Efficient dustproof and ventilation method and system for charging pile based on intelligent sensing regulation

Abstract

The application relates to the technical field of intelligent dust prevention, and discloses a charging pile efficient ventilation dust prevention method and system based on intelligent sensing regulation, which comprises the following steps: first, detecting the environment in the pile to analyze ventilation demand and determine dust prevention priority, adjusting a ventilation regulation component and detecting performance according to the ventilation demand and the dust prevention priority, and calculating an air flow regulation rate; second, combining a dust prevention index and a real-time load index to calculate a ventilation energy consumption ratio, and constructing a multi-mode regulation architecture; third, regulating the component logic based on the architecture, obtaining component ventilation logic, analyzing air flow coverage, and inquiring an optimized path; and finally, monitoring dust deposition state, dynamically adjusting a wind control index, identifying an efficient regulation node, and thus formulating an adaptive dust prevention scheme. The application can improve the reliability and safety of charging pile operation.

Description

TECHNICAL FIELD

[0001] The application relates to a charging pile efficient ventilation and dust prevention method and system based on intelligent sensing regulation, and belongs to the technical field of intelligent dust prevention. BACKGROUND

[0002] The charging pile is an infrastructure equipment for providing power supply for electric vehicles, and is usually divided into an alternating current charging pile (slow charging) and a direct current charging pile (fast charging). Since the charging pile is exposed to the outdoor complex environment for a long time, the internal electrical elements are prone to performance attenuation or failure due to dust accumulation and high temperature.

[0003] At present, the ventilation and dust prevention of the charging pile usually adopts a traditional mechanical ventilation fan combined with a filter screen, ventilation is performed through timed operation or manual control, and the filter screen also needs to be replaced regularly to maintain the dust prevention effect. However, this method cannot accurately regulate according to the real-time running temperature of the charging pile, the dynamic change of the environmental dust concentration and the like, and has problems of low ventilation efficiency, early filter screen blockage and energy waste, thereby causing safety hazards due to untimely heat dissipation of the charging pile. Therefore, a charging pile efficient ventilation and dust prevention method based on intelligent sensing regulation is needed to improve the reliability and safety of the charging pile operation. SUMMARY

[0004] The application provides a charging pile efficient ventilation and dust prevention method and system based on intelligent sensing regulation, which mainly aims to improve the reliability and safety of the charging pile operation.

[0005] To achieve the above-mentioned purpose, the application provides a charging pile efficient ventilation and dust prevention method based on intelligent sensing regulation, which comprises the following steps:

[0006] Obtaining an in-pile detection environment corresponding to a target charging pile, analyzing a ventilation demand corresponding to the target charging pile based on the in-pile detection environment, determining a dust prevention priority corresponding to the target charging pile according to the ventilation demand;

[0007] Adjusting a ventilation regulation component corresponding to the target charging pile based on the dust prevention priority, performing performance detection on the ventilation regulation component to obtain a component performance parameter, and calculating an air flow regulation rate corresponding to the ventilation regulation component based on the component performance parameter;

[0008] Detecting a dust prevention index corresponding to the air flow regulation rate, calculating a ventilation energy consumption ratio corresponding to the target charging pile based on the dust prevention index and a real-time load index of the target charging pile, and constructing a multi-mode regulation architecture corresponding to the target charging pile based on the ventilation energy consumption ratio;

[0009] Based on the multi-mode regulation architecture, the ventilation regulation component is logically regulated to obtain a component ventilation logic, the airflow coverage range corresponding to the component ventilation logic is analyzed, and an airflow optimization path in the airflow coverage range is queried;

[0010] The dust deposition state in the airflow optimization path is monitored, the air control index corresponding to the ventilation regulation component is dynamically adjusted based on the dust deposition state, the efficient regulation node in the air control index is identified, and the adaptive dust prevention scheme corresponding to the target charging pile is formulated based on the efficient regulation node.

