Intelligent vacuum sealer control method, device and equipment and storage medium

Abstract

The invention provides an intelligent vacuum sealer control method, device and equipment and a storage medium, and the method comprises the steps: carrying out the periodic sampling of the output voltage of a battery pack through an analog-to-digital conversion module, and generating the electric quantity percentage data of a current battery based on a preset percentage conversion table; acquiring a plurality of air pressure original values through a vacuum degree sensor, and performing deviation comparison on vacuum degree values corresponding to the air pressure original values and a preset target vacuum threshold value to generate a vacuum degree deviation value; and if the vacuum degree deviation value meets the preset tolerance range, generating a target PWM duty ratio according to the electric quantity percentage data and the vacuum degree deviation value, and outputting a driving signal according to the target PWM duty ratio to perform vacuum pumping. Through the implementation of the scheme, the intelligent control efficiency of the vacuum sealing machine in the vacuum sealing process can be effectively improved, and the sealing success rate is improved while the battery endurance time is prolonged.

Description

Technical Field

[0001] The present application relates to the field of food packaging technology, and in particular to a control method, device, equipment and storage medium for an intelligent vacuum sealing machine. Background Art

[0002] With the growing demand for food preservation and portable packaging at home, smart vacuum sealers, as an important tool for improving food preservation, are becoming increasingly popular in home kitchens, convenient catering pre-processing, and small food processing workshops. For example, in everyday households, users want to vacuum seal leftover meals, pre-prepared preserved foods, or easily oxidized dried goods to extend shelf life and avoid cross-contamination. However, traditional vacuum sealing equipment often uses a mechanical relay control structure with limited functionality and a lack of intelligent control strategies, resulting in high sealing failure rates and frequent problems that affect user experience. Summary of the Invention

[0003] The present application provides a method, device, equipment and storage medium for controlling an intelligent vacuum sealing machine, which are used to solve the problem of high sealing failure rate caused by the lack of intelligent control strategy in related technologies.

[0004] A first aspect of the present application provides a method for controlling an intelligent vacuum sealing machine, the method comprising: Initializing the peripheral modules of the control system according to the power startup signal, and obtaining a set of initialization operating parameters corresponding to each peripheral module; wherein the peripheral modules include an analog-to-digital conversion module; The analog-to-digital conversion module periodically samples the output voltage of the battery pack to obtain a battery voltage sampling value, and performs linear interpolation conversion on the voltage sampling value based on a preset percentage conversion table to generate current battery power percentage data; Obtaining an original air pressure value through a vacuum sensor connected to the target container, performing a deviation comparison between a current vacuum value corresponding to the original air pressure value and a preset target vacuum threshold value to generate a vacuum deviation value; If the vacuum degree deviation value satisfies a preset tolerance range, a target PWM duty cycle is generated according to the power percentage data and the vacuum degree deviation value, and a driving signal is output according to the target PWM duty cycle to perform vacuum pumping.

[0005] Optionally, in a first implementation of the first aspect of the present application, the step of periodically sampling the battery pack output voltage by the analog-to-digital conversion module to obtain battery voltage sampling values, and performing linear interpolation conversion on the voltage sampling values ​​based on a preset percentage conversion table to generate current battery power percentage data includes: By setting a resistor divider network at the voltage node between the positive and negative electrodes of the battery pack, an analog voltage signal after voltage division is obtained; Periodically sampling the analog voltage signal based on the sampling instruction of the analog-to-digital conversion module to generate a voltage sampling sequence consisting of multiple voltage sampling points; Generate an average voltage value of the current battery by processing multiple voltage sampling points in the voltage sampling sequence, and perform interval matching between the average voltage value and each voltage segment in a preset percentage conversion table to determine a conversion segment identifier; According to the upper and lower limits of the conversion interval corresponding to the conversion segment identifier and the upper and lower limits of the corresponding mapped power percentage, the average voltage value is linearly interpolated within the segment to generate corresponding power percentage data.

[0006] Optionally, in a second implementation of the first aspect of the present application, after the step of periodically sampling the battery pack output voltage by the analog-to-digital conversion module to obtain battery voltage sampling values, and performing linear interpolation conversion on the voltage sampling values ​​based on a preset percentage conversion table to generate current battery power percentage data, the method includes: Generate a deviation by performing a difference calculation between the power percentage data and a power reference value corresponding to the power calibration coefficient of the initialization operating parameter set; Comparing the deviation with a preset first deviation threshold, and generating a calibration flag signal if the deviation exceeds the first deviation threshold; Extracting the interpolation slope parameters preset in the percentage conversion table for each voltage segment according to the calibration flag signal, and generating an adjusted correction slope coefficient by performing a differential analysis between the current voltage sampling value and the standard value recorded in the previous calibration cycle; By performing a covering replacement operation on the interpolation slope parameter, the correction slope coefficient is written into the corresponding position of the percentage conversion table, and the voltage percentage mapping rule called in the subsequent conversion process is updated.

[0007] Optionally, in a third implementation of the first aspect of the present application, the step of obtaining an original air pressure value through a vacuum sensor connected to the target container, performing a deviation comparison between a current vacuum value corresponding to the original air pressure value and a preset target vacuum threshold, and generating a vacuum deviation value includes: Acquire multiple vacuum pressure analog signals by collecting analog signal output from a vacuum sensor connected to the target container through a gas path, and executing a multi-cycle sampling instruction based on a preset time interval; Performing analog-to-digital conversion on the multiple vacuum pressure analog signals to generate a corresponding sequence of original air pressure values; Generate an effective air pressure value inside the target container by filtering and weighted averaging the numerical samples in the original air pressure numerical sequence; Converting the effective air pressure value into a current vacuum value according to a linear conversion coefficient relationship of the vacuum sensor; The current vacuum value is compared with a preset target vacuum threshold value to generate a vacuum deviation value.

[0008] Optionally, in a fourth implementation of the first aspect of the present application, if the vacuum degree deviation value satisfies a preset tolerance range, generating a target PWM duty cycle according to the power percentage data and the vacuum degree deviation value, and outputting a drive signal for vacuum pumping according to the target PWM duty cycle includes: Generate a corresponding first duty cycle component and a corresponding second duty cycle component by respectively calling a duty cycle reference mapping table preset in the control system for the power percentage data and the vacuum degree deviation value; Generating a corresponding target PWM duty cycle by weightedly fusing the first duty cycle component and the second duty cycle component; wherein the weighting coefficient is determined in an initialization parameter set according to a current battery state level and a vacuum regulation priority; Generating a driving signal with pulse width modulation characteristics by loading the target PWM duty cycle into a PWM control channel register; The driving signal is used to control the on / off state of the switch tube in the power driving module, output the driving voltage to the vacuum pump motor end, and drive the vacuum pump to perform vacuum extraction.

