Multi-cavity isolation ventilation control method and device of positive pressure type explosion-proof power distribution cabinet

Abstract

The application discloses a positive pressure type explosion-proof power distribution cabinet multi-cavity isolation ventilation control method and device, and relates to the technical field of power distribution cabinet ventilation. The method comprises the following steps: collecting a plurality of independent cavities of the positive pressure type explosion-proof power distribution cabinet, generating a first inner cavity pressure data set for real-time explosion-proof monitoring, and triggering an isolation control instruction; injecting gas into N risk cavities for pressure stabilization, generating N positive pressure values, and activating a plurality of directional ventilation paths; adjusting and monitoring fan speed data of the N risk cavities to determine a second inner cavity pressure data set, combining the N positive pressure values for judgment, backtracking according to a judgment result to determine abnormal fan speed data; triggering a multi-level interlock protection instruction for fault response according to the abnormal fan speed data, and adaptively controlling ventilation of the plurality of independent cavities according to a response result. The technical problem of slow response of the power distribution cabinet ventilation control in the prior art is solved, and the technical effect of improving the ventilation control response speed is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution cabinet ventilation, in particular to a multi-cavity isolation ventilation control method and device for a positive pressure type explosion-proof power distribution cabinet. BACKGROUND

[0002] The positive pressure type explosion-proof power distribution cabinet continuously injects clean gas into the cabinet body to form a certain positive pressure higher than the external environment, so as to prevent flammable gas from entering the cabinet and achieve the explosion-proof effect. With the complication of industrial equipment, the structure of the power distribution cabinet presents a multi-cavity independent distribution feature, and different cavities bear different functional modules, which puts forward higher requirements for air pressure control and ventilation management. In the prior art, unified ventilation or single sensing control is usually used, which has problems such as untimely ventilation control response, inaccurate risk cavity identification, and lagging fault linkage processing, and it is difficult to meet the actual needs of multi-cavity partition high reliable operation. SUMMARY

[0003] The present application provides a multi-cavity isolation ventilation control method and device for a positive pressure type explosion-proof power distribution cabinet, which solves the technical problem of untimely ventilation control response of the power distribution cabinet in the prior art.

[0004] In a first aspect, the present application provides a multi-cavity isolation ventilation control method for a positive pressure type explosion-proof power distribution cabinet, which comprises:

[0005] Collecting a plurality of independent cavities of the positive pressure type explosion-proof power distribution cabinet to generate a first internal cavity pressure data set for real-time explosion-proof monitoring and triggering an isolation control instruction; starting a positive pressure maintaining unit based on the isolation control instruction to inject gas into N risk cavities for pressure stabilization, generating N positive pressure values, and activating a plurality of directional ventilation paths of the N risk cavities, N being an integer greater than or equal to 1 and less than or equal to the number of all independent cavities; adjusting the fan speed data of the N risk cavities according to the plurality of directional ventilation paths and monitoring and determining a second internal cavity pressure data set of the N risk cavities, determining fan abnormal speed data according to the second internal cavity pressure data set in combination with the N positive pressure values, and performing backtracking analysis according to the determination result; triggering a multi-level interlock protection instruction for fault response according to the fan abnormal speed data, and performing adaptive ventilation control on the plurality of independent cavities according to the response result.

[0006] In a second aspect, the present application provides a multi-cavity isolation ventilation control device for a positive pressure type explosion-proof power distribution cabinet, which comprises:

[0007] The data acquisition module acquires a plurality of independent chambers of the positive pressure type explosion-proof power distribution cabinet, generates a first inner chamber pressure data set for real-time explosion-proof monitoring, and triggers an isolation control instruction. The pressure stabilizing module starts a positive pressure maintaining unit based on the isolation control instruction to stabilize the pressure by injecting gas into N risk chambers, generates N positive pressure values, activates a plurality of directional ventilation paths of the N risk chambers, and N is an integer greater than or equal to 1 and less than or equal to the number of all independent chambers. The analysis module adjusts fan speed data of the N risk chambers according to the plurality of directional ventilation paths, monitors and determines a second inner chamber pressure data set of the N risk chambers, determines fan abnormal speed data based on the determination result, and determines fan abnormal speed data based on the determination result. The control module triggers a multi-level interlock protection instruction for fault response according to the fan abnormal speed data, and performs adaptive ventilation control on the plurality of independent chambers according to the response result.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0009] First, a plurality of independent chambers of the positive pressure type explosion-proof power distribution cabinet are acquired, a first inner chamber pressure data set is generated for real-time explosion-proof monitoring, and an isolation control instruction is triggered. Then, based on the isolation control instruction, a positive pressure maintaining unit is started to stabilize the pressure by injecting gas into N risk chambers, N positive pressure values are generated, and a plurality of directional ventilation paths of the N risk chambers are activated, N is an integer greater than or equal to 1 and less than or equal to the number of all independent chambers. Then, the fan speed data of the N risk chambers is adjusted according to the plurality of directional ventilation paths, and the second inner chamber pressure data set of the N risk chambers is determined by monitoring, the determination result is combined with the N positive pressure values, the fan abnormal speed data is determined by backtracking analysis. Finally, according to the fan abnormal speed data, a multi-level interlock protection instruction is triggered for fault response, and adaptive ventilation control is performed on the plurality of independent chambers according to the response result. The technical problem of the prior art that the power distribution cabinet ventilation control response is not timely is solved, and the technical effect of improving the ventilation control response speed is achieved. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0011] Figure 1 The flowchart of the multi-chamber isolation ventilation control method of the positive pressure type explosion-proof power distribution cabinet provided by the embodiments of the present application is shown.

[0012] Figure 2 A structure schematic diagram of the multi-cavity isolation ventilation control device of the positive pressure type explosion-proof power distribution cabinet is provided for the embodiments of the present application.

[0013] Legend: data acquisition module 11, voltage stabilizing module 12, analysis module 13, control module 14. DETAILED DESCRIPTION

[0014] The present application provides a multi-cavity isolation ventilation control method and device for a positive pressure type explosion-proof power distribution cabinet, which solves the technical problem of untimely response of the ventilation control of the power distribution cabinet in the prior art.

[0015] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0016] It should be noted that the terms “comprising” and “having” are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server comprising a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or modules not clearly listed or inherent to the process, method, product or device.

[0017] Embodiment one, as shown in the present application provides a multi-cavity isolation ventilation control method for a positive pressure type explosion-proof power distribution cabinet, wherein the method comprises: Figure 1

[0018] The multiple independent cavities of the positive pressure type explosion-proof power distribution cabinet are collected to generate a first inner cavity pressure data set for real-time explosion-proof monitoring, and a isolation control instruction is triggered.

[0019] In the embodiments of the present application, the pressure state is collected based on the pressure sensor in the independent cavity to obtain the first inner cavity pressure data set; the first inner cavity pressure data set is compared with the explosion-proof threshold interval preset by the system in real time, wherein the explosion-proof threshold interval is set according to historical operation data and environmental requirements; if the inner cavity pressure value of any one independent cavity in the first inner cavity pressure data set is continuously low for multiple sampling points (such as 3 continuous sampling points) below the lower limit value of the explosion-proof threshold interval, the system determines that there is a pressure abnormal risk in the cavity, generates a corresponding inner cavity pressure abnormality identifier, and triggers a isolation control instruction (immediately closes the explosion-proof isolation valve between the cavity and the adjacent cavity to realize cavity-level physical isolation).