[0011] Optionally, the dust prevention priority corresponding to the target charging pile is determined according to the ventilation demand, comprising:

[0012] The airflow rate data corresponding to the ventilation demand is analyzed;

[0013] Based on the airflow rate data, the dust retention area corresponding to the target charging pile is divided;

[0014] The dust particle concentration in the dust retention area is collected;

[0015] The dust accumulation risk index corresponding to the dust particle concentration is analyzed;

[0016] Based on the dust accumulation risk index, the dust prevention priority corresponding to the target charging pile is determined.

[0017] Optionally, the ventilation regulation component corresponding to the target charging pile is adjusted based on the dust prevention priority, comprising:

[0018] According to the dust prevention priority, the ventilation grid interval corresponding to the target charging pile is detected;

[0019] The ventilation grid node corresponding to the ventilation grid interval is divided;

[0020] The airflow disturbance amplitude corresponding to the ventilation grid node is analyzed;

[0021] The inefficient grid node with the airflow disturbance amplitude lower than the preset threshold value is screened;

[0022] According to the inefficient grid node, the ventilation regulation component corresponding to the target charging pile is adjusted.

[0023] Optionally, the airflow regulation rate corresponding to the ventilation regulation component is calculated based on the component performance parameter, comprising:

[0024] The airflow regulation rate corresponding to the ventilation regulation component is calculated by using the following formula:

[0025]

[0026] wherein, V adj represents the airflow adjustment rate corresponding to the ventilation regulation component, n represents the number of categories corresponding to the ventilation regulation component, i represents the category index corresponding to the ventilation regulation component, a i represents the weight coefficient of the i-th category component, P i represents the performance parameter value of the i-th category component, R i represents the resistance-related parameter of the i-th category component, m represents the total number of component performance parameters, j represents the number index of the component performance parameter, β j represents the influence coefficient corresponding to the j-th component performance parameter, D j represents the parameter measured value corresponding to the j-th component performance parameter.

[0027] Optionally, the ventilation energy consumption ratio corresponding to the target charging pile is calculated based on the dust prevention index in combination with a real-time load index of the target charging pile, comprising:

[0028] The ventilation energy consumption ratio corresponding to the target charging pile is calculated by using the following formula:

[0029]

[0030] wherein, E r represents the ventilation energy consumption ratio corresponding to the target charging pile, ρ represents the dust prevention index weight, DI represents the dust prevention index, σ represents the load index weight, LI represents the real-time load index, γ represents the energy consumption weight coefficient, t1 and t2 respectively represent the starting time and the ending time of the ventilation energy consumption period, P(t) represents the ventilation energy consumption function, δ represents the noise weight coefficient, and NI represents the noise index.

[0031] Optionally, the multi-mode regulation architecture corresponding to the target charging pile is constructed based on the ventilation energy consumption ratio, comprising:

[0032] extracting a dynamic fluctuation component in the ventilation energy consumption ratio;

[0033] calculating a heat dissipation demand threshold corresponding to the target charging pile according to the dynamic fluctuation component;

[0034] analyzing a fan speed gradient corresponding to the heat dissipation demand threshold;

[0035] collecting multi-mode closed-loop data corresponding to the target charging pile according to the fan speed gradient;

[0036] constructing a multi-mode regulation architecture corresponding to the target charging pile based on the multi-mode closed-loop data.

[0037] Optionally, the multi-mode regulation architecture is based on logical regulation of the ventilation regulation component to obtain component ventilation logic, including:

[0038] Resolving the ventilation regulation mode corresponding to the multi-mode regulation architecture;

[0039] Querying mode regulation data under the ventilation regulation mode;

[0040] Based on the mode regulation data, locate the core ventilation node in the ventilation regulation component;

[0041] Integrate the ventilation start-stop timing corresponding to the core ventilation node;

[0042] Based on the ventilation start-stop timing, the ventilation regulation component is logically regulated to obtain the component ventilation logic.

[0043] Optionally, the query of the air flow optimization path in the air flow coverage range includes:

[0044] Determine the high-turbulence area corresponding to the air flow coverage range;

[0045] Extract the vortex intensity index in the high-turbulence area;

[0046] Based on the vortex intensity index, calculate the air flow disturbance coefficient of the high-turbulence area;

[0047] Analyze the air flow disturbance trajectory corresponding to the air flow disturbance coefficient;

[0048] Based on the air flow disturbance trajectory, query the air flow optimization path in the air flow coverage range.

