Field intensity adjusting method based on electrostatic preservation

Abstract

The invention belongs to the technical field of food preservation, and particularly relates to a field intensity adjusting method based on electrostatic preservation. According to the method, a dynamic field intensity adjusting instruction is generated by obtaining biological impedance data of a to-be-preserved object, the phase difference and the voltage amplitude of the multi-electrode plate array are controlled to form a three-dimensional rotating electric field, and space uniform distribution of field intensity is achieved. The ozone generation module operates independently of an electric field, and the ozone concentration is dynamically adjusted through an ultraviolet light source. A high-frequency short-pulse mode or a low-frequency long-pulse mode is matched according to the type of the object, and the antibacterial and fresh-keeping effects are optimized. Self-adaptive field intensity adjustment is achieved through biological impedance feedback, the three-dimensional rotating electric field and ozone decoupling control are combined, the freshness retaining uniformity and safety are improved, the shelf life is prolonged, and energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of food preservation, and in particular to a field intensity regulation method based on electrostatic preservation. Background Art

[0002] In the storage and transportation of fresh food, electrostatic preservation technology, owing to its non-thermal properties, is considered a key development direction. Existing electrostatic preservation technologies primarily inhibit microbial activity by applying a constant high-voltage electric field, but this approach has significant limitations. Traditional solutions generally employ a fixed field strength and a single direction, making it difficult to adapt to the varying physical characteristics of different object types (such as size, shape, and moisture content). This results in large fluctuations in antibacterial efficacy and localized field attenuation or overload. Furthermore, ozone, used as an auxiliary sterilization method, is typically generated in conjunction with a high-voltage electric field, and concentration regulation depends on the intensity of the corona discharge. This can easily lead to ozone runaway due to field fluctuations, posing the risk of oxidative damage or exceeding specified limits. Furthermore, existing technologies lack the ability to dynamically respond to changes in the freshness of an object. Adjustment of preservation parameters relies on empirically pre-set parameters, making it difficult to adapt to the real-time state changes in complex storage and transportation environments, resulting in wasted energy and shortened shelf life.

[0003] In recent years, some improved solutions have attempted to optimize preservation by adjusting electric field intensity or introducing multi-frequency pulses. However, these improvements remain limited by uneven two-dimensional electric field distribution and low ozone control precision. This is particularly true when handling odd-shaped objects or storing and transporting mixed products. Traditional technologies struggle to balance antibacterial efficiency with cell viability protection, limiting their potential for large-scale application. Therefore, a comprehensive solution is urgently needed that overcomes the limitations of field intensity distribution, achieves precise and controllable sterilization, and adapts to dynamic preservation needs. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a field intensity regulation method based on electrostatic preservation.

[0005] The present invention provides a field intensity regulation method based on electrostatic preservation, comprising the following steps: Obtaining bioimpedance data of the object to be preserved; generating a dynamic field intensity adjustment instruction according to the bioimpedance data; Based on the dynamic field intensity regulation instruction, the phase difference and voltage amplitude of each electrode in the multi-electrode array are controlled to form a three-dimensional rotating electric field; controlling the ozone generation module to adjust the ozone concentration independently of the operating state of the three-dimensional rotating electric field; According to the type of the object to be preserved, a pulsed field intensity mode is selected and the corresponding frequency parameters are matched.

[0006] Optionally, acquiring bioimpedance data includes: The dielectric constant change of the surface of the object to be preserved is collected in real time by a non-contact capacitive coupling sensor; A cell membrane integrity index is calculated according to the change in the dielectric constant as the bioimpedance data.

[0007] Optionally, adjusting the ozone concentration includes: When the cell membrane integrity index is lower than a preset threshold, the ultraviolet light source of the ozone generation module is triggered to start; The irradiation intensity of the ultraviolet light source is dynamically adjusted based on the real-time ozone concentration data fed back by the gas concentration sensor.

[0008] Optionally, the pulsed field intensity mode includes: High-frequency short-pulse field strength is used in the initial stage of preservation, and switched to low-frequency long-pulse field strength in the middle and late stages of preservation.

[0009] Optionally, the forming of the three-dimensional rotating electric field includes: A sinusoidal voltage with a phase difference of 45° is applied to multiple circularly distributed plates, causing the electric field lines to rotate periodically along a circular path.

[0010] Optionally, the method further includes: Real-time monitoring of the temperature and humidity data of the environment where the objects to be preserved are located; The field intensity parameters and pulse frequency of the three-dimensional rotating electric field are dynamically adjusted according to the temperature and humidity data.

[0011] Optionally, the method further includes: Based on a multi-physics response surface model, multi-objective optimization is performed on the field intensity parameters, pulse frequency, and ozone concentration; The weight allocation strategy of the multi-objective optimization is iteratively updated through a reinforcement learning algorithm, and the multi-objective optimization includes balancing the inhibition of microbial survival rate, nutrient retention rate and energy consumption.

[0012] Optionally, the training of the multi-physics response surface model includes: Collecting historical preservation data, including initial bioimpedance, environmental parameters, and preservation results of different types of objects; Combining the real-time monitored cell membrane integrity indicators with ozone concentration data, a dynamic mapping relationship between field strength, frequency, and ozone concentration is established. The dynamic mapping relationship is iteratively tuned through the reinforcement learning algorithm to generate a parameter matching rule library.

[0013] Optionally, the method further includes: When it is detected that the ozone concentration exceeds the safety threshold, an exception handling mechanism is triggered, which includes: Pausing the operation of the three-dimensional rotating electric field and starting the exhaust equipment; Based on the multi-physics response surface model and real-time environmental parameters, the field intensity parameters are recalculated and a revised dynamic field intensity adjustment instruction is generated.

[0014] Optionally, the method further includes: Dividing the area into multiple independent fresh-keeping areas according to the spatial distribution of the objects to be kept fresh; For each of the independent fresh-keeping areas, the corresponding three-dimensional rotating electric field parameters are independently adjusted based on the local bioimpedance data.

[0015] The present invention has the following technical effects: The present invention fundamentally changes the limitations of traditional electrostatic preservation technology that relies on preset parameters by introducing a real-time dynamic feedback mechanism for bio-impedance data. By collecting changes in the dielectric constant of the object surface through a non-contact capacitive coupling sensor, it can directly reflect the integrity of the cell membrane, and then convert the bio-impedance data into field intensity adjustment instructions. This mechanism enables the field intensity parameters of the three-dimensional rotating electric field to be dynamically adjusted with the freshness of the object. For example, for fruits and vegetables with high moisture content in the early stage, the proliferation of microorganisms can be quickly suppressed by increasing the field intensity. For objects whose cell membranes begin to age in the middle and late stages, the field intensity is reduced to avoid excessive damage to the cell structure, thereby achieving precise adaptation of the preservation parameters and solving the problem of "over-preservation" or "under-preservation" caused by fixed field intensity in traditional technologies.