[0020] ​Further, the multiple independent chambers of the positive pressure type explosion-proof power distribution cabinet are collected to generate a first inner chamber pressure data set for real-time explosion-proof monitoring, and a isolation control instruction is triggered. The method comprises the following steps:

[0021] M pressure sensors deployed in each independent chamber are used to synchronously collect the multiple independent chambers according to a sampling weight coefficient and a sampling frequency, to obtain a first inner chamber pressure data set, M being an integer greater than or equal to 3; historical explosion-proof record data of the positive pressure type explosion-proof power distribution cabinet are called, an explosion-proof threshold interval is set, and the first inner chamber pressure data set is continuously compared with the explosion-proof threshold interval; when the inner chamber pressure data of any one independent chamber in the first inner chamber pressure data set at a plurality of continuous sampling points is lower than the lower limit value of the explosion-proof threshold interval, an inner chamber pressure abnormality identifier is determined; N risk chambers are determined according to the inner chamber pressure abnormality identifier, and the N risk chambers are subjected to pressure monitoring to obtain a pressure drop amplitude parameter, and the isolation control instruction is triggered according to the pressure drop amplitude parameter.

[0022] At least M pressure sensors are deployed in each independent chamber, M being an integer greater than or equal to 3, and the pressure sensors are respectively arranged at the top, middle and bottom positions of the chamber. The data collected at each position is given different sampling weight coefficients according to the functional differences of the physical positions, so as to improve the accuracy and sensitivity of the pressure state reflection. For example, the top pressure data weight is set to 0.4 (for sensitive sensing of the top pressure change caused by gas accumulation), the middle pressure data weight is set to 0.3 (for representing the average pressure level of the chamber as a whole), and the bottom pressure data weight is set to 0.3 (for identifying the low-position pressure abnormality caused by potential leakage). The pressure sensors synchronously collect the multiple independent chambers according to a unified sampling frequency (for example, 50 Hz), and generate a current average effective pressure value of each chamber through weighted fusion, to form a first inner chamber pressure data set.

[0023] The historical explosion-proof record data of the power distribution cabinet are called, and a corresponding explosion-proof threshold interval is set based on the historical data characteristics, for example, the interval range is [8.5kPa, 12.5kPa]. The system continuously compares the first inner chamber pressure data set with the threshold interval point by point, and when it is detected that the inner chamber pressure values of any one independent chamber at a plurality of continuous sampling points (for example, three or more continuous sampling points) are lower than the lower limit value of the threshold interval, the system generates an inner chamber pressure abnormality identifier, indicating that the chamber has a risk of loss of positive pressure.

[0024] Further, the system determines N risk chambers (N is an integer greater than or equal to 1 and less than or equal to the number of independent chambers) that currently require focused response based on the abnormality identification, and performs high-frequency sampling in a short period to obtain a pressure drop amplitude parameter for the N chambers, wherein the pressure drop amplitude parameter is formed by calculating the pressure change rate per unit time (for example, ΔP / Δt), to determine whether the pressure is in a rapid decay state. If the pressure drop amplitude parameter of any risk chamber exceeds a set critical drop rate threshold, the system immediately triggers an isolation control instruction.

[0025] The positive pressure maintenance unit is started based on the isolation control instruction to inject gas into the N risk chambers to generate N positive pressure values, and activate the multiple directional ventilation paths of the N risk chambers, N is an integer greater than or equal to 1 and less than or equal to the number of all independent chambers.

[0026] When the isolation control instruction is triggered, the system automatically starts the positive pressure maintenance unit to inject gas into the identified N risk chambers and perform pressure stabilization control operation, N is an integer greater than or equal to 1 and less than or equal to the number of all independent chambers. The positive pressure maintenance unit includes a plurality of gas supply interface modules, pressure injection control valve groups, gas flow adjustment components, and pressure feedback controllers, etc., which functions to inject gas (such as clean compressed air or inert gas) into the designated chamber in a directional manner, and adjust the gas injection flow rate and duration in real time, thereby generating N positive pressure values for explosion protection. Based on the N positive pressure values, combined with the ventilation model analysis results, the multiple directional ventilation paths of each risk chamber are activated. The directional ventilation paths include a main exhaust path, a secondary flow guide path, and a gas sealing buffer path, and the activation sequence of the paths is synchronized with the process of establishing positive pressure, to ensure the airflow directionality, safety and isolation during ventilation.

[0027] Further, the positive pressure maintenance unit is started based on the isolation control instruction to inject gas into the N risk chambers to generate N positive pressure values, and activate the multiple directional ventilation paths of the N risk chambers, the method comprising:

[0028] The positive pressure maintenance unit is started based on the isolation control instruction to divide the N risk chambers according to risk levels, determine high-risk chambers and low-risk chambers; the high-risk chambers are injected with gas by the positive pressure maintenance unit according to a ring injection mode to generate a first chamber pressure curve; the low-risk chambers are injected with gas by the positive pressure maintenance unit according to a bottom-priority injection mode to generate a second chamber pressure curve; the first chamber pressure curve and the second chamber pressure curve are analyzed for pressure stabilization to generate N positive pressure values; and the N risk chambers are analyzed for ventilation based on the N positive pressure values to determine the multiple directional ventilation paths.

[0029] Based on the isolation control instruction, the initialization parameters of the positive pressure maintenance unit are called, the pressure injection module and the ventilation analysis module are started, and for the N risk chambers identified, intelligent division is performed according to the risk level to form a high-risk chamber set and a low-risk chamber set. The risk level division is completed according to the intracavity pressure anomaly identifier and the pressure drop amplitude parameter, and the chambers with high pressure drop rate and serious continuous over-limit sampling points are preferentially considered to be divided into the high-risk level.

[0030] For high-risk chambers, the positive pressure maintenance unit adopts a ring injection mode for gas injection, that is, gas (such as inert gas or clean air) is released synchronously at multiple positions circumferentially inside the chamber, ensuring uniform distribution of the gas and rapid formation of an overall pressure rise process, thereby generating a corresponding first chamber pressure curve that presents a rapid rise and tends to be stable.

[0031] For low-risk chambers, a bottom-priority injection mode is adopted, that is, gas is injected preferentially from the gas inlet at the lower part of the chamber, gradually dispersing the combustible gas that may accumulate upward, and controlling the pressure to slowly rise, thereby generating a corresponding second chamber pressure curve that reflects a steady rise without obvious fluctuations in the pressure evolution process.

[0032] The system performs steady pressure analysis on the first chamber pressure curve and the second chamber pressure curve, specifically: fitting processing is performed on each curve to extract characteristic indexes such as rise time, stable time, maximum pressure, and final steady-state pressure; based on the comparison result with the set explosion-proof positive pressure threshold interval, the target steady pressure value of each risk chamber is finally output, forming N positive pressure values.

[0033] Based on the N positive pressure values, the system combines chamber structure parameters, airflow path topology, and ventilation simulation models to perform ventilation analysis on the N risk chambers, determines the paths required for gas dissipation in each chamber, and dynamically generates a plurality of directional ventilation paths according to the wind pressure gradient and spatial layout conditions, including the main exhaust path, the auxiliary air guide path, the buffer pressure isolation path, etc., to ensure that the gas can be orderly discharged along the predetermined direction after injection, avoiding pressure stringing or combustible gas backflow diffusion between chambers.

