Battery state self-adaptive electric firework cooking stove precise control system and hot pot cooking stove

Abstract

The invention relates to the technical field of battery state control, in particular to a battery state self-adaptive electric firework cooking stove accurate control system and a hot pot cooking stove. The influence of voltage transient disturbance and internal resistance change on charge estimation is compressed within a small error range, the resolution of the battery bearing capacity on numerical scales is improved, and paragraph evolution screening is executed on difference amplitude and paragraph continuity by adopting a genetic algorithm. Short-time random disturbance and persistent lag offset are distinguished in numerical value expression, output power adjustment does not depend on an over-wide safety margin any more, a charge iteration structure set, charge lag output and bearable current values are combined according to segments to form a charge ratio chain group, power distribution segments are subjected to synchronous order division in the resistance dimension and the temperature rise dimension, and the charge iteration structure set, the charge lag output and the bearable current values are combined according to the segments to form a charge ratio chain group. And the battery capacity utilization rate and the unit time heat output stability are simultaneously improved.

Description

Technical Field

[0001] This invention relates to the field of battery state control technology, and in particular to a precise control system for an electric flame hot pot stove with battery state adaptive control and the hot pot stove itself. Background Technology

[0002] The field of battery state control technology specifically refers to control technologies that acquire, determine, calculate remaining capacity, calculate output capacity, and implement power mapping measures for rechargeable battery packs. This technology typically includes using voltage sampling circuits to monitor terminal voltage, current sampling loops to monitor discharge current, temperature sensing units to monitor temperature rise, equivalent circuit models, and state estimation algorithms to calculate the state of charge and health status. Based on the calculation results, stable, limited, and reduced output control is applied to external loads. The goal of this field is to maintain stable output and safe power supply when battery capacity decreases, impedance increases, and temperature shifts.

[0003] A battery state adaptive electric flame hot pot stove precision control system refers to a control system that takes battery state as input and electric flame hot pot stove heating power as output. The system aims to match the electric heating power of the hot pot stove with the real-time discharge capacity of the battery, so that the heating output is consistent with the battery's load capacity. This avoids output failure due to voltage drop, avoids overcurrent triggering protection actions, and maintains heating temperature stability. The expected effects of the system include maintaining stable discharge process, suppressing overcurrent caused by sudden load increase in the battery, and ensuring that the heating temperature of the hot pot stove is maintained in the set temperature range and the fluctuation range does not exceed the quantifiable threshold.

[0004] Existing battery state control technologies rely on a set of state variables, consisting of terminal voltage sampling, discharge current sampling, and temperature sensing, combined with equivalent circuit models and state estimation algorithms, to determine the state of charge and health. Based on the calculation results, stable output, limited output, and reduced output control are then applied to the load. Structurally, this approach primarily focuses on overall magnitude judgments, lacking a fine-grained representation of the subtle changes in terminal voltage and discharge current over time. The fluctuation patterns and hysteresis characteristics of the state of charge within a cycle are difficult to extract independently. Short-term voltage drops and impedance transitions are often superimposed on long-term capacity decay in the same judgment result, leading to a conservative control strategy. For load scenarios such as electric hot pot stoves where quantitative requirements exist for heating temperature zones and temperature fluctuation ranges, the output remains at a low level for extended periods in scenarios with slowly changing loads. The available battery capacity is not fully utilized, resulting in insufficient heat output per unit time, prolonged heating time, and delayed temperature recovery. While power supply safety can be maintained within acceptable limits, there are significant shortcomings in temperature control accuracy, power response smoothness, and energy utilization efficiency. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and propose a battery state adaptive electric flame hot pot stove precision control system and hot pot stove.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a battery state adaptive electric flame hot pot stove precision control system includes: State deduction module: The battery output voltage, discharge circuit current and RC branch voltage obtained by the sensor are compared with the terminal voltage and branch voltage and the charge is corrected by Kalman filtering. The charge iterative structure set is generated by comparing the terminal voltage and branch voltage and correcting the charge. Charge lag screening module: Based on the charge iteration structure set, the difference between the iteration charge and the previous period charge is split, and the difference chain is optimized by a genetic algorithm before replacement and maintenance. The single value output is determined by comparing the replacement charge with the voltage value of the RC branch, and the charge lag output is obtained. Electric flame energy level division module: Based on the charge iteration structure set and the charge hysteresis output, the two charges and the current carrying capacity are merged and split to form a ratio chain. The ratio chain is compared with the resistance segment of the electric flame hot pot stove heat conduction component to determine the energy level segment. The temperature rise index value is judged and the energy level is adjusted to obtain the energy level determination signal. Power sequence reconstruction module: Based on the energy level determination signal, the power value is determined by mapping the energy level determination signal to periodic segments, the power difference between adjacent segments is accumulated to form a difference sequence, and stable segments are screened by comparing the difference sequence with the screening threshold to construct a reconstructed power sequence; Hot pot stove heating execution module: Based on the reconstructed power sequence, extract the reconstructed power sequence segments and determine the corresponding power values, align the power values ​​with the heat-conducting plate segments of the electric flame hot pot stove heating element, organize the alignment content to form a trigger link, and obtain a heating trigger command group.

[0007] As a further embodiment of the present invention, the charge iteration structure set includes a voltage-to-charge group, a voltage chain sequence value, and a structure item segment chain set; the charge hysteresis output includes a charge difference chain group, a charge replacement sequence, and a single-point chain; the energy level determination signal includes a charge ratio chain group, an energy level column, and an indicator item; the reconstructed power sequence includes a power segment chain group, a difference segment group, and a periodic power sequence product; and the heating trigger command group includes a heating mapping chain group, a trigger row chain, and a control sequence.

[0008] As a further aspect of the present invention, the state deduction module includes: Voltage Comparison Submodule: The module obtains the terminal voltage value of the battery output terminal, the discharge current value of the discharge circuit, and the voltage value of the RC branch through sensors. Kalman filtering is introduced to correct the difference between the terminal voltage and the branch voltage. The terminal voltage and the branch voltage are compared and the load is corrected. The current is added to form a sequence and then merged into a load segment to generate a voltage comparison load group. Voltage chain construction submodule: Based on the voltage-to-charge group, the discharge current is added sequentially and the branch voltage is superimposed to form an extended sequence. The extended sequence is compared with the voltage-to-charge group segment by segment and the corresponding segment is confirmed. The corresponding segments are aggregated into chain item values ​​to generate voltage chain sequence values. The charge screening submodule: Based on the voltage chain sequence value, it compares the charge within the sequence value with the charge of the previous period according to the segment difference to identify the fluctuating segment, removes the fluctuating segment and retains the stable charge segment, and merges the stable charge segment into a structural item group segment chain set in sequence to generate a charge iteration structure set.