[0016] The phase-difference control technology of the multi-electrode array applies a sinusoidal voltage with a 45° phase difference to the circularly distributed electrodes, causing the electric field lines to rotate periodically along a circular path. This three-dimensional electric field pattern overcomes the limitations of traditional two-dimensional planar electric fields, effectively eliminating the phenomenon of electric field edge attenuation, and significantly improving the uniformity of field intensity distribution across the surface of the object. This is particularly suitable for fruits and vegetables with irregular shapes or uneven surfaces (such as strawberries and broccoli), avoiding the problem of microbial residue caused by insufficient electric field coverage in local areas. At the same time, the dynamic stimulation generated by the rotating electric field can delay the deactivation of cell metabolism, further extending the shelf life.

[0017] The independent control design of the ozone generation module and the three-dimensional electric field completely decouples the correlation between ozone concentration and electric field parameters by catalytically decomposing oxygen to generate ozone through ultraviolet light sources. In traditional technologies, ozone generation relies on corona discharge in high-voltage electric fields, resulting in uncontrollable ozone concentration fluctuations with field strength, which can easily cause oxidative damage. In this solution, the ozone concentration is controlled by the ultraviolet light intensity through a closed-loop gas sensor. This can not only improve the ozone-assisted sterilization effect in scenarios with high microbial loads, but also actively inhibit ozone release when a decrease in cell membrane integrity is detected, avoiding secondary oxidative damage and achieving dual protection of safety and antibacterial efficiency.

[0018] The pulsed field strength mode adopts a phased application strategy, optimized in stages according to the characteristics of the preservation cycle. In the initial stage of preservation, high-frequency, short-pulse field strength is used, leveraging instantaneous high field strength to quickly penetrate the microbial cell membrane, while short pulse intervals can reduce cell electrolytic damage. In the middle and late stages, it switches to low-frequency, long-pulse field strength, using a gentle electric field to inhibit metabolic enzyme activity and maintain cell membrane stability. This phased strategy not only inhibits the explosive growth of microorganisms, but also takes into account the long-term protection of cell activity, avoiding the energy waste and cell fatigue effects caused by the traditional continuous electric field mode.

[0019] In addition, the dynamic fusion control of ambient temperature and humidity data enables field intensity regulation to respond to external environmental changes in real time. For example, in high-humidity environments, the pulse frequency is automatically reduced to reduce electric field energy loss, or in low-temperature environments, the field intensity is increased to compensate for the increased cold tolerance of microorganisms, thereby improving the robustness of freshness preservation in complex storage and transportation scenarios. The collaborative optimization of multi-physics response surface models and reinforcement learning algorithms further incorporates conflicting objectives such as inhibition rate, nutrient retention rate, and energy consumption into a unified framework. Through historical data training and real-time feedback iteration, a global optimal parameter combination is generated, breaking through the limitations of traditional empirical parameter adjustment.

[0020] The spatial zoning control mechanism divides the freshness-keeping area into independent zones and, based on local bioimpedance data, differentially adjusts the electric field parameters. This mechanism effectively addresses mixed-product storage and transportation or stacked objects. For example, in a cold chain container, a low field strength mode is used in the well-ventilated upper levels, while the field strength is increased in the densely stacked lower levels to penetrate the gaps between objects. This eliminates the freshness-keeping blind spots caused by uneven distribution of objects with traditional technologies. This mechanism significantly improves the technology's applicability in large-scale storage and transportation scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 A schematic flow chart of a field intensity regulation method based on electrostatic preservation provided by an embodiment of the present invention; Figure 2 A schematic diagram of a multi-electrode plate array provided in an embodiment of the present invention; Figure 3 A schematic structural diagram of a field intensity regulation device based on electrostatic preservation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0024] Figure 1 A schematic flow chart of a field intensity regulation method based on electrostatic preservation provided in an embodiment of the present invention includes the following steps: S1. Obtaining bioimpedance data of the object to be preserved; S2. Generate dynamic field intensity adjustment instructions based on bioimpedance data; S3, based on the dynamic field intensity regulation instruction, controlling the phase difference and voltage amplitude of each electrode in the multi-electrode array to form a three-dimensional rotating electric field; S4, controlling the ozone generation module to operate independently of the three-dimensional rotating electric field to adjust the ozone concentration; S5. Select a pulsed field intensity mode and match corresponding frequency parameters according to the type of the object to be preserved.

[0025] When obtaining the bio-impedance data of the object to be preserved, the electrical characteristic parameters of the object's surface are collected in real time through the bio-impedance detection module deployed inside the preservation equipment. The bio-impedance data is obtained through high-frequency signal excitation and response analysis, reflecting the integrity of the object's cell membrane and changes in the conductivity of the intracellular fluid. The dynamic field intensity regulation instruction is generated by the central controller based on the changing trend of the bio-impedance data, and the instruction signal is transmitted to the driving circuit of the multi-electrode plate array through the digital-to-analog conversion module. The multi-electrode plate array consists of a plurality of independent plates distributed in a ring, and each plate is connected to an adjustable phase high-voltage power supply. According to the dynamic field intensity regulation instruction, the central controller applies a sinusoidal voltage with a fixed phase difference to each plate, so that a continuously changing potential gradient is formed between adjacent plates, thereby generating a three-dimensional electric field rotating in the circumferential direction in the central area of the annular array. Figure 2 A schematic diagram of a multi-electrode array according to an embodiment of the present invention includes electrodes 21 and objects to be preserved 22. The intensity distribution of the three-dimensional rotating electric field is determined by the spacing between the electrodes, the voltage amplitude, and the phase difference, ensuring uniform coverage of the electric field lines in space and no directional attenuation.

[0026] The ozone generation module operates independently of the electric field generation system of the multi-electrode array, and includes an ultraviolet light source and a gas catalytic reaction chamber. The irradiation intensity of the ultraviolet light source is dynamically adjusted by the real-time ozone concentration data fed back by the gas concentration sensor. The central controller controls the on and off and power output of the ultraviolet light source through pulse width modulation technology to maintain the ozone concentration within the preset safety range. The selection of the pulsed field intensity mode is pre-set according to the type of object to be preserved. The central controller has a built-in classification database to match the optimal pulse parameter combination for different categories of objects. For example, for berries with thin and tender skin and vigorous respiratory metabolism, a high-frequency short pulse mode is selected to quickly inhibit surface microorganisms; for root and stem objects with dense structure and low water content, a low-frequency long pulse mode is used to penetrate the surface tissue to achieve deep antibacterial.