[0034] Further, the ventilation analysis on the N risk chambers according to the N positive pressure values to determine the plurality of directional ventilation paths includes:

[0035] A plurality of first ventilable points are determined by performing chamber ventilation analysis on the N positive pressure values in combination with the high-risk chamber; a plurality of second ventilable points are determined by performing chamber ventilation analysis on the N positive pressure values in combination with the low-risk chamber; ventilation efficiency calculation is performed based on the plurality of first ventilable points and the plurality of second ventilable points to determine a main ventilation path and a backup ventilation path; double matching of the main ventilation path and the backup ventilation path is performed based on the plurality of first ventilable points to obtain a double-redundancy sealing path; matching of the backup ventilation path is performed based on the plurality of second ventilable points to obtain an energy-saving backup path; and path directional analysis is performed on the double-redundancy sealing path and the energy-saving backup path to determine a plurality of directional ventilation paths.

[0036] The system retrieves the positive pressure value corresponding to each chamber in the N risk chambers, and combines the chamber structure diagram and the air flow parameter model to perform hierarchical ventilation analysis and processing on the high-risk chamber and the low-risk chamber respectively. For the high-risk chamber, the system identifies the potential node area where the gas can be quickly discharged according to the higher positive pressure maintenance value, and determines a plurality of first ventilable points by comprehensively considering factors such as chamber structure dead angle, pipeline resistance, wind resistance model, etc. The first ventilable points are usually located at the upper part of the chamber or the weak structure area of the air outlet direction, which facilitates the construction of an efficient pressure relief path. For the low-risk chamber, the system performs slow-release ventilation simulation according to the lower positive pressure value, and identifies the path point where the air flow can naturally float or be guided step by step, to determine a plurality of second ventilable points. The second ventilable points are generally arranged at the bottom diagonal line, the entrance of the guide channel, or the position of the air outlet interface near the middle part, to ensure the optimization of ventilation efficiency and energy consumption.

[0037] Based on the plurality of first ventilable points and the plurality of second ventilable points, the system constructs a chamber internal gas flow map, performs numerical calculation on the air pressure gradient between each point, executes a ventilation efficiency evaluation model, and comprehensively outputs indexes such as gas update rate, pressure difference regulation ability, and channel damping influence, to determine the main ventilation path and the backup ventilation path of each risk chamber. The main ventilation path is the optimal path that meets the fastest gas replacement and pressure relief rate, and the backup ventilation path is the secondary path that can still maintain effective exhaust under the condition that the main path fails or the air pressure is unbalanced.

[0038] On the basis of obtaining the main path and the standby path, the system further performs path double matching operation on the plurality of first ventilable point positions of the high-risk chamber, respectively matches the inlet and outlet endpoints of the ventilation main path and the ventilation standby path, establishes a path space topology reconstruction model, and forms a double-redundant sealing path with double-path switchable capability to improve the stability and emergency capability of the ventilation process. At the same time, for the plurality of second ventilable point positions, the system screens the path combination with the smallest gas flow energy consumption, the shortest path and the best sealing integrity in the ventilation standby path set, establishes a standby energy-saving path, which can be used to maintain the basic positive pressure state under non-emergency scenarios, while reducing the energy consumption of the fan. The system performs path directional analysis on the double-redundant sealing path and the standby energy-saving path, including path direction simulation, mutual interference verification, cross airflow determination, etc., to ensure the logical independence and physical isolation between different paths, and outputs a plurality of directional ventilation paths.

[0039] According to the plurality of directional ventilation paths, the fan speed data of the N risk chambers is adjusted and the second internal cavity pressure data set of the N risk chambers is determined, the N positive pressure values are combined according to the second internal cavity pressure data set to make a judgment, and the fan abnormal speed data is determined according to the judgment result.

[0040] The system dispatches the activated directional ventilation path, respectively corresponding to the ventilation main path or standby path in the N risk chambers, on the basis of which the chamber fan control module is called to set the initial speed value of the fan device of each risk chamber, which is calculated by the wind resistance function through parameters such as ventilation path length, path resistance coefficient, target positive pressure value, and is adjusted in real time through PID control logic. The system starts the fan operation, and continuously collects the speed change data of each fan at a preset sampling frequency, forms N groups of fan real-time operation data sequences, and synchronously collects the internal pressure change data of the corresponding risk chamber, generates the corresponding second internal cavity pressure data set, wherein each group of second internal cavity pressure data is aligned with the fan speed change data by time stamp to constitute a one-to-one mapping.

[0041] Based on the historical stable pressure curve model of the target chamber and the current N positive pressure values, the system calculates the pressure change deviation value and performs stability judgment processing on the second internal cavity pressure data set, including: if the pressure value of a certain chamber does not reach the corresponding positive pressure target value ± error tolerance interval in continuous T sampling periods, it is judged as a pressure abnormality unqualified state; if the pressure curve of a certain chamber has nonlinear fluctuation, or the pressure jumps repeatedly, it is judged as a pressure instability state; if the pressure of a certain chamber is stable to reach the target positive pressure value without obvious shock, it is judged as a normal stable pressure state.

[0042] After completing the determination, the system initiates fan operation data backtracking analysis on the chambers in the "pressure abnormality unmet state" or "air pressure instability state", combines the aforementioned fan speed data sequence, fan current load state, and air duct pressure difference response model to perform abnormal behavior tracing. Specifically, it detects whether the fan has abnormal speed drop, abnormal speed increase, or invalid high-speed operation; compares the fan operating state with the standard operating interval to identify speed points that deviate by more than a preset threshold; and determines whether the speed deviation is caused by impeller loss, ventilation pipe blockage, air duct backflow, or other physical reasons. Finally, the system outputs the fan abnormal speed data, corresponding time period, risk chamber number, and abnormal type.

[0043] Further, the fan speed data of the N risk chambers is adjusted according to the plurality of directional ventilation paths, and the second internal cavity pressure data set of the N risk chambers is determined and monitored, the second internal cavity pressure data set is combined with the N positive pressure values to determine, and the fan abnormal speed data is determined based on the determination result, the method comprising:

[0044] According to the plurality of directional ventilation paths of the N risk chambers, fan speed data is collected to obtain fan speed data; based on the plurality of directional ventilation paths, temperature collection is performed on the N risk chambers in reverse to obtain a chamber temperature set, the fan speed data is adjusted and updated according to the chamber temperature set to generate fan speed update data; according to the fan speed update data, pressure monitoring is performed on the N risk chambers to obtain a second internal cavity pressure data set; the second internal cavity pressure data set is compared with the N positive pressure values, and when the second internal cavity pressure data set has not recovered to the target positive pressure interval, an abnormal associated chamber is determined; based on the abnormal associated chamber, the fan speed update data is backtracked to determine the fan abnormal speed data.

[0045] Based on the determined plurality of directional ventilation paths, the system calls the fan state collection module under the corresponding path, groups the fan equipment matched by each ventilation path in the N risk chambers according to the chamber number, and collects fan speed data, wherein the fan speed data includes the current speed value, speed regulation mode, running time, and speed change trend of the fan in each directional ventilation path. The system performs path reverse heat conduction modeling analysis on the plurality of directional ventilation paths, based on the ventilation path layout, fan operating state, and historical environmental response curve, arranges multiple temperature sensing points at the end and middle section of the ventilation path, reversely collects the airflow temperature change after the ventilation flow passes through the chamber, and forms a chamber temperature set of the risk chamber. The chamber temperature set contains dynamic temperature data of multiple representative points in each chamber during the ventilation process.

[0046] The system fuses and analyzes the chamber temperature set with the fan speed data, updates and optimizes the fan speed according to the following rules, and generates fan speed update data. Specifically: if the temperature of a certain chamber changes less than the preset temperature difference threshold, it is determined that the air volume is insufficient, and the speed of the fan on the corresponding ventilation path is appropriately increased; if the temperature difference suddenly changes or the temperature rising trend exceeds the set interval, it is determined that there may be heat accumulation or air flow blockage, triggering iterative evaluation of the fan speed; if the temperature does not decrease significantly with the increase of the fan speed, a ventilation mode switching strategy is adopted to adjust the fan operation logic.

