, and generates the working condition sampling fluctuation parameter set, as shown in Table 2.

[0030] Table 2 Abnormal segment working condition sampling fluctuation parameter set

[0031] The stable matching judgment submodule calls the standard deviation of the effective value of the current in the working condition sampling fluctuation parameter set, divides it into three levels of stable, overload and light load according to the set current fluctuation interval, judges whether the combination in the stability identification table is consistent after matching the corresponding interval segment of the power change rate, screens the unmatched combination and extracts the time interval index range to obtain the stability deviation time interval group; The stable matching judgment submodule calls the standard deviation of the effective value of the current in the working condition sampling fluctuation parameter set, for example, calls A, divides it into three levels of stable, overload and light load according to the set current fluctuation interval, which is obtained by experiment under different load conditions and statistics of current standard deviation, for example, when the induction cooker is normally and stably heated, the current standard deviation is usually between A, at this time, it is divided into a stable level, when the load is too large (such as uneven bottom or coil abnormality), the current standard deviation will significantly increase to A, divided into an overload level, when the load is too small (such as empty burning or removal of cookware), the current standard deviation is usually less than A, divided into a light load level, matches A to the overload level, judges whether the combination in the stability identification table is consistent after matching the corresponding interval segment of the power change rate, for example, under the overload level, the corresponding power change rate interval is usually large fluctuation, for example, greater than W / s, and if the power change rate is very small under the overload level, for example, less than W / s, then the combination is determined to be inconsistent, the stability identification table, for example, stipulates that "stable current standard deviation-small power fluctuation", "overload current standard deviation-large power fluctuation", "light load current standard deviation-small power fluctuation" are consistent combinations, screen the inconsistent combination and extract the time interval index range, for example, if the inconsistent combination of "overload current standard deviation" and "small power fluctuation" of the power change rate appears, the corresponding time interval index is extracted, and the stability deviation time interval group is obtained.

[0032] The power regulation execution submodule calls the paragraph index in the stability deviation time interval group, judges the adjustment direction according to the positive and negative directions of the temperature rise slope corresponding to the time interval, adjusts the original output power value sequence by a set amplitude, respectively modifies and combines to generate a continuous power data group in the time interval to obtain a sampling section working condition correction data set; The power regulation execution submodule calls the paragraph index in the stability deviation time interval group, for example, calls the time interval with index , judges the adjustment direction according to the positive and negative directions of the temperature rise slope corresponding to the time interval, for example, in the time interval corresponding to index , the temperature rise slope curve is , the slope direction is positive, indicating that the temperature is continuously rising, and the adjustment direction is determined to be negative, i.e. reducing the power, and adjusting the original output power value sequence by a set amplitude, the set amplitude being a preset adjustment step of the induction cooker control system according to the temperature change rate and the power level, for example, being set to the current power value , i.e. W, the adjustment amplitude is W, and the continuous power data set is generated by combining the modified data in the time period, for example, the original power value in the time period is modified to , wherein is the set adjustment amplitude, and the modified power data is W, W, W, and the modified data is combined into the original data sequence to obtain the sampling section working condition modified data set.

[0033] The above is only a preferred embodiment of the present application, and does not limit the present application in other forms. Any skilled person in the art can modify or change the above disclosed technical content to obtain equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made to the above embodiments without departing from the technical solution of the present application, according to the technical essence of the present application, still belong to the protection scope of the technical solution of the present application.

Claims

1. An electromagnetic stove data acquisition and processing system, characterized in that, The system comprises: The power jump identification module obtains the active power sampling sequence in the running period of the induction cooker control mainboard, screens the points with a rate greater than the power change rate threshold as mutation candidate points, calls the change trend direction of the power values before and after the index position of the mutation candidate points, identifies the jump points, and generates the power jump section labeling result; The response correction module compares the difference between the theoretical value and the collected power value point by point based on the section index range in the power jump section labeling result, adjusts the sampling time forward if the difference is greater than the hysteresis response limit difference value, and obtains the power delay segment correction sampling sequence; The trend sampling analysis module calculates the temperature fluctuation degree value based on the corresponding time period of the power delay segment correction sampling sequence, marks the fluctuation amplitude abnormal window, records the window segment with the same temperature mean value change direction in the continuous three windows, and obtains the temperature control trend sampling state list; The temperature control abnormality judgment module calls the temperature control trend sampling state list, combines whether the current peak sampling sequence in the paragraph appears three or more times as a judgment condition, labels the abnormality grade mark, and obtains the temperature control abnormality segment label set in the sampling period.