[0027] During the preservation process, bioimpedance data is continuously updated and input into the dynamic field intensity regulation algorithm. The algorithm adaptively adjusts the field strength and rotation speed of the three-dimensional rotating electric field based on the impedance change rate. When a significant decrease in cell membrane integrity is detected, the algorithm automatically increases the field strength amplitude and accelerates the electric field rotation speed to enhance the physical breakdown effect on microorganisms; when cell activity tends to stabilize, the field strength is gradually reduced to reduce energy loss. The operation of the ozone generation module is decoupled from the electric field parameters. It independently adjusts the amount of ozone released according to the microbial load level, avoiding the problem of uncontrolled ozone concentration caused by high-voltage electric field fluctuations in traditional technologies. The pulse mode is switched in stages according to the preset strategy during the preservation period. In the early stage, high-frequency short pulses quickly establish an antibacterial barrier, and in the middle and late stages, low-frequency long pulses maintain cell homeostasis, achieving a balance between energy consumption and preservation efficiency.

[0028] Through this implementation, the three-dimensional rotating electric field evenly covers the surface and gaps of objects, preventing microbial residues caused by insufficient field strength in localized areas. Independent regulation of ozone concentration effectively prevents excessive oxidative damage, preserving the color and nutritional composition of objects. The pulse mode's phased adaptation strategy balances antibacterial efficiency with cell viability protection, extending the shelf life. The overall solution, through dynamic feedback and parameter decoupling, significantly improves preservation uniformity, safety, and adaptability.

[0029] In some embodiments, obtaining bioimpedance data includes: The dielectric constant change of the surface of the object to be preserved is collected in real time through a non-contact capacitive coupling sensor; The cell membrane integrity index was calculated based on the change in dielectric constant as bioimpedance data.

[0030] The contactless capacitive coupling sensor is integrated into the detection area of the preservation equipment, and its surface is covered with an insulating dielectric layer to prevent direct contact with the object to be preserved. The sensor is excited by a high-frequency AC signal, applying an alternating electric field to the object's surface via the transmitting electrode. The receiving electrode captures the response signal reflecting the dielectric properties of the object. The change in dielectric constant is determined by analyzing the phase difference and amplitude attenuation between the excitation and received signals, specifically the combined effect of the membrane capacitance of the epidermal cells and the cytoplasmic conductivity of the object. The sensor array is arranged in a grid pattern covering the preservation area, ensuring data acquisition from multiple angles to eliminate the influence of object shape on detection accuracy.

[0031] The dielectric constant change data is converted into a digital signal by a signal conditioning circuit and then input into the central processing unit, which has a built-in analytical algorithm based on a bioelectric model. The algorithm maps the dielectric constant change into cell membrane integrity indicators, including parameters such as cell membrane rupture rate, ion channel activity, and the degree of intracellular fluid leakage. Among them, the cell membrane rupture rate is calculated by correlation analysis between the dielectric loss factor and the relaxation time constant, the ion channel activity is inferred by inverse derivation of the low-frequency dielectric response characteristics, and the degree of intracellular fluid leakage is assessed by the trend of conductivity changes in the high-frequency band. The analytical algorithm combines fuzzy logic rules to perform a weighted fusion of multi-dimensional parameters, generating a standardized cell membrane integrity indicator as the bioimpedance data output.

[0032] During the detection process, the frequency of the high-frequency AC signal must be adapted to the physiological characteristics of the target object. For example, for fruits and vegetables with thick waxy skin (such as apples), higher-frequency excitation is used to penetrate the surface barrier and obtain information about the deeper cellular state. For objects with thin, tender skin and high water content (such as strawberries), medium- and low-frequency bands are selected to reduce signal attenuation. The sensor array's dynamic scanning mode can simultaneously monitor the differences in dielectric properties of different parts of the object, and combined with a spatial interpolation algorithm, it generates a global distribution map of cell membrane integrity.

[0033] Through the above-mentioned implementation, non-contact detection avoids the physical damage that may be caused by traditional electrode contact measurement, and is particularly suitable for objects such as berries with sensitive skin. Dynamic analysis of changes in dielectric constant can accurately reflect the evolution of cell membrane status with shelf life, providing a real-time basis for field intensity control. The combination of a grid sensor layout and a spatial interpolation algorithm effectively eliminates detection deviations caused by object placement or posture, ensuring the spatial representativeness and consistency of bioimpedance data. The analytical algorithm based on the bioelectric model improves the ability to interpret complex physiological states through multi-parameter fusion, avoiding the limitations of single electrical parameter analysis.

[0034] In some embodiments, adjusting the ozone concentration comprises: When the cell membrane integrity index is lower than the preset threshold, the ultraviolet light source of the ozone generation module is triggered to start; The irradiation intensity of the ultraviolet light source is dynamically adjusted through the real-time ozone concentration data fed back by the gas concentration sensor.

[0035] The ozone generation module consists of a UV light source array, a gas catalytic reaction chamber, and an ozone concentration sensor. The UV light source array utilizes deep UV LEDs with a wavelength range that covers the most efficient band for ozone generation. The LEDs are arranged in a honeycomb pattern at the top of the reaction chamber, ensuring that oxygen is fully exposed to UV radiation as it flows through. The interior of the gas catalytic reaction chamber is coated with a titanium dioxide photocatalyst, which, under UV light excitation, catalyzes the decomposition of oxygen molecules into ozone. Ozone concentration sensors are deployed at key locations in the fresh-keeping area to monitor ozone concentration changes in the gas environment in real time and feed this data back to the central controller.

[0036] When the cell membrane integrity index falls below a preset threshold, the central controller sends a start command to the UV light source array. The triggering logic for this start command is based on a comprehensive assessment of the cell membrane rupture rate and ion channel activity. For example, when the cell membrane rupture rate exceeds a critical value and ion channel activity continues to decline, it is determined that the risk of microbial invasion has increased, and ozone-assisted sterilization needs to be initiated. The initial irradiation intensity of the UV light source is dynamically calculated based on the difference between the current ozone concentration and the target value. A proportional-integral-differential algorithm is used to adjust the driving current of the LED lamp beads, so that the ozone concentration quickly approaches the preset safety range.

[0037] During the dynamic regulation of ozone concentration, the oxygen flow rate within the gas catalytic reaction chamber is controlled by a micro-air pump, and this flow rate is adjusted in tandem with the intensity of the UV light source. For example, when the ozone concentration is detected to be below the target value, the central controller simultaneously increases the air pump speed and the LED drive current to increase ozone production per unit time. Conversely, when the concentration approaches the upper safety limit, the air pump speed is reduced and the UV light intensity is weakened to prevent peroxidation damage. The coupled control of oxygen flow rate and UV light intensity is achieved through a lookup table method, with preset adjustment parameter combinations for different concentration deviations to ensure response speed and stability.