[0047] Based on the fan speed update data, the pressure collection operation is re-performed on the N risk chambers to obtain a new second inner cavity pressure data set. The system compares and analyzes the second inner cavity pressure data set with the target positive pressure values corresponding to the N risk chambers: if any risk chamber in the second inner cavity pressure data set has not recovered to the tolerance interval of the positive pressure value (i.e. lower than the lower limit of the target positive pressure or unstable fluctuation occurs), the chamber is marked as an abnormal associated chamber; if all chamber pressures are stable, it is determined that the fan adjustment is effective, and the process is ended.

[0048] The system performs a backtracking analysis of the fan speed update data for the abnormal associated chamber. Specifically: backtrack the speed change trajectory of the fan in the abnormal associated chamber for the last T time periods, and combine the corresponding temperature change and air pressure response to identify the mismatch mode of speed and environmental response; determine whether the fan has an abnormal working state, such as speed increase but no significant pressure increase (idling), speed drop (failure), or frequent fluctuation (control instability); extract the fan speed data of the abnormal period and form a fan abnormal speed data set.

[0049] Further, the second inner cavity pressure data set is compared with the N positive pressure values, and when the second inner cavity pressure data set has not recovered to the target positive pressure interval, an abnormal associated chamber is determined, the method comprising:

[0050] The second inner cavity pressure data set is compared with the N positive pressure values point by point, and when there are continuous multiple data points in the second inner cavity pressure data set that do not reach the target positive pressure value interval, a non-recovery identifier is generated, the N risk chambers are matched according to the non-recovery identifier, and a non-recovery chamber is determined; pressure fluctuation analysis is performed on the non-recovery chamber according to the non-recovery identifier, and when the pressure fluctuation value is greater than the preset fluctuation threshold, a instability identifier is generated; the instability identifier and the non-recovery identifier are associated and determined as an abnormal association identifier; the abnormal association identifier is verified in the pressure recovery stage, and the abnormal associated chamber is constructed.

[0051] The system performs point-by-point time series comparison of each chamber pressure value in the second inner cavity pressure data set with N positive pressure values, where the N positive pressure values represent the target positive pressure interval that each risk chamber should reach, and the target positive pressure interval includes an upper limit value and a lower limit value, used to reflect the safe pressure range required to be maintained inside the chamber. The system performs continuous analysis on the pressure monitoring data in each chamber. When the pressure data of a certain chamber does not reach the target positive pressure interval corresponding to the chamber (i.e., the data value is below the lower limit or above the upper limit) in consecutive T sampling periods, an unrecovered identifier is generated in the chamber. The system matches the states of the N risk chambers according to the unrecovered identifier, and filters out all chambers with unrecovered identifiers as a candidate unrecovered chamber set. Then, pressure fluctuation analysis is performed on each chamber in the unrecovered chamber set, specifically including: extracting the continuous pressure change data of each chamber in the unrecovered stage; calculating the pressure fluctuation value (such as the difference between the maximum value and the minimum value, or the standard deviation per unit time) in this stage; if the pressure fluctuation value of a certain chamber is greater than a preset fluctuation threshold value (reflecting the stability control tolerance of the system), an instability identifier is generated in the chamber, indicating that the chamber not only has not recovered to the target pressure interval, but also shows strong volatility, and there is a risk of pressure control instability.

[0052] The system cross-comparisons the unrecovered identifier and the instability identifier, performs joint processing, filters out chambers that have both unrecovered state and pressure instability state, and marks these chambers with an abnormal association identifier. The system further performs pressure recovery stage verification analysis on the identified abnormal association chambers to determine whether these chambers have spontaneous pressure recovery, effective external control intervention or recovery trend stabilization in the subsequent time period. If the recovery judgment rule (such as continuous pressure rise, fluctuation value drop to within the threshold value, etc.) is not met, the chamber is finally included in the abnormal association chamber set.

[0053] Further, based on the abnormal association chamber, the fan speed update data is analyzed to determine the abnormal fan speed data, the method comprising:

[0054] The system traverses the abnormal association chamber and performs pressure anomaly analysis to determine multiple pressure anomaly time periods. According to the multiple pressure anomaly time periods, the system performs abnormal mutation backtracking on the fan speed update data to determine mutation abnormal rising patterns and mutation abnormal falling patterns. According to the multiple pressure anomaly time periods, the system performs abnormal oscillation backtracking on the fan speed update data to determine oscillation abnormal patterns. According to the multiple pressure anomaly time periods, the system performs abnormal drift backtracking on the fan speed update data to determine positive drift abnormal patterns and negative drift abnormal patterns. According to the mutation abnormal rising patterns and the mutation abnormal falling patterns, the system performs time sequence association verification on the fan speed update data to obtain first fan abnormal speed data. According to the oscillation abnormal patterns, the system performs time sequence association verification on the fan speed update data to obtain second fan abnormal speed data. According to the positive drift abnormal patterns and the negative drift abnormal patterns, the system performs time sequence association verification on the fan speed update data to obtain third fan abnormal speed data.

[0055] The system traverses the identified abnormal association chamber, extracts time sequence data related to pressure instability in the chamber, performs pressure anomaly analysis, and determines multiple significant pressure anomaly time periods. The pressure anomaly time period is a time period in which the chamber pressure continuously deviates from the target positive pressure interval and is accompanied by obvious fluctuations, serving as a positioning index for backtracking analysis.

[0056] The system uses multiple pressure anomaly time periods as backtracking windows to perform multi-mode abnormal analysis on the fan speed update data in the corresponding time period, including mutation abnormal analysis, oscillation abnormal analysis, and drift abnormal analysis.

[0057] Mutation abnormal analysis: scan the continuous slope of fan speed data change in each pressure anomaly time period; when the fan speed of a certain sampling point increases by more than 15% compared to the previous time point, the system marks it as a mutation abnormal rising pattern and associates it with an electrical short circuit risk; when the fan speed decreases by more than 15% compared to the previous time point, the system marks it as a mutation abnormal falling pattern and associates it with a mechanical jamming or rotor seizure risk; combined with the corresponding abnormal chamber number and speed change time point, the system records the abnormal fan number and abnormal time period corresponding to all mutation abnormal patterns.

[0058] Oscillation abnormal analysis: the system performs waveform scanning on the fan speed data in each pressure anomaly time period and extracts wave peaks and troughs; when the speed difference between a single wave peak and the adjacent trough is more than 10%, the system records it as an effective oscillation; if the interval time of two consecutive oscillations is less than 6 seconds, the system determines it as a high-frequency fault oscillation pattern and accumulates the oscillation frequency; high-frequency oscillations exceeding the threshold are marked as oscillation abnormal patterns, indicating that the fan electrical control system has disturbance feedback or speed regulator abnormalities.

[0059] Drift anomaly analysis: the system performs fan speed trend line fitting for each pressure anomaly period, calculates the speed change rate per minute; if the speed trend line continuously rises in a positive direction by more than 8% per minute within any time period, it is marked as a positive drift anomaly pattern, indicating the risk of fan overload operation; if the speed trend line continuously falls in a negative direction by more than 8% per minute within any time period, it is marked as a negative drift anomaly pattern, indicating the risk of fan bearing aging or severe wear; all speed drift trend data that meet this condition will be classified into the drift anomaly set.

[0060] The system performs time series correlation verification analysis according to the above three types of anomaly patterns respectively: according to the mutation anomaly rising pattern and the mutation anomaly falling pattern, the system traverses the full time series of fan speed update data, extracts the fan number and its corresponding speed data that highly overlap with the pressure anomaly time point, and outputs the first fan abnormal speed data; according to the oscillation anomaly pattern, the system compares the fan control response time, extracts the difference between the oscillation period and the system feedback mechanism, and determines the stability of the adjustment link, and outputs the second fan abnormal speed data; according to the positive drift anomaly pattern and the negative drift anomaly pattern, the system performs causal relationship modeling combined with the chamber temperature rise trend, extracts the operation load history record of the corresponding fan, and outputs the third fan abnormal speed data.

[0061] According to the fan abnormal speed data, the system triggers multi-level interlock protection instructions for fault response, and performs adaptive ventilation control on multiple independent chambers according to the response results.

[0062] The system classifies the fault level based on the fan abnormal speed data, quantifies the score according to the severity of the anomaly pattern (such as mutation anomaly > oscillation anomaly > drift anomaly) and the number of fans that have anomalies, and generates a fault level index. According to the fault level index, the system matches the multi-level interlock protection rule table, and triggers the first-level interlock protection instruction, the second-level interlock protection instruction, and the third-level interlock protection instruction in sequence.

[0063] After the execution of the multi-level interlock protection instruction, the system receives the feedback fault response results in real time, including pressure recovery degree, temperature drop rate, fan self-checking state, ventilation path recovery situation and other information parameters. Based on the fault response results, the system performs adaptive ventilation control for each chamber, specifically: for the chambers that have completed pressure recovery, the system automatically adjusts the fan speed and direction according to the ventilation efficiency model, and gradually restores the normal operation mode; for the chambers that have not met the response results or have residual anomaly labels, the system prolongs the ventilation period, dynamically plans the directional ventilation path, and if necessary, links the standby fan group to enter the auxiliary air supply state; for high-risk areas or recurrent fault chambers, the system performs specific rhythm ventilation strategies (such as intermittent ventilation + load cycle analysis), and marks them as key monitoring objects.

[0064] Further, the method comprises:

[0065] The first fan abnormal speed data is used to interlock multiple independent chambers to generate a speed step response, and a first interlock protection instruction is generated. The first interlock protection instruction is used to traverse N risk chambers to perform fault matching, and the matching successful fault data is used as a first response result. The second fan abnormal speed data is used to interlock multiple independent chambers to generate a speed fluctuation response, and a second interlock protection instruction is generated. The second interlock protection instruction is used to traverse N risk chambers to perform fault matching, and the matching successful fault data is used as a second response result. The third fan abnormal speed data is used to interlock multiple independent chambers to generate a speed drift response, and a third interlock protection instruction is generated. The third interlock protection instruction is used to traverse N risk chambers to perform fault matching, and the matching successful fault data is used as a third response result.

[0066] The first fan abnormal speed data is used to interlock multiple independent chambers to generate a speed step response, and a first interlock protection instruction is generated. The first interlock protection instruction is used to traverse N risk chambers to perform fault matching, and the matching successful fault data is used as a first response result. The second fan abnormal speed data is used to interlock multiple independent chambers to generate a speed fluctuation response, and a second interlock protection instruction is generated. The second interlock protection instruction is used to traverse N risk chambers to perform fault matching, and the matching successful fault data is used as a second response result. The third fan abnormal speed data is used to interlock multiple independent chambers to generate a speed drift response, and a third interlock protection instruction is generated. The third interlock protection instruction is used to traverse N risk chambers to perform fault matching, and the matching successful fault data is used as a third response result.

[0067] The second fan abnormal speed data is used to interlock multiple independent chambers to generate a speed fluctuation response, and a second interlock protection instruction is generated. The second interlock protection instruction is used to traverse N risk chambers to perform fault matching, and the matching successful fault data is used as a second response result. The third fan abnormal speed data is used to interlock multiple independent chambers to generate a speed drift response, and a third interlock protection instruction is generated. The third interlock protection instruction is used to traverse N risk chambers to perform fault matching, and the matching successful fault data is used as a third response result.

[0068] Based on the third fan abnormal speed data, multiple independent chambers are interlocked to perform speed drift response processing, identify slow positive or negative offset behavior (for example, the speed change rate exceeds 8% / min), construct a three-level interlocking protection instruction, and the three-level interlocking protection instruction is used to schedule fan operation load, linkage redundant fan access, execute bearing self-check or lubrication activation command, etc. The system continues to traverse N risk chambers according to the three-level interlocking protection instruction, matches the ventilation efficiency decline area or fan operation degradation trend section caused by such slow change characteristics, determines the corresponding chamber and fan state data, forms a third response result, and calls it when executing subsequent non-continuous air supply and adaptive pressure release strategy.

[0069] Further, according to the response result, adaptive ventilation control is performed on multiple independent chambers, and the method comprises:

[0070] The multiple independent chambers are divided in descending order according to chamber pressure data to obtain high-pressure chambers, medium-pressure chambers, and low-pressure chambers; one-way airflow ventilation analysis is performed on the high-pressure chambers, the medium-pressure chambers, and the low-pressure chambers according to the first response result, the multiple directional ventilation paths are matched and reconstructed according to the first analysis result, and a first ventilation airflow chain is determined; one-way airflow ventilation analysis is performed on the high-pressure chambers, the medium-pressure chambers, and the low-pressure chambers according to the second response result, the multiple directional ventilation paths are matched and reconstructed according to the second analysis result, and a second ventilation airflow chain is determined; one-way airflow ventilation analysis is performed on the high-pressure chambers, the medium-pressure chambers, and the low-pressure chambers according to the third response result, the multiple directional ventilation paths are matched and reconstructed according to the third analysis result, and a third ventilation airflow chain is determined; path length determination is performed based on the first ventilation airflow chain, the second ventilation airflow chain, and the third ventilation airflow chain, ventilation airflow chains with a path length greater than a preset ventilation length threshold are compensated, and a compensation result is generated; path efficiency analysis is performed according to the compensation result combined with the first ventilation airflow chain, the second ventilation airflow chain, and the third ventilation airflow chain, and path ventilation efficiency is obtained to perform adaptive ventilation control on multiple independent chambers.

[0071] Firstly, a plurality of independent chambers are arranged in descending order according to the respective chamber pressure data, and are divided into high-pressure chambers, medium-pressure chambers and low-pressure chambers according to the pressure interval boundaries, and each type of chamber is marked with the current air pressure stability level. Then, the system performs one-way air flow analysis on the three types of chambers based on the first response result (triggered by the first fan abnormal speed data). Specifically, the natural ventilation gradient from the high-pressure chamber to the medium-pressure or low-pressure chamber is determined; the ventilation structure matching the gradient direction is selected from the original multiple directional ventilation paths; the selected paths are matched and reconstructed (including valve state adjustment, air flow direction reversal control, etc.), thereby forming a first ventilation air flow chain for guiding the air flow from the high-pressure area to the low-pressure area, and relieving the local positive pressure accumulation. Subsequently, the system performs one-way air flow analysis on the three types of chambers according to the second response result (triggered by the fan oscillation type abnormality), and focuses on identifying the fluid disturbance interval caused by pressure fluctuation, and performing dynamic impedance analysis and vortex prediction on the existing directional ventilation path, to reconstruct a second ventilation air flow chain for building a redundant air guide path with oscillation relief capability. Then, according to the third response result (triggered by the fan drift type abnormality), the same one-way air flow analysis is performed on the three types of chambers, focusing on detecting the long-term pressure difference maintenance trend between chambers. The system reconstructs the structure of the directional ventilation path based on the pressure balance relationship between the long-term positive pressure and negative pressure chambers, and generates a third ventilation air flow chain to build a slow adjustment type stable ventilation network. Next, the system determines the path length of the three ventilation air flow chains, and compares the physical length of each ventilation path with the preset ventilation length threshold. For the ventilation air flow chain whose length exceeds the threshold, the system compensates the flow rate or configures a parallel short path according to the path end chamber pressure compensation model, to generate a corresponding compensation result. Finally, based on the first ventilation air flow chain, the second ventilation air flow chain and the third ventilation air flow chain and their compensation results, the system performs path efficiency analysis, including ventilation response time delay evaluation, air flow stability analysis, pressure recovery speed statistics, and comprehensive analysis of the above efficiency parameters, to generate the ventilation efficiency evaluation result of each path.