[0009] Optionally, in a fifth implementation of the first aspect of the present application, the method further includes: When the vacuum pump is currently stopped, the vacuum feedback data within the preset sampling period is compared to obtain a stable vacuum state flag; Outputting a sealing start instruction to the control system according to the stable vacuum state flag, and controlling the driving input end of the sealing heating module by loading a heating control PWM signal with a preset duty cycle value, driving the heating element to heat-seal the target container within a preset heating duration; During the heat sealing process, the heating duration is counted down by the time base generated by the timer module, and a stop signal is output to the heating control PWM module after the timing ends to terminate the sealing heating process.

[0010] Optionally, in a sixth implementation of the first aspect of the present application, the peripheral module further includes: a communication module, and the method further includes: By parsing the serial control instruction received by the communication module, an instruction identifier for instructing the control system to enter an upgrade state is obtained, and a firmware upgrade state flag is generated according to the instruction identifier; Performing a soft reset operation on the control system according to the firmware upgrade status flag, reloading the startup logic, and reading the firmware upgrade status flag during the startup process, calling the boot area program entry address based on the firmware upgrade status flag, and jumping to a preset firmware receiving boot area; By performing data comparison on the firmware data packets received by the communication module, a corresponding valid write data block is generated, and a Flash storage address range is determined according to the address field of the valid write data block; New firmware content is generated by erasing the target Flash address within the Flash storage address range and writing data to the valid write data block.

[0011] A second aspect of the present application provides an intelligent vacuum sealing machine control device, which is used to implement an intelligent vacuum sealing machine control method. The intelligent vacuum sealing machine control device includes: An acquisition module, configured to initialize peripheral modules of the control system according to a power startup signal and acquire a set of initialization operating parameters corresponding to each peripheral module; wherein the peripheral modules include an analog-to-digital conversion module; an analog-to-digital conversion module, configured to periodically sample the output voltage of the battery pack to obtain a battery voltage sampling value, and perform linear interpolation conversion on the voltage sampling value based on a preset percentage conversion table to generate current battery power percentage data; a comparison module, configured to obtain an original air pressure value through a vacuum sensor connected to a target container, perform a deviation comparison between a current vacuum value corresponding to the original air pressure value and a preset target vacuum threshold value, and generate a vacuum deviation value; The vacuum pumping module is configured to generate a target PWM duty cycle according to the power percentage data and the vacuum degree deviation value if the vacuum degree deviation value satisfies a preset tolerance range, and output a driving signal for vacuum pumping according to the target PWM duty cycle.

[0012] A third aspect of an embodiment of the present application provides an electronic device, comprising a memory and a processor, wherein the processor is configured to execute a computer program stored on the memory. When the processor executes the computer program, the processor implements the steps of the intelligent vacuum sealing machine control method provided in the first aspect of the embodiment of the present application.

[0013] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the intelligent vacuum sealing machine control method provided in the first aspect of the embodiment of the present application are implemented.

[0014] In summary, according to the intelligent vacuum sealing machine control method, device, equipment and storage medium provided by the present application scheme, through the implementation of the present application scheme, the target PWM duty cycle is generated according to the power percentage data and the vacuum degree deviation value, thereby realizing dynamic adjustment of the vacuum pump power, which can effectively improve the intelligent control efficiency of the vacuum sealing machine during the vacuum sealing process, and improve the sealing success rate while extending the battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flow chart of a control method for an intelligent vacuum sealer provided in an embodiment of the present application; Figure 2 A schematic diagram of a program module of a smart vacuum sealer control device provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to make the purpose, features, and advantages of the invention of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of this application.

[0017] In order to solve the problem of high sealing failure rate caused by lack of intelligent control strategy in related technologies, the embodiment of the present application provides a control method for an intelligent vacuum sealing machine, such as Figure 1 This is a flow chart of the intelligent vacuum sealing machine control method provided in this embodiment. The intelligent vacuum sealing machine control method includes the following steps: Step 110 : Initialize the peripheral modules of the control system according to the power startup signal, and obtain the initialization operation parameter set corresponding to each peripheral module.

[0018] In the embodiment, the peripheral module includes but is not limited to an analog-digital conversion module, and the peripheral module of the control system is initialized according to a power-on signal, mainly through a single-chip microcomputer to perform basic configuration on the connected external functional units, including clock source setting, IO port direction definition, interrupt control registration and loading operation of module activation state. The initialization objects cover analog-digital conversion modules (such as ADC channels), display interfaces, PWM generators and communication interfaces, and key running parameters previously saved from a non-volatile memory (such as an EEPROM), such as the last power calibration coefficient, the sealing frequency count value, the battery capacity level and the like, are also read, so as to provide a reference benchmark for subsequent modules. Whether the initialization process is correct or not directly determines whether the whole system is in a controllable and executable state, so it constitutes a logical starting point in the intelligent control flow of the sealing machine, and has a basic influence on the effectiveness of the subsequent sampling and control links.

[0019] In step 120, the output voltage of the battery pack is periodically sampled by the analog-digital conversion module to obtain a battery voltage sampling value, and the voltage sampling value is linearly interpolated and converted based on a preset percentage conversion table to generate the power percentage data of the current battery.

[0020] In the embodiment, the output voltage of the battery pack is periodically sampled by the analog-digital conversion module, mainly to convert the analog voltage signal into a processable digital quantity. Since the voltage of the lithium battery fluctuates with the use state and time, a resistance voltage dividing network is needed to reduce the total voltage of about 12V to the level range acceptable by the single-chip microcomputer ADC, and continuous multiple sampling is performed according to the set period. After the sampling data is statistically averaged to remove the instantaneous interference value, it is compared with the previously established voltage-percentage conversion table, the voltage segment is looked up and linear interpolation in the segment is performed, and finally the current power percentage value is converted. The value is not only used for battery power display, but also provides an input parameter for subsequent power regulation strategy, and is the core reference basis for energy-saving control and over-discharge protection mechanism.

[0021] In some embodiments, the output voltage of the battery pack is periodically sampled by an analog-to-digital conversion module to obtain a battery voltage sampling value, and the voltage sampling value is linearly interpolated and converted based on a preset percentage conversion table to generate the current battery power percentage data, including: setting a resistor divider network for the voltage node between the positive and negative poles of the battery pack to obtain the divided analog voltage signal; periodically sampling the analog voltage signal based on the sampling instruction of the analog-to-digital conversion module to generate a voltage sampling sequence consisting of multiple voltage sampling points; processing the multiple voltage sampling points in the voltage sampling sequence to generate the average voltage value of the current battery, and matching the average voltage value with each voltage segment in the preset percentage conversion table to determine the conversion segment identifier; performing intra-segment linear interpolation on the average voltage value according to the upper and lower limits of the conversion interval corresponding to the conversion segment identifier and the corresponding mapped upper and lower limits of the power percentage to generate the corresponding power percentage data.

[0022] In this embodiment, a voltage detection channel is provided between the positive and negative electrodes of the battery pack. Because the battery pack output voltage can be significantly higher than the input range that the analog-to-digital conversion module can directly process, a resistor divider network is constructed to reduce the original voltage to the operating voltage range of the ADC channel. This resistor divider network consists of two series resistors, connected between the positive and negative electrodes of the battery. Based on Ohm's law, a proportional voltage is generated at the intermediate node. This node serves as the output of the divided analog voltage signal, which is then fed into the microcontroller ADC channel for subsequent sampling. The analog-to-digital conversion module initiates periodic sampling operations based on the sampling interval set in the control logic, continuously reading the analog voltage signal. During each sampling, the ADC channel converts the analog signal into a digital value and stores the conversion result in a buffer queue, forming a voltage sampling sequence. Because the analog signal may be affected by factors such as electromagnetic interference, fluctuations in the battery's internal resistance, or changes in loading, the result of a single sampling may exhibit jitter. Therefore, the system performs multiple sampling operations within a unit cycle to obtain multiple voltage sampling points to reflect the instantaneous stable voltage level. Statistical averaging is performed on the acquired voltage sampling sequence. This process performs a weighted or equally weighted summation of the sampling points and takes the average value, thereby filtering out outliers and suppressing transient spikes. The result is an average value representing the battery's current output voltage. To convert this average voltage into a human-interpretable battery capacity percentage, a pre-designed voltage-to-capacity conversion table is used. This conversion table divides the entire battery voltage operating range into multiple segments. For example, 3.0V to 4.2V can be divided into several segments, each corresponding to a battery capacity percentage range, such as 20% to 30% or 30% to 40%. During this process, the system first identifies the voltage segment to which the average voltage value belongs. This method compares the average voltage with the predefined segment upper and lower limits in the conversion table to determine which segment it falls within. After identification, the two upper and lower limits of the mapped battery capacity corresponding to the segment are extracted from the table and set as the left and right boundary values, respectively. To ensure a consistent and seamless mapping of voltage to battery capacity, the system generates battery capacity percentage data by linearly interpolating the voltage values ​​within the segment. Linear interpolation is a numerical calculation method used to estimate the intermediate unknown value between two known points. In this scenario, the interpolation calculation formula is: Battery percentage = Left Battery + (Current Voltage - Left Boundary Voltage) × (Right Boundary Battery - Left Boundary Battery) / (Right Boundary Voltage - Left Boundary Voltage). This formula ensures that there will be no sudden changes or error accumulation when converting voltage values ​​into battery percentages in different segments. For example, if a segment is set to 3.70V to 3.85V, the corresponding battery range is 40% to 50%. When the average voltage is 3.775V, the interpolation result is 45%, which means a linear and smooth transition within this range.Ultimately, the calculated battery percentage data serves as one of the input variables for subsequent vacuum pump power control and is also used for battery display or low-battery alarm logic on the human-machine interface. This method boasts a simple structure, efficient computation, and controllable accuracy. It can adapt to various battery conditions and further enhances the system's self-correction capabilities by dynamically adjusting the conversion table slope parameters.

[0023] In some embodiments, after the step of periodically sampling the output voltage of the battery pack through the analog-to-digital conversion module to obtain the battery voltage sampling value, and performing linear interpolation conversion on the voltage sampling value based on a preset percentage conversion table to generate the current battery power percentage data, the method includes: generating a deviation by calculating the difference between the power percentage data and the power reference value corresponding to the power calibration coefficient of the initialization operating parameter set; comparing the deviation with a preset first deviation threshold, and generating a calibration flag signal if the deviation exceeds the first deviation threshold; extracting the interpolation slope parameter preset in the percentage conversion table for each voltage segment according to the calibration flag signal, and generating an adjusted correction slope coefficient by performing a differential analysis on the current voltage sampling value and the standard value recorded in the last calibration cycle; writing the correction slope coefficient into the corresponding position of the percentage conversion table by performing an overwriting replacement operation on the interpolation slope parameter, and updating the voltage percentage mapping rule called in the subsequent conversion process.

[0024] In this embodiment, after completing the voltage-to-percentage conversion, the currently generated battery percentage data is compared with the baseline battery value corresponding to the battery calibration coefficient in the operating parameter set loaded during device initialization. The battery calibration coefficient is a slope correction factor or reference generated after the last complete charge-discharge cycle. It represents the standard battery conversion reference value at a specific voltage point after actual measurement. This reference value is stored as a percentage in non-volatile memory and loaded during system initialization to correct for conversion errors caused by battery aging, internal resistance drift, voltage platform offset, etc. The calculation process uses the currently converted battery percentage as input and obtains the corresponding baseline battery value within the same voltage range from the calibration coefficient. The difference between the two is then calculated to obtain a deviation. This deviation reflects the degree of deviation between the voltage percentage estimate and the historical calibration result. Since slight deviations can be caused by short-term disturbances such as environmental noise and current load changes, the system compares this deviation with a preset first deviation threshold to determine whether to trigger the calibration process. The first deviation threshold is a signed boundary value. Its value is set based on the system's error tolerance design principles and is expressed in percentage points, with a typical value of ±5%. If the current deviation exceeds this threshold, it indicates that the interpolation accuracy of the current conversion table has deviated from the actual state and requires correction. Therefore, the system generates a calibration flag signal when this condition is met, which notifies the subsequent update mechanism to begin. After the calibration flag signal is activated, the control logic extracts the interpolation slope parameter for the current voltage range from the percentage conversion table. This parameter is the scaling factor used in the linear interpolation calculation, which maps the voltage difference to the battery percentage difference. To update this slope parameter, the system first reads the voltage standard value recorded in the previous calibration cycle from the EEPROM and performs a differential analysis with the current measured voltage sample value. This difference, combined with the deviation, establishes a new linear relationship: a new interpolation slope is constructed with the voltage difference as the independent variable and the percentage deviation as the dependent variable. This slope can be derived using the slope formula Y / X, where Y represents the percentage deviation and X represents the voltage difference, thereby generating the adjusted correction slope coefficient. To ensure that the newly generated slope takes effect immediately, the system overwrites the original slope value of the corresponding voltage segment in the original percentage conversion table, that is, replaces the original slope coefficient with the corrected slope coefficient and writes it to the slope parameter storage unit of the corresponding segment in the conversion table. This write operation simultaneously updates the running buffer area in RAM and the persistent area in EEPROM, ensuring that the calibrated and corrected conversion parameters are called during subsequent sampling and conversion processes. This update action is not only used for the current calculation process, but also continues to work after the next system restart or low-battery wake-up, thereby ensuring that the power estimation logic has memory and tracking.For example, if battery performance degrades slightly over multiple consecutive operating cycles, this mechanism can automatically adjust the estimated curve by sequentially correcting the slope, ensuring that the device's displayed power level matches the actual available power, avoiding the risk of false alarms or shutdowns. This technical approach combines error determination, historical verification, and dynamic adjustment of interpolation coefficients to provide dynamic closed-loop compensation for battery status management.

[0025] Step 130 : Obtain an original air pressure value through a vacuum sensor connected to the target container, compare the current vacuum value corresponding to the original air pressure value with a preset target vacuum threshold value, and generate a vacuum deviation value.

[0026] In this embodiment, an analog-to-digital conversion module samples the signal from the vacuum sensor connected to the target container, enabling real-time monitoring of the container's internal pressure state. The vacuum sensor outputs the measured pressure as a voltage. The control system collects this voltage signal at multiple time points and performs analog-to-digital conversion to obtain a set of raw data points representing the current container vacuum state. To reduce errors caused by random fluctuations, the system filters and weighted averages these raw values ​​to generate currently valid vacuum data. This data is then compared against a preset vacuum target value. The resulting deviation is not only used to determine whether the current vacuum state meets the target but is also subsequently used to calculate the adjustment parameters of the drive signal, making it an indispensable key variable in the control logic.

[0027] In some embodiments, the steps of obtaining an original air pressure value through a vacuum sensor connected to a target container, performing a deviation comparison between a current vacuum value corresponding to the original air pressure value and a preset target vacuum threshold, and generating a vacuum deviation value include: collecting analog signal output through a vacuum sensor connected to an air path of the target container, and obtaining a plurality of vacuum pressure analog signals based on a multi-cycle sampling instruction executed at a preset time interval; performing analog-to-digital conversion on the plurality of vacuum pressure analog signals to generate a corresponding original air pressure value sequence; generating an effective air pressure value inside the target container by filtering and weighted averaging the numerical samples in the original air pressure value sequence; converting the effective air pressure value into a current vacuum value according to a linear conversion coefficient relationship of the vacuum sensor; and performing a deviation comparison between the current vacuum value and the preset target vacuum threshold to generate a vacuum deviation value.

[0028] In this embodiment, a vacuum sensor is connected to the interior of the container via an air path structure, ensuring that the sensor's sensing end and the container form a closed pressure transmission path, allowing real-time monitoring of internal air pressure changes. The sensor converts external air pressure changes into a voltage output signal through its built-in piezoresistive structure or capacitive sensing element, generating a sampled analog voltage. This voltage signal continuously changes with changes in the container's internal pressure and is connected to an analog-to-digital conversion interface in the control system. To achieve high temporal resolution and reflect pressure fluctuation trends, the system triggers multi-cycle sampling instructions based on a preset control frequency, reading the sensor output at regular intervals to continuously acquire vacuum pressure analog signals at multiple time points. For example, if sampling is performed every 50 milliseconds, 10 analog signal data points can be obtained within 500 milliseconds, forming a structured pressure sampling sequence that describes the short-term dynamic profile of the current vacuum state. The analog-to-digital conversion module then performs digital-to-analog conversion on each analog voltage sampling point, mapping the continuous voltage value into a corresponding digital code. This code represents the raw air pressure value, typically expressed in ADC code values ​​or corresponding millivolt levels. After analog-to-digital conversion, multiple sampling points form a numerical sequence. This sequence reflects subtle variations at different sampling points due to pressure perturbations, electromagnetic interference, or sensor zero drift, and therefore cannot be used directly as a basis for vacuum determination. To improve signal representativeness and stability, this numerical sequence requires filtering and weighted averaging. Filtering can employ sliding window, median filtering, or exponentially weighted algorithms to eliminate extremely fluctuating data points to suppress the influence of high-frequency noise. Weighted averaging combines the different weights in the sampling sequence to more closely reflect the current system state, for example, giving higher weights to the latest data points to enhance the algorithm's responsiveness to short-term trends. After processing, a single effective pressure value is obtained, representing the statistical characteristics of the multiple sampling data points. It should be noted that this effective pressure value is an intermediate quantity and still requires conversion to a physical quantity based on sensor characteristics. During the product design phase, vacuum sensors are designed with a clearly defined linear output characteristic, meaning that their output voltage maintains an approximately linear relationship with the sensed air pressure. The control system then reverse-calculates the effective air pressure using a manufacturer-provided or experimentally calibrated coefficient to determine the current true container vacuum level. For example, an output of 0.5V represents 1000hPa, and an output of 4.5V represents 0hPa. Based on the corresponding voltage-pressure relationship, the system establishes a conversion equation to convert the effective air pressure into the difference from standard atmospheric pressure, which is then expressed in units of vacuum. After completing the vacuum conversion, the current vacuum level is compared against a pre-set vacuum target threshold for deviation. The target vacuum threshold is a standard reference point defined in the device's operating strategy. For example, a value of 200hPa indicates that the container is considered vacuum-completed when the air pressure falls below this value.The control system generates a vacuum deviation value by calculating the difference between the current vacuum level and the target threshold. This deviation can be positive or negative. A positive value indicates that the target vacuum state has not yet been reached, while a negative value or zero indicates that the vacuum conditions have met or slightly exceeded the set standard. This deviation serves as a direct input for determining whether to initiate sealing, extend the pumping time, or dynamically adjust the pump speed. The entire acquisition, conversion, and comparison process forms a closed-loop vacuum control logic, which can improve the accuracy of pumping judgment and response stability without adding external components.

[0029] Step 140 : If the vacuum degree deviation value satisfies the preset tolerance range, a target PWM duty cycle is generated according to the power percentage data and the vacuum degree deviation value, and a driving signal is output according to the target PWM duty cycle to perform vacuum pumping.

[0030] It is understood that PWM, or pulse width modulation, is a technique for controlling average power output by adjusting the duration of a high-level pulse at a fixed frequency. In this embodiment, if the current vacuum level is detected to be within a set tolerance, the system combines the aforementioned battery percentage data with the vacuum level deviation value as input parameters for the PWM duty cycle generation logic. By searching a duty cycle reference mapping table corresponding to these two variables and performing a weighted fusion calculation, the target PWM signal's duty cycle value is obtained. This value is then loaded into the control unit's PWM register, generating a drive signal with the corresponding frequency and duty cycle. This signal is then transmitted to the vacuum pump motor via the MOSFET power switch module, achieving precise vacuum control. Through this mechanism, the device can automatically reduce power output when the battery level is low, and temporarily increase suction when the battery level is sufficient or the vacuum distance is large, thereby balancing sealing efficiency and battery life.

[0031] In some embodiments, if the vacuum degree deviation value satisfies a preset tolerance range, a target PWM duty cycle is generated based on the power percentage data and the vacuum degree deviation value, and a drive signal is output according to the target PWM duty cycle to perform vacuum pumping, including: generating a corresponding first duty cycle component and a second duty cycle component by calling a preset duty cycle reference mapping table in the control system for the power percentage data and the vacuum degree deviation value respectively; generating a corresponding target PWM duty cycle by weighted fusion processing of the first duty cycle component and the second duty cycle component; wherein the weighting coefficient is determined in the initialization parameter set according to the current battery status level and the vacuum regulation priority; generating a drive signal with pulse width modulation characteristics by loading the target PWM duty cycle into the PWM control channel register; controlling the on-off state of the switch tube in the power drive module by the drive signal, outputting a drive voltage to the vacuum pump motor end, and driving the vacuum pump to perform vacuum pumping.

[0032] In this embodiment, to ensure the vacuum pump achieves driving capacity that matches the current system state under different operating conditions, the previously calculated battery percentage and vacuum deviation values ​​are used as input parameters. Two sets of duty cycle reference mapping tables pre-set within the control system are used to generate two basic duty cycle sub-components. These mapping tables are lookup tables containing multiple data items corresponding to intervals, recording the PWM duty cycle values ​​that should be used within specific battery percentage ranges or vacuum deviation ranges. For example, a battery percentage range of 20% to 40% may correspond to a lower duty cycle value, while a larger vacuum deviation value indicates that the container is still far from the target vacuum state and should be assigned a higher duty cycle to increase suction capacity. The output of the battery percentage mapping table is the first duty cycle component, which reflects the control limit imposed by the battery power on the pump power. The output of the vacuum deviation mapping table is the second duty cycle component, which reflects the vacuum state's response to the suction demand. The system performs a weighted fusion of these two duty cycle components to construct a final target PWM duty cycle. The core of this fusion algorithm is the introduction of a weighting coefficient to balance the two input factors. This weighting coefficient is not a static value but is determined in the initialization parameter set based on the current battery state level and vacuum control priority. The battery state level is a tiered information based on battery charge percentage, indicating the system's tolerance for power consumption. The vacuum control priority indicates the priority given to vacuum accuracy or sealing performance in the current task. This information may be derived from user parameter settings or task type switching. For example, when set to a high sealing level, the system gives higher weight to vacuum deviation. However, when the battery is nearing critical charge, energy conservation is prioritized, with the battery duty cycle taking precedence. The target PWM duty cycle is generated by multiplying the first component by the battery weight and the second component by the vacuum weight, and then adding the two together, thus implementing a dynamically adaptable control strategy. Once the target duty cycle value is obtained, it is directly written into the duty cycle register of the PWM control module. Based on the target duty cycle, the control system generates a corresponding drive signal within each PWM cycle, causing the output terminal to switch on and off at a specified ratio within each cycle, thereby varying the energy drawn from the load. Once generated, this drive signal is output via a control pin to the power driver module, which contains MOSFET switches or other types of power transistors for current conversion. The PWM signal controls the switching states by controlling their on and off timing, generating a periodic drive voltage wave across the pump motor. Ultimately, the power driver module converts the drive signal into a dynamically changing voltage or current and transmits it to the vacuum pump motor, forming a closed-loop speed regulation scheme that ensures the motor achieves the required driving force under the current system state. For example, in situations where low power consumption and high vacuum are required, the system maintains a moderate duty cycle while ensuring the battery remains stable, thus avoiding energy overdrafts. When sufficient power is available or rapid vacuuming is required, the system automatically increases the duty cycle, increasing both the speed and pumping speed, thus balancing efficiency and power conservation.

[0033] In some embodiments, when the current vacuum pump is stopped, a state comparison is performed on the vacuum feedback data within a preset sampling period to obtain a stable vacuum state flag; a sealing start instruction is output to the control system based on the stable vacuum state flag, and the driving input end of the sealing heating module is controlled by loading a heating control PWM signal with a preset duty cycle value, and the heating element is driven to perform thermal sealing on the target container within a preset heating duration; during the thermal sealing process, the heating duration is counted down by the time base generated by the timer module, and a stop signal is output to the heating control PWM module after the timing ends to terminate the sealing heating process.

[0034] In this embodiment, after the vacuum pump is stopped and the vacuum pump is completed, the control system does not immediately perform the sealing operation. Instead, it initiates a vacuum status confirmation process within a preset sampling cycle. This process performs a structured status comparison of multiple cycles of vacuum feedback data retained from the previous stage to determine whether the container interior is in a stable vacuum environment. The vacuum feedback data comes from the completed ADC sampling and conversion mechanism. The system analyzes the continuity of the vacuum values ​​in the sampling sequence. If the vacuum values ​​at multiple consecutive sampling points are within the target threshold range and the fluctuation amplitude is within the set jitter tolerance, the current state is determined to meet the pressure stability requirements for the sealing operation. Once this condition is met, the system generates a logical flag signal, defined as a "stable vacuum status flag," indicating that the container has reached a state suitable for sealing and heating. This not only ensures a successful sealing rate but also prevents sealing failures or film deformation caused by unstable vacuum or instantaneous back pressure. After the stable vacuum status flag is generated, the system outputs a sealing start command to the control unit through control logic, which triggers the operation of the heating module. The heating module is driven using a PWM control signal, a pulse-width modulation (PWM) signal. This signal adjusts the average power output of the heating element by controlling the duration of the high-level pulse within each cycle. To ensure consistent heat seal strength and prevent overheating, the system does not employ dynamic adjustment. Instead, it loads a preset fixed duty cycle value from an initialization parameter set to generate a constant-duty PWM waveform, which is then loaded into the heating control channel register. This PWM waveform is output by the control system to the drive input of the sealing heating module, driving the power switching device to transfer power to the sealing heater, enabling it to operate continuously at the set power level. The heater applies heat to the edge of the vacuum bag through thermal conduction, causing the material to melt and fuse under pressure, completing the final sealing of the package structure. During the heating process, the system requires strict control of the heating time using an internal timer module. This timer module is a timing structure implemented using hardware-based timing logic. It generates a precise time reference signal by setting an initial count value and periodic shifting. This time reference signal operates in a countdown manner, decreasing from the set heating duration to zero, and triggers a termination signal when the timer reaches zero. This termination signal is fed into the heating control logic, which issues a shutdown command to the PWM control module. The system then terminates the PWM wave output, thereby cutting off the input power to the sealing heating module. Triggering the termination signal not only shuts down the heating process but also marks the end of a complete sealing process in the control logic. This signal can be used for subsequent sealing completion feedback displays, operation log recording, and as a judgment condition for entering standby mode.This process uses control logic and hardware timers to build a precise, controllable, and closed-loop sealing control process, which can effectively balance sealing integrity, power control, and equipment status consistency, ensuring that the packaging process maintains high reliability and consistency under different task conditions.

[0035] In some embodiments, the peripheral module of the control system also includes a communication module, which parses the serial control instructions received by the communication module to obtain an instruction identifier for instructing the control system to enter an upgrade state, and generates a firmware upgrade status flag according to the instruction identifier; performs a soft reset operation on the control system according to the firmware upgrade status flag, reloads the startup logic, and reads the firmware upgrade status flag during the startup process, calls the boot area program entry address based on the firmware upgrade status flag, and jumps to the preset firmware receiving boot area; generates a corresponding valid write data block by performing data comparison on the firmware data packet received by the communication module, and determines the Flash storage address range according to the address field of the valid write data block; generates new firmware content by erasing the valid write data block to the target Flash address within the Flash storage address range and writing data.

[0036] Specifically, to enable remote reconfiguration of the device firmware, an external data access channel is established through a communication module. This communication module supports a serial protocol to receive formatted instruction sets, where each instruction is organized into a specific data structure, including a command header, instruction code, parameter field, and checksum. The control system parses the instruction data stream received by the communication module byte by byte, identifies the instruction code field, and compares it with a built-in command dictionary to determine whether the received instruction is a control instruction for activating the system into firmware upgrade mode. If the instruction content matches a firmware upgrade command, the control unit generates a firmware upgrade status flag based on the parsing result. This flag is a specific variable residing in volatile or non-volatile storage space that indicates whether the system is in the upgrade boot process during operation. After the firmware upgrade status flag is set, the system immediately performs a soft reset. A soft reset is a process that reinitializes the controller's execution process through internal control commands without requiring a power cycle or restart of the MCU. During the reset process, the system reloads the boot entry function and executes the initialization sequence in the boot logic. During this sequence, the controller accesses the upgrade status flag in the storage location and, based on the flag status, determines whether the system needs to jump to the boot area. If the status bit is set to the upgrade state, the system interrupts the main program startup process and calls the entry point of the pre-set boot area program. This entry point is located in the boot area segment designated in the Flash memory, which contains program logic dedicated to handling firmware reception and writing. The program counter forces a jump to this address, transferring control to the boot program and entering the firmware data reception channel configuration phase. After the boot program is initialized, the system receives the firmware data stream from the host computer or control terminal through the communication module. This data is organized into packets, each consisting of an address field, a data field, a checksum field, and a flag field. The control system structurally parses each packet and performs a field comparison. First, the data field is checked to verify transmission integrity. Then, the address field is extracted to determine the write location. The firmware version and address continuity are compared to generate a verified valid write data block. This data block is the executable write unit after verification and address validity verification. It contains the target write address and corresponding content to ensure that the write address is not written to the wrong location or that the existing operating area is damaged. After the system obtains a valid write data block, it calls the Flash write interface function based on its target address information, first performing a page-level erase operation on the specified address segment. Once the erase is complete, the system writes the data fields within the data block to the target address in byte or word-line format. A write confirmation mechanism and interrupt protection logic are incorporated into the write process to ensure that the write state is not interrupted by voltage fluctuations or interruptions. Once all data blocks are written, a complete image area with the new firmware content is generated. This area structurally replaces the original application segment and, after verification, serves as the main program entry point for system logic migration.In this way, the entire process from receiving instructions, determining jumps, receiving data to firmware reconstruction is completed, providing the device with a flexible, stable and breakpoint-protected remote upgrade mechanism.

[0037] According to the intelligent vacuum sealer control method provided by the present application, the peripheral modules of the control system are initialized according to the power start signal, and the initialization operating parameter set corresponding to each peripheral module is obtained; the output voltage of the battery pack is periodically sampled through the analog-to-digital conversion module to obtain the battery voltage sampling value, and the voltage sampling value is linearly interpolated and converted based on a preset percentage conversion table to generate the current battery power percentage data; the original air pressure value is obtained through a vacuum sensor connected to the target container, and the current vacuum value corresponding to the original air pressure value is compared with the preset target vacuum threshold value for deviation to generate a vacuum deviation value; if the vacuum deviation value meets the preset tolerance range, a target PWM duty cycle is generated according to the power percentage data and the vacuum deviation value, and a drive signal is output according to the target PWM duty cycle to perform vacuum extraction. Through the implementation of the present application, the target PWM duty cycle is generated according to the power percentage data and the vacuum deviation value, realizing dynamic adjustment of the vacuum pump power, which can effectively improve the intelligent control efficiency of the vacuum sealer during the vacuum sealing process, and improve the sealing success rate while extending the battery life.

[0038] Figure 2 The present invention provides an intelligent vacuum sealing machine control device, which can be used to implement the intelligent vacuum sealing machine control method in the above embodiment. Figure 2 As shown, the intelligent vacuum sealing machine control device mainly includes: The acquisition module 10 is used to initialize the peripheral modules of the control system according to the power startup signal and obtain the initialization operation parameter set corresponding to each peripheral module; wherein the peripheral module includes an analog-to-digital conversion module; The analog-to-digital conversion module 20 is configured to periodically sample the output voltage of the battery pack through the analog-to-digital conversion module to obtain a battery voltage sampling value, and perform linear interpolation conversion on the voltage sampling value based on a preset percentage conversion table to generate current battery power percentage data; A comparison module 30 is configured to obtain an original air pressure value through a vacuum sensor connected to a target container, compare a current vacuum value corresponding to the original air pressure value with a preset target vacuum threshold value, and generate a vacuum deviation value; The vacuum pumping module 40 is configured to generate a target PWM duty cycle according to the power percentage data and the vacuum degree deviation value if the vacuum degree deviation value satisfies a preset tolerance range, and output a driving signal for vacuum pumping according to the target PWM duty cycle.

[0039] In an optional implementation of the present embodiment, the analog-to-digital conversion module is specifically used to: obtain the divided analog voltage signal by setting a resistor divider network for the voltage node between the positive and negative poles of the battery pack; periodically sample the analog voltage signal based on the sampling instruction of the analog-to-digital conversion module to generate a voltage sampling sequence consisting of multiple voltage sampling points; generate the average voltage value of the current battery by processing the multiple voltage sampling points in the voltage sampling sequence, and match the average voltage value with each voltage segment in the preset percentage conversion table to determine the conversion segment identifier; perform intra-segment linear interpolation on the average voltage value according to the upper and lower limits of the conversion interval corresponding to the conversion segment identifier and the corresponding mapped upper and lower limits of the power percentage to generate corresponding power percentage data.

[0040] In an optional implementation of this embodiment, the analog-to-digital conversion module is also used to: generate a deviation by calculating the difference between the power percentage data and the power reference value corresponding to the power calibration coefficient of the initialization operating parameter set; compare the deviation with a preset first deviation threshold, and if the deviation exceeds the first deviation threshold, generate a calibration flag signal; extract the interpolation slope parameters preset in the percentage conversion table for each voltage segment according to the calibration flag signal, and generate an adjusted correction slope coefficient by performing a differential analysis on the current voltage sampling value and the standard value recorded in the last calibration cycle; write the correction slope coefficient into the corresponding position of the percentage conversion table by performing an overwriting replacement operation on the interpolation slope parameter, and update the voltage percentage mapping rule called in the subsequent conversion process.

[0041] In an optional implementation of the present embodiment, the comparison module is specifically used to: collect analog signal outputs through a vacuum sensor connected to the gas path of the target container, and obtain multiple vacuum pressure analog signals based on a multi-cycle sampling instruction executed at preset time intervals; perform analog-to-digital conversion on the multiple vacuum pressure analog signals to generate a corresponding original air pressure numerical sequence; generate an effective air pressure value inside the target container by filtering and weighted averaging the numerical samples in the original air pressure numerical sequence; convert the effective air pressure value into a current vacuum value based on the linear conversion coefficient relationship of the vacuum sensor; perform a deviation comparison between the current vacuum value and a preset target vacuum threshold to generate a vacuum deviation value.

[0042] In an optional implementation of the present embodiment, the vacuum pumping module is specifically used to: generate a corresponding first duty cycle component and a second duty cycle component by respectively calling a preset duty cycle reference mapping table in the control system for the power percentage data and the vacuum degree deviation value; generate a corresponding target PWM duty cycle by weighted fusion processing of the first duty cycle component and the second duty cycle component; wherein the weighting coefficient is determined in the initialization parameter set according to the current battery status level and the vacuum regulation priority; generate a drive signal with pulse width modulation characteristics by loading the target PWM duty cycle into the PWM control channel register; control the on-off state of the switch tube in the power drive module by the drive signal, output the drive voltage to the vacuum pump motor end, and drive the vacuum pump to perform vacuum pumping.

[0043] In an optional implementation of this embodiment, the heating and sealing module is specifically used to: perform a state comparison on the vacuum feedback data within a preset sampling period when the current vacuum pump is stopped, and obtain a stable vacuum state flag; output a sealing start instruction to the control system according to the stable vacuum state flag, and control the driving input end of the sealing heating module by loading a heating control PWM signal with a preset duty cycle value, and drive the heating element to heat-seal the target container within a preset heating duration; during the heat sealing process, the heating duration is counted down by the time base generated by the timer module, and a stop signal is output to the heating control PWM module after the timing ends to terminate the sealing heating process.

[0044] In an optional implementation of this embodiment, the communication module is used to: parse the serial control instructions received by the communication module to obtain an instruction identifier for instructing the control system to enter an upgrade state, and generate a firmware upgrade status flag according to the instruction identifier; perform a soft reset operation on the control system according to the firmware upgrade status flag, reload the startup logic, and read the firmware upgrade status flag during the startup process, call the boot area program entry address based on the firmware upgrade status flag, and jump to the preset firmware receiving boot area; generate a corresponding valid write data block by performing data comparison on the firmware data packet received by the communication module, and determine the Flash storage address range according to the address field of the valid write data block; generate new firmware content by erasing the valid write data block to the target Flash address within the Flash storage address range and writing data.

[0045] According to the intelligent vacuum sealing machine control device provided by the present application, the peripheral modules of the control system are initialized according to the power start signal, and the initialization operating parameter set corresponding to each peripheral module is obtained; the output voltage of the battery pack is periodically sampled through the analog-to-digital conversion module to obtain the battery voltage sampling value, and the voltage sampling value is linearly interpolated and converted based on a preset percentage conversion table to generate the current battery power percentage data; the original air pressure value is obtained through a vacuum sensor connected to the target container, and the current vacuum value corresponding to the original air pressure value is compared with the preset target vacuum threshold value for deviation to generate a vacuum deviation value; if the vacuum deviation value meets the preset tolerance range, a target PWM duty cycle is generated according to the power percentage data and the vacuum deviation value, and a drive signal is output according to the target PWM duty cycle to perform vacuum extraction. Through the implementation of the present application solution, the target PWM duty cycle is generated according to the power percentage data and the vacuum deviation value, realizing dynamic adjustment of the vacuum pump power, which can effectively improve the intelligent control efficiency of the vacuum sealing machine during the vacuum sealing process, and improve the sealing success rate while extending the battery life.

[0046] According to the application plan Figure 3 An electronic device provided in an embodiment of the present application can be used to implement the intelligent vacuum sealer control method in the aforementioned embodiment, mainly comprising: The processor 301 can be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application; the memory 302 can be implemented in the form of ROM (Read Only Memory), a static storage device, a dynamic storage device, or RAM (Random Access Memory). The memory 302 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program codes are stored in the memory 302 and are called by the processor 301 to execute the multimodal recognition model training method of the embodiments of this application; the input / output interface 303 is used to implement information input and output; the communication interface 304 is used to implement communication interaction between this device and other devices, and communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.); the bus 305 transmits information between the various components of the device (such as the processor 301, memory 302, input / output interface 303 and communication interface 304); wherein the processor 301, memory 302, input / output interface 303 and communication interface 304 are connected to each other within the device through the bus 305.

[0047] The computer-readable storage medium stores a computer program that, when executed by a processor, implements the intelligent vacuum sealer control method described in the aforementioned embodiments. Furthermore, the computer-readable storage medium may be a USB flash drive, a removable hard drive, a read-only memory (ROM), RAM, a magnetic disk, or an optical disk, among other media capable of storing program code.

[0048] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0049] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0050] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A control method for an intelligent vacuum sealing machine, characterized in that: include: Initializing the peripheral modules of the control system according to the power startup signal, and obtaining a set of initialization operating parameters corresponding to each peripheral module; wherein the peripheral modules include an analog-to-digital conversion module; The analog-to-digital conversion module periodically samples the output voltage of the battery pack to obtain a battery voltage sampling value, and performs linear interpolation conversion on the voltage sampling value based on a preset percentage conversion table to generate current battery power percentage data; Obtaining an original air pressure value through a vacuum sensor connected to the target container, performing a deviation comparison between a current vacuum value corresponding to the original air pressure value and a preset target vacuum threshold value to generate a vacuum deviation value; If the vacuum degree deviation value satisfies a preset tolerance range, a target PWM duty cycle is generated according to the power percentage data and the vacuum degree deviation value, and a driving signal is output according to the target PWM duty cycle to perform vacuum pumping.

2. The intelligent vacuum sealing machine control method according to claim 1, characterized in that: The step of periodically sampling the output voltage of the battery pack by the analog-to-digital conversion module to obtain battery voltage sampling values, and performing linear interpolation conversion on the voltage sampling values ​​based on a preset percentage conversion table to generate current battery power percentage data includes: By setting a resistor divider network at the voltage node between the positive and negative electrodes of the battery pack, an analog voltage signal after voltage division is obtained; Periodically sampling the analog voltage signal based on the sampling instruction of the analog-to-digital conversion module to generate a voltage sampling sequence consisting of multiple voltage sampling points; Generate an average voltage value of the current battery by processing multiple voltage sampling points in the voltage sampling sequence, and perform interval matching between the average voltage value and each voltage segment in a preset percentage conversion table to determine a conversion segment identifier; According to the upper and lower limits of the conversion interval corresponding to the conversion segment identifier and the upper and lower limits of the corresponding mapped power percentage, the average voltage value is linearly interpolated within the segment to generate corresponding power percentage data.

3. The intelligent vacuum sealing machine control method according to claim 2, characterized in that: After the steps of periodically sampling the output voltage of the battery pack by the analog-to-digital conversion module to obtain battery voltage sampling values, and performing linear interpolation conversion on the voltage sampling values ​​based on a preset percentage conversion table to generate current battery power percentage data, the method includes: Generate a deviation by performing a difference calculation between the power percentage data and a power reference value corresponding to the power calibration coefficient of the initialization operating parameter set; Comparing the deviation with a preset first deviation threshold, and generating a calibration flag signal if the deviation exceeds the first deviation threshold; Extracting the interpolation slope parameters preset in the percentage conversion table for each voltage segment according to the calibration flag signal, and generating an adjusted correction slope coefficient by performing a differential analysis between the current voltage sampling value and the standard value recorded in the previous calibration cycle; By performing a covering replacement operation on the interpolation slope parameter, the correction slope coefficient is written into the corresponding position of the percentage conversion table, and the voltage percentage mapping rule called in the subsequent conversion process is updated.

4. The intelligent vacuum sealing machine control method according to claim 1, characterized in that: The step of obtaining an original air pressure value through a vacuum sensor connected to the target container, performing a deviation comparison between a current vacuum value corresponding to the original air pressure value and a preset target vacuum threshold value, and generating a vacuum deviation value includes: Acquiring analog signal output from a vacuum sensor connected to the gas path of the target container, and acquiring multiple vacuum pressure analog signals based on a multi-cycle sampling instruction executed at a preset time interval; Performing analog-to-digital conversion on the multiple vacuum pressure analog signals to generate a corresponding sequence of original air pressure values; Generate an effective air pressure value inside the target container by filtering and weighted averaging the numerical samples in the original air pressure numerical sequence; Converting the effective air pressure value into a current vacuum value according to a linear conversion coefficient relationship of the vacuum sensor; The current vacuum value is compared with a preset target vacuum threshold value to generate a vacuum deviation value.

5. The intelligent vacuum sealing machine control method according to claim 1, characterized in that: If the vacuum degree deviation value satisfies a preset tolerance range, generating a target PWM duty cycle according to the power percentage data and the vacuum degree deviation value, and outputting a driving signal for vacuum pumping according to the target PWM duty cycle, the step includes: Generate a corresponding first duty cycle component and a corresponding second duty cycle component by respectively calling a duty cycle reference mapping table preset in the control system for the power percentage data and the vacuum degree deviation value; Generating a corresponding target PWM duty cycle by weightedly fusing the first duty cycle component and the second duty cycle component; wherein a weighted coefficient of the weighted fusion is determined in an initialization parameter set according to a current battery state level and a vacuum regulation priority; Generating a driving signal with pulse width modulation characteristics by loading the target PWM duty cycle into a PWM control channel register; The driving signal is used to control the on / off state of the switch tube in the power driving module, output the driving voltage to the vacuum pump motor end, and drive the vacuum pump to perform vacuum extraction.

6. The intelligent vacuum sealing machine control method according to claim 5, characterized in that: The method further comprises: When the vacuum pump is currently stopped, the vacuum feedback data within the preset sampling period is compared to obtain a stable vacuum state flag; Outputting a sealing start instruction to the control system according to the stable vacuum state flag, and controlling the driving input end of the sealing heating module by loading a heating control PWM signal with a preset duty cycle value, driving the heating element to heat-seal the target container within a preset heating duration; During the heat sealing process, the heating duration is counted down by the time base generated by the timer module, and a stop signal is output to the heating control PWM module after the timing ends to terminate the sealing heating process.

7. The intelligent vacuum sealing machine control method according to claim 1, characterized in that: The peripheral module further includes: a communication module, and the method further includes: By parsing the serial control instruction received by the communication module, an instruction identifier for instructing the control system to enter an upgrade state is obtained, and a firmware upgrade state flag is generated according to the instruction identifier; Performing a soft reset operation on the control system according to the firmware upgrade status flag, reloading the startup logic, and reading the firmware upgrade status flag during the startup process, calling the boot area program entry address based on the firmware upgrade status flag, and jumping to a preset firmware receiving boot area; By performing data comparison on the firmware data packets received by the communication module, a corresponding valid write data block is generated, and a Flash storage address range is determined according to the address field of the valid write data block; New firmware content is generated by erasing the target Flash address within the Flash storage address range and writing data to the valid write data block.

8. An intelligent vacuum sealing machine control device, characterized in that: The intelligent vacuum sealing machine control device is used to implement the intelligent vacuum sealing machine control method according to claim 1, and the intelligent vacuum sealing machine control device includes: An acquisition module, configured to initialize peripheral modules of the control system according to a power startup signal and acquire a set of initialization operating parameters corresponding to each peripheral module; wherein the peripheral modules include an analog-to-digital conversion module; an analog-to-digital conversion module, configured to periodically sample the output voltage of the battery pack to obtain a battery voltage sampling value, and perform linear interpolation conversion on the voltage sampling value based on a preset percentage conversion table to generate current battery power percentage data; a comparison module, configured to obtain an original air pressure value through a vacuum sensor connected to a target container, perform a deviation comparison between a current vacuum value corresponding to the original air pressure value and a preset target vacuum threshold value, and generate a vacuum deviation value; The vacuum pumping module is configured to generate a target PWM duty cycle according to the power percentage data and the vacuum degree deviation value if the vacuum degree deviation value satisfies a preset tolerance range, and output a driving signal for vacuum pumping according to the target PWM duty cycle.

9. An electronic device, characterized in that: The device comprises a memory and a processor, wherein: The processor is configured to execute a computer program stored in the memory; When the processor executes the computer program, the steps of the intelligent vacuum sealing machine control method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent vacuum sealing machine control method according to any one of claims 1 to 7 are implemented.

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