2. The electromagnetic stove data acquisition and processing system according to claim 1, characterized in that, The power jump section labeling result is specifically the jump point index group, the section start and end timestamp, the change rate peak amplitude, and the trend direction turning label. The power delay segment correction sampling sequence includes the reconstructed power time sequence, the sampling point synchronization mark, the difference mapping before and after correction, and the time axis offset value list. The temperature control trend sampling state list is specifically the window number, the temperature mean value change direction label, the fluctuation abnormal window mark, and the continuous offset sequence index. The temperature control abnormality segment label set in the sampling period includes the abnormality segment time range, the abnormality grade identification, the current distortion type code, and the continuous abnormality window number.

3. The electromagnetic stove data acquisition and processing system according to claim 2, characterized in that, The power jump identification module comprises: The sampling sequence construction submodule obtains the active power sampling sequence in the running period of the induction cooker control mainboard, calls the power difference value between any two adjacent sampling values and the sampling time interval, groups the difference value ratio into a continuous sequence in time sequence, uniformly converts the numerical units in the sequence, establishes the power change rate sample data corresponding to the continuous time, generates the sampling segment change rate sequence, and generates the sampling segment change rate sequence. The rate mutation extraction submodule calls the value of each sampling point in the sampling segment change rate sequence, judges the size relationship point by point with the set power change rate threshold, screens 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 the adjacent five groups of data and judges whether the average change direction value is reversed, obtains the sampling point position that meets the direction reversal condition and the amplitude mutation at the same time, and obtains the power mutation candidate point index group. The jump section marking submodule calls each pair of adjacent mutation points in the power mutation candidate point index set, sequentially obtains the power variation amplitude before and after sampling, and calculates the average fluctuation amplitude between the two points, compares the average fluctuation amplitude with a set step amplitude standard value, judges whether the amplitude mutation section belongs to the amplitude mutation section, screens the point pairs meeting the conditions and marks the start and end sampling positions and time stamps, establishes a jump marking field and constructs a complete record in the index order, and generates a power jump section marking result.

4. The electromagnetic induction cooker data acquisition and processing system according to claim 3, characterized in that, The response correction module comprises: The parameter extraction submodule extracts the current effective value sequence, the voltage effective value sequence and the active power sampling value sequence at the sampling position corresponding to the section based on the start and end indexes in the power jump section marking result, structurally organizes the voltage, current and power sampling data in time sequence, establishes a synchronous sampling set of the section, and generates a power section synchronous parameter sequence; The theoretical power calculation submodule calls the current effective value sequence and the voltage effective value sequence in the power section synchronous parameter sequence, sequentially extracts each group of corresponding sampling values, calculates the theoretical active power estimation value of each sampling point, maps the position structure of the original power sampling sequence, establishes the corresponding group of the theoretical value and the collected value, and generates a sampling point theoretical power sequence; The data time sequence correction submodule compares the sampling point theoretical power sequence with the original power value sequence in the power section synchronous parameter sequence point by point, calculates the difference value and screens the sampling points with a difference value greater than a set hysteresis response limit difference value, extracts the voltage variation trend in the corresponding sampling point, and adjusts the power sampling time point index based on the voltage slope, reorders the points meeting the conditions after correction, and reconstructs the power data column after correction in the time axis to obtain a power delay section corrected sampling sequence.