[0072] Based on the ventilation efficiency analysis result, the fan speed and path control logic are automatically optimized to realize adaptive ventilation control of the plurality of independent chambers to meet the positive pressure maintenance requirements under different risk levels, and ensure that the positive pressure type explosion-proof power distribution cabinet still maintains a stable explosion-proof safety interval under multiple abnormal fan states.

[0073] In summary, the embodiments of the present application have at least the following technical effects:

[0074] Firstly, the multiple independent chambers of the positive pressure type explosion-proof power distribution cabinet are collected to generate a first inner chamber pressure data set for real-time explosion-proof monitoring, triggering an isolation control instruction. Then, based on the isolation control instruction, a positive pressure maintenance unit is started to inject gas into N risk chambers to maintain pressure, generating N positive pressure values, activating multiple directional ventilation paths of the N risk chambers, and N is an integer greater than or equal to 1 and less than or equal to the number of all independent chambers. Then, the fan speed data of the N risk chambers is adjusted according to the multiple directional ventilation paths, and the second inner chamber pressure data set of the N risk chambers is determined by monitoring, and the second inner chamber pressure data set is combined with the N positive pressure values for determination, and the fan abnormal speed data is determined according to the determination result. Finally, a multi-level interlock protection instruction is triggered for fault response according to the fan abnormal speed data, and adaptive ventilation control is performed on the multiple independent chambers according to the response result. The technical problem of the prior art that the ventilation control response of the power distribution cabinet is not timely is solved, and the technical effect of improving the ventilation control response speed is achieved.

[0075] In the second embodiment, based on the same inventive concept as the multi-chamber isolation ventilation control method of the positive pressure type explosion-proof power distribution cabinet in the foregoing embodiments, as shown in the following table, the present application provides a multi-chamber isolation ventilation control device for a positive pressure type explosion-proof power distribution cabinet. Figure 2 As shown in the following table, the present application provides a multi-chamber isolation ventilation control device for a positive pressure type explosion-proof power distribution cabinet.

[0076] The data acquisition module 11 collects the multiple independent chambers of the positive pressure type explosion-proof power distribution cabinet to generate a first inner chamber pressure data set for real-time explosion-proof monitoring, triggering an isolation control instruction; the pressure stabilizing module 12 starts a positive pressure maintenance unit based on the isolation control instruction to inject gas into N risk chambers to maintain pressure, generating N positive pressure values, and activating multiple directional ventilation paths of the N risk chambers, and N is an integer greater than or equal to 1 and less than or equal to the number of all independent chambers; the analysis module 13 adjusts the fan speed data of the N risk chambers according to the multiple directional ventilation paths, and determines the second inner chamber pressure data set of the N risk chambers by monitoring, and determines the fan abnormal speed data according to the determination result by combining the second inner chamber pressure data set with the N positive pressure values; the control module 14 triggers a multi-level interlock protection instruction for fault response according to the fan abnormal speed data, and performs adaptive ventilation control on the multiple independent chambers according to the response result.

[0077] Further, the data acquisition module 11 is used to execute the following method:

[0078] The M pressure sensors arranged in each independent chamber synchronously collect the multiple independent chambers according to a sampling frequency according to a sampling weight coefficient, to obtain a first inner cavity pressure data set, M being an integer greater than or equal to 3; historical explosion-proof record data of the positive pressure type explosion-proof power distribution cabinet is called, an explosion-proof threshold interval is set, and the first inner cavity pressure data set is continuously compared with the explosion-proof threshold interval; when the inner cavity pressure data of any one independent chamber at multiple continuous sampling points in the first inner cavity pressure data set is lower than the lower limit value of the explosion-proof threshold interval, an inner cavity pressure abnormality identifier is determined; N risk chambers are determined according to the inner cavity pressure abnormality identifier, pressure monitoring is performed on the N risk chambers, a pressure drop amplitude parameter is obtained, and the isolation control instruction is triggered according to the pressure drop amplitude parameter.

[0079] Further, the pressure stabilizing module 12 is configured to execute the following method:

[0080] Based on the isolation control instruction, the positive pressure maintaining unit divides the N risk chambers according to risk levels, determines high-risk chambers and low-risk chambers, injects gas into the high-risk chambers according to a ring injection mode through the positive pressure maintaining unit, generates a first chamber pressure curve, injects gas into the low-risk chambers according to a bottom-priority injection mode through the positive pressure maintaining unit, generates a second chamber pressure curve, performs pressure stabilization analysis according to the first chamber pressure curve and the second chamber pressure curve, generates N positive pressure values, and performs ventilation analysis on the N risk chambers according to the N positive pressure values, to determine the multiple directional ventilation paths.

[0081] Further, the pressure stabilizing module 12 is configured to execute the following method:

[0082] According to the N positive pressure values, chamber ventilation analysis is performed on the high-risk chambers to determine multiple first ventilatable points, chamber ventilation analysis is performed on the low-risk chambers according to the N positive pressure values to determine multiple second ventilatable points, chamber ventilation efficiency calculation is performed based on the multiple first ventilatable points and the multiple second ventilatable points, to determine a main ventilation path and a backup ventilation path, double matching of the main ventilation path and the backup ventilation path is performed based on the multiple first ventilatable points, to obtain a double-redundancy sealing path, backup energy-saving path matching is performed based on the multiple second ventilatable points, to obtain a backup energy-saving path, path directional analysis is performed on the double-redundancy sealing path and the backup energy-saving path, to determine the multiple directional ventilation paths.

[0083] Further, the analysis module 13 is configured to execute the following method:

[0084] The fan speed is collected according to the plurality of directional ventilation paths of the N risk chambers, fan speed data is obtained; the temperature of the N risk chambers is collected in reverse based on the plurality of directional ventilation paths, a chamber temperature set is obtained, the fan speed data is updated based on the chamber temperature set, and fan speed update data is generated; the pressure of the N risk chambers is monitored according to the fan speed update data, and a second inner cavity pressure data set is obtained; the second inner cavity pressure data set is compared with the N positive pressure values, and when the second inner cavity pressure data set has not recovered to the target positive pressure interval, an abnormal associated chamber is determined; the fan speed update data is traced back based on the abnormal associated chamber, and abnormal fan speed data is determined.

[0085] Further, the analysis module 13 is configured to perform the following method:

[0086] The second inner cavity pressure data set is compared with the N positive pressure values point by point, and when a plurality of consecutive data points in the second inner cavity pressure data set do not reach the target positive pressure value interval, a non-recovery identifier is generated, the N risk chambers are matched according to the non-recovery identifier, and a non-recovery chamber is determined; when the pressure fluctuation value is greater than the preset fluctuation threshold, a instability identifier is generated; the instability identifier and the non-recovery identifier are associated and combined to determine an abnormal association identifier; the abnormal association identifier is verified in a pressure recovery stage, and the abnormal associated chamber is constructed.