[0049] Optionally, the dynamic adjustment of the air control index corresponding to the ventilation regulation component based on the dust deposition state includes:

[0050] Resolving the deposition thickness distribution data corresponding to the dust deposition state;

[0051] Determine the dust aggregation level corresponding to the deposition thickness distribution data;

[0052] Analyze the ventilation braking gradient corresponding to the dust aggregation level;

[0053] Match the dynamic wind pressure threshold corresponding to the ventilation braking gradient;

[0054] Based on the dynamic wind pressure threshold, dynamically adjust the air control index corresponding to the ventilation regulation component.

[0055] In order to solve the above problems, the present application also provides a charging pile efficient ventilation dust prevention system based on intelligent sensing regulation, the system comprises:

[0056] The priority determination module is configured to acquire an in-pile detection environment corresponding to the target charging pile, analyze ventilation demand corresponding to the target charging pile based on the in-pile detection environment, and determine a dust prevention priority corresponding to the target charging pile according to the ventilation demand.

[0057] The rate calculation module is configured to adjust a ventilation regulation component corresponding to the target charging pile based on the dust prevention priority, perform performance detection on the ventilation regulation component, obtain a component performance parameter, and calculate an air flow adjustment rate corresponding to the ventilation regulation component based on the component performance parameter.

[0058] The architecture construction module is configured to detect a dust prevention index corresponding to the air flow adjustment rate, calculate a ventilation energy consumption ratio corresponding to the target charging pile based on the dust prevention index in combination with a real-time load index of the target charging pile, and construct a multi-mode regulation architecture corresponding to the target charging pile based on the ventilation energy consumption ratio.

[0059] The path query module is configured to perform logical regulation on the ventilation regulation component based on the multi-mode regulation architecture, obtain a component ventilation logic, analyze an air flow coverage range corresponding to the component ventilation logic, and query an air flow optimization path in the air flow coverage range.

[0060] The scheme formulation module is configured to monitor a dust deposition state in the air flow optimization path, dynamically adjust a wind control index corresponding to the ventilation regulation component based on the dust deposition state, identify an efficient regulation node in the wind control index, and formulate an adaptive dust prevention scheme corresponding to the target charging pile based on the efficient regulation node.

[0061] Compared with the problems described in the background art, the present application can master the key information such as the internal temperature, humidity and dust concentration of the charging pile in real time by acquiring the in-pile detection environment corresponding to the target charging pile, accurately identify potential overheating and dust accumulation risks, provide data support for dynamically adjusting the ventilation and dust prevention strategy, significantly improve the ventilation efficiency and reduce energy consumption, and can also provide early warning of electrical component performance degradation or fault risks. Based on the dust prevention priority, the ventilation regulation component corresponding to the target charging pile is adjusted to realize precise adaptation of the dust prevention measures, thereby improving the dust prevention effect and avoiding energy waste caused by excessive intervention, ensuring efficient and safe operation of the charging pile in different dust environments. Further, by detecting the dust prevention index corresponding to the airflow regulation rate, the filter screen interception efficiency and whether the airflow can effectively block dust can be judged, and hidden troubles such as filter screen blockage can be found in time, which is beneficial to targeted maintenance, ensures the cleanliness of the charging pile, avoids faults caused by dust accumulation, prolongs the service life of the equipment, and ensures stable and reliable operation. Further, by logically regulating the ventilation regulation component based on the multi-mode regulation architecture, the component ventilation logic is obtained, the intelligent switching and precise matching of the ventilation strategy can be realized, and by analyzing the regulation data under different modes, the core ventilation node is located and the start-stop timing is optimized, which can dynamically balance the heat dissipation, dust prevention and energy consumption demand of the charging pile, and ensure stable and efficient operation of the charging pile in multiple scenarios. Finally, by monitoring the dust deposition state in the airflow optimization path, the filter screen load and airflow dead angle can be judged, and the path parameters can be cleaned and maintained or adjusted in time, thereby avoiding dust blockage of key components, improving the dust prevention performance of the charging pile, prolonging the service life of the equipment and ensuring stable operation of the charging pile in high dust environment. Therefore, the charging pile efficient ventilation and dust prevention method and system based on intelligent sensing regulation provided by the embodiment of the present application can improve the reliability and safety of the operation of the charging pile. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 A flowchart of the charging pile efficient ventilation and dust prevention method based on intelligent sensing regulation provided by an embodiment of the present application is shown.