5. The electromagnetic stove data acquisition and processing system according to claim 4, characterized in that, The trend sampling analysis module comprises: The temperature window division submodule extracts the temperature sampling value sequence output by the thermistor sensor in the time period based on the time period corresponding to the power delay section corrected sampling sequence, divides the sequence into a plurality of continuous and non-overlapping time windows according to a set sampling window length, records the start and end time index positions and corresponding temperature values of the time windows, and generates multi-window temperature sampling grouping data; The fluctuation degree judgment submodule calls each window data in the multi-window temperature sampling grouping data, extracts the maximum value, the minimum value and the average value of the temperature in the window, calculates the fluctuation degree value of each window, and determines whether the fluctuation degree value exceeds the temperature fluctuation range threshold value, marks the windows with a fluctuation degree greater than the threshold value as abnormal, and obtains a temperature fluctuation abnormal window index set; The mean value trend identification submodule calls the temperature fluctuation abnormal window index set, sequentially extracts the temperature sampling mean value of each corresponding window, judges the change direction in time sequence order, records whether the mean value change direction is consistent in each three continuous windows, screens the window paragraphs meeting the continuous change consistency condition, and obtains a temperature control trend sampling state list.

6. The electromagnetic induction cooker data acquisition and processing system according to claim 5, characterized in that, The temperature control abnormality judgment module comprises: The waveform distortion screening submodule calls all window sequences in the temperature control trend sampling state list, extracts a continuous paragraph that meets the conditions of consistent change direction of mean values of three consecutive windows and is marked as temperature fluctuation abnormality, extracts a current peak value sampling sequence in the corresponding time interval, and identifies positive and negative half-wave extreme points in each period. Whether the asymmetry difference is greater than the current distortion recognition threshold is compared, and the number of periods with asymmetry is accumulated. If the count value is greater than or equal to three, the corresponding paragraph is marked, and a waveform distortion consistent paragraph number list is obtained. The abnormality level labeling submodule calls each paragraph number in the waveform distortion consistent paragraph number list, matches the corresponding start and end time, sets an abnormality label level identification code in the same time interval, sets a level score according to the weighted integral result of the current asymmetry period count and the number of consecutive offset windows in the paragraph, calculates the level value of each paragraph and binds it to the corresponding paragraph, and obtains a temperature control abnormality paragraph label set in the sampling period.

7. The electromagnetic induction cooker data acquisition and processing system according to claim 6, characterized in that, The system further comprises: The working condition balance data calibration module divides the time interval according to the current standard deviation based on the time stamp of each paragraph in the temperature control abnormality paragraph label set in the sampling period, matches the power change rate distribution interval, judges whether the combination falls into the specification comparison range in the load stability identification table, adjusts the time period target output power according to the temperature rise rate change direction if it does not match, and obtains a sampling label working condition correction data set. The sampling label working condition correction data set specifically refers to a corrected target power setting sequence, a stability matching level label, an adjustment amplitude record, and an original and corrected difference value.

8. The electromagnetic induction cooker data acquisition and processing system according to claim 7, characterized in that, The working condition balance data calibration module comprises: The data extraction submodule collects the current effective value sequence, the output power sequence and the coil temperature sampling sequence in the corresponding time range in the device operation record based on the start and end time stamps of each paragraph in the temperature control abnormality paragraph label set in the sampling period, calculates the current effective value standard deviation, the adjacent power sampling point difference sequence and the temperature rise slope curve in the time period respectively, and generates a working condition sampling fluctuation parameter set; The stability matching judgment submodule calls the current effective value standard deviation in the working condition sampling fluctuation parameter set, divides it into three levels of stability, overload and light load according to the set current fluctuation interval, matches the corresponding interval segment of the power change rate, judges whether it is consistent with the combination in the stability identification table, screens the unmatched combination and extracts the time period index range, and obtains a stability deviation time interval group; The power adjustment execution submodule calls the paragraph index in the stability deviation time interval group, judges the adjustment direction according to the positive and negative directions of the temperature rise slope corresponding to the time period, adjusts the original output power value sequence by a set amplitude, and generates a continuous power data group after merging in the time period to obtain a sampling label working condition correction data set.

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