[0087] Further, the analysis module 13 is configured to perform the following method:

[0088] The abnormal associated chamber is traversed for pressure anomaly analysis, and a plurality of pressure anomaly time periods are determined; the fan speed update data is traced back for abnormal mutation according to the plurality of pressure anomaly time periods, and a mutation abnormal rising mode and a mutation abnormal falling mode are determined; the fan speed update data is traced back for abnormal oscillation according to the plurality of pressure anomaly time periods, and an oscillation abnormal mode is determined; the fan speed update data is traced back for abnormal drift according to the plurality of pressure anomaly time periods, and a positive drift abnormal mode and a negative drift abnormal mode are determined; the fan speed update data is traversed according to the mutation abnormal rising mode and the mutation abnormal falling mode for time sequence association verification, and first fan abnormal speed data is obtained; the fan speed update data is traversed according to the oscillation abnormal mode for time sequence association verification, and second fan abnormal speed data is obtained; the fan speed update data is traversed according to the positive drift abnormal mode and the negative drift abnormal mode for time sequence association verification, and third fan abnormal speed data is obtained.

[0089] Further, the control module 14 is configured to perform the following method:

[0090] Based on the first fan abnormal speed data, a plurality of independent chambers are interlocked for speed step response to generate a first interlocking protection instruction; according to the first interlocking protection instruction, N risk chambers are traversed for fault matching, and the matching successful fault data is taken as a first response result; based on the second fan abnormal speed data, a plurality of independent chambers are interlocked for speed fluctuation response to generate a second interlocking protection instruction; according to the second interlocking protection instruction, N risk chambers are traversed for fault matching, and the matching successful fault data is taken as a second response result; based on the third fan abnormal speed data, a plurality of independent chambers are interlocked for speed drift response to generate a third interlocking protection instruction; according to the third interlocking protection instruction, N risk chambers are traversed for fault matching, and the matching successful fault data is taken as a third response result.

[0091] Further, the control module 14 is used to execute the following method:

[0092] The plurality of independent chambers are divided in descending order according to chamber pressure data to obtain high-pressure chambers, medium-pressure chambers and low-pressure chambers; the high-pressure chambers, the medium-pressure chambers and the low-pressure chambers are analyzed for one-way air flow ventilation according to the first response result, the plurality of directional ventilation paths are matched and reconstructed according to the first analysis result, and a first ventilation air flow chain is determined; the high-pressure chambers, the medium-pressure chambers and the low-pressure chambers are analyzed for one-way air flow ventilation according to the second response result, the plurality of directional ventilation paths are matched and reconstructed according to the second analysis result, and a second ventilation air flow chain is determined; the high-pressure chambers, the medium-pressure chambers and the low-pressure chambers are analyzed for one-way air flow ventilation according to the third response result, the plurality of directional ventilation paths are matched and reconstructed according to the third analysis result, and a third ventilation air flow chain is determined; path length determination is performed based on the first ventilation air flow chain, the second ventilation air flow chain and the third ventilation air flow chain, ventilation air flow chains with path lengths greater than a preset ventilation length threshold are compensated to generate a compensation result; path efficiency analysis is performed according to the compensation result in combination with the first ventilation air flow chain, the second ventilation air flow chain and the third ventilation air flow chain to obtain path ventilation efficiency, and adaptive ventilation control is performed on the plurality of independent chambers.

[0093] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The above describes a specific embodiment of the present application. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0094] The above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0095] The specification and drawings are only exemplary and illustrative of the present application and are considered to cover any and all modifications, variations, combinations or equivalents that are within the scope of the present application. Obviously, various modifications and changes can be made to the present application by those skilled in the art without departing from the scope of the present application. Thus, it is intended that the present application cover the modifications and changes as they come within the scope of the application, and that the scope of the application be limited only by the claims.

Claims

1. A multi-cavity isolation ventilation control method of a positive pressure type explosion-proof power distribution cabinet, characterized in that, The method comprises: Collecting a plurality of independent chambers of the positive pressure type explosion-proof power distribution cabinet to generate a first inner chamber pressure data set for real-time explosion-proof monitoring, and triggering an isolation control instruction; Based on the isolation control instruction, a positive pressure maintaining unit is started to inject gas into N risk chambers to maintain pressure, generate N positive pressure values, and activate a plurality of directional ventilation paths of the N risk chambers, N is an integer greater than or equal to 1 and less than or equal to the number of all independent chambers; According to the fan speed data of the N risk chambers through the plurality of directional ventilation paths, the second inner chamber pressure data set of the N risk chambers is adjusted and monitored to determine the fan abnormal speed data; According to the fan abnormal speed data, a multi-level interlock protection instruction is triggered for fault response, and a plurality of independent chambers are adaptively ventilated according to the response result.

2. The multi-cavity isolation ventilation control method of the positive pressure type explosion-proof power distribution cabinet according to claim 1, characterized in that, Collecting a plurality of independent chambers of the positive pressure type explosion-proof power distribution cabinet to generate a first inner chamber pressure data set for real-time explosion-proof monitoring, and triggering an isolation control instruction, the method comprising: According to the sampling weight coefficient, the M pressure sensors deployed in each independent chamber are synchronously collected at a sampling frequency to obtain the first inner chamber pressure data set, M is an integer greater than or equal to 3; Retrieve historical explosion-proof record data of the positive pressure type explosion-proof power distribution cabinet, set an explosion-proof threshold interval, and continuously compare the first inner chamber pressure data set with the explosion-proof threshold interval; When the inner chamber pressure data of any one independent chamber in the first inner chamber pressure data set is lower than the lower limit value of the explosion-proof threshold interval for a plurality of consecutive sampling points, an inner chamber pressure abnormality identifier is determined; According to the inner chamber pressure abnormality identifier, N risk chambers are determined, and the N risk chambers are pressure monitored to obtain a pressure drop amplitude parameter, and the isolation control instruction is triggered according to the pressure drop amplitude parameter.

3. The multi-cavity isolation ventilation control method of the positive pressure type explosion-proof power distribution cabinet according to claim 1, characterized in that, Based on the isolation control instruction, a positive pressure maintaining unit is started to inject gas into N risk chambers to maintain pressure, generate N positive pressure values, and activate a plurality of directional ventilation paths of the N risk chambers, N is an integer greater than or equal to 1 and less than or equal to the number of all independent chambers; Based on the isolation control instruction, the positive pressure maintaining unit is started to divide the N risk chambers according to risk levels, determine high-risk chambers and low-risk chambers; Through the positive pressure maintaining unit, the high-risk chambers are injected with gas according to the annular injection mode to generate a first chamber pressure curve; Through the positive pressure maintaining unit, the low-risk chambers are injected with gas according to the bottom priority injection mode to generate a second chamber pressure curve; According to the first chamber pressure curve and the second chamber pressure curve, pressure stabilization analysis is performed to generate N positive pressure values; According to the N positive pressure values, ventilation analysis is performed on the N risk chambers to determine the plurality of directional ventilation paths.

4. The multi-cavity isolation ventilation control method of the positive pressure type explosion-proof power distribution cabinet according to claim 3, characterized in that, According to the N positive pressure values, ventilation analysis is performed on the N risk chambers to determine the plurality of directional ventilation paths, the method comprising: According to the N positive pressure values, chamber ventilation analysis is performed on the high-risk chambers to determine a plurality of first ventilable points; Determine a plurality of second ventilable points according to the N positive pressure values in combination with the low-risk chamber for chamber ventilation analysis; Determine a ventilation main path and a ventilation backup path based on the plurality of first ventilable points and the plurality of second ventilable points for chamber ventilation efficiency calculation; Perform double matching of the ventilation main path and the ventilation backup path based on the plurality of first ventilable points to obtain a double-redundancy sealing path; Perform matching of the ventilation backup path based on the plurality of second ventilable points to obtain a backup energy-saving path; Perform path directional analysis on the double-redundancy sealing path and the backup energy-saving path to determine the plurality of directional ventilation paths.

5. The multi-compartment isolation ventilation control method of a positive pressure type explosion-proof power distribution cabinet according to claim 1, characterized in that, Adjust and monitor fan speed data of N risk chambers according to the plurality of directional ventilation paths to determine a second internal cavity pressure data set of the N risk chambers, determine according to the second internal cavity pressure data set in combination with the N positive pressure values, perform backtracking analysis according to the determination result to determine fan abnormal speed data, the method comprising: Collect fan speed data according to the plurality of directional ventilation paths of N risk chambers to obtain fan speed data; Collect temperature of N risk chambers in reverse according to the plurality of directional ventilation paths to obtain a chamber temperature set, adjust and update the fan speed data according to the chamber temperature set to generate fan speed update data; Perform pressure monitoring on N risk chambers according to the fan speed update data to obtain a second internal cavity pressure data set; Compare the second internal cavity pressure data set with the N positive pressure values, and when the second internal cavity pressure data set has not recovered to a target positive pressure interval, determine an abnormal associated chamber; Perform backtracking analysis on the fan speed update data based on the abnormal associated chamber to determine the fan abnormal speed data.

6. The multi-cavity isolation ventilation control method of the positive pressure type explosion-proof power distribution cabinet according to claim 5, characterized in that, Compare the second internal cavity pressure data set with the N positive pressure values, and when the second internal cavity pressure data set has not recovered to a target positive pressure interval, determine an abnormal associated chamber, the method comprising: Point-by-point compare the second internal cavity pressure data set with the N positive pressure values, and when a plurality of consecutive data points of the second internal cavity pressure data set do not reach a target positive pressure value interval, generate a non-recovery identifier, match N risk chambers according to the non-recovery identifier to determine a non-recovery chamber; Perform pressure fluctuation analysis on the non-recovery chamber according to the non-recovery identifier, and when the pressure fluctuation value is greater than a preset fluctuation threshold, generate a instability identifier; Combine the instability identifier with the non-recovery identifier to determine an abnormal association identifier; Perform pressure recovery stage verification on the abnormal association identifier to construct the abnormal associated chamber.

7. The multi-cavity isolation ventilation control method of the positive pressure type explosion-proof power distribution cabinet according to claim 5, characterized in that, Perform backtracking analysis on the fan speed update data based on the abnormal associated chamber to determine the fan abnormal speed data, the method comprising: Perform pressure abnormality analysis on the abnormal associated chamber to determine a plurality of pressure abnormality time periods; Perform abnormal mutation backtracking on the fan speed update data according to the plurality of pressure abnormality time periods to determine a mutation abnormality rising mode and a mutation abnormality falling mode; According to the multiple pressure anomaly time periods, the fan speed update data is backtracked for abnormal oscillation to determine an oscillation anomaly mode; According to the multiple pressure anomaly time periods, the fan speed update data is backtracked for abnormal drift to determine a positive drift anomaly mode and a negative drift anomaly mode; According to the mutation anomaly rising mode and the mutation anomaly falling mode, the fan speed update data is traversed for time sequence correlation verification to obtain first fan abnormal speed data; According to the oscillation anomaly mode, the fan speed update data is traversed for time sequence correlation verification to obtain second fan abnormal speed data; According to the positive drift anomaly mode and the negative drift anomaly mode, the fan speed update data is traversed for time sequence correlation verification to obtain third fan abnormal speed data.

8. The multi-cavity isolation ventilation control method of the positive pressure type explosion-proof power distribution cabinet according to claim 7, characterized in that, According to the fan abnormal speed data, a multi-level interlock protection instruction is triggered for fault response, and according to a response result, a plurality of independent chambers are adaptively controlled, and the method comprises: Based on the first fan abnormal speed data, a plurality of independent chambers are interlocked for speed step response to generate a first-level interlock protection instruction; According to the first-level interlock protection instruction, N risk chambers are traversed for fault matching, and the matching successful fault data is taken as a first response result; Based on the second fan abnormal speed data, a plurality of independent chambers are interlocked for speed fluctuation response to generate a second-level interlock protection instruction; According to the second-level interlock protection instruction, N risk chambers are traversed for fault matching, and the matching successful fault data is taken as a second response result; Based on the third fan abnormal speed data, a plurality of independent chambers are interlocked for speed drift response to generate a third-level interlock protection instruction; According to the third-level interlock protection instruction, N risk chambers are traversed for fault matching, and the matching successful fault data is taken as a third response result.

9. The multi-cavity isolation ventilation control method of the positive pressure type explosion-proof power distribution cabinet according to claim 8, characterized in that, According to the response result, a plurality of independent chambers are adaptively controlled, and the method comprises: A plurality of independent chambers are divided in descending order according to chamber pressure data to obtain high-pressure chambers, medium-pressure chambers and low-pressure chambers; According to the first response result, the high-pressure chambers, the medium-pressure chambers and the low-pressure chambers are analyzed for one-way air flow ventilation, the plurality of directional ventilation paths are matched and reconstructed according to a first analysis result, and a first ventilation air flow chain is determined; According to the second response result, the high-pressure chambers, the medium-pressure chambers and the low-pressure chambers are analyzed for one-way air flow ventilation, the plurality of directional ventilation paths are matched and reconstructed according to a second analysis result, and a second ventilation air flow chain is determined; According to the third response result, the high-pressure chambers, the medium-pressure chambers and the low-pressure chambers are analyzed for one-way air flow ventilation, the plurality of directional ventilation paths are matched and reconstructed according to a third analysis result, and a third ventilation air flow chain is determined; Based on the first ventilation air flow chain, the second ventilation air flow chain and the third ventilation air flow chain, path length determination is performed, ventilation air flow chains with path lengths greater than a preset ventilation length threshold are compensated, and a compensation result is generated; According to the compensation result, path efficiency analysis is performed on the first ventilation airflow chain, the second ventilation airflow chain and the third ventilation airflow chain, and path ventilation efficiency is obtained for adaptive ventilation control of the multiple independent chambers.

10. A multi-cavity isolation ventilation control device for a positive pressure explosion-proof power distribution cabinet, characterized in that, The method for implementing the multi-cavity isolation ventilation control of the positive pressure type explosion-proof power distribution cabinet according to any one of claims 1-9, the device comprises: A data acquisition module: acquires the multiple independent chambers of the positive pressure type explosion-proof power distribution cabinet, generates a first inner cavity pressure data set for real-time explosion-proof monitoring, and triggers an isolation control instruction; A pressure stabilizing module: based on the isolation control instruction, starts a positive pressure maintaining unit to inject gas into N risk chambers for pressure stabilization, generates N positive pressure values, and activates multiple directional ventilation paths of the N risk chambers, N is an integer greater than or equal to 1 and less than or equal to the number of all independent chambers; An analysis module: adjusts fan speed data of the N risk chambers according to the multiple directional ventilation paths, determines a second inner cavity pressure data set of the N risk chambers, determines fan abnormal speed data according to the second inner cavity pressure data set in combination with the N positive pressure values, and performs backtracking analysis according to the determination result; A control module: triggers a multi-level interlock protection instruction for fault response according to the fan abnormal speed data, and performs adaptive ventilation control on the multiple independent chambers according to the response result.

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