Intelligent power distribution and energy consumption management system of negative pressure cabin equipment

Abstract

The invention relates to the technical field of intelligent power distribution and micro-grid control, in particular to an intelligent power distribution and energy consumption management system of negative pressure cabin equipment, which comprises a state acquisition module used for acquiring a real-time differential air pressure signal between an internal reference environment and an external reference environment of each container type negative pressure purification module; the equivalent mapping module is used for mapping state information of internal equipment of the container type negative pressure purification module into virtual impedance parameters on the direct current shared bus; the voltage regulation and control module is used for remodeling the equivalent node voltage at the position where the pneumoelectric coupling node box is connected to the direct current shared bus in real time; the energy self-adaptive allocation module is used for forming a node voltage difference on the direct current shared bus according to the equivalent node voltage so as to provide peak power for the corresponding module in a transient load state; according to the invention, microsecond-level cross-cabin support of transient peak power is realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent power distribution and microgrid control technology, specifically to an intelligent power distribution and energy management system for negative pressure chamber equipment. Background Technology

[0002] The essence of intelligent power distribution and energy management for negative pressure chamber equipment is to coordinate and allocate the power supply demand and internal energy storage of multiple negative pressure purification modules under the condition of limited main grid input, so as to ensure the safety and stability of the negative pressure environment. Existing multi-module power distribution management methods usually adopt centralized dispatch communication or configure large-capacity energy storage devices independently for each node. Centralized dispatch mainly relies on the central programmable logic controller for communication polling, and issues power dispatch instructions after determining the pressure difference change through software. Although independently configured energy storage can cope with the peak and valley electricity consumption of a single point, it is difficult to achieve cross-module power sharing within the microgrid. Centralized power distribution architecture addresses global energy balance under normal steady-state conditions. However, when faced with transient depressurization caused by sudden door opening, the serial link from pressure change to software judgment and then to power dispatch will generate significant system delays, which cannot meet the transient peak power response requirements urgently needed by the wind turbine. In addition, conventional control methods are highly susceptible to interference from external turbulent pulses, which can generate false alarms and cause unnecessary redundant power allocation on the DC shared bus. Furthermore, there is a lack of local absorption methods for residual energy feedback within the system. Therefore, how to overcome the communication delay of the central dispatch and achieve self-organized cross-module mutual power supply and dynamic energy closed loop for instantaneous peak power among multiple modules under the condition of capped total input power of weak power grid has become a problem to be solved. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent power distribution and energy consumption management system for negative pressure chamber equipment, and to solve the following technical problems: By utilizing air pressure mapping and node potential difference, a hardware-level power exchange channel that does not require communication is constructed to achieve extremely low latency power exchange and rapid recovery of transient negative pressure for multi-compartment modules based on hardware-level response under conditions of weak grid and limited total input power. At the same time, it realizes closed-loop management of excess feedback power within the microgrid, and can accurately quantify and assess the deep stagnation and degradation state of the internal air filter without the need for additional wind speed equipment.

[0004] The objective of this invention can be achieved through the following technical solutions: The intelligent power distribution and energy management system for the negative pressure chamber equipment includes: a rectifier and voltage stabilizer cabinet deployed at the main power grid access end, a DC shared busbar running through multiple containerized negative pressure purification modules, and an air-electric coupling node box built into each module; the containerized negative pressure purification module is equipped with an air filter, a negative pressure fan, and a heat energy conversion load unit; the air-electric coupling node box includes a bidirectional DC-DC converter, a supercapacitor, and a microcontroller; the air-electric coupling node box also includes: The status acquisition module is used to acquire real-time differential air pressure signals between the inside of each of the containerized negative pressure purification modules and the external reference environment; The equivalent mapping module is used to establish an equivalent circuit model of gas-electric isomorphism based on the real-time differential air pressure signal, and to map the state information of the internal equipment of the containerized negative pressure purification module to the virtual impedance parameters on the DC shared bus according to the preset air pressure-impedance mapping relationship. The voltage regulation module is used by the microcontroller to reshape the equivalent node voltage at the DC shared bus of the gas-electric coupling node box in real time by changing the duty cycle of the power switching transistor in the bidirectional DC-DC converter based on the received real-time differential air pressure signal and the virtual impedance parameters. An energy adaptive allocation module is used to form a node voltage difference on the DC shared bus based on the equivalent node voltage, and to physically drive the remaining electrical energy in the supercapacitors in each of the gas-electric coupling node boxes to be collected across modules based on the node voltage difference, so as to provide peak power to the corresponding module in transient load state.

[0005] Optionally, methods for establishing gas-electric isomorphic equivalent circuit models include: Obtain the preset standard negative pressure reference value and the preset total input power limit value of the main grid access terminal; map the standard negative pressure reference value to the reference bus voltage of the bidirectional DC-DC converter; obtain the real-time dust holding capacity parameter of the air filter in each of the containerized negative pressure purification modules, and positively map the real-time dust holding capacity parameter to the static foundation compensation current of the gas-electric coupling node box; Based on the reference bus voltage and the static foundation compensation current, the initial circuit structure state of the gas-electric isomorphic equivalent circuit model is generated.

[0006] Optionally, the method for reshaping the equivalent node voltage at the DC shared bus of the gas-electric coupling node box in real time by changing the duty cycle of the power switching transistors in the bidirectional DC-DC converter includes: Calculate the real deviation between the real-time differential air pressure signal and the standard negative pressure reference value; compare the real deviation with a preset tolerance deviation threshold; if the real deviation is in the range below the tolerance deviation threshold, control the microcontroller to maintain the current duty cycle of the power switch and keep the current equivalent node voltage unchanged. If the real number of the deviation is at a critical point equal to the tolerance deviation threshold, the historical duty cycle sequence stored in the system memory is extracted for compensation and fine-tuning to lock the critical equivalent node voltage. If the real number of the deviation is in the range higher than the tolerance deviation threshold, it is determined that a transient airflow depressurization event has occurred. According to the preset droop control logic, the virtual impedance parameter corresponding to the containerized negative pressure purification module that caused the transient airflow depressurization event is reduced, and the duty cycle of the power switch is increased synchronously to generate a target equivalent node voltage with low potential drop characteristics, replacing the current equivalent node voltage.

[0007] Optionally, the method for cross-module aggregation of residual electrical energy in each of the gas-electric coupling node boxes based on the node voltage difference includes: Under the constraint of the preset total input power limit at the main grid access end, when the airflow transient pressure loss event occurs and the target equivalent node voltage is generated, a low potential gap is generated during the target access phase of the DC shared bus. Activate the supercapacitor in the adjacent containerized negative pressure purification module that has not experienced a bias voltage fault to form a distributed energy storage pool. Based on the node potential difference distribution at the circuit hardware level, the electrical energy in the distributed energy storage pool automatically flows in reverse along the physical path with the lowest impedance in the DC shared bus to the containerized negative pressure purification module where the transient airflow depressurization event occurs, and the duration during which the environmental pressure is restored to the standard negative pressure reference value is controlled within a preset pressure recovery time threshold.

[0008] Optionally, the system also includes an energy consumption closed-loop recovery module. The method executed by the energy consumption closed-loop recovery module after completing the instantaneous high load intervention includes: continuously monitoring the recovery slope of the real-time differential pressure signal and analyzing the pressure field recovery process; generating a pressure field balance determination command based on the pressure field recovery process. In response to the pressure field balance determination command, the virtual impedance parameter corresponding to the containerized negative pressure purification module that experienced the transient airflow depressurization event is increased, and the cross-module current transmission link used to collect the remaining electrical energy is cut off; the reverse feedback electrical energy parameter generated by the emergency braking of the negative pressure fan speed is monitored in real time. According to the preset system load power supply sequence table, the thermal energy conversion load unit with the lowest power supply priority is extracted from the non-working thermal energy conversion load units as the receiving object. The reverse feedback electrical energy is guided and transmitted to the heat energy conversion load unit, which is the recipient, so that the excess kinetic energy is completely converted into heat energy for local storage, thus achieving a complete energy loop within the microgrid system.

[0009] Optionally, the system also includes a health self-diagnosis module, and the methods for health self-diagnosis include: When the entire system is in a voltage stabilization closed state, the real bus sustaining current data on the DC shared bus branch is read in real time. Based on the extracted node admittance matrix of the gas-electric isomorphic equivalent circuit model, inverse estimation is performed to obtain the predicted value of the standard sustaining current. By comparing the actual bus sustaining current data with the standard sustaining current prediction value, the global current drift rate during system operation is obtained. The aging index of the global current drift rate extraction module is used to quantitatively assess the degree of effectiveness degradation of the air filter inside the containerized negative pressure purification module.

[0010] Optionally, methods for quantifying the degree of decline include: The global current drift rate variables are recorded according to a preset sampling time period and synthesized into a drift rate evolution sequence. For each segment in the drift rate evolution sequence, a linear slope extraction process with a sliding analysis window is performed to obtain the current drift trend value for the corresponding time period. The extracted current drift trend values ​​are compared sequentially with the preset lifetime state critical threshold stored in the system. If the current drift trend value is less than the preset lifespan state critical threshold, the health status is determined to be up to standard and a daily log is recorded. If the current drift trend value is equal to the preset lifetime state critical threshold, a monitoring warning code is generated; If the current drift trend value is greater than the preset life state critical threshold, the hardware identifier of the out-of-standard node is located, maintenance work order data containing location information and fault mechanism analysis results is compiled and output to the operation and maintenance terminal.

[0011] Optionally, the method for obtaining the real-time differential pressure signal between the interior and external reference environment of each of the containerized negative pressure purification modules includes: The hardware sensor node is called to collect high-frequency raw environmental omnidirectional pressure data, and the high-frequency raw environmental omnidirectional pressure data is smoothed by a bandpass filter component to obtain the base pressure component. Integrate the ventilation speed setting parameters within the associated area, and retrieve the appropriate background turbulence interference spectrum from the stored nonlinear fluid dynamics frequency domain fingerprint database; Adaptive cancellation frequency control parameters are generated by comparing with the background turbulence interference spectrum lines, and the passband structure of the bandpass filter component is reset; An offsetting operation involving a deconvolution operator is performed to separate and remove the environmental coupling noise remnants from the base pressure component, resulting in the real-time differential pressure signal after filtering out the coupling noise.

[0012] The beneficial effects of this invention are: 1. This invention reshapes the equivalent node voltage in real time on the DC shared bus by mapping the differential air pressure signal to a virtual impedance and adjusting the converter duty cycle; the low potential gap generated when the airflow is transiently depressurized can physically drive the remaining electrical energy in the supercapacitor of the adjacent module to automatically flow back along the lowest impedance path; this mechanism gets rid of the communication judgment delay of traditional centralized scheduling and realizes microsecond-level cross-compartment support for transient peak power under the condition of the main grid power capping. 2. This invention uses sensor data to obtain the base pressure component and combines it with a nonlinear fluid dynamics frequency domain fingerprint library to extract adaptive cancellation frequency control parameters, thereby resetting the passband structure of the bandpass filter component; it uses a built-in deconvolution operator to perform cancellation calculations, which can accurately remove environmental coupling noise reverberation; this avoids signal distortion interference caused by external turbulent pulses and spatial structures, prevents false alarms of air pressure from causing unnecessary redundant power allocation, and improves the stability of system energy allocation; 3. After the transient energy borrowing ends, the present invention promptly cuts off the surge current link by monitoring the recovery slope of the air pressure field and monitors the reverse feedback power generated by the emergency braking of the fan in real time. The system guides the feedback power to the heat energy conversion load unit as the recipient, and converts it completely into heat energy for local storage. This mechanism avoids the excess energy impacting the bus during the deceleration phase, which would cause local overvoltage, and realizes safe and local closed-loop management of energy within the system. 4. Under the stabilizing closed state, this invention estimates the predicted standard maintenance current value in reverse based on the transformation matrix of the gas-electric isomorphic model, and compares it with the actual bus current to solve the global current drift rate; by performing sliding analysis on the drift rate evolution sequence to extract the linear trend value, the degradation degree of the core filter is quantitatively assessed; this method can accurately perform health self-diagnosis without adding external wind measurement hardware, upgrading the single-point aging static deviation to a forward-looking dynamic life warning. Attached Figure Description

[0013] The invention will now be further described with reference to the accompanying drawings.

[0014] Figure 1 This is a schematic diagram of the intelligent power distribution and energy consumption management system for the negative pressure chamber equipment provided in this application embodiment. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Please see Figure 1 The intelligent power distribution and energy management system for the negative pressure chamber equipment includes: a rectifier and voltage stabilizer cabinet deployed at the main power grid access end, a DC shared bus that runs through multiple containerized negative pressure purification modules, and an air-electric coupling node box built into each module; the containerized negative pressure purification module is equipped with an air filter, a negative pressure fan, and a heat energy conversion load unit; the air-electric coupling node box includes a bidirectional DC-DC converter, a supercapacitor, and a microcontroller; The gas-electric coupling node box also includes: a status acquisition module, used to acquire real-time differential air pressure signals between the inside of each of the containerized negative pressure purification modules and the external reference environment; The equivalent mapping module is used to establish an equivalent circuit model of gas-electric isomorphism based on the real-time differential air pressure signal, and to map the state information of the internal equipment of the containerized negative pressure purification module to the virtual impedance parameters on the DC shared bus according to the preset air pressure-impedance mapping relationship. The voltage regulation module is used by the microcontroller to reshape the equivalent node voltage at the DC shared bus of the gas-electric coupling node box in real time by changing the duty cycle of the power switching transistor in the bidirectional DC-DC converter based on the received real-time differential air pressure signal and the virtual impedance parameters. An energy adaptive allocation module is used to form a node voltage difference on the DC shared bus based on the equivalent node voltage, and to physically drive the remaining electrical energy in the supercapacitors in each of the gas-electric coupling node boxes to be collected across modules based on the node voltage difference, so as to provide peak power to the corresponding module in transient load state.

[0017] This embodiment provides an intelligent power distribution and energy consumption management mechanism for negative pressure chamber equipment; specifically, the system is applied to a temporarily built containerized fever clinic, where the main power grid provides only 10kW of input power to the top, and three containerized negative pressure purification modules are arranged along the medical staff corridor, which are referred to as Chamber A, Chamber B and Chamber C respectively. A rectifier and voltage regulator cabinet is installed at the main power grid access end to rectify the restricted AC power into a stable DC output, such as a 380V DC bus. This DC shared bus runs through the three compartments, and each compartment is equipped with a pneumatic-electric coupling node box. The node box integrates at least a bidirectional DC-DC converter, a supercapacitor, a microcontroller, and local acquisition and drive interfaces corresponding to the air filter, negative pressure fan, and thermal energy conversion load unit. Specifically, the status acquisition module continuously acquires real-time differential pressure signals inside and outside each cabin; here, inside and outside can be understood as the pressure difference between the cabin and the external corridor environment or the external atmospheric environment; for ease of explanation, the standard negative pressure target can be set to -15Pa; when cabin A is in steady state, the real-time differential pressure signal is -15Pa, cabin B is -16Pa, and cabin C is -14Pa. The equivalent mapping module does not use air pressure only as an alarm value, but maps the air pressure state to the circuit parameter space. To illustrate with a set of example data, the preset air pressure-impedance mapping relationship can be simplified as follows: the closer the pressure difference is to 0Pa, the higher the risk of airtightness failure, and the lower the corresponding virtual impedance, so as to draw more power from the bus. The more stable the pressure difference is below the target value, the higher the corresponding virtual impedance, maintaining only basic power supply; for example, when cabin A is -15Pa, the mapped virtual impedance is 2.0Ω; when cabin B is -16Pa, it corresponds to 2.2Ω; and when cabin C is -14Pa, it corresponds to 1.8Ω. The voltage regulation module is executed by the microcontroller. After receiving the above differential voltage signal and virtual impedance parameters, the microcontroller directly adjusts the pulse width modulation duty cycle of the power switch of the bidirectional DC-DC converter to change the equivalent node voltage at the point where the node is connected to the DC shared bus. For ease of understanding, the equivalent node voltages of nodes A, B, and C in steady state can be set to 379V, 380V, and 378V, respectively. When the door of compartment A is opened and the pressure difference rapidly deteriorates from -15Pa to -6Pa, the mapping module reduces the virtual impedance of compartment A from 2.0Ω to 0.7Ω, and the microcontroller correspondingly increases the PWM duty cycle, so that the node of compartment A exhibits a low potential drop characteristic corresponding to the drop amplitude, for example, from 379V to 365V. Since other nodes on the shared busbar still maintain a high potential, the remaining electrical energy in compartments B and C, as well as the energy stored in their supercapacitors, flows directly along the busbar to compartment A under the drive of the potential difference, without going through the central dispatch communication. In this process, the energy adaptive allocation module is manifested as a distributed power collection mechanism that relies on the physical laws of hardware; for example, in steady state, cabins B and C only need 1.5kW and 1.2kW respectively to maintain negative pressure, while their node boxes respectively contain supercapacitor energy that can be released in a short time. When a sudden demand for 4.5kW peak power from the wind turbines occurs in compartment A, the rectifier and voltage regulator cabinet maintains a total input of no more than 10kW. The additional transient power required by compartment A is automatically supplemented by the voltage difference between the nodes of compartments B and C and local energy storage. As a result, the wind turbines in compartment A receive instantaneous support exceeding the single mains power quota, while compartments B and C, due to closed doors and sufficient negative pressure margin, only temporarily transfer the remaining power without affecting the overall safety boundary. As an anomaly handling mechanism, if the barometric pressure sensor of a certain compartment temporarily loses communication connection, the microcontroller can read the differential pressure signal in the most recent effective sampling period and freeze the current virtual impedance. At the same time, the node of that compartment is set to a restricted power state to avoid erroneously triggering large current accumulation when the sensor is abnormal. If the state of charge of a supercapacitor in a certain compartment is lower than the preset lower limit, such as below 20%, then the compartment will only maintain basic power supply and will not participate in external cross-module support; if the impedance of a local branch of the shared bus suddenly increases, such as due to poor contact of the plug-in terminal, the remaining power will preferentially gather along the path with the lowest impedance, and the nodes that do not participate in support will continue to maintain their own negative pressure to avoid chain instability. For example, in the above-mentioned temporary fever clinic scenario, when an emergency patient is pushed into compartment A, medical staff quickly open the compartment door, and the negative pressure in compartment A instantly drops to -6Pa. At this time, there is no need for the central programmable logic controller to poll or wait for the AC contactor to act. The node box of compartment A directly changes the duty cycle of the local converter according to the differential pressure signal, forming a low potential gap on the DC shared bus. The distributed energy storage and idle power of compartments B and C are automatically integrated into compartment A to increase the speed of the negative pressure fan in compartment A. The purpose of this step is to reconstruct the original serial link of air pressure change - software judgment - power dispatch into a physical self-organizing link where air pressure change directly reshapes the node potential and power is automatically collected. This enables zero-delay mutual power supply of multi-compartment modules under weak grid conditions and ensures transient negative pressure recovery capability.

[0018] In a preferred embodiment of the present invention, the method for establishing a gas-electric isomorphic equivalent circuit model includes: obtaining a preset standard negative voltage reference value and a preset total input power limit value at the main grid access terminal; mapping the standard negative voltage reference value to the reference bus voltage of the bidirectional DC-DC converter; The real-time dust holding capacity parameters of the air filters in each of the containerized negative pressure purification modules are obtained. The real-time dust holding capacity parameters are multiplied by a preset resistance conversion coefficient to obtain an equivalent compensation value. The equivalent compensation value is set as the static basic compensation current of the gas-electric coupling node box in a linear proportion. Based on the reference bus voltage and the static basic compensation current, the initial circuit structure state of the gas-electric isomorphic equivalent circuit model is generated.

[0019] This embodiment provides an initialization modeling step for a gas-electric isomorphic equivalent circuit model. Specifically, after the temporary fever clinic is hoisted and connected to the weak power grid, the system does not enter the dynamic energy borrowing mode, but first establishes the initial circuit structure state of the entire multi-compartment microgrid to provide a baseline for subsequent transient response. Specifically, the standard negative voltage reference value and the total input power at the main grid connection point are obtained; for example, the standard negative voltage reference value is set to -15Pa, and the total input power limit is set to 10kW; the standard negative voltage reference value is mapped to the reference bus voltage of the bidirectional DC-DC converter; for ease of implementation, a set of preset mapping tables can be used, instead of being limited to a unique functional relationship; for example, -15Pa can be mapped to a 380V reference bus voltage, -12Pa to 372V, and -8Pa to 360V; in this way, each node operates around 380V during steady-state maintenance; Furthermore, the system collects real-time dust holding capacity parameters of the air filters in each compartment; the dust holding capacity can be derived from the cumulative estimated value of the filter differential pressure, the converted value of the running time, or the blockage monitoring unit built into the filter assembly; to illustrate with a set of example data, the dust holding capacity of the filter in compartment A is 20 units, that in compartment B is 35 units, and that in compartment C is 10 units. The preset resistance conversion coefficient is set to 0.02A / unit, and the equivalent compensation values ​​are 0.4A, 0.7A and 0.2A respectively. The system then sets the equivalent compensation value as the static basic compensation current of each node box in a linear proportion, which means that even if the hatch is closed and personnel do not move, a higher maintenance current needs to be reserved for the corresponding fan just to overcome the filter resistance and maintain basic ventilation. The initial circuit structure can be generated based on the reference bus voltage and the static foundation compensation current. For example, the initial model of compartment A is a 380V reference node + 0.4A foundation compensation branch, compartment B is a 380V reference node + 0.7A foundation compensation branch, and compartment C is a 380V reference node + 0.2A foundation compensation branch. If we further consider that the initial state of charge of the supercapacitors is 80%, 75%, and 90%, then we can add node energy storage margin parameters to the model to form an initial bus topology that is closer to the real system. If the filter's dust holding capacity is ignored based solely on the air pressure setpoint, there is a drawback: the chambers may appear to be maintained near the same negative pressure target, but the actual maintaining current of the fan may have diverged significantly. A filter with a higher degree of clogging will cause some compartments to inherently consume more power under the same pressure difference. If this difference is not reflected in the model initialization, the subsequent judgment of the energy flow direction may deviate from the actual operating conditions. Therefore, this implementation introduces a static base compensation current so that the model not only reflects the instantaneous air pressure state, but also includes the differences in filter aging and damping. As an exception handling mechanism, if the dust holding capacity parameter of a certain filter cannot be obtained temporarily, the most recent valid dust holding capacity value can be retrieved for that compartment. If historical values ​​are also not available, the factory default value is used, such as 15 units, corresponding to a basic compensation current of 0.3A, to ensure the integrity of the model. If the total input power limit of the main grid is lowered by the operation and maintenance personnel during operation, for example, from 10kW to 8kW, the system regenerates the initial circuit structure state and simultaneously compresses the upper limit of the energy that can be borrowed by each compartment to prevent overall overload. For example, during the startup phase after the deployment of the three modules A, B, and C, although all three modules are set to a target negative pressure of -15Pa, the static basic compensation current of module B is modeled as 0.7A, which is higher than that of modules A and C, because it has been running continuously for several days and the filter has accumulated a lot of dust. In this way, when a sudden access control is opened, the system can know in advance that the basic load of module B itself is too high and it is not suitable for long-term large-scale external support, but only suitable for short-term transient mutual assistance. The purpose of this step is to establish a unified initial state for each node on the DC shared bus, with wind resistance differences and power supply limit constraints, so as to achieve data-driven mapping and bounded support during subsequent dynamic allocation.