[0063] Figure 2 An architecture diagram corresponding to the multi-mode regulation architecture in the charging pile efficient ventilation and dust prevention method based on intelligent sensing regulation provided by an embodiment of the present application is shown.

[0064] Figure 3 A module diagram of the charging pile efficient ventilation and dust prevention system based on intelligent sensing regulation provided by an embodiment of the present application is shown.

[0065] The purpose of the present application, functional features and advantages will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0066] It is to be understood that the specific embodiments described herein are merely illustrative of the present application and do not limit the present application.

[0067] The embodiment of the present application provides a charging pile efficient ventilation and dust prevention method based on intelligent sensing regulation. The execution subject of the charging pile efficient ventilation and dust prevention method based on intelligent sensing regulation includes but is not limited to at least one of electronic devices such as a server, a terminal and the like which can be configured to execute the method provided by the embodiment of the present application. In other words, the charging pile efficient ventilation and dust prevention method based on intelligent sensing regulation can be executed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to a single server, a server cluster, a cloud server or a cloud server cluster and the like.

[0068] Embodiment 1:

[0069] Referring to Figure 1 Fig. 1 shows a flowchart of the charging pile efficient ventilation and dust prevention method based on intelligent sensing regulation provided by an embodiment of the present application. In the embodiment, the charging pile efficient ventilation and dust prevention method based on intelligent sensing regulation includes:

[0070] S1, obtaining a pile-in detection environment corresponding to a target charging pile, analyzing a ventilation demand corresponding to the target charging pile based on the pile-in detection environment, and determining a dust prevention priority corresponding to the target charging pile according to the ventilation demand.

[0071] The present application can master the key information such as internal temperature, humidity and dust concentration of the charging pile in real time by obtaining the pile-in detection environment corresponding to the target charging pile, accurately identify potential overheating and dust accumulation risks, provide data support for dynamically adjusting the ventilation and dust prevention strategy, significantly improve the ventilation efficiency and reduce the energy consumption, and also can early warn the performance degradation or fault hidden danger of electrical components.

[0072] The target charging pile refers to a specific charging pile individual which needs to be ventilated and dust controlled, and can be a single or multiple charging pile devices in different scenarios (such as outdoor public areas, industrial parks and the like). For example, a direct current fast charging pile in a specific highway service area which is charging an electric vehicle needs to be monitored and controlled as a target charging pile due to high-power operation heat production and outdoor dust environment. The pile-in detection environment refers to real-time physical environment data inside the charging pile collected by various sensors, including but not limited to key point temperature (such as power module, cable joint), air humidity, dust concentration, air flow rate and the like. For example, the temperature sensor measures that the IGBT module temperature of a certain charging pile is 65℃, the laser dust sensor detects that the internal dust accumulation amount reaches 80 μg / m 3The data collectively constitute the pile internal detection environment of the pile, and the pile internal detection environment corresponding to the target charging pile can be acquired by an environment perception technology, such as real-time collection of internal environment data of the charging pile by a temperature sensor, a humidity sensor, a smoke sensor, and the like.

[0073] Further, based on the pile internal detection environment, ventilation requirements corresponding to the target charging pile are analyzed, real-time operation states of the charging pile can be accurately matched, ventilation intensity and mode are dynamically planned according to internal temperature, dust concentration, and the like, safety hazards caused by insufficient heat dissipation in a high-temperature environment can be avoided, and unnecessary ventilation energy consumption can be reduced in a low-dust environment.