[0009] As a further embodiment of the present invention, the Kalman filter extracts the difference between the terminal voltage and the branch voltage based on the point-by-point difference and uses the difference as the observation input. It uses the state group consisting of the previous cycle's charge value, the previous cycle's RC branch voltage value, and the current discharge current to form a predicted value. It integrates the predicted value and the observation input to form an updated value, and superimposes the updated value onto the charge value and the RC branch voltage value to form a corrected state group.

[0010] As a further aspect of the present invention, the charge hysteresis screening module includes: Charge difference splitting submodule: Based on the charge iteration structure set, the iterative charge is subtracted from the previous period charge item by item to generate a difference sequence. The difference sequence is split into a difference chain according to the amplitude. A genetic algorithm is introduced to perform segment evolution and screening on the difference chain to generate an optimized segment sequence. The difference chains are sorted in order and merged into a segment set. Finally, the charge difference chain group is generated. Load replacement and retention submodule: Based on the load difference chain group, the difference and hysteresis threshold are compared by segment and the satisfied segment is identified. The load of the satisfied segment is replaced and the original load of the unsatisfied segment is retained. The replaced load and the retained load are arranged in sequence to form a chain group and generate the load replacement and retention sequence. The charge segment setting submodule: Based on the charge replacement sequence, it compares the charge and RC branch voltage values ​​in the sequence segment by segment and identifies the stable segment. It extracts the stable segments in sequence to form a single-point chain, organizes the single-point chain into a state output and adjusts the segment characteristics to obtain the charge hysteresis output.

[0011] As a further aspect of the present invention, the genetic algorithm is based on a multi-segment structure of a differential chain. Each segment within the differential chain is sequentially numbered, and the numbered sequence is used as the initial population. After each numbered sequence in the crossover population forms a new sequence along the segment position, some segment positions are randomly replaced to generate a mutated sequence. The initial sequence, crossover sequence, and mutated sequence are combined according to the difference amplitude and segment continuity to calculate the sequence fitness. A new population is formed by screening out sequences with higher fitness. The crossover, mutation, and screening operations are repeated until the population sequence changes tend to a stable state. The numbered sequence obtained by the final screening is output as the segment evolution result.

[0012] As a further aspect of the present invention, the electric flame energy level division module includes: Charge ratio construction submodule: Based on the charge iteration structure set and the charge hysteresis output, merge two charge values ​​along the same segment index and compare them synchronously with the carryable current value to generate a ratio sequence, split the ratio sequence segment by segment and locate the amplitude segment, and organize the amplitude segments in sequence to form a chain group to generate a charge ratio chain group. Energy level segment determination submodule: Based on the charge ratio chain group, the ratio of each segment in the ratio chain group is synchronously matched with the resistance segment of the heat conduction component of the electric flame hot pot stove to form an energy level column. The energy level column and temperature rise index are compared by segment and the energy level value is adjusted. The adjusted energy level values ​​are merged into an indicator item to obtain the energy level determination signal.

[0013] As a further aspect of the present invention, the power sequence reconstruction module includes: Power segment mapping submodule: Based on the energy level determination signal, expand the energy level determination signal along the periodic sequence, determine the segment power value by referring to the periodic segment, accumulate the segment power values ​​in sequence to form a continuous power chain, organize the continuous power chain into a segment sequence group, and generate a power segment chain group. Differential Segment Screening Submodule: Based on the power segment chain group, the adjacent power values ​​in the chain group are subtracted in sequence to form a differential segment group. The differential segment group is compared according to the threshold range and stable segments are identified. The stable segments are merged into a periodic power sequence in sequence, and the finished periodic power sequence is output to construct the reconstructed power sequence.

[0014] As a further aspect of the present invention, the hot pot stove heating execution module includes: Power alignment construction submodule: Based on the reconstructed power sequence, extract the reconstructed power sequence in paragraph order and match each paragraph with the power value. Align the corresponding power value with the heat-conducting plate segment of the electric flame hot pot stove heating element point by point to form an alignment chain. Organize the alignment chain in order to generate a heating mapping chain group. Trigger Link Organizing Submodule: Based on the heating mapping chain group, expand the power alignment points within the heating mapping chain group along the sequence and connect the alignment points with the corresponding heating body action segments. Organize the connection items point by point to form a trigger line chain. After the trigger line chain items are bundled, they are arranged into a control sequence to obtain the heating trigger command group.

[0015] A battery-state adaptive electric flame hot pot stove with precise control includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the above-mentioned battery-state adaptive electric flame hot pot stove precise control system.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: 1. In this invention, by comparing the voltage value at the battery output terminal with the voltage value of the RC branch and using Kalman filtering to correct the load, the influence of voltage transient disturbances and internal resistance changes on the load estimation is compressed to a smaller error range, and the resolution of the battery's load capacity on the numerical scale is improved, thus reducing the deviation between the discharge capacity assessment result and the actual output capacity. 2. In this invention, by using a genetic algorithm to perform segment evolution screening on the difference amplitude and segment continuity, short-term random disturbances and persistent lag offsets are distinguished in numerical expression. The contribution of control decision-making to medium- and long-term carrying capacity and transient peak load is more clearly defined, and output power adjustment no longer depends on an excessively wide safety margin. 3. In this invention, by merging the charge iteration structure set with the charge hysteresis output and the current carrying capacity into a charge ratio chain group, the power distribution segment is simultaneously divided into stages in the resistance and temperature rise dimensions, the heating temperature fluctuation of the hot pot stove is compressed within a preset value range, and the battery capacity utilization rate and the heat output stability per unit time are improved at the same time. Attached Figure Description

[0017] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0018] 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.

[0019] Example 1 Please see Figure 1 This invention provides a technical solution: a precise control system for an electric flame hot pot stove with battery state adaptive control includes: State deduction module: The battery output voltage, discharge circuit current and RC branch voltage obtained by the sensor are compared with the terminal voltage and branch voltage and the charge is corrected by Kalman filtering. The charge iterative structure set is generated by comparing the terminal voltage and branch voltage and correcting the charge. Charge lag screening module: Based on the charge iteration structure set, the difference between the iterative charge and the previous period charge is split, and the difference chain is optimized by genetic algorithm before replacement and maintenance. The single value output is determined by comparing the replacement charge with the voltage value of the RC branch, and the charge lag output is obtained. Electric flame energy level division module: Based on the charge iteration structure set and charge hysteresis output, the two charges and the current carrying capacity are merged and split to form a ratio chain. The ratio chain is compared with the resistance segment of the electric flame hot pot stove heat conduction component to determine the energy level segment. The temperature rise index value is judged and the energy level is adjusted to obtain the energy level judgment signal. Power sequence reconstruction module: Based on the energy level determination signal, the power value is determined by mapping the energy level determination signal to periodic segments. The power difference between adjacent segments is accumulated to form a difference sequence. Stable segments are screened by comparing the difference sequence with the screening threshold to construct the reconstructed power sequence. Hot pot stove heating execution module: Based on the reconstructed power sequence, extract the segments of the reconstructed power sequence and determine the corresponding power values. Align the power values ​​with the heat-conducting plate segments of the electric flame hot pot stove heating element, organize the alignment content to form a trigger link, and obtain the heating trigger command group.

[0020] The charge iteration structure set includes a voltage-to-charge group, voltage chain sequence values, and a structure item segment chain set. The charge hysteresis output includes a charge difference chain, a charge replacement sequence, and a single-point chain. The energy level determination signal includes a charge ratio chain, an energy level column, and an indicator item. The reconstructed power sequence includes a power segment chain, a difference segment group, and a periodic power sequence product. The heating trigger command group includes a heating mapping chain, a trigger line chain, and a control sequence.

[0021] The state deduction module includes: Voltage Comparison Submodule: The module obtains the terminal voltage value of the battery output terminal, the discharge current value of the discharge circuit, and the voltage value of the RC branch through sensors. Kalman filtering is introduced to correct the difference between the terminal voltage and the branch voltage. The terminal voltage and the branch voltage are compared and the load is corrected. The current is added to form a sequence and then merged into a load segment to generate a voltage comparison load group. Voltage chain construction submodule: Based on the voltage-to-charge group, the discharge current is added sequentially and superimposed with the branch voltage to form an extended sequence. The extended sequence is compared with the voltage-to-charge group segment by segment and the corresponding segment is confirmed. The corresponding segments are then aggregated into chain item values ​​to generate voltage chain sequence values. Charge screening submodule: Based on the voltage chain sequence value, the load in the sequence value is compared with the load of the previous period by segment difference to identify the fluctuating segment, the fluctuating segment is eliminated and the stable load segment is retained. The stable load segments are sequentially merged into the structural item group segment chain set to generate the charge iteration structure set. Voltage Comparison Submodule: Based on the sensor output voltage, discharge current, and RC branch voltage, a discrete state-space model is constructed using a Kalman filter algorithm. The state vector contains two quantities: charge and RC branch voltage. The state transition matrix is ​​set as a 2x2 matrix. The first row and first column have a value of 1.0, and the first row and second column have a value of -0.01, derived from the statistical results of the RC branch voltage change during discharge of a 48V 50Ah battery pack at 10A current with a sampling interval of 0.1 seconds in a 25°C environment. The second row and first column have a value of 0.0, and the second row and second column have a value of 0.99, derived from the average measured value of the RC branch voltage attenuation ratio of adjacent sampling points. The observation matrix has values ​​of 1.0 and 0.0 in each of the first and second rows. The process noise covariance matrix is... The corner term is set to 0.0001, based on the square order of magnitude of the residuals of the load estimation and the voltage estimation of the RC branch. The observation noise covariance is set to 0.0005, based on the statistical result that the noise standard deviation of the terminal voltage sensor is about 0.02 volts under the 48-volt range. In each 0.1-second sampling period, the predicted state quantity is calculated using the state quantity and matrix parameters of the previous moment, and the load estimation and RC branch voltage estimation are corrected by the gain of the Kalman filter algorithm. The power increment is calculated by the corrected load estimation and the discharge current according to the sampling interval. Under the 10-amp discharge condition, the power increment per cycle is 1 amp-second. The 600 consecutive sampling points are divided into a segment, and the starting load, ending load and average discharge current of each segment are recorded. The data of each segment are merged in chronological order to generate a voltage-to-load comparison group. The voltage chain construction submodule reads the discharge current and RC branch voltage data of each segment based on the voltage-to-charge ratio. The discharge current is forward-accumulated at a sampling step of 0.1 seconds and normalized using a 50 amp-hour capacity value to form a current integral normalized sequence. The sequence value range is limited to 0 to 1. A linear superposition operation is performed on the current integral normalized sequence and the RC branch voltage sequence, with superposition weights of 0.7 and 0.3. The weight ratio is statistically set based on the fitting proportion of voltage curves collected from a 48V battery pack under 5A and 10A discharge conditions. The resulting extended sequence is divided into windows of 20 sampling points, each window covering 2 seconds of data. The calculations for each window are then performed. The mean of the extended sequence is calculated, and the neighboring load segments are retrieved in the voltage reference load group according to the time range. The mean load of the load segment and the mean voltage of the RC branch are read to form a comparison vector containing the window time range, the mean of the extended sequence, the mean of the load, and the mean of the RC branch voltage. The Euclidean distance metric is used to calculate the numerical difference between the comparison vectors of multiple windows and the reference vector. The window with the smallest numerical difference is selected to establish a correspondence with the reference load segment. The window time range, the normalized mean of the current integral, the mean voltage of the RC branch, and the mean of the load are combined into a chain term record. The chain term record sequence is established for all windows of the 30-minute discharge process and arranged by time index to generate the voltage chain sequence value. The charge screening submodule reads the average charge value of each chain item in the current discharge cycle based on the voltage chain sequence value, and retrieves the average charge value of the indexed chain items at the same time in the previous discharge cycle from the historical database. The historical database is derived from comparative test data of 48V 50Ah battery packs under the same ambient temperature and discharge current conditions. A differential operation is performed on the average charge values ​​of the two cycles to form a charge difference sequence. In 10 comparative tests, the steady-state charge difference was mostly between 0.00 and 0.02Ah, while the fluctuation difference was commonly greater than 0.03Ah. Chain items with an absolute difference less than 0.02Ah were marked as steady-state candidate segments, and chain items with an absolute difference greater than or equal to 0.03Ah were marked as fluctuation candidate segments. The continuity of the fluctuation candidate segments was then verified. When the number of consecutive chain terms reaches 3 or more, they are included in the fluctuation segment set. The average value of the RC branch voltage of the candidate stable load segment is read and the difference is calculated with the average value of the corresponding RC branch voltage in the previous cycle. In the 48V battery pack test, the chain term with an absolute value of RC branch voltage difference not exceeding 0.2V is identified as a stable voltage chain term. When the absolute value of the load difference is less than 0.02A and the absolute value of the RC branch voltage difference does not exceed 0.2V, it is included in the final stable load segment. The time interval between adjacent segments in the final stable load segment is checked in chronological order. Segments with a time interval not exceeding 5 seconds are merged into a single structural term. The start and end time, average load, average RC branch voltage, and discharge current statistics are recorded after merging. All structural terms are arranged in order into a structural term segment chain set to generate a charge iteration structural set.