[0020] In a preferred embodiment of the present invention, the method for reshaping the equivalent node voltage at the DC shared bus of the gas-electric coupling node box in real time by changing the duty cycle of the power switching transistor in the bidirectional DC-DC converter includes: calculating the real number of the deviation between the real-time differential air pressure signal and the standard negative pressure reference value; The real deviation is compared with a preset tolerance deviation threshold; if the real deviation is less than the tolerance deviation threshold, the microcontroller is controlled to maintain the current duty cycle of the power switch and keep the current equivalent node voltage unchanged. If the real number of the deviation is equal to the tolerance deviation threshold, then the historical duty cycle time series stored in the microcontroller memory within a preset time period is extracted and the arithmetic mean is calculated as the target fine-tuning value. The duty cycle of the power switch is adjusted to the target fine-tuning value, and the current equivalent node voltage is locked. If the real number of the deviation is greater than the tolerance deviation threshold, it is determined that a transient airflow depressurization event has occurred. According to the preset differential pressure-impedance droop control relationship, the virtual impedance parameter corresponding to the containerized negative pressure purification module where the transient airflow depressurization event occurred is reduced, and the duty cycle of the power switch is simultaneously increased to generate a target equivalent node voltage with low potential drop characteristics, replacing the current equivalent node voltage.

[0021] This embodiment provides a real-time node voltage reshaping step; specifically, after the initial modeling is completed, the system enters the continuous operation phase, and the microcontroller processes the real-time differential pressure signal of each cabin in a loop with a fixed sampling period, such as 20ms, and adopts different duty cycle control strategies according to the magnitude of the deviation. Specifically, the system first calculates the real deviation between the real-time differential pressure signal and the standard negative pressure reference value. For ease of explanation, the standard negative pressure of -15Pa is still used as an example. If the current detection value in cabin A is -14Pa, the real deviation value is 1Pa. If it is -12Pa, the real deviation value is 3Pa. The system then compares the real deviation value with the preset tolerance deviation threshold, for example, the threshold is set to 3Pa. When the actual deviation is less than the threshold, it indicates that although the negative voltage fluctuates slightly, it is still within a safe and acceptable range. At this time, the microcontroller maintains the current duty cycle, for example, 45%, which corresponds to an equivalent node voltage of 378V. This can avoid frequent voltage adjustment due to small disturbances and reduce the vibration of the fan and converter. When the real deviation equals the threshold, it indicates that the system is on the critical edge. If the duty cycle is adjusted significantly at this time, normal disturbances may be misjudged as sudden events, causing unnecessary energy borrowing. Therefore, this implementation introduces a mean fine-tuning mechanism based on the historical duty cycle time series. For example, if 50 duty cycle sampling values ​​are recorded in the past second, fluctuating around 44%, 45%, and 46% respectively, and the arithmetic mean is calculated to be 45.2%, then 45.2% is used as the target fine-tuning value to slightly pull the duty cycle of the power switch back to this mean point, so that the current equivalent node voltage is locked at a more stable reference position. When the real deviation exceeds the threshold, the system determines that a transient depressurization event has occurred. At this time, the virtual impedance of the cabin is reduced according to the preset differential pressure-impedance droop control relationship. Specifically, this droop control relationship is not a simple table lookup jump, but a reconstruction and constraint calculation based on the linear droop adjustment rule that evolves from differential pressure to damping. ; The reshaped virtual impedance parameter is denoted as . The steady-state foundation impedance locked during the normal maintenance period is denoted as... The above deviation in real form is denoted as The tolerance deviation threshold is denoted as The system's preset impedance droop factor is denoted as Its corresponding physical level pressure-resistive feedback mapping sensitivity; for example, when the detection is -7Pa, the deviation is a real number. The value is 8 Pa; if the steady-state foundation impedance is taken... Tolerance deviation threshold Impedance droop factor Then it can be deduced that: ; The system equates this part of the value reflecting the deterioration gap to the short-term absorption pre-emptive characteristics of the busbar, and simultaneously expands the duty cycle to generate a target equivalent node voltage with a lower potential; for example, when the duty cycle of compartment A increases from 45% to 58%, the node voltage decreases from 378V to 364V; since other nodes of the busbar are still in the high potential region, this voltage drop will immediately trigger the current of the other compartments to converge to compartment A. If no intermediate level equal to the threshold is set, and only the less than and greater than are processed, the node voltage may frequently switch between borrowing and not borrowing energy under critical conditions such as half-open hatches, wind speed changes, and personnel movement, resulting in bus power flow oscillation. Therefore, this implementation introduces the historical duty cycle average as the target fine-tuning value to form a buffer layer to absorb boundary jitter. As an anomaly handling mechanism, if the historical duty cycle time series is not long enough, for example, the system has just started and has not yet accumulated 1 second of data, the mean is calculated using the currently available samples; if the number of samples is lower than the minimum available number, for example, less than 5 sampling points, the current duty cycle is kept unchanged. If the deviation real number changes abruptly but the duration is less than the set duration threshold, such as only one sampling period, the system can combine the continuous over-threshold judgment strategy and require that the threshold be exceeded for 2 to 3 consecutive sampling periods before it is considered a transient undervoltage, so as to filter out occasional interference; if the microcontroller detects that the duty cycle adjustment is close to the hardware limit, such as exceeding 95%, it will not continue to increase it, but will report the status as local limit operation and give priority to calling the supercapacitor discharge support. For example, during the nighttime reception phase of the fever clinic, the door of cabin A was pushed open by about half, and the pressure difference changed from -15Pa to -12Pa in a short time, with a deviation of 3Pa. The system did not immediately start large-scale energy borrowing, but first read the average duty cycle of the past second and fine-tuned the duty cycle of cabin A to 45.2% to maintain node stability. Several hundred milliseconds later, if the hatch is fully opened and the pressure difference continues to deteriorate to -7Pa, the real number of the deviation jumps to 8Pa. Only then does the system recognize it as a transient depressurization event and call the physical droop mapping rule to quickly pull down the voltage of the A-cell node, triggering cross-cell support. The purpose of this step is to suppress boundary oscillations while maintaining response speed through hierarchical judgment and a duty cycle reshaping mechanism with deterministic mapping constraints, thereby achieving a fast, accurate, circuit-level response to real underpressure events.

[0022] In a preferred embodiment of the present invention, the method for cross-module aggregation of the remaining electrical energy in each of the gas-electric coupling node boxes based on the node voltage difference includes: under the constraint of the preset total input power limit at the main grid access end, when the gas transient pressure loss event occurs and the target equivalent node voltage is generated, a low potential gap is generated at the corresponding access position of the DC shared bus. The supercapacitor in the adjacent containerized negative pressure purification module that did not experience the transient airflow depressurization event is activated to form a distributed energy storage pool. Based on the node potential difference distribution at the circuit hardware level, the electrical energy in the distributed energy storage pool automatically flows in reverse along the physical path with the lowest impedance in the DC shared bus to the containerized negative pressure purification module that experienced the transient airflow depressurization event, and the duration during which the environmental pressure is restored to the standard negative pressure reference value is controlled within a preset pressure restoration time threshold.

[0023] This embodiment provides a physical collection step for cross-module residual electrical energy; specifically, after the aforementioned node voltage has been rapidly reshaped due to the pressure loss event, the system further utilizes the potential difference on the shared bus to automatically backflow the distributed energy storage in the non-pressure loss chamber into the pressure loss chamber. Specifically, the power input limit of the main power grid is always a prerequisite constraint; for example, the upper limit of the total input power on site is 10kW. When the system is in steady state, compartments A, B and C consume 2.5kW, 2.0kW and 1.8kW respectively, totaling 6.3kW, leaving a margin of 3.7kW. When a transient depressurization event occurs in compartment A and generates the target equivalent node voltage, a low potential gap is formed at the A node. This low potential gap is not a logical priority label, but a physical voltage drop point. For example, the A node drops to 364V, while compartments B and C remain at 378V and 379V respectively. At this time, adjacent compartments that have not experienced a depressurization event activate their respective supercapacitors; activation means that the node box controller causes the supercapacitors to enter the dischargeable support mode through the bidirectional DC-DC converter. For example, the supercapacitor in compartment B can provide 2kW of additional power in 0.5 seconds, and compartment C can provide 1.5kW of additional power in 0.5 seconds, thus forming a distributed energy storage pool; Since there is a clear potential difference on the DC shared bus, and the impedance of the bus section between B and A is lower than that between C and A, the current will preferentially flow to A via the BA path with the lowest impedance, and the remaining part will be supplemented by the CA path; the whole process does not require central coordination to calculate the flow direction, it is only necessary to ensure that each node is in a dischargeable state. A simplified data extrapolation can be used to illustrate this: If a sudden power surge in compartment A requires an additional 3kW peak power, and the main grid can add 1kW under the constraint of the limit, then the remaining 2kW gap needs to be made up by distributed energy storage. If the equivalent resistance of the BA path is 0.05Ω and that of the CA path is 0.08Ω, then under the same voltage difference, compartment B will bear a larger proportion of transient discharge, for example, 1.2kW, while compartment C will bear 0.8kW. The negative pressure fan in compartment A quickly increased its speed, pulling the negative pressure back from -7Pa to -15Pa, and the recovery time was controlled within the preset pressure recovery time threshold, such as within 0.3 seconds. If only local energy storage is available without a shared bus across modules, each module must be equipped with a large-capacity energy storage system, which increases the system's weight and extends the system construction cycle. If there is a shared bus but no low-potential gap triggering mechanism, the other modules cannot spontaneously form a support power flow at the physical level. Therefore, this implementation emphasizes the dual conditions of node voltage difference and distributed supercapacitors to form the necessary technical closed loop for cross-module aggregation. As an anomaly handling mechanism, if the current state of charge of the supercapacitors in adjacent compartments is insufficient, for example, if compartment B has only 15% charge remaining, which is lower than the discharge threshold of 20%, then compartment B will exit the distributed energy storage pool, and compartment C and the main grid will jointly support compartment A; if all adjacent compartments are under high load and cannot discharge, the system will degrade to the main grid's limited power supply, and at the same time trigger the local wind turbine's maximum allowable speed and non-critical load shedding strategy to prioritize the negative pressure recovery of the depressurized compartments. If the pressure recovery fails to meet the target within the preset time threshold, for example, if it only recovers to -11Pa after 0.3 seconds, the system can determine that there is a deeper fault in the cabin, such as a failed door seal or a severely clogged filter, and proceed to the subsequent diagnostic process. For example, during peak emergency hours, two medical staff passed by the outside of compartment B at the same time without opening the door, while the door of compartment A was fully opened to receive patients; after a low potential gap was formed at the node of compartment A, compartment B, being the closest and having the lowest bus connection impedance, preferentially released supercapacitor energy to compartment A, and compartment C, as a second support source, supplemented the remaining power, thereby enabling compartment A to recover to around -15Pa within 0.3 seconds without breaching the infection protection boundary; The purpose of this step is to construct a hardware-level power sharing channel that does not require communication by utilizing the shared bus and node potential difference, under the condition that the total input power is capped, so as to realize the cross-compartment borrowing of transient peak power.

[0024] In a preferred embodiment of the present invention, the system further includes an energy consumption closed-loop recovery module. The method executed by the energy consumption closed-loop recovery module after completing the instantaneous high load intervention includes: continuously monitoring the recovery slope of the real-time differential air pressure signal and analyzing the air pressure field recovery process; generating an air pressure field balance determination command according to the air pressure field recovery process; and, in response to the air pressure field balance determination command, increasing the virtual impedance parameter corresponding to the containerized negative pressure purification module that experienced the transient airflow depressurization event and cutting off the cross-module current transmission link used to collect the remaining electrical energy. Real-time monitoring of the reverse feedback electrical energy parameters generated by the emergency braking of the negative pressure fan speed; according to the preset system load power supply sequence table, the thermal energy conversion load unit with the lowest power supply priority in the non-working state is extracted as the receiving object; the reverse feedback electrical energy is guided and transmitted to the receiving thermal energy conversion load unit, so that the excess kinetic energy is completely converted into thermal energy for local storage, achieving a complete energy loop within the microgrid system.

[0025] This embodiment provides an energy consumption closed-loop recovery mechanism after the transient support ends. Specifically, although the aforementioned cross-module energy borrowing can quickly restore negative pressure, if it is not withdrawn in time after the negative pressure is restored, the bus may still maintain unnecessary cross-module current, and even form reverse feedback power accumulation when the wind turbine slows down. Therefore, this embodiment adds pressure field balance judgment and residual energy transfer link in the recovery phase. Specifically, the system continuously monitors the recovery slope of the real-time differential pressure signal; the recovery slope can be understood as the speed at which the pressure difference falls back to the standard negative pressure value per unit time; for example, if the recovery of cabin A from -7Pa to -15Pa is completed within 0.2 seconds, the average recovery slope is approximately -40Pa / s. The system does not only look at whether it reaches -15Pa at a certain moment, but also analyzes whether its recovery trend tends to be gradual. For example, if the pressure difference change is less than 0.2Pa within 5 consecutive sampling periods, it can be determined that the pressure field has entered the equilibrium stage. A pressure field equilibrium determination command is generated to control the A module to increase the virtual impedance, for example, from 0.6Ω to 1.8Ω, and then to a steady state of 2.0Ω, thereby cutting off the aforementioned cross-module current transmission link and causing the B and C modules to exit the support state. When the wind turbine support ends and the speed decreases, the wind turbine motor may generate reverse feedback energy due to inertia; the system monitors the parameters of this reverse feedback energy in real time. For example, it detects that 0.12kJ is fed back within 50ms when the wind turbine in compartment A brakes in an emergency. If this energy is allowed to impact the bus, it may cause local overvoltage. To this end, the system has a pre-set load power supply sequence table, which lists the priority of the heat energy conversion load units, such as disinfection preheater - backup electric heating unit - bulkhead anti-condensation heating strip. The unit that is currently not in operation and is the last in the sequence is given priority as the load to be supplied. The last in the sequence means that the load has the least impact on the core ventilation negative pressure task at the current moment. The system guides the reverse feedback electrical energy to the receiving object; for example, if the current standby electric heating unit of compartment A is in a shutdown state, after detecting the feedback energy, the node box controls it to absorb the energy in a controlled manner for a short time, converting it into heat and storing it in the local heating structure of the compartment; in this way, the excess kinetic energy on the bus is locally absorbed, forming an internal energy loop. If the fan support is simply disconnected after the negative pressure is restored without addressing the accumulation of feedback energy during the recovery and emergency braking phases, two drawbacks will occur: first, the cross-compartment support link will be disconnected too late, causing negative pressure instability in other compartments; second, the feedback energy will have nowhere to be released and may impact the busbar. Therefore, this implementation method solves the energy balance problem after the transient adjustment is completed by restoring the slope analysis, balance determination, virtual impedance recovery, and feedback energy heat conversion through a continuous link. As an abnormal handling mechanism, if all thermal energy conversion load units are in operation, or their current temperature rise is close to the safe limit, the system will no longer introduce feedback energy to the thermal load, but will prioritize the use of feedback energy for supercapacitor recharging. If the supercapacitor is also close to full charge, for example, with a state of charge higher than 95%, then the braking resistor dissipation branch is activated as a final protection measure to prevent bus overvoltage; if the recovery slope analysis shows that although the pressure difference is close to -15Pa, there is still periodic oscillation, then the generation of the pressure field balance determination command is temporarily suspended, and small-scale support is maintained until the oscillation disappears. For example, after the patient transfer is completed in compartment A, the compartment door is closed, and the pressure difference recovers to -15Pa within 0.2 seconds; the system detects that the recovery slope approaches zero within several consecutive sampling cycles, and then gradually increases the virtual impedance of compartment A to cut off the support flow from compartments B and C. The A compartment fan generates regenerative energy due to rapid deceleration. The system directs this energy into the currently idle anti-condensation heating strips on the bulkhead, ensuring that excess energy does not leave the local microgrid. The purpose of this mechanism is to ensure that the system quickly returns to a steady state after the transient energy borrowing ends, and to safely, locally and controllably convert excess feedback power into usable heat energy, thereby achieving closed-loop energy management within the microgrid.

[0026] In a preferred embodiment of the present invention, the system further includes a health self-diagnosis module. The health self-diagnosis method includes: when the entire system is in a voltage-stabilized closed state, reading the real bus sustaining current data on the DC shared bus branch in real time; extracting the corresponding node admittance matrix based on the gas-electric isomorphic equivalent circuit model, solving the linear equation in combination with the set node voltage data, and calculating the theoretical standard sustaining current prediction value. The difference between the actual bus sustaining current data and the standard sustaining current prediction value is calculated, and the difference is divided by the standard sustaining current prediction value to obtain the global current drift rate during system operation; the global current drift rate is directly used as a percentage module aging index to quantitatively evaluate the effective degradation degree of the air filter component inside the containerized negative pressure purification module.

[0027] This embodiment provides a health self-diagnosis mechanism; specifically, when the system enters the stable closed state, that is, when there is no sudden door opening action in each compartment and the bus voltage is stable, the system uses the gas-electric isomorphic model combined with the actual node operation characteristics to back-calculate the theoretical maintenance current, and then compares it with the actual maintenance current to quantify the degree of effective degradation of components such as filters. Specifically, the system reads the actual bus sustaining current data of each branch of the DC shared bus; to illustrate the calculation mechanism of the diagnostic process, a set of typical data is used as an example: during steady-state operation at night, the actual measured bus sustaining current data of the corresponding branches of compartments A, B, and C are 5.2A, 6.8A, and 4.6A, respectively. Meanwhile, the system extracts the corresponding 3×3 node admittance matrix based on the gas-electric isomorphic equivalent circuit model. The node admittance matrix here is constructed strictly in accordance with Kirchhoff's current law for circuits. The diagonal terms are the sum of the load admittance to ground of the corresponding node and the mutual admittance of all connected bus lines, while the off-diagonal terms are the negative bias values ​​of the mutual admittance corresponding to the bus line impedance of the physical connection between nodes. Assume the physical parameters for the model are as follows: Due to differences in damping, the ground admittance of modules A, B, and C are respectively calibrated to be 0.013S, 0.015S, and 0.011S; the mutual admittance between physical lines is set as the mutual admittance between the first and second modules. Inter-cathode between the first and third modules Inter-cathode between the second and third modules ; Based on this, the nodal admittance matrix can be obtained. The first line corresponds to cabin A with [0.313S, -0.200S, -0.100S]; the second line corresponds to cabin B with [-0.200S, 0.365S, -0.150S]; the third line corresponds to cabin C with [-0.100S, -0.150S, 0.261S]; Furthermore, combining the set node voltage data, such as the current system operation feedback observation stable voltage distribution as the node voltage of compartment A. B-class cabin node voltage C-cabin node voltage ; The system solves the aforementioned admittance parameters using standard linear equations, thereby constructing a predicted current column vector composed of the expected extracted current values ​​from each node. The measured stable voltages are then used to construct a column vector of node voltages. Perform matrix multiplication: ; The predicted current column vector is denoted as... The nodal admittance matrix is ​​denoted as The node voltage column vector is denoted as Based on this, the theoretically predicted standard sustaining current can be calculated; taking cabin A as an example, its corresponding predicted standard sustaining current is... for: ; With discrete precision, a reference value of 5.0A can be used; similarly, the predicted value IB for cabin B is approximately 5.5A, and the predicted value IC for cabin C is approximately 4.3A. The system sequentially calculates the difference between the actual bus sustaining current data and the standard sustaining current prediction value, and divides this difference by the standard sustaining current prediction value to obtain the global current drift rate during system operation; based on the above data, the drift rate of compartment B can be calculated as follows: The calculation yields approximately 23.6%. Similarly, the drift rate of cabin A is... Class C is Under homogeneous gas pressure isomorphic conditions, a high drift rate means that in order to maintain the same negative pressure balance, a higher holding current needs to be drawn. Therefore, the system directly uses the global current drift rate obtained from this deviation as the percentage module aging index for rating output. This indicator can be used to quantitatively assess the effectiveness of the air filter components inside the containerized negative pressure purification module. For example, if the drift rate of the B compartment exceeds 20%, it means that the filter is clogged beyond the set threshold, resulting in a significant deterioration of the filter's air resistance. If only simple time reciprocal statistics or fixed-mode observation are used, it is easy to be misjudged by short-term sampling errors caused by complex external environment. This implementation method introduces a defined diagonal physical interconnection structure and standard equation calculation to establish an automated non-destructive testing method for the entire microgrid system that can accurately evaluate the deep blockage state of the filter element without the need for additional hardware wind speed equipment. As an anomaly response mechanism, if the system is not currently in a voltage stabilization closed state, such as when the access control is not fully closed or a large load heat engine is connected, causing nonlinear large-amplitude traction oscillations in the DC bus, in order to shield against noise interference and protect diagnostic accuracy, the output of indicators will be temporarily suspended and silent bypass protection will be implemented; if a local sudden circuit failure at a certain junction point causes a verification mismatch in the calculation, the local area will be isolated first to prevent cascading false alarms. For example, after a certain emergency transfer station has been running continuously for a set period of time, a deep state diagnosis is triggered when there are no personnel entering or leaving and the airflow is closed. The central controller points out through matrix solving that the original standard power consumption calculation point for the pressure maintenance of the B chamber is about 5.5A. However, the feedback value of the current sensor rises abnormally and stops at 6.8A. Based on the calculated 23.6% aging attenuation ratio, the system quickly alarms and prompts the operation and maintenance terminal that the component is in a state that urgently needs maintenance and replacement. The purpose of this mechanism is to transform the inherent physical properties of power distribution transmission into key identification features for assessing the potential risks of ventilation blockage and attenuation in negative pressure chambers through rigorous logical algorithms, thereby effectively improving and solidifying the overall system's safety and reliability monitoring dimensions.

[0028] In a preferred embodiment of the present invention, the method for quantitatively assessing the degree of actual degradation includes: recording the historical global current drift rate variable according to a preset sampling time period, and synthesizing it into a drift rate evolution sequence; performing linear slope extraction processing of a sliding analysis window on each sub-segment in the drift rate evolution sequence to obtain the current drift trend value for the corresponding time period; The system sequentially compares all extracted current drift trend values ​​with the preset lifespan state critical threshold stored in the system; if the current drift trend value is less than the preset lifespan state critical threshold, the system determines that the health status meets the standard and records the daily log; if the current drift trend value is equal to the preset lifespan state critical threshold, a monitoring warning code is generated. If the current drift trend value is greater than the preset life state critical threshold, the hardware identifier of the out-of-standard node is located, maintenance work order data containing location information and fault mechanism analysis results is compiled and output to the operation and maintenance terminal.

[0029] This embodiment provides a quantitative assessment step for aging trends. Specifically, the aforementioned drift rate can reflect the degree of aging at a certain moment. However, in real operation and maintenance scenarios, a single anomaly may be caused by short-term environmental changes or occasional load fluctuations. If maintenance instructions are issued directly based on this, false alarms are likely to occur. Therefore, this embodiment further analyzes the evolution trend of the historical drift rate. Specifically, the system records the global current drift rate variable according to a preset sampling time period; for example, the drift rate of compartment B is recorded every 4 hours, and after 5 consecutive samplings, the sequence is: 8%, 9%, 10.5%, 12%, 13.5%; the system then performs linear slope extraction processing of a sliding analysis window on each sub-segment of the drift rate evolution sequence; for ease of explanation, the window length can be set to 3 sampling points; The first window corresponds to [8%, 9%, 10.5%], and its drift trend value can be approximated as increasing by 1.25% per sampling period; the second window corresponds to [9%, 10.5%, 12%], and the trend value is approximately 1.5%; the third window corresponds to [10.5%, 12%, 13.5%], and the trend value is still approximately 1.5%. The system compares these trend values ​​with a preset critical threshold for the lifespan state, for example, the threshold is set to increase by 1.2% per sampling period; if the trend value is less than the threshold, it means that although there is drift, the growth is slow and it is still within the acceptable aging range, and the system only records daily logs. If the trend value equals the threshold, a monitoring warning code is generated to prompt maintenance personnel to pay attention to the compartment; if the trend value is greater than the threshold, it indicates that aging is accelerating, and the hardware identifier of the node exceeding the standard should be located, such as compartment B - filter component - high efficiency air filter - position 2, and a maintenance work order containing location, time, trend value and fault mechanism analysis results should be compiled and sent to the maintenance terminal. The fault mechanism analysis results here can be generated based on the drift characteristics and the historical template library; for example, if the current drift rate rises slowly over a long period of time, it usually corresponds to dust accumulation in the filter. If the rate of change of the drift rate within the set time window exceeds the preset threshold and is accompanied by a decrease in fan efficiency, it may be due to the coupling effect caused by partial collapse of the filter or abnormal impedance of the shortwave ultraviolet ballast. If there is only the current aging index without a time trend, the system cannot distinguish between two completely different maintenance risks: one that is slightly high but stable and the other that is rapidly deteriorating. Therefore, this implementation extracts the linear slope through a sliding window, upgrading the static deviation into a dynamic life trend. As an exception handling mechanism, if the number of historical data points is insufficient to form a complete window, the data is accumulated first, and no trend judgment is output. If there are individual missing points in the drift rate evolution sequence, they can be interpolated using two adjacent valid values. However, if the number of interpolation exceeds the preset upper limit, the trend analysis in this round will fail. If the trend values ​​of multiple windows show alternating positive and negative fluctuations, it indicates that the cabin may be greatly disturbed by environmental factors. The system can increase the threshold or extend the observation period to avoid misjudgment. For example, after the fever clinic operated continuously for a week, the drift rate evolution sequence of the B compartment showed a continuous increase. The trend value of the last three sliding windows all reached 1.5%, which is higher than the critical threshold of 1.2% for the life state. Based on this, the system automatically located the second-level high-efficiency filter component of the B compartment and generated a maintenance work order to push to the operation and maintenance terminal, suggesting that it be replaced during the off-peak hours that do not affect the outpatient admission. The purpose of this step is to extend the assessment of single-point aging to the identification of degradation trends over time, thereby enabling more stable and forward-looking maintenance decisions.