[0038] The ozone generation module operates completely independently of the multi-electrode array's electric field generation system. A central controller allocates independent data channels and control threads for ozone control, preventing interference with the ozone generation process from adjustments to electric field parameters. For example, when the intensity of the three-dimensional rotating electric field fluctuates significantly due to changes in bioimpedance, ozone concentration control maintains stable operation based on its own closed-loop logic. This decoupled design ensures that ozone concentration depends solely on the microbial load and cell state, rather than the electric field strength, fundamentally avoiding the risk of ozone explosions caused by uncontrollable high-voltage discharges in traditional technologies.

[0039] The UV light source's lifespan is protected by intermittent pulse drive. When ozone concentration remains stable, the central controller switches the UV light source to low-frequency pulse mode. This periodic on-off switching reduces heat buildup in the lamp, extending its lifespan. The pulse duty cycle is adaptively adjusted based on real-time ozone concentration fluctuations. For example, a low duty cycle is used when concentration fluctuations are minimal, while a higher duty cycle is used to enhance regulation. The deep UV LED's heat dissipation substrate is coupled to the reaction chamber wall with thermally conductive silicone to ensure temperature stability during long-term operation.

[0040] Through the above implementation, the ozone generation module can respond quickly when the risk of microbial invasion increases, providing precise and controllable ozone-assisted sterilization, while avoiding oxidative damage caused by excessive concentration. Independent control logic ensures the decoupling of ozone regulation and electric field parameters, eliminating the safety hazards caused by parameter coupling in traditional technologies. The coordinated adjustment of gas flow rate and ultraviolet light intensity takes into account both ozone generation efficiency and equipment life, and the dynamic pulse drive strategy further optimizes energy consumption and reliability. The photocatalyst coating of the catalytic reaction chamber improves the oxygen conversion efficiency, allowing low-intensity ultraviolet light to maintain the required ozone concentration, reducing overall power consumption.

[0041] In some embodiments, the pulsed field strength mode comprises: High-frequency short-pulse field strength is used in the initial stage of preservation, and switched to low-frequency long-pulse field strength in the middle and late stages of preservation.

[0042] The pulsed field intensity mode phase switching is implemented through a timing logic module and dynamic parameter database embedded in the central controller. The timing logic module pre-sets multiple control nodes based on the stages of the freshness cycle, with each node corresponding to a specific pulse parameter combination. The dynamic parameter database stores the freshness phase definitions for different categories of objects. For example, the freshness cycle for berries is divided into the initial (rapid antibacterial phase) and the mid-to-late (steady-state maintenance phase), while that for root vegetables is divided into the initial, mid-to-late, and late stages. The central controller dynamically determines the current freshness phase and triggers the pulsed mode switch based on a combination of real-time clock and bioimpedance data.

[0043] The pulse control unit is integrated into the multi-electrode array's drive circuit. After receiving pulse mode commands from the central controller, it generates a pulse signal with the corresponding frequency and duty cycle. The high-frequency, short-pulse mode drives the plate voltage using a narrow pulse width and high-repetition-rate square wave, creating an instantaneous, high-intensity electric field that quickly penetrates microbial cell membranes and inhibits their activity. The low-frequency, long-pulse mode uses a wide pulse width and low-repetition-rate square wave to generate a continuous, gentle electric field, maintaining cell membrane stability and reducing energy loss. The drive circuit utilizes a topology combining insulated-gate bipolar transistors and fast-recovery diodes to ensure high-fidelity output of both high- and low-frequency pulse signals.

[0044] In the initial stage of preservation, the central controller calls the high-frequency short pulse configuration parameters in the dynamic parameter database according to the object type, and the driving circuit applies a high-frequency pulse voltage to the multi-electrode array. In this stage, the electric field acts on the surface of the object with short, high-intensity intermittent shocks, quickly destroying the membrane structure of attached microorganisms while avoiding cell electrolysis damage caused by continuous high voltage. When it is detected that the bioimpedance data tends to be stable or reaches the preset time threshold, the timing logic module determines that it has entered the middle and late stages, and the central controller switches to low-frequency long pulse mode. Low-frequency pulses enhance the penetration effect on deep tissues by extending the single electric field action time, inhibiting internal enzyme activity and slowing down the metabolic rate, while reducing the pulse repetition frequency to reduce overall energy consumption.

[0045] The pulse mode switching process uses a gradual transition strategy to avoid the impact of sudden changes in electric field parameters on the object. After the switching command is triggered, the central controller gradually adjusts the pulse frequency and duty cycle. For example, over several cycles, the repetition frequency of the high-frequency pulse is linearly reduced to the target low-frequency value, while the pulse width is correspondingly extended. During the transition period, the bioimpedance detection module continuously monitors changes in cell membrane integrity. If abnormal fluctuations are detected (such as a sudden increase in cell membrane rupture rate), the switching process is suspended and the parameters of the previous stage are restored. The transition is then restarted after the data stabilizes.

[0046] Through the above implementation method, the phased switching of the pulsed field intensity mode can adapt to the dynamic needs of the preservation cycle. In the initial stage, high-frequency short pulses quickly establish an antibacterial barrier, effectively curbing the explosive proliferation of microorganisms; in the middle and late stages, low-frequency long pulses reduce the interference of electric field energy on cell activity while maintaining the antibacterial effect, avoiding the loss of nutrients caused by excessive damage. The collaborative design of timing logic and dynamic parameter library ensures that the switching process accurately matches the physiological characteristics of objects of different categories, and the gradual transition strategy further reduces the risk of physical impact caused by mode switching. The overall solution optimizes energy consumption distribution while improving the preservation efficiency through phased pulse regulation, achieving the dual goals of antibacterial efficiency and cell protection.

[0047] In some embodiments, forming a three-dimensional rotating electric field comprises: A sinusoidal voltage with a phase difference of 45° is applied to multiple circularly distributed plates, causing the electric field lines to rotate periodically along a circular path.

[0048] The multi-electrode plate array adopts a circular symmetrical layout, with the plates fixed at equal angular intervals in the circumferential slots of the insulating bracket, and the electrical connections between adjacent plates are completely isolated. Each plate is connected to a multi-channel adjustable phase power supply via an independent high-voltage cable, and the power supply output is equipped with a sine wave signal generator and power amplifier. The central controller sends a synchronous trigger signal to the multi-channel power supply to ensure that the voltage waveforms of all plates are strictly time-aligned. The phase difference parameter is dynamically allocated by the central controller based on preset rules. A fixed phase offset angle is applied between adjacent plates, so that the overall potential distribution of the multi-electrode plate array exhibits periodic rotation characteristics.

[0049] The amplitude and frequency of the sinusoidal voltage are uniformly set by a central controller, and the multi-channel power supply generates a sinusoidal signal with the same amplitude, frequency, and increasing phase difference according to the instructions. For example, when the circular array includes eight plates, the phase difference between adjacent plates is set to 45 degrees. The phase base of the first plate is zero degrees, and the subsequent plates increase by 45 degrees in sequence. The phase of the eighth plate is 315 degrees. Under this configuration, the potential distribution of the multi-plate array rotates along a circular path at a fixed angular velocity, forming a closed three-dimensional rotating electric field. The direction of potential rotation is achieved by adjusting the order of increasing phase difference. For example, the phase difference is distributed in a clockwise or counterclockwise order, and the electric field lines rotate accordingly.

[0050] The rotation speed of the electric field is determined by both the sine wave frequency and the number of plates. A central controller adjusts the rotation speed by varying the fundamental frequency of the sine wave signal. Increasing the frequency accelerates the rotation of the electric field lines, increasing the number of times they cover the surface per unit time, making it suitable for high-risk scenarios with rapid microbial proliferation. Decreasing the frequency slows the rotation speed, making it suitable for low-energy maintenance during the steady-state preservation phase. The potential gradient distribution is achieved by adjusting the amplitude of the sine wave. Increasing the amplitude increases the electric field strength, but this is limited by the plate spacing and the withstand voltage threshold of the insulating medium.

[0051] The annular layout of the multi-electrode array, combined with the phase difference control strategy, enables the electric field lines to form a spiral propulsion path in three-dimensional space. The electric field intensity at any point on the surface of an object varies sinusoidally with time, eliminating the anisotropy of the intensity distribution of traditional fixed-direction electric fields. For objects with uneven surfaces or porous internal structures, the dynamic coverage characteristics of the rotating electric field can penetrate interstitial areas and inhibit the growth of hidden microorganisms. The incremental phase difference design between the plates ensures the continuity of the potential waveform and avoids electric field distortion or sudden changes in intensity at the junction of adjacent plates.

[0052] Through the above implementation, the three-dimensional rotating electric field is uniformly distributed in both spatial and temporal dimensions, overcoming the edge attenuation and directional limitations of the traditional two-dimensional planar electric field. The dynamic rotation characteristics enhance the continuous physical effect of the electric field on microorganisms, preventing them from developing adaptive tolerance due to the fixed direction of the electric field. The spiral potential gradient distribution enhances the penetration of the electric field into objects with complex structures, ensuring full coverage of the antibacterial effect. The coordinated regulation of phase difference and frequency further adapts to the dynamic needs of different preservation scenarios, taking into account both antibacterial efficiency and energy consumption optimization.

[0053] In some embodiments, the method further comprises: Real-time monitoring of the temperature and humidity data of the environment where the objects to be preserved are located; The field intensity parameters and pulse frequency of the three-dimensional rotating electric field are dynamically adjusted according to temperature and humidity data.

[0054] Temperature and humidity sensors are distributed and installed on the interior walls of fresh-keeping equipment and in key locations on shelves, collecting real-time temperature and humidity data from different areas. Sensor signals are transmitted via shielded cables to the signal conditioning circuit. After filtering and amplification, they are converted into digital signals and input into the central controller. The central controller, equipped with a built-in environmental parameter analysis algorithm, compares the temperature and humidity data with preset thresholds and generates dynamic adjustment instructions.

[0055] Field intensity parameters are adjusted by varying the voltage amplitude across the multi-electrode array. For example, when ambient temperature rises, the central controller reduces the plate voltage amplitude to minimize electric field energy loss. When humidity rises, the voltage is increased to enhance the electric field's ability to penetrate moist air. Pulse frequency is adjusted by modifying the pulse repetition period of the driver circuit. In high-temperature environments, the pulse interval is shortened to maintain the antibacterial effect, while in low-temperature environments, the interval is extended to reduce energy consumption.

[0056] The operating parameters of the multi-electrode array are linked to environmental data, and a central controller dynamically optimizes the electric field strength and pulse timing based on real-time temperature and humidity fluctuations. For example, in high-humidity environments, where microbial activity is enhanced, the controller simultaneously increases the field strength and speeds up the pulse frequency to enhance microbial inhibition. In low-temperature, dry environments, the field strength is reduced and the pulse interval is lengthened to avoid excessive interference with cellular metabolism. An environmental parameter analysis algorithm, combined with fuzzy control rules, balances antibacterial needs with energy efficiency to generate the optimal parameter combination.

[0057] Dynamic fusion control of temperature and humidity data ensures the adaptability of the three-dimensional rotating electric field to diverse environmental conditions. For example, when the door of a fresh-keeping device is opened, causing a sudden change in temperature and humidity, the controller responds quickly, temporarily adjusting the field strength and pulse parameters, and then gradually returning to the baseline mode once the environment stabilizes. This mechanism effectively mitigates external interference and maintains the stability of the fresh-keeping effect.

[0058] Through the aforementioned implementation, the real-time monitoring and dynamic adjustment of ambient temperature and humidity data enables the three-dimensional rotating electric field to adapt to changing external conditions, optimizing both antibacterial efficacy and energy efficiency. The coordinated control of field intensity and pulse parameters enhances the robustness of the preservation system in complex environments, avoiding the degradation of preservation efficiency associated with traditional technologies due to environmental fluctuations.

[0059] In some embodiments, the method further comprises: Based on the multi-physics response surface model, multi-objective optimization of field intensity parameters, pulse frequency, and ozone concentration was performed; The weight allocation strategy of multi-objective optimization is iteratively updated through reinforcement learning algorithm. The multi-objective optimization includes balancing the inhibition of microbial survival rate, nutrient retention rate and energy consumption.

[0060] The construction of a multi-physics response surface model is based on the fusion analysis of historical preservation data and real-time monitoring parameters. Historical data covers the initial bioimpedance, temperature, humidity, and ozone concentration of different types of objects under various environmental conditions, as well as corresponding preservation results indicators, including antibacterial rate, nutrient retention rate, and energy consumption data. Real-time monitoring parameters are continuously collected through a sensor network, including the current object's bioimpedance, ambient temperature and humidity, and ozone concentration. The central controller inputs historical and real-time data into the model training module, and uses a multivariate nonlinear regression algorithm to establish a mapping relationship between field strength parameters, pulse frequency, ozone concentration, and preservation effect.

[0061] The multi-objective optimization process is implemented by defining a trade-off function for conflicting objectives. A central controller sets the optimization targets for the inhibition rate, nutrient retention rate, and energy consumption, constructing a multi-objective optimization problem. The objective function is converted into a single objective function using a weighted summation method, with the weight coefficients dynamically adjusted by a reinforcement learning algorithm. Initially, the weight coefficients are preset based on expert experience; for example, a higher weight is assigned to the inhibition rate to quickly control microbial risks. During the optimization process, the reinforcement learning algorithm updates the weight coefficients using a policy gradient method based on real-time feedback on the preservation effect, gradually approaching the Pareto optimal solution.

[0062] The state space of the reinforcement learning algorithm is defined as the combination of the current field strength parameters, pulse frequency, ozone concentration, and environmental parameters, while the action space is the direction and magnitude of weight coefficient adjustments. The reward function is calculated based on the improvement in the inhibition rate, the change in nutrient retention rate, and the increase or decrease in energy consumption. For example, a positive reward is given when the inhibition rate increases without a significant decrease in nutrient retention; a negative penalty is applied if energy consumption surges without improving the preservation effect. Through an exploration and exploitation mechanism, the algorithm explores optimal weight allocation strategies based on prior knowledge driven by historical data and combined with real-time feedback.

[0063] The multiphysics response surface model and reinforcement learning algorithm work together through an iterative optimization cycle. During each optimization cycle, the model generates candidate combinations of field intensity, pulse, and ozone parameters based on the current weight coefficients. After actual application, it obtains preservation effect data. This data is fed back to the reinforcement learning algorithm to update the weights, thereby driving the model to generate more optimal parameter combinations. This iteration continues until pre-defined convergence conditions are met, such as when the fluctuation range of the weight coefficients falls below a threshold or the preservation effect indicator stabilizes.

[0064] Through the above implementation, the multi-physics response surface model can accurately characterize the nonlinear relationships between complex parameters, providing a reliable predictive foundation for multi-objective optimization. Reinforcement learning algorithms overcome the limitations of traditional fixed weight assignments through dynamic weight adjustment, adaptively balancing the conflicting demands of antibacterial control, preservation, and energy consumption. An iterative optimization mechanism ensures that preservation parameters evolve in real time with environmental changes and object states, avoiding the rigidity of empirical parameter adjustment. Collaborative design enhances the system's decision-making intelligence in dynamic scenarios, maximizing comprehensive preservation effectiveness.

[0065] In some embodiments, training a multiphysics response surface model includes: Collect historical preservation data, including initial bioimpedance, environmental parameters, and preservation results of different types of objects; Combining the real-time monitored cell membrane integrity indicators with ozone concentration data, a dynamic mapping relationship between field strength, frequency, and ozone concentration is established. The dynamic mapping relationship is iteratively tuned through the reinforcement learning algorithm to generate a parameter matching rule library.

[0066] Historical preservation data is collected through a sensor network deployed across multiple preservation devices, covering the initial state and preservation results of different categories of objects under diverse environmental conditions. Initial bioimpedance data is acquired via contactless capacitive coupling sensors, recording the cell membrane integrity, epidermal conductivity, and dielectric properties of objects upon storage. Environmental parameters, including temperature, humidity, and ozone concentration, are collected synchronously in real time by a distributed sensor array. Preservation results data, including antibacterial rate, nutrient retention rate, weight loss rate, and energy consumption indicators, are stored in conjunction with equipment operation logs through laboratory testing. A central controller categorizes historical data by category and environmental conditions and stores it in a cloud database, forming a multidimensional dataset for model training.

[0067] The establishment of a dynamic mapping relationship is achieved by integrating historical data with real-time monitoring parameters. The central controller matches the real-time collected cell membrane integrity indicators and ozone concentration data with records of the same category and similar environmental conditions in the historical data set, extracting the historically optimal combination of field strength parameters, pulse frequency, and ozone concentration as the initial mapping benchmark. The model training module uses a supervised learning algorithm, with initial bioimpedance and environmental parameters as input features, and field strength parameters, pulse frequency, and ozone concentration as output labels, to construct a multi-input and multi-output nonlinear regression model. Regularization and cross-validation strategies are introduced during the training process to prevent overfitting and improve the model's generalization ability.

[0068] The iterative tuning process of the reinforcement learning algorithm is based on real-time feedback on the preservation effect. After the central controller applies the current parameter combination to the preservation process, it obtains real-time evaluation results of the inhibition rate, nutrient retention rate, and energy consumption through the sensor network and converts them into reward signals for reinforcement learning. The algorithm updates the model weights based on the reward signal and adjusts the mapping relationship between field strength parameters, pulse frequency, and ozone concentration. For example, when the inhibition rate does not meet expectations but the energy consumption is low, the algorithm tends to increase the field strength weight; when the nutrient retention rate drops significantly, the ozone concentration weight is reduced to inhibit oxidative damage. During the iterative process, the model gradually converges to the Pareto optimal solution set by exploring the preservation effects of different parameter combinations, generating a parameter matching rule library that adapts to dynamic conditions.

[0069] The generation of the parameter matching rule base relies on the results of reinforcement learning strategy optimization. The central controller encodes the iteratively tuned mapping relationship into a decision tree rule set, with each rule corresponding to the optimal parameter combination for a specific category, environmental conditions, and cell state. The rule base is stored in a hierarchical structure, with the top layer categorized by category, the middle layer clustered by environmental conditions, and the bottom layer segmented by cell membrane integrity indicators. In real-time application, the controller quickly retrieves and loads the optimal parameter combination by matching the current category, environmental parameters, and cell state, while retaining autonomous learning capabilities to cope with uncovered scenarios.

[0070] Through the above implementation method, the systematic accumulation of historical data provides a reliable foundation for model training, the establishment of dynamic mapping relationships enables the precise characterization of complex parameter relationships, and reinforcement learning tuning ensures the dynamic evolution of the rule base. The three elements work together to enable the multi-physics response surface model to adapt to different preservation scenarios, optimizing the matching accuracy and real-time performance of parameter combinations, and breaking through the limitations of traditional static models. The hierarchical structure of the dynamic rule base balances retrieval efficiency and flexibility, providing support for rapid decision-making in complex storage and transportation environments.

[0071] In some embodiments, the method further comprises: When the ozone concentration is detected to exceed the safety threshold, the exception handling mechanism is triggered. The exception handling mechanism includes: Pause the operation of the three-dimensional rotating electric field and start the exhaust equipment; Based on the multi-physics response surface model and real-time environmental parameters, the field intensity parameters are recalculated and the corrected dynamic field intensity adjustment instructions are generated.

[0072] The ozone concentration monitoring module consists of multiple gas sensors evenly distributed across the top, bottom, and sidewall vents of the fresh-keeping equipment, providing real-time monitoring of gas concentrations at various locations. Sensor signals are aggregated via a multiplexer and transmitted to a central controller. This controller uses a sliding average filter to eliminate transient noise interference and calculate the regional average and peak ozone concentrations. If the peak ozone concentration in any area exceeds a preset safety threshold, the central controller triggers an exception handling mechanism, generating a priority interrupt signal to suspend the operation of the current three-dimensional rotating electric field.

[0073] After the interrupt signal is triggered, the central controller sends a start command to the exhaust equipment. The exhaust equipment includes a centrifugal fan and air ducts, and the fan speed is regulated in stages using pulse-width modulation technology. Initially, it runs at maximum speed to quickly displace the high-concentration ozone gas within the fresh-keeping equipment. Once the concentration drops below the safety threshold, the speed is gradually reduced to a maintenance level to prevent dehydration of the object surface caused by airflow shock. During the exhaust process, the ozone generation module's UV light source is simultaneously turned off, and an ozone concentration sensor continuously monitors the data and feeds it back to the central controller, creating a closed-loop control system.

[0074] Field intensity parameters are recalculated based on a multi-physics response surface model and real-time environmental data. After pausing the three-dimensional rotating electric field, the central controller uses a historically optimized parameter library for field intensity settings of the same product category and similar environmental conditions as a baseline. This is combined with current temperature, humidity, and residual ozone concentration data to generate revised field intensity parameters through an interpolation algorithm. A penalty factor is introduced during this correction process. If ozone concentration exceeds the specified limit, the field intensity amplitude is automatically reduced and the pulse frequency is capped to prevent a secondary concentration runaway caused by aggressive parameterization after the electric field resumes.

[0075] The exhaust system's shutdown conditions are determined by both ozone concentration and airflow stability. Once ozone concentration drops to a safe threshold and remains stable, the central controller delays for a period of time, allowing airflow to stabilize, before gradually resuming operation of the three-dimensional rotating electric field. A progressive field strength loading strategy is employed during the initial recovery phase. For example, starting at 50% of the corrected field strength value and then gradually increasing it linearly to the target value prevents sudden changes in electric field parameters from causing additional stress to the subject's cells. During this progressive loading process, the bioimpedance detection module tracks changes in cell membrane integrity in real time. If any abnormal fluctuations are detected, loading is immediately interrupted and the parameters recalibrated.

[0076] Through the above-described implementation, the abnormal handling mechanism for ozone concentration exceeding the limit achieves rapid response and safe recovery. Closed-loop exhaust control effectively prevents oxidative damage caused by excessive ozone accumulation, and the progressive field strength loading strategy balances antibacterial effects with cell protection. The parameter correction process combines historical data with real-time feedback to ensure that the operating parameters after the electric field is restored meet both preservation requirements and prevent repeated ozone concentration violations. The overall solution ensures safety while maximizing the continuity of freshness and minimizing the impact of abnormal events on the overall freshness cycle.

[0077] In some embodiments, the method further comprises: Divide the space into multiple independent fresh-keeping areas according to the spatial distribution of the objects to be preserved; For each independent fresh-keeping area, the corresponding three-dimensional rotating electric field parameters are independently adjusted based on the local bioimpedance data.

[0078] An infrared laser scanning array and weight distribution sensors are deployed within the fresh-keeping equipment to obtain real-time spatial distribution and stacking density of objects to be preserved. The scanned data is fed into a spatial parsing algorithm, which divides the fresh-keeping area into multiple independent sub-areas. Each sub-area corresponds to a set of electrode combinations in the multi-electrode array. The division rules are based on object density thresholds and gap detection. For example, high-density stacked areas are marked as independent sub-areas, while low-density, loose areas are merged into adjacent sub-areas. Each sub-area is assigned a unique control identifier and is linked to a local bioimpedance detection module.

[0079] Local bioimpedance data is collected within each sub-area using contactless capacitive coupling sensors embedded in a grid pattern on the shelf surfaces corresponding to the sub-area. Each sub-area's sensor network operates independently, collecting dielectric constant change data. The data is pre-processed by the regional controller and then transmitted to the central controller, which generates local dynamic field intensity adjustment commands. The central controller maintains an independent parameter database for each sub-area, storing historical field intensity data, environmental parameters, and optimization target weights.

[0080] Independent adjustment of the three-dimensional rotating electric field parameters is achieved through phase difference and voltage amplitude control of the sub-regional electrode groups. Based on local bioimpedance data, the central controller sends differentiated phase difference instructions to the electrode groups in each sub-region. For example, high-density sub-regions use a smaller phase difference to enhance electric field penetration, while low-density sub-regions use a larger phase difference to expand the electric field coverage. The voltage amplitude is dynamically adjusted based on the local microbial load, increasing the voltage in high-load sub-regions to enhance antibacterial strength and reducing the voltage in low-load sub-regions to reduce energy consumption.

[0081] Electric field isolation between sub-areas is achieved through time-division multiplexing and phase synchronization. A central controller assigns staggered electric field activation periods to adjacent sub-areas to avoid overlapping interference caused by simultaneous operation. The phase difference between the plates of adjacent sub-areas is set to complementary angles. For example, sub-area A uses a clockwise phase increment, while sub-area B uses a counterclockwise phase increment. This allows the electric field lines at the junction to naturally cancel each other out.

[0082] Through the above-described implementation, the spatial zoning control mechanism enables precise electric field regulation tailored to differences in object distribution. Enhanced field strength in high-density areas penetrates gaps between accumulations to suppress hidden microorganisms, while moderate field strength in low-density areas avoids energy waste. Independent processing of local bioimpedance data ensures that parameters in each sub-region are tailored to their specific preservation needs, eliminating local blind spots or overloads caused by traditional global unified control. Phase synchronization and time-sharing multiplexing effectively isolate interference between sub-regions, maintaining a stable and coordinated overall electric field distribution.

[0083] The following is an exemplary description of a field intensity adjustment device based on electrostatic preservation corresponding to the method provided in an embodiment of the present invention.

[0084] Figure 3 This is a schematic diagram of the structure of a field intensity regulation device based on electrostatic preservation provided by an embodiment of the present invention. The device may include: The multi-plate array module 301 includes a plurality of independent plates distributed in a ring, each plate being connected to a phase-adjustable high-voltage power supply; The bio-impedance detection module 302 includes a non-contact capacitive coupling sensor for collecting dielectric constant data of the surface of the object to be preserved; The ozone control module 303 includes a UV light source and a gas concentration sensor and operates independently of the multi-electrode array module; The pulse control unit 304 is used to switch between high-frequency short pulse and low-frequency long pulse modes according to the object type; the plates of the multi-plate array module are fan-shaped stainless steel structures, the phase difference between adjacent plates is 45°, and the plate spacing is adjusted by an electric slide rail.

[0085] The multi-plate array module consists of multiple independent plates distributed in a ring. The plates are made of stainless steel with good electrical conductivity and covered with an insulating ceramic coating to prevent arc discharge. Each plate is independently connected to a multi-channel, phase-adjustable high-voltage power supply via a high-voltage cable. The power output is equipped with a sine wave generator module and a power amplifier circuit. The plate spacing is adjusted by an electric slide mechanism. The slide is driven by a stepper motor. The central controller sends a pulse signal to control the motor step amount to achieve precise control of the plate spacing. The center area of the circular array is the area with uniform electric field coverage. The objects to be preserved are placed in this area. Reducing the plate spacing can enhance the electric field strength, while increasing it improves uniformity. The plate is designed as a fan-shaped stainless steel structure. The fan angle matches the number of plates in the circular array. Adjacent plates are fixed by insulating brackets. The brackets have reserved slide slots for adjusting the plate spacing. The arc-shaped edges of the sector plates are processed into a serrated shape to uniformly distribute the electric field lines and reduce the tip discharge effect. The phase difference of the sinusoidal voltage between adjacent plates is fixed at 45°. When the number of plates changes, the phase difference is dynamically adjusted according to the equally divided angle of the circular array.

[0086] The bioimpedance detection module includes a non-contact capacitive coupling sensor array. The sensor adopts an interdigitated electrode structure, and the electrode surface is gold-plated to reduce contact resistance. The sensor array is embedded in the loading platform of the fresh-keeping equipment in a grid form, and each sensor corresponds to a detection unit. The high-frequency signal generator of the detection unit generates a sinusoidal excitation signal, which is applied to the surface of the object through the transmitting electrode. After the receiving electrode captures the response signal, the phase and amplitude information are extracted through the phase-locked amplifier. The signal processing unit converts the raw electrical signal into a digital quantity, which is analyzed into a cell membrane integrity indicator through a bioelectric model. The layout density of the sensor array is set according to the minimum characteristic size of the object to ensure that the detection resolution meets the requirements of objects of different shapes.

[0087] The ozone control module consists of a UV light source array, a gas catalytic reaction chamber, and an ozone concentration sensor. The UV light source utilizes deep UV LED lamp beads, arranged in a honeycomb pattern at the top of the reaction chamber. A quartz glass window is located below to ensure efficient UV light transmission. The interior walls of the reaction chamber are coated with a nano-titanium dioxide photocatalytic coating, and the outside is connected to an oxygen input pipe and an ozone output diffuser. The ozone concentration sensor operates on an electrochemical principle, with a probe positioned along the airflow circulation path in the fresh-keeping area. It monitors ozone concentration in real time and provides feedback to a central controller. The UV light source's drive circuit is physically isolated from the multi-electrode array's power supply system, achieving decoupled control through independent power supply lines and signal channels.

[0088] The pulse control unit is integrated into the multi-electrode array drive circuit and comprises two independent circuits: a high-frequency pulse generator and a low-frequency pulse generator. The high-frequency pulse generator uses MOSFET switching devices to output narrow pulses with nanosecond rising edges; the low-frequency pulse generator, based on an IGBT module, generates microsecond-wide pulses. The central controller switches the outputs of the two circuits via a digital signal selector, and the pulse parameters are written to the driver chip via a digital-to-analog conversion module. The synchronization of the pulse signals is ensured by a master clock source, ensuring that the pulse timing of each plate in the multi-electrode array is strictly aligned.

[0089] The electric slide adjustment mechanism consists of linear guides, a stepper motor, and a position encoder. The linear guides are arranged parallel to the radial direction of the circular array, and the plate base is connected to the guides via a slider. The stepper motor receives pulse direction commands from the central controller, driving the slider along the guides. The position encoder provides real-time feedback on the current position of the plate. The slide travel range is set according to the electric field strength and uniformity requirements. The minimum spacing is limited by the plate voltage rating, and the maximum spacing is determined by the size of the fresh-keeping area. During the adjustment process, the central controller dynamically optimizes the plate spacing based on real-time electric field strength detection data to ensure uniform field strength distribution within the target area.

[0090] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0091] With the above-described preferred embodiments of the present invention as a guide, and with reference to the above description, relevant personnel are fully capable of making various changes and modifications without departing from the technical scope of this invention. The technical scope of this invention is not limited to the contents of the specification and must be determined according to the scope of the claims.

Claims

1. A field intensity regulation method based on electrostatic preservation, characterized in that: The following steps are involved: Obtaining bioimpedance data of the object to be preserved; generating a dynamic field intensity adjustment instruction according to the bioimpedance data; Based on the dynamic field intensity regulation instruction, the phase difference and voltage amplitude of each electrode in the multi-electrode array are controlled to form a three-dimensional rotating electric field; controlling the ozone generation module to adjust the ozone concentration independently of the operating state of the three-dimensional rotating electric field; According to the type of the object to be preserved, a pulsed field intensity mode is selected and the corresponding frequency parameters are matched.

2. The method according to claim 1, characterized in that The obtaining of bioimpedance data comprises: The dielectric constant change of the surface of the object to be preserved is collected in real time by a non-contact capacitive coupling sensor; A cell membrane integrity index is calculated according to the change in the dielectric constant as the bioimpedance data.

3. The method according to claim 2, characterized in that The ozone concentration adjustment comprises: When the cell membrane integrity index is lower than a preset threshold, the ultraviolet light source of the ozone generation module is triggered to start; The irradiation intensity of the ultraviolet light source is dynamically adjusted based on the real-time ozone concentration data fed back by the gas concentration sensor.

4. The method according to claim 1, wherein The pulsed field strength mode includes: High-frequency short-pulse field strength is used in the initial stage of preservation, and switched to low-frequency long-pulse field strength in the middle and late stages of preservation.

5. The method according to claim 1, wherein The forming of the three-dimensional rotating electric field includes: A sinusoidal voltage with a phase difference of 45° is applied to multiple circularly distributed plates, causing the electric field lines to rotate periodically along a circular path.

6. The method according to claim 1, characterized in that The method further comprises: Real-time monitoring of the temperature and humidity data of the environment where the objects to be preserved are located; The field intensity parameters and pulse frequency of the three-dimensional rotating electric field are dynamically adjusted according to the temperature and humidity data.

7. The method according to claim 1, characterized in that The method further comprises: Based on a multi-physics response surface model, multi-objective optimization is performed on the field intensity parameters, pulse frequency, and ozone concentration; The weight allocation strategy of the multi-objective optimization is iteratively updated through a reinforcement learning algorithm, and the multi-objective optimization includes balancing the inhibition of microbial survival rate, nutrient retention rate and energy consumption.

8. The method according to claim 7, characterized in that The training of the multi-physics response surface model includes: Collecting historical preservation data, including initial bioimpedance, environmental parameters, and preservation results of different types of objects; Combining the real-time monitored cell membrane integrity indicators with ozone concentration data, a dynamic mapping relationship between field strength, frequency, and ozone concentration is established. The dynamic mapping relationship is iteratively tuned through the reinforcement learning algorithm to generate a parameter matching rule library.

9. The method according to claim 7, characterized in that The method further comprises: When it is detected that the ozone concentration exceeds the safety threshold, an exception handling mechanism is triggered, which includes: Pausing the operation of the three-dimensional rotating electric field and starting the exhaust equipment; Based on the multi-physics response surface model and real-time environmental parameters, the field intensity parameters are recalculated and a revised dynamic field intensity adjustment instruction is generated.

10. The method according to claim 1, characterized in that The method further comprises: Dividing the area into multiple independent fresh-keeping areas according to the spatial distribution of the objects to be kept fresh; For each of the independent fresh-keeping areas, the corresponding three-dimensional rotating electric field parameters are independently adjusted based on the local bioimpedance data.

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