[0074] The ventilation requirements refer to ventilation strategy parameters required by the target charging pile to maintain safe and stable operation, which are analyzed according to the pile internal detection environment (such as temperature field distribution, dust concentration, equipment load, and the like), and include ventilation intensity (such as fan speed and air flow rate), ventilation direction (such as directional purging area), ventilation duration, and dust prevention measures (such as filter screen cleaning frequency), for example, when it is detected that the internal power module temperature of the charging pile reaches 70 DEG C and the dust concentration is low, the ventilation requirement is to start a high-speed fan to perform strong convection heat dissipation on the high-temperature area; if the dust concentration is excessive, the ventilation path is adjusted to reduce dust accumulation, and the filter screen self-cleaning function is triggered, and the analysis of the ventilation requirements corresponding to the target charging pile can be achieved by an experimental test method, such as deployment of a temperature and humidity sensor array in a simulated environment to monitor actual ventilation effect, so as to obtain the ventilation requirements.

[0075] Further, according to the ventilation requirements, dust prevention priorities corresponding to the target charging pile are determined, resource allocation between heat dissipation and dust prevention is dynamically balanced, dust accumulation affecting equipment performance is avoided, and the hindering of dust prevention measures to ventilation efficiency is reduced, so that the adaptability of the charging pile in different scenarios is improved.

[0076] The dust prevention priorities refer to hierarchical determination of urgency and importance of dust prevention work of the target charging pile according to air flow rate data, dust retention area, dust particle concentration, and dust accumulation risk index, and the higher the priority is, the more dust prevention measures such as strengthening filter screen filtration, starting the self-cleaning function, and adjusting the ventilation strategy need to be taken in priority, so as to guarantee safe and stable operation of the charging pile.

[0077] As an embodiment of the present application, the dust prevention priority of the target charging pile is determined according to the ventilation demand, including: analyzing airflow rate data corresponding to the ventilation demand; dividing a dust retention area corresponding to the target charging pile based on the airflow rate data; collecting dust particle concentration in the dust retention area; analyzing a dust accumulation risk index corresponding to the dust particle concentration; and determining the dust prevention priority of the target charging pile based on the dust accumulation risk index.

[0078] The airflow rate data is the flow velocity data of the airflow in each region inside the target charging pile collected by a wind speed sensor or other equipment, usually in units of meters per second (m / s), which reflects the flow state of air inside the charging pile. For example, the airflow rate is higher near the ventilation port, and relatively lower in the corners. It is a key basis for judging the ventilation efficiency and airflow distribution. The dust retention area is a specific area inside the charging pile where dust particles are prone to deposit and stay due to low airflow rate and poor air flowability. For example, the corners inside the charging pile, the gaps between the cooling fins, the recesses of the circuit joints, etc. Due to the difficulty of effective flushing of airflow, dust is more likely to adhere and accumulate in these areas, affecting the normal operation of the equipment. The dust particle concentration is the number or mass of dust particles contained in a unit volume of air in the dust retention area. Common units are particles per cubic meter or micrograms per cubic meter. It is detected by a laser particulate matter sensor or other equipment. The higher the value, the more serious the dust pollution in the area, and the greater the potential harm to the internal components of the charging pile. The dust accumulation risk index is a quantitative index calculated by a specific algorithm based on dust particle concentration and other data, combined with the sensitivity of the internal components of the charging pile to dust, the operating environment, and other factors. It is used to assess the risk of dust accumulation causing equipment failure or performance degradation. The higher the index, the greater the possibility of equipment failure caused by dust accumulation.

[0079] Further, the analyzing the airflow rate data corresponding to the ventilation demand can be achieved by a fluid dynamics simulation method, such as simulating the internal airflow field distribution of the charging pile using ANSYS Fluent software to obtain the airflow rate data; the dividing the dust retention area corresponding to the target charging pile can be achieved by a particle trajectory tracking algorithm, such as identifying the low-speed airflow area by using the Lagrangian particle tracking method combined with the CFD simulation result to obtain the dust retention area; the collecting the dust particle concentration in the dust retention area can be achieved by a laser scattering detection technology, such as using a TSIDustTrak aerosol monitor to measure the real-time particulate matter concentration to obtain the dust particle concentration; the analyzing the dust accumulation risk index corresponding to the dust particle concentration can be achieved by a weighted scoring model, such as constructing a risk assessment matrix combined with parameters such as particulate matter concentration, retention time, and equipment sensitivity to obtain the dust accumulation risk index; and the determining the dust prevention priority of the target charging pile can be achieved by a multi-criteria decision analysis method, such as comprehensively evaluating factors such as dust accumulation risk and maintenance cost by using the AHP hierarchical analysis method to obtain the dust prevention priority.