[0030] In a preferred embodiment of the present invention, the method for obtaining the real-time differential air pressure signal between the interior of each of the containerized negative pressure purification modules and the external reference environment includes: calling the hardware sensor node through the status acquisition module to collect high-frequency raw environmental omnidirectional pressure data, and using a bandpass filter component to smooth the high-frequency raw environmental omnidirectional pressure data to obtain the base pressure component. Obtain the current ventilation speed setting parameters within the associated area, retrieve and call up the background turbulence interference spectrum curve that matches the current ventilation speed setting parameters from the background fluid dynamics feature database stored locally in the system, extract the adaptive cancellation frequency control parameters by referring to the background turbulence interference spectrum curve, and adjust the upper and lower cutoff frequencies of the bandpass filter component using the adaptive cancellation frequency control parameters. The deconvolution algorithm configured by the microcontroller is used to perform cancellation calculation operations to separate and remove environmental coupling noise from the base pressure component, thereby obtaining the real-time differential air pressure signal after filtering out coupling noise.

[0031] This embodiment provides a method for enhancing the acquisition of real-time differential pressure signals; specifically, within the aforementioned operation control and dispatch system, the remapping of node voltage distribution is highly dependent on the authenticity of input differential pressure fluctuations; If the strong frequency airflow impact generated by personnel movement or sudden crosswinds in the connecting corridor is directly transmitted to the control center, the system may misjudge this instantaneous disturbance as a critical depressurization in the cabin, causing unnecessary redundant power allocation actions on the DC shared bus; therefore, this implementation introduces an interference stripping and analytical reconstruction calculation framework for turbulent interference channels. Specifically, the status acquisition module calls hardware sensor nodes at the bottom layer to quickly collect high-frequency raw environmental omnidirectional pressure data from the environmental side; the system can use a sampling frequency of, for example, 200Hz to record continuous change sequences, and use a bandpass filter component to perform edge softening processing, so as to output the base pressure component that represents the pressure base change trend in a smooth way by filtering out transient spike pulses. The system obtains the current ventilation speed setting parameters in the associated area, such as detecting that the large circulating fan in the corridor is in the upper limit mode of the air outlet speed parameter of 2.5m / s; The system then performs a search in the locally stored background hydrodynamic feature database to find and lock the background turbulence interference spectrum curve that highly matches the 2.5 m / s parameter. It should be noted that in actual external building space structures, strong airflow impact is not simply superimposed white noise, but rather manifests as a delay mapping and system-level damping transmission caused by the influence of the building space structure on the pulse airflow with strong physical kinetic energy. This time-domain signal broadening and aliasing effect caused by the influence of hydrodynamic characteristics will cause significant convolution morphological distortion in the signal received by the sensor; In response to this situation, the system compares and matches the extracted specific background turbulence interference spectrum curve features to quickly extract adaptive cancellation frequency control parameters, thereby adjusting the upper and lower cutoff frequencies of the bandpass filter component to prevent energy loss across high-correlation frequency bands. Furthermore, the core cancellation calculation operation is performed using the deconvolution algorithm configured by the microcontroller; the system extracts a time-domain discrete array representing the distortion of the corridor conduction structure response from the feature spectrum as the equivalent channel convolution kernel for prior reconstruction, for example, presenting an energy shift reflection factor with weighted feedback of [0.2, 0.6, 0.2], and the microcontroller uses this to perform inverse deconvolution operation on the distorted and masked base pressure component using the Wiener constraint mechanism; Specifically, to avoid gain amplification runaway caused by directly applying filtering inverse calculation, the microcontroller converts the timing basis pressure component into an equivalent frequency domain response sequence. Furthermore, the equivalent channel convolution kernel extracted a priori is expressed as a transduced response function. Within the framework of Wiener's inverse model, a signal-to-noise penalty constant is introduced to prevent zero-frequency pole divergence. ; Specifically, the adaptive determination calculation method is as follows: extract the frequency domain power spectrum within a preset steady-state time window, calculate the ratio of the integrated power of the base white noise frequency band to the total power of the entire frequency band as the distribution ratio value, and multiply this distribution ratio value by the system's preset benchmark penalty coefficient to obtain the signal-to-noise penalty constant. Its value can be adaptively determined by the proportion of steady-state airflow white noise substrate power distribution, and the following stripping criterion is applied: ; Wherein, the conduction response function The conjugate value is denoted as The true pressure spectrum after structural transmission distortion compensation is denoted as ; Represents the conduction response function The power spectral density; in this formula This is the aforementioned equivalent frequency domain response sequence; the formula is calculated by superimposing the values ​​in the denominator. It effectively resists the operational oscillations caused by direct division in the low signal-to-noise frequency band; For frequency domain frequency variables; The system re-executes the inverse frequency domain analysis and pushes back to the time domain discrete waveform to separate and remove environmental coupling noise. After restoration, the false alarm waveform that originally showed slow damping and kept dropping to -12Pa can be converged and corrected, and the output is a real -14.7Pa narrow-range stable differential pressure waveform that has not caused physical leakage. Thus, the real-time differential pressure signal after filtering out coupling noise is obtained, avoiding the erroneous triggering of subsequent unnecessary energy cross-module mutual assistance linkage mechanisms. If we rely solely on linear decay processing or time windowing smoothing, while ignoring the multiplicative destructive characteristics of multipath convolution brought about by structural transmission, it is easy to treat the envelope distortion error that originally required reverse deconstruction as the true value and transmit it inward, inducing unnecessary node voltage readjustment actions; this processing avoids and corrects the computational deviation when the traditional architecture rigidly strips the flow field noise. As an anomaly handling mechanism, if the database cannot find a corresponding template that perfectly overlaps with the value of the sudden irregular airflow in a short period of time, the response factor is approximated by extracting the nearest templates at both ends and performing reference fusion weighting. Once the reverse calculation encounters extreme irreversible interference, causing multi-order fluctuations and making the calculation unstable, the microcontroller center will immediately cut off the deconvolution feedback path, retreat to use the primary baseband buffer waveform to maintain the initial power supply of the base and suspend the abnormal allocation command. For example, when multiple people pass through the gate, the turbulence generated enters the gap in the connecting corridor of the A compartment, causing the system to detect a false alarm of continuous low pressure. Relying on the deconvolution algorithm module to perform step-by-step blanking and restoration calculations on the flow resistance reflection of this structure under frequency domain penalty, the system confirms that the A door itself has not experienced an airtight failure. Therefore, it does not start PWM pulse width modulation, but maintains the existing stable state to ensure that the power supply is not affected by the external wind pressure. The purpose is to apply the Wiener deconvolution reconstruction method with constraints to perform waveform analysis on the spatial transmission forced transformation characteristics in wind pressure transmission, so as to ensure that all energy dispatch judgment benchmark signals are not misled by external environmental clutter, thereby improving the basic operational safety of the system.

[0032] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. An intelligent power distribution and energy consumption management system for negative pressure chamber equipment, characterized in that, include: The system includes a rectifier and voltage regulator cabinet deployed at the main power grid access end, a DC shared bus that runs through multiple containerized negative pressure purification modules, and a gas-electric coupling node box built into each module. The containerized negative pressure purification module is equipped with an air filter, a negative pressure fan, and a heat energy conversion load unit. The gas-electric coupling node box includes a bidirectional DC-DC converter, a supercapacitor, and a microcontroller. The pneumatic-electric coupling node box also includes: The status acquisition module is used to acquire real-time differential air pressure signals between the inside of each of the containerized negative pressure purification modules and the external reference environment; The equivalent mapping module is used to establish an equivalent circuit model of gas-electric isomorphism based on the real-time differential air pressure signal, and to map the state information of the internal equipment of the containerized negative pressure purification module to the virtual impedance parameters on the DC shared bus according to the preset air pressure-impedance mapping relationship. The voltage regulation module is used by the microcontroller to reshape the equivalent node voltage at the DC shared bus of the gas-electric coupling node box in real time by changing the duty cycle of the power switching transistor in the bidirectional DC-DC converter based on the received real-time differential air pressure signal and the virtual impedance parameters. An energy adaptive allocation module is used to form a node voltage difference on the DC shared bus based on the equivalent node voltage, and to physically drive the remaining electrical energy in the supercapacitors in each of the gas-electric coupling node boxes to be collected across modules based on the node voltage difference, so as to provide peak power to the corresponding module in transient load state.

2. The intelligent power distribution and energy consumption management system for the negative pressure chamber equipment according to claim 1, characterized in that, Methods for establishing gas-electric isomorphic equivalent circuit models include: Obtain the preset standard negative pressure reference value and the preset total input power limit value of the main grid access terminal; map the standard negative pressure reference value to the reference bus voltage of the bidirectional DC-DC converter; obtain the real-time dust holding capacity parameter of the air filter in each of the containerized negative pressure purification modules, and positively map the real-time dust holding capacity parameter to the static foundation compensation current of the gas-electric coupling node box; Based on the reference bus voltage and the static foundation compensation current, the initial circuit structure state of the gas-electric isomorphic equivalent circuit model is generated.

3. The intelligent power distribution and energy consumption management system for the negative pressure chamber equipment according to claim 2, characterized in that, The method for reshaping the equivalent node voltage at the DC shared bus of the gas-electric coupling node box in real time by changing the duty cycle of the power switching transistors in the bidirectional DC-DC converter includes: Calculate the real deviation between the real-time differential air pressure signal and the standard negative pressure reference value; compare the real deviation with a preset tolerance deviation threshold; if the real deviation is in the range below the tolerance deviation threshold, control the microcontroller to maintain the current duty cycle of the power switch and keep the current equivalent node voltage unchanged. If the real number of the deviation is at a critical point equal to the tolerance deviation threshold, the historical duty cycle sequence stored in the system memory is extracted for compensation and fine-tuning to lock the critical equivalent node voltage. If the real number of the deviation is in the range higher than the tolerance deviation threshold, it is determined that a transient airflow depressurization event has occurred. According to the preset droop control logic, the virtual impedance parameter corresponding to the containerized negative pressure purification module that caused the transient airflow depressurization event is reduced, and the duty cycle of the power switch is increased synchronously to generate a target equivalent node voltage with low potential drop characteristics, replacing the current equivalent node voltage.

4. The intelligent power distribution and energy consumption management system for the negative pressure chamber equipment according to claim 3, characterized in that, The method for cross-module aggregation of residual electrical energy in each of the gas-electric coupling node boxes based on the node voltage difference includes: Under the constraint of the preset total input power limit at the main grid access end, when the airflow transient pressure loss event occurs and the target equivalent node voltage is generated, a low potential gap is generated during the target access phase of the DC shared bus. Activate the supercapacitor in the adjacent containerized negative pressure purification module that has not experienced a bias voltage fault to form a distributed energy storage pool. Based on the node potential difference distribution at the circuit hardware level, the electrical energy in the distributed energy storage pool automatically flows in reverse along the physical path with the lowest impedance in the DC shared bus to the containerized negative pressure purification module where the transient airflow depressurization event occurs, and the duration during which the environmental pressure is restored to the standard negative pressure reference value is controlled within a preset pressure recovery time threshold.

5. The intelligent power distribution and energy consumption management system for the negative pressure chamber equipment according to claim 4, characterized in that, The system also includes an energy consumption closed-loop recovery module. The method executed by the energy consumption closed-loop recovery module after completing the instantaneous high load intervention includes: continuously monitoring the recovery slope of the real-time differential pressure signal and analyzing the pressure field recovery process; generating a pressure field balance determination command based on the pressure field recovery process. In response to the pressure field balance determination command, the virtual impedance parameter corresponding to the containerized negative pressure purification module that experienced the transient airflow depressurization event is increased, and the cross-module current transmission link used to collect the remaining electrical energy is cut off; the reverse feedback electrical energy parameter generated by the emergency braking of the negative pressure fan speed is monitored in real time. According to the preset system load power supply sequence table, the thermal energy conversion load unit with the lowest power supply priority is extracted from the non-working thermal energy conversion load units as the receiving object. The reverse feedback electrical energy is guided and transmitted to the heat energy conversion load unit, which is the recipient, so that the excess kinetic energy is completely converted into heat energy for local storage, thus achieving a complete energy loop within the microgrid system.

6. The intelligent power distribution and energy consumption management system for the negative pressure chamber equipment according to claim 5, characterized in that, The system also includes a health self-diagnosis module, and the methods for health self-diagnosis include: When the entire system is in a voltage stabilization closed state, the real bus sustaining current data on the DC shared bus branch is read in real time. Based on the extracted node admittance matrix of the gas-electric isomorphic equivalent circuit model, inverse estimation is performed to obtain the predicted value of the standard sustaining current. By comparing the actual bus sustaining current data with the standard sustaining current prediction value, the global current drift rate during system operation is obtained. The aging index of the global current drift rate extraction module is used to quantitatively assess the degree of effectiveness degradation of the air filter inside the containerized negative pressure purification module.

7. The intelligent power distribution and energy consumption management system for the negative pressure chamber equipment according to claim 6, characterized in that, Methods for quantifying the degree of decline include: The global current drift rate variables are recorded according to a preset sampling time period and synthesized into a drift rate evolution sequence. For each segment in the drift rate evolution sequence, a linear slope extraction process with a sliding analysis window is performed to obtain the current drift trend value for the corresponding time period. The extracted current drift trend values ​​are compared sequentially with the preset lifetime state critical threshold stored in the system. If the current drift trend value is less than the preset lifespan state critical threshold, the health status is determined to be up to standard and a daily log is recorded. If the current drift trend value is equal to the preset lifetime state critical threshold, a monitoring warning code is generated; If the current drift trend value is greater than the preset life state critical threshold, the hardware identifier of the out-of-standard node is located, maintenance work order data containing location information and fault mechanism analysis results is compiled and output to the operation and maintenance terminal.

8. The intelligent power distribution and energy consumption management system for the negative pressure chamber equipment according to claim 1, characterized in that, The method for obtaining the real-time differential pressure signal between the interior and external reference environment of each of the containerized negative pressure purification modules includes: The hardware sensor node is called to collect high-frequency raw environmental omnidirectional pressure data, and the high-frequency raw environmental omnidirectional pressure data is smoothed by a bandpass filter component to obtain the base pressure component. Integrate the ventilation speed setting parameters within the associated area, and retrieve the appropriate background turbulence interference spectrum from the stored nonlinear fluid dynamics frequency domain fingerprint database; Adaptive cancellation frequency control parameters are generated by comparing with the background turbulence interference spectrum lines, and the passband structure of the bandpass filter component is reset; An offsetting operation involving a deconvolution operator is performed to separate and remove the environmental coupling noise remnants from the base pressure component, resulting in the real-time differential pressure signal after filtering out the coupling noise.

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