[0022] Kalman filtering extracts the differences between the terminal voltage and the branch voltage point by point and uses the differences as the observation input. It uses the state group consisting of the previous cycle's charge value, the previous cycle's RC branch voltage value, and the current discharge current to form a predicted value. It integrates the predicted value with the observation input to form an updated value. The updated value is then superimposed on the charge value and the RC branch voltage value to form a corrected state group. Kalman filtering, according to the formula:

[0023] in: For the first Sampling period load estimate For the first Sampling period load estimate To update the gain coefficient for the load, This is the weighting factor for the terminal voltage difference. The weighting coefficient for the change in current. The weighting coefficient for temperature change. This is the weighting factor for open-circuit voltage offset. For the first The voltage measurement value at the sampling period is as follows. For the first Sampling period estimated value of RC branch voltage For the first Discharge current measurement value during sampling period For the first Discharge current measurement value during sampling period For the first Temperature measurement value during sampling period For reference temperature, For the first Sampling period open-circuit voltage estimate This is the nominal open-circuit voltage. For discrete sampling period index; Execution process: During each sampling period, the state deduction process involves the control processing unit reading the stored load estimate from the previous period. and the discharge current of the previous cycle and pre-calibrated reference temperature and nominal open circuit voltage Simultaneously obtain the current cycle terminal voltage from the voltage sampling channel, current sampling channel, and temperature sampling channel. Estimated voltage values ​​of RC branch Discharge current ,temperature And the open-circuit voltage estimate given by the open-circuit voltage estimation unit. In the arithmetic unit, calculate the four increments within the parentheses sequentially according to the formula structure, where the terminal voltage difference is calculated by... The change in current is given by The given temperature change is from The open-circuit voltage offset is given by Given the values, the four increments are then compared with their corresponding weights. , , , Multiply and sum in the same accumulator register to generate combined observation inputs. Use the covariance recursion result to determine the load update gain within the same period. Multiply the combined observations by Obtain the correction amount and Add them together to generate the current cycle load estimate. At the end of this cycle The charge iteration structure set is written for subsequent charge lag screening, electric flame energy level division and power sequence reconstruction, enabling the entire battery state adaptive electric flame hot pot stove precision control system to complete the charge update calculation involving multiple state variables such as voltage, current, temperature and open circuit voltage within a single calculation link.

[0024] The charge hysteresis screening module includes: The charge difference splitting submodule is based on the charge iteration structure set. It subtracts the iterative charge from the previous period charge item by item to generate a difference sequence. The difference sequence is split into a difference chain according to the magnitude. A genetic algorithm is introduced to perform segment evolution and screening on the difference chain to generate an optimized segment sequence. The difference chains are sorted in order and merged into a segment set. Finally, the charge difference chain group is generated. Load replacement and retention submodule: Based on the load difference chain group, the difference and hysteresis threshold are compared segment by segment and the satisfied segment is identified. The load of the satisfied segment is replaced and the original load of the unsatisfied segment is retained. The replaced load and the retained load are arranged in order to form a chain group and generate the load replacement and retention sequence. Charge segment set value submodule: Based on the charge replacement sequence, the load and RC branch voltage values ​​in the sequence are compared segment by segment to identify the stable segment. The stable segment is extracted in sequence to form a single-point chain. The single-point chain is organized into a state output and the segment characteristics are adjusted to obtain the charge hysteresis output. The charge difference decomposition submodule reads the iterative charge values ​​and previous cycle charge values ​​within each time period based on the charge iteration structure set. Using two complete discharge cycle data collected from a 48V 50Ah battery pack under conditions of 25°C, 10A discharge current, and a sampling interval of 0.1 seconds as a reference, it performs a step-by-step subtraction operation between the iterative charge and the previous cycle charge at the same time index position, generating a difference sequence arranged in chronological order. It records the absolute value of each difference in the difference sequence, and records with an absolute value less than 0.02Ah are classified into smaller sub-modules. Amplitude grouping is performed. Records with absolute amplitude values ​​between 0.02 amp-hours and 0.05 amp-hours are classified into the medium amplitude group, and records with absolute amplitude values ​​greater than 0.05 amp-hours are classified into the large amplitude group. The three groups are then sequentially connected according to their time indices to form three difference quantum chains. These three difference quantum chains are then combined in parallel according to the order of small amplitude, medium amplitude, and large amplitude to form a complete difference chain. A genetic algorithm is introduced to perform segment evolution and selection on the difference chains. Integer encoding is used to combine the start and end indices of each segment in the difference chain into chromosome gene pairs. The initial population size is 50 chromosomes, with a crossover probability of 0.7 and a mutation probability of 0.1. During fitness calculation, the average absolute value of the difference within each chromosome's covered segment, the standard deviation of the absolute value of the difference, and the segment length are weighted and summed with weights of 0.5, 0.3, and 0.2, respectively. A roulette wheel selection operation is performed based on fitness ranking. Selected chromosome pairs are cross-linked by exchanging the start and end index combination segments. In the mutation operation, the selected chromosomes are randomly shifted one and two sampling segments by the start and end indices. The selection, crossover, and mutation steps are repeated until the fitness change amplitude is lower than the pre-statistically obtained threshold of 0.01 ampere-hour difference for 20 consecutive generations. The final generation chromosome set is mapped back to the difference chain segment index and organized into an optimized segment sequence. The difference records corresponding to the optimized segment sequence are merged into multiple continuous segment sets according to time order. Each segment set is numbered and organized into a segment set. All difference records, time indices, and amplitude grouping labels within the segment set are summarized to generate a load difference chain group. The load replacement submodule reads the difference values ​​and corresponding iterative loads within each difference segment based on the load difference chain group. The hysteresis threshold is set to 0.03 AH based on statistical results from multiple comparative tests of the 48V 50AH battery pack. For each difference segment, the average and peak absolute values ​​of the difference are calculated. If either the average or peak value is greater than or equal to 0.03 AH, the segment is marked as a hysteresis satisfied segment. If both the average and peak values ​​are less than 0.03 AH, the segment is marked as a hysteresis unsatisfied segment. For each time index position within a hysteresis satisfied segment, the previous period's load is replaced with the iterative load. The replacement operation is recorded as a replacement load entry using the time index as the primary key. For hysteresis unsatisfied segments... Within each time index position, the previous cycle load is retained as a hold load entry. The time index is traversed in order from the discharge start point to the discharge end point. When the time index belongs to the replacement load entry set, the replacement load value is written. When the time index belongs to the hold load entry set, the hold load value is written. The output load value and the corresponding time index record are recorded. The source type is marked as replacement and hold at each time index position. A straightening operation is performed on the time index segments of the same source type that are continuously distributed in time. The start time, end time, source type and average load value within each segment are recorded. The continuous segment records are arranged in chronological order and organized into a chain structure data set to generate a load replacement and hold sequence. The charge segment setting submodule reads the charge values ​​and time ranges within each continuous segment based on the charge alternation sequence. Simultaneously, it extracts the voltage value sequence of the RC branch within the same time range from the RC branch voltage measurement record according to the time index. Statistical analysis is performed on the RC branch voltage changes in the 48V 50Ah battery pack test data. The stable segment determination criteria are set as follows: the absolute value of the charge difference within the segment is no greater than 0.02Ah, and the difference between the peak and valley values ​​of the RC branch voltage is no greater than 0.2V. The difference between the peak and valley values ​​of the charge and the difference between the peak and valley values ​​of the RC branch voltage are calculated for each continuous segment within the time range. When both differences meet the above stability conditions, the continuous segment is marked as a stable segment. The charge values ​​at each sampling time within the stable segment's time range are extracted as a single-point sequence. For each stable segment, samples are taken every 5 times according to the time index within the single-point sequence. A representative point is extracted, and the load value of the representative point is combined with the corresponding RC branch voltage value to form a single-point chain entry. The time index, load value, and RC branch voltage value of the representative point are recorded. The single-point chain entries generated in the stable section are arranged in chronological order and divided into multiple state segments. The average load value and the average RC branch voltage value are calculated in each state segment, and the state segment number, time range, and average parameters are recorded. All state segments are arranged into a state output sequence. At the same time, based on the correspondence between load and RC branch voltage obtained from multiple tests of the 48V battery pack, the load value and RC branch voltage value in each state segment are adjusted to the center value of the corresponding statistical interval. The average parameter information of the state segment in the state output sequence is updated. The output content of each state segment is summarized to obtain the charge hysteresis output.

[0025] The genetic algorithm, based on the multi-segment structure of the differential chain, sequentially numbers each segment within the differential chain and uses the numbered sequence as the initial population. After the numbered sequences in the crossover population form new sequences along the segment positions, some segment positions are randomly replaced to generate mutation sequences. The initial sequence, crossover sequence, and mutation sequence are combined according to the difference amplitude and segment continuity to calculate the sequence fitness, and sequences with higher fitness are selected to form a new round of population. The crossover, mutation, and selection operations are repeated until the population sequence changes tend to a stable state, and the final selected numbered sequence is output as the segment evolution result. Genetic algorithm, according to the formula:

[0026] in: For the first chromosome fitness value, For chromosome indexing, The normalized weight coefficient is one. The normalized weighting coefficient is two. The normalized weighting coefficient is three. The normalized weighting coefficient is four. For the first The average absolute value of the difference in charge within the differential segment of chromosome coverage. For all chromosomes in the current population The maximum value in, For the first Standard deviation of the absolute value of the difference in charge within the differential segment of chromosome coverage. For all chromosomes in the current population The maximum value in, For the first The length of the differential segment covered by each chromosome. For all chromosomes in the current population The maximum value in, For the first The average absolute value of the voltage offset of the resistive-capacitive branch within the differential segment covered by each chromosome. For all chromosomes in the current population The maximum value in; Execution process: First, based on the start and end indices of the recorded segments in the difference chain, extract the corresponding segment samples of each chromosome in the charge difference sequence. Then, call the arithmetic unit to accumulate the absolute values ​​of all differences within the segment and divide by the number of sampling points before writing the result. The relative absolute value of the difference Calculate the average of the squared deviations and take the square root. The number of sampling points is directly counted and written. Then, voltage samples are read from the RC branch voltage data buffer according to the same time index, the difference is calculated with the steady-state target voltage value, the absolute value is taken, and the average is written. Complete the analysis of all chromosomes in the current population. , , , After calculation, the control processing unit searches for its maximum value across the entire population and writes the maximum average difference into the database. Write the maximum standard deviation into Write the maximum length Write the maximum voltage offset Weighting coefficient , , , During the system calibration phase, the parameters were obtained by performing a grid search on multiple sets of discharge and temperature test samples. During the online operation phase, the parameters were read in one go from the parameter area. In each generation of genetic iteration, the control processing unit will... , , , and , , , By combining the improved formulas, division, multiplication, and addition operations are performed sequentially within the arithmetic unit to obtain the fitness values ​​of each chromosome. ,Will The sequence is sent to the roulette wheel selection logic and crossover mutation logic, driving the charge difference segment index sequence to be continuously updated in multiple generations of evolution, thus building the segment evolution basis for the charge lag output in the battery state adaptive electric flame hot pot stove precision control system.

[0027] The electric flame energy level classification module includes: The charge ratio construction submodule: Based on the charge iteration structure set and charge hysteresis output, it merges two charge values ​​along the same segment index and compares them synchronously with the carryable current value to generate a ratio sequence. It then splits the ratio sequence segment by segment and locates the amplitude segment. Finally, it organizes the amplitude segments in order to form a chain group and generates a charge ratio chain group. Energy level segment determination submodule: Based on the charge ratio chain group, the ratio of each segment in the ratio chain group is synchronously matched with the resistance segment of the heat conduction component of the electric flame hot pot stove to form an energy level column. The energy level column and temperature rise index are compared according to the segment ratio and the energy level value is adjusted. The adjusted energy level values ​​are merged into an indicator item to obtain the energy level determination signal. The charge ratio construction submodule reads charge records and carrying current values ​​arranged by time index based on the charge iteration structure set and charge lag output. It aligns charge records with the same time index, groups the iterated charge values ​​and lag charge values ​​within each time period, and adds the carrying current value. For time periods with time index gaps, it uses a piecewise linear interpolation algorithm to supplement the charge and carrying current data. The interpolation time interval is 0.1 seconds, and the interpolation data comes from the measured charge and current values ​​at two adjacent time points. The supplementation is completed by distributing charge and current values ​​proportionally along the time axis. The total charge value is obtained by adding the iterated charge values ​​and lag charge values ​​within each time period group. The carrying current value within the same data group is multiplied by a constant of 1 hour to obtain the reference charge value. The total charge value is then used as the reference charge value. Divide by the reference electrical quantity value to obtain a dimensionless ratio value. Write the ratio value, time index, and corresponding two load values ​​and carrying current value into a record. Arrange the records continuously in time order to form an initial ratio sequence. Split the initial ratio sequence according to the ratio value. Records with ratio values ​​less than 0.5 are assigned to the low amplitude segment, records with ratio values ​​between 0.5 and 1.2 are assigned to the medium amplitude segment, and records with ratio values ​​greater than 1.2 are assigned to the high amplitude segment. The low amplitude segment, medium amplitude segment, and high amplitude segment are merged according to time continuity. Records with continuous time intervals of no more than 5 seconds are merged into the same amplitude segment to form several amplitude segment units. The amplitude segment units are organized in time order. Record the start and end time, ratio value range, and corresponding load statistics of each amplitude segment unit. Summarize the amplitude segment unit structure to generate a load ratio chain group. The energy level segment determination submodule reads the range and time start / end information of the ratio values ​​within each amplitude segment unit based on the charge ratio chain group. It retrieves the resistance segment table from the design and testing data of the electric flame hot pot stove's heat-conducting components, dividing the overall resistance measurement results of the heat-conducting components into resistance intervals in 2-ohm steps: 0 to 2 ohms, 2 to 4 ohms, 4 to 6 ohms, and above 6 ohms. Simultaneously, it retrieves the temperature rise index table from the temperature rise experiment database, dividing the temperature rise index into five levels, from level 1 to level 5, based on values ​​obtained from testing 10 prototypes at 25 degrees Celsius. Statistical results of temperature curves measured under different input powers were used to locate the corresponding thermal component resistance segments for each amplitude segment unit in the resistance segment table. The lookup rule was to extract the area resistance measurement results from the multi-point measurement data of the thermal component based on the working area index of the thermal component corresponding to the amplitude segment time range, compare the resistance measurement results with the boundaries of each resistance interval, and select the resistance segment containing the resistance measurement results as the matching resistance segment. For the amplitude segment units of the matched resistance segment, a hierarchical mapping algorithm was used to map them to the temperature rise index table, and the ratio value range was mapped to the temperature rise in a linear segmented manner. The index levels are assigned by corresponding lower ratio values ​​to lower temperature rise index levels and higher ratio values ​​to higher temperature rise index levels. Within each amplitude segment unit, the resistance segment number and temperature rise index level are recorded. After obtaining the resistance segment number and temperature rise index level, integer energy level values ​​are generated through each amplitude segment unit. The generation process involves normalizing the resistance segment numbers sequentially to integers 1 to 4, and normalizing the temperature rise index level to integer levels 1 to 5. These two integers are then combined using a weighted ratio of 3:7. The 3-part weight is allocated to the resistance segment number, and the 7-part weight is allocated to the temperature rise index, etc. The energy level is calculated by weighted summation and rounding the result to obtain the integer value of the energy level. The integer values ​​of the energy level are arranged in chronological order to form a column structure data. For each time period, the integer value of the energy level, the corresponding resistance segment number, and the temperature rise index level are recorded. The entire column of data is recorded as the energy level column. The energy level columns are merged in chronological order. Records with the same integer value of the energy level in adjacent time periods are merged into a single indicator item. The merged time range of each indicator item, the integer value of the energy level, the associated resistance segment information, and the temperature rise index information are recorded. The indicator items are arranged in a sequence to obtain the energy level determination signal.

[0028] The power sequence reconstruction module includes: Power segment mapping submodule: Based on the energy level determination signal, the energy level determination signal is expanded along the periodic sequence, the segment power value is determined by referring to the periodic segment, the segment power values ​​are sequentially accumulated to form a continuous power chain, the continuous power chain is organized into a segment sequence group, and a power segment chain group is generated. Differential Segment Screening Submodule: Based on power segment chain groups, adjacent power values ​​within the chain group are subtracted sequentially to form differential segment groups. The differential segment groups are compared according to the threshold range and stable segments are identified. The stable segments are merged into periodic power sequences in sequence, and the finished periodic power sequence is output to construct the reconstructed power sequence. Power Segment Mapping Submodule: Based on the energy level determination signal, read the energy level value and period number corresponding to each time index, and match the energy level value with the preset power table. In the power table, the power corresponding to energy level 1 is 500 watts, energy level 2 is 800 watts, energy level 3 is 1200 watts, energy level 4 is 1600 watts, and energy level 5 is 2000 watts. Using the period number and sampling interval, the power value of each energy level is mapped and written into the power sequence arranged in time order. The period segment is divided according to the period number with 10 sampling points as a window length. The arithmetic mean power value of the power sequence in each period segment is calculated and the start time, end time and corresponding average power value of the period are recorded. The average power values ​​of each period segment are accumulated in ascending order of period number to form a continuous power chain. The continuous power chain is divided into multiple power segment units according to the period boundary. The start and end time, average power value and period number of each power segment unit are organized into a segment record set. All segment records are arranged in time order to generate a power segment chain group. The differential segment filtering submodule reads the average power value and start and end time of each segment arranged chronologically based on the power segment chain. It subtracts the average power values ​​of adjacent segments sequentially. For each segment starting from the second segment, it records the difference value by subtracting the average power value of the previous segment from the current segment's average power value. This difference value, along with the corresponding time indices of the two segments, is stored in the differential segment group. The preset power difference stability threshold range is determined statistically from multiple sets of prototype test data. The lower limit of the stability threshold is 0 watts, and the upper limit is 100 watts. The module compares each difference value in the differential segment group with the upper and lower limits of the stability threshold. When the absolute value of the difference is within the range of 0 watts to 100 watts... Stable markers are recorded intermittently. When the absolute value of the difference is greater than 100 watt-hours, unstable markers are recorded. A continuous segment identification algorithm is used to find time-continuously distributed segments in the stable marker sequence. The sets of stable markers with a continuous length of not less than 3 segments are merged into a stable segment set. The stable segment set is arranged into multiple periodic segments in chronological order. The average power value within each periodic segment is calculated, and the segment start time, end time, and average power value are recorded. The periodic segments are arranged and connected according to the discharge cycle number and time sequence to form a complete power sequence. The finished periodic power sequence is output. The finished periodic power sequence is registered as a reconstructed power sequence in the control logic.

[0029] The hot pot stove heating execution module includes: Power alignment construction submodule: Based on the reconstructed power sequence, extract the reconstructed power sequence in paragraph order and match each paragraph with the power value. Align the corresponding power value with the heat-conducting plate segment of the electric flame hot pot stove heating element point by point to form an alignment chain. Organize the alignment chain in order to generate a heating mapping chain group. Trigger Link Organizing Submodule: Based on the heating mapping chain group, expand the power alignment points within the heating mapping chain group along the sequence and connect the alignment points with the corresponding action segments of the heating body. Organize the connection items point by point to form the trigger line chain. After the trigger line chain items are bundled, they are arranged into a control sequence to obtain the heating trigger instruction group. The power alignment submodule extracts the reconstructed power records of each segment in sequence based on the reconstructed power sequence. It establishes segment numbers using power segment indices and timestamps, numbered sequentially from 1 to N. The segment numbers are matched with the segment numbers of the heat-conducting plates in the heating element of the electric flame hot pot stove. There are 8 heat-conducting plate segments, numbered from H1 to H8. The matching rule is to take the remainder of the segment number divided by 8 and add 1 to obtain the heat-conducting plate segment number. The reconstructed power records of each segment are written to the target heat-conducting plate segment number. The reconstructed power values ​​are divided into power levels: power below 800 watts is recorded as Level 1 power, 800 watts to 1200 watts as Level 2 power, 1200 watts to 1600 watts as Level 3 power, and above 1600 watts as Level 4 power. Four power levels are defined, with power level codes written in each segment record, from 1 to 4 representing the four power levels mentioned above. Using a sequential coding combination algorithm, the segment number, timestamp, power value, power level, and heat-conducting sheet segment number fields are combined in each mapping record to generate a single-point alignment record. All single-point alignment records are sorted by time sequence and segment number to form a sequential alignment chain. Adjacent records in the alignment chain with the same heat-conducting sheet segment number and the same power level code are merged into a single segment record through interval merging processing. The start time, end time, heat-conducting sheet segment number, and unified power level value are recorded. The segment records are then organized into a two-dimensional sequence list structure according to the time sequence and heat-conducting sheet segment number sequence to generate a heating mapping chain group. The trigger link organization submodule expands each power alignment record along the sequence based on the heating mapping chain. Using a sequential traversal algorithm, it reads the heat-conducting plate segment number, power level, and time range one by one, starting from the earliest record. For each record, it uses a preset action template table to find the corresponding heating element action segment. Within the action template table, power level 1 is mapped to a low duty cycle trigger mode, power level 2 to a medium duty cycle trigger mode, power level 3 to a high duty cycle trigger mode, and power level 4 to a full power trigger mode. Simultaneously, it maps the heat-conducting plate segment number to the hardware drive channel number: H1 corresponds to drive channel CH1, H2 to drive channel CH2, and so on, up to H8 corresponding to CH8. Trigger entries are generated on each alignment record. The trigger entry fields include trigger start time, trigger end time, drive channel number, duty cycle, and time range. The duty cycle level encoding and trigger priority encoding information are generated. The trigger priority is set according to the geometric position of the heat-conducting plate segment on the bottom of the pot. The priority encoding of the central area is 3, the priority encoding of the edge area is 1, and the priority encoding of the middle ring area is 2. The trigger entries are written into the intermediate trigger row list. Entries with the same time range and drive channel number in the overlapping intermediate trigger row list are merged and adjusted. The overlapping time period is retained according to the priority encoding, and the entry with the larger priority value is retained. If necessary, the time range is divided into non-overlapping time segments. The sorted trigger row chain is generated and reordered in ascending order of time. Each record is assigned a sequence number, starting from 1 and incrementing, to form a control sequence containing the sequence number, time range, drive channel number, duty cycle level encoding, and priority encoding. The control sequence entries are output to obtain the heating trigger command group.

[0030] 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 precise control system for an electric flame hot pot stove with battery state adaptive control, characterized in that, The system includes: State deduction module: The battery output voltage, discharge circuit current and RC branch voltage obtained by the sensor are compared with the terminal voltage and branch voltage and the charge is corrected by Kalman filtering. The charge iterative structure set is generated by comparing the terminal voltage and branch voltage and correcting the charge. Charge lag screening module: Based on the charge iteration structure set, the difference between the iteration charge and the previous period charge is split, and the difference chain is optimized by a genetic algorithm before replacement and maintenance. The single value output is determined by comparing the replacement charge with the voltage value of the RC branch, and the charge lag output is obtained. Electric flame energy level division module: Based on the charge iteration structure set and the charge hysteresis output, the two charges and the current carrying capacity are merged and split to form a ratio chain. The ratio chain is compared with the resistance segment of the electric flame hot pot stove heat conduction component to determine the energy level segment. The temperature rise index value is judged and the energy level is adjusted to obtain the energy level determination signal. Power sequence reconstruction module: Based on the energy level determination signal, the power value is determined by mapping the energy level determination signal to periodic segments, the power difference between adjacent segments is accumulated to form a difference sequence, and stable segments are screened by comparing the difference sequence with the screening threshold to construct a reconstructed power sequence; Hot pot stove heating execution module: Based on the reconstructed power sequence, extract the reconstructed power sequence segments and determine the corresponding power values, align the power values ​​with the heat-conducting plate segments of the electric flame hot pot stove heating element, organize the alignment content to form a trigger link, and obtain a heating trigger command group.

2. The battery-state adaptive electric flame hot pot stove precision control system according to claim 1, characterized in that, The charge iteration structure set includes a voltage-to-charge group, a voltage chain sequence value, and a structure item segment chain set. The charge hysteresis output includes a charge difference chain group, a charge replacement sequence, and a single-point chain. The energy level determination signal includes a charge ratio chain group, an energy level column, and an indicator item. The reconstructed power sequence includes a power segment chain group, a difference segment group, and a periodic power sequence product. The heating trigger command group includes a heating mapping chain group, a trigger row chain, and a control sequence.

3. The battery-state adaptive electric flame hot pot stove precision control system according to claim 1, characterized in that, The state deduction module includes: Voltage Comparison Submodule: The module obtains the terminal voltage value of the battery output terminal, the discharge current value of the discharge circuit, and the voltage value of the RC branch through sensors. Kalman filtering is introduced to correct the difference between the terminal voltage and the branch voltage. The terminal voltage and the branch voltage are compared and the load is corrected. The current is added to form a sequence and then merged into a load segment to generate a voltage comparison load group. Voltage chain construction submodule: Based on the voltage-to-charge group, the discharge current is added sequentially and the branch voltage is superimposed to form an extended sequence. The extended sequence is compared with the voltage-to-charge group segment by segment and the corresponding segment is confirmed. The corresponding segments are aggregated into chain item values ​​to generate voltage chain sequence values. The charge screening submodule: Based on the voltage chain sequence value, it compares the charge within the sequence value with the charge of the previous period according to the segment difference to identify the fluctuating segment, removes the fluctuating segment and retains the stable charge segment, and merges the stable charge segment into a structural item group segment chain set in sequence to generate a charge iteration structure set.

4. The battery-state adaptive electric flame hot pot stove precision control system according to claim 1, characterized in that, The Kalman filter extracts the difference between the terminal voltage and the branch voltage point by point and uses the difference as the observation input. It uses the state group consisting of the previous cycle's charge value, the previous cycle's RC branch voltage value, and the current discharge current to form a predicted value. It integrates the predicted value with the observation input to form an update value. The update value is then superimposed on the charge value and the RC branch voltage value to form a corrected state group.

5. The battery-state adaptive electric flame hot pot stove precision control system according to claim 1, characterized in that, The charge hysteresis screening module includes: Charge difference splitting submodule: Based on the charge iteration structure set, the iterative charge is subtracted from the previous period charge item by item to generate a difference sequence. The difference sequence is split into a difference chain according to the amplitude. A genetic algorithm is introduced to perform segment evolution and screening on the difference chain to generate an optimized segment sequence. The difference chains are sorted in order and merged into a segment set. Finally, the charge difference chain group is generated. Load replacement and retention submodule: Based on the load difference chain group, the difference and hysteresis threshold are compared by segment and the satisfied segment is identified. The load of the satisfied segment is replaced and the original load of the unsatisfied segment is retained. The replaced load and the retained load are arranged in sequence to form a chain group and generate the load replacement and retention sequence. The charge segment setting submodule: Based on the charge replacement sequence, it compares the charge and RC branch voltage values ​​in the sequence segment by segment and identifies the stable segment. It extracts the stable segments in sequence to form a single-point chain, organizes the single-point chain into a state output and adjusts the segment characteristics to obtain the charge hysteresis output.

6. The battery-state adaptive electric flame hot pot stove precision control system according to claim 1, characterized in that, The genetic algorithm, based on the multi-segment structure of the differential chain, sequentially numbers each segment within the differential chain and uses the numbered sequence as the initial population. After each numbered sequence in the crossover population forms a new sequence along the segment position, some segment positions are randomly replaced to generate a mutated sequence. The initial sequence, crossover sequence, and mutated sequence are combined according to the difference amplitude and segment continuity to calculate the sequence fitness, and a new population is formed by screening out sequences with higher fitness. The crossover, mutation, and screening operations are repeated until the population sequence changes tend to a stable state, and the numbered sequence obtained by the final screening is output as the segment evolution result.

7. The battery state adaptive electric flame hot pot stove precision control system according to claim 1, characterized in that, The electric flame energy level division module includes: Charge ratio construction submodule: Based on the charge iteration structure set and the charge hysteresis output, merge two charge values ​​along the same segment index and compare them synchronously with the carryable current value to generate a ratio sequence, split the ratio sequence segment by segment and locate the amplitude segment, and organize the amplitude segments in sequence to form a chain group to generate a charge ratio chain group. Energy level segment determination submodule: Based on the charge ratio chain group, the ratio of each segment in the ratio chain group is synchronously matched with the resistance segment of the heat conduction component of the electric flame hot pot stove to form an energy level column. The energy level column and temperature rise index are compared by segment and the energy level value is adjusted. The adjusted energy level values ​​are merged into an indicator item to obtain the energy level determination signal.

8. The battery state adaptive electric flame hot pot stove precision control system according to claim 1, characterized in that, The power sequence reconstruction module includes: Power segment mapping submodule: Based on the energy level determination signal, expand the energy level determination signal along the periodic sequence, determine the segment power value by referring to the periodic segment, accumulate the segment power values ​​in sequence to form a continuous power chain, organize the continuous power chain into a segment sequence group, and generate a power segment chain group. Differential Segment Screening Submodule: Based on the power segment chain group, the adjacent power values ​​in the chain group are subtracted in sequence to form a differential segment group. The differential segment group is compared according to the threshold range and stable segments are identified. The stable segments are merged into a periodic power sequence in sequence, and the finished periodic power sequence is output to construct the reconstructed power sequence.

9. The battery state adaptive electric flame hot pot stove precision control system according to claim 1, characterized in that, The hot pot stove heating execution module includes: Power alignment construction submodule: Based on the reconstructed power sequence, extract the reconstructed power sequence in paragraph order and match each paragraph with the power value. Align the corresponding power value with the heat-conducting plate segment of the electric flame hot pot stove heating element point by point to form an alignment chain. Organize the alignment chain in order to generate a heating mapping chain group. Trigger Link Organizing Submodule: Based on the heating mapping chain group, expand the power alignment points within the heating mapping chain group along the sequence and connect the alignment points with the corresponding heating body action segments. Organize the connection items point by point to form a trigger line chain. After the trigger line chain items are bundled, they are arranged into a control sequence to obtain the heating trigger command group.

10. A battery-state adaptive electric flame hot pot stove with precise control, comprising a memory and a processor, characterized in that, The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the battery state adaptive electric flame hot pot stove precision control system according to any one of claims 1 to 9.

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