[0080] S2, based on the dust prevention priority, adjusting the ventilation regulation component corresponding to the target charging pile, performing performance detection on the ventilation regulation component to obtain component performance parameters, and calculating the airflow adjustment rate corresponding to the ventilation regulation component based on the component performance parameters.

[0081] Based on the dust prevention priority, the ventilation regulation component corresponding to the target charging pile is adjusted, which can achieve precise adaptation of dust prevention measures, thereby improving the dust prevention effect, avoiding energy waste caused by excessive intervention, and ensuring efficient and safe operation of the charging pile in different dust environments.

[0082] The ventilation regulation component refers to various hardware devices for adjusting the internal ventilation and dust prevention effect of the target charging pile, including variable-speed ventilation fans, intelligent louvers, self-cleaning filter screens, and guide vanes, etc. By controlling the operating parameters (such as fan speed, louver opening, etc.) of these components, the internal airflow distribution can be optimized, and the ventilation and dust prevention efficiency can be improved.

[0083] As an embodiment of the present application, based on the dust prevention priority, adjusting the ventilation regulation component corresponding to the target charging pile includes: detecting the ventilation grid interval corresponding to the target charging pile according to the dust prevention priority; dividing the ventilation grid nodes corresponding to the ventilation grid interval; analyzing the airflow disturbance amplitude corresponding to the ventilation grid nodes; screening low-efficiency grid nodes with airflow disturbance amplitude lower than a preset threshold; and adjusting the ventilation regulation component corresponding to the target charging pile according to the low-efficiency grid nodes.

[0084] The ventilation grid interval refers to a plurality of region sets divided according to certain rules inside the target charging pile, which is used for systematic analysis of internal airflow distribution and dust movement. For example, the inside of the charging pile can be divided into several cubic regions according to the length, width and height directions, and each region is a ventilation grid interval. The ventilation grid node refers to a representative monitoring or calculation position point selected in the ventilation grid interval, which is a key point for obtaining airflow data. Like the coordinate points in a three-dimensional coordinate system, by arranging sensors or performing simulation calculation at these nodes, the airflow velocity, pressure and other parameters at the position can be obtained to evaluate the ventilation state of the interval. The airflow disturbance amplitude refers to the fluctuation degree of airflow velocity, direction and other parameters at the ventilation grid node relative to the stable state. For example, at a certain node, the airflow velocity frequently changes in a short time, and the speed difference value is the airflow disturbance amplitude of the node. The larger the amplitude, the more unstable the airflow, which can affect dust diffusion and settlement. The preset threshold refers to a numerical standard for measuring whether the airflow disturbance amplitude meets the standard according to the ventilation requirements of the normal operation of the charging pile. When the airflow disturbance amplitude of the node is lower than the threshold, it indicates that the ventilation effect of the node is poor and needs to be further optimized. The low-efficiency grid node refers to the ventilation grid node whose airflow disturbance amplitude is lower than the preset threshold. Due to unstable airflow or too low flow rate, dust is prone to accumulate at this node, which cannot effectively realize the functions of ventilation, heat dissipation and dust prevention, and is the area that needs to be improved in ventilation regulation.

[0085] Further, the detection of the ventilation grid interval corresponding to the target charging pile can be realized by three-dimensional point cloud reconstruction technology, such as using a depth camera combined with a SLAM algorithm to construct a three-dimensional model of the internal space of the charging pile, thereby obtaining the ventilation grid interval. The division of the ventilation grid nodes corresponding to the ventilation grid interval can be realized by finite element mesh division method, such as using ANSYS Meshing tool to generate structured hexahedral mesh, thereby obtaining the ventilation grid nodes. The analysis of the airflow disturbance amplitude corresponding to the ventilation grid nodes can be realized by computational fluid dynamics simulation, such as calculating the velocity fluctuation standard deviation of each grid node based on k-ε turbulence model, thereby obtaining the airflow disturbance amplitude. The screening of the low-efficiency grid nodes with airflow disturbance amplitude lower than the preset threshold can be realized by image processing technology, such as applying OpenCV library to threshold segmentation and connected component analysis on the airflow field visualization result, thereby obtaining the low-efficiency grid nodes. The adjustment of the ventilation regulation component corresponding to the target charging pile can be realized by fuzzy logic control, such as establishing a Mamdani-type fuzzy controller based on airflow velocity and temperature feedback, thereby obtaining the ventilation regulation component.

[0086] The application can guarantee the stability of the dustproof effect of ventilation, early warn equipment failure, reduce the risk of sudden shutdown, prolong the service life of the component and improve the overall operation efficiency of the charging pile by detecting the performance of the ventilation regulation component to obtain component performance parameters.

[0087] The component performance parameters refer to quantitative indicators for measuring the running state and functional performance of the ventilation regulation component, covering component physical characteristics, operating parameters and energy efficiency performance, for example, the performance parameters of the variable-speed ventilation fan include real-time speed (rpm), air volume (m 3 / h), air pressure (Pa) and power consumption (kW); the parameters of the intelligent louver include opening percentage, response time (s) and sealing performance indicators; the parameters of the self-cleaning filter screen include filtration efficiency (interception rate for a specific particle size of dust), resistance (Pa) and cumulative use time, etc. These parameters are collected in real time by sensors and are the core basis for evaluating whether the component performance meets the standards and whether the regulation strategy is effective. Optionally, the performance detection of the ventilation regulation component can be realized by a dynamic response test method, such as using a step response test combined with a LabVIEW data acquisition system to measure the response time and overshoot of fan speed adjustment, thereby obtaining the component performance parameters.

[0088] Further, based on the component performance parameters, the ventilation regulation component corresponding air flow regulation rate is calculated, which can accurately match the real-time ventilation demand of the charging pile. By dynamically converting the actual air flow speed through fan speed, air pressure and other data, it ensures that the high-temperature or high-dust area obtains reasonable air volume, and realizes intelligent and accurate control of the heat management and dust prevention of the charging pile.

[0089] The air flow regulation rate refers to a quantitative indicator for measuring the regulation of air flow speed by the ventilation regulation component, reflecting the speed at which the component changes the air flow speed under specific working conditions, for example, when detecting a high-temperature area inside the charging pile, it is calculated by formula that the air flow rate at a certain place needs to be increased from the current 5 m / s to 8 m / s to enhance heat dissipation, and the "8 m / s" is the air flow regulation rate at this place.

[0090] As an embodiment of the application, based on the component performance parameters, the ventilation regulation component corresponding air flow regulation rate is calculated, including:

[0091] The ventilation regulation component corresponding air flow regulation rate is calculated by the following formula:

[0092]

[0093] V adjrepresents the airflow adjustment rate corresponding to the ventilation regulation component, n represents the number of categories corresponding to the ventilation regulation component, i represents the category index corresponding to the ventilation regulation component, a i represents the weight coefficient of the i-th category component, P i represents the performance parameter value of the i-th category component, R i represents the resistance-related parameter of the i-th category component, m represents the total number of component performance parameters, j represents the index of the number of component performance parameters, b j represents the influence coefficient corresponding to the j-th component performance parameter, D j represents the measured value of the j-th component performance parameter.

[0094] Further, the weight coefficient refers to a proportional coefficient (dimensionless, value 0-1) reflecting the relative importance of the ventilation regulation component or environmental parameter in airflow adjustment, for example, the fan plays a dominant role in airflow speed, and its weight coefficient can be set to 0.6; the filter screen mainly affects airflow resistance, and the weight coefficient is set to 0.3; the influence weight of environmental temperature on gas density can be set to 0.1; the performance parameter value refers to a specific value that can reflect the working state and performance of each category of ventilation regulation component, for example, for the fan, it can be wind pressure, air volume, speed, etc.; for the louver, it can be the opening size; for the filter screen, it can be the filtering efficiency, air permeability, etc. These values are basic data for evaluating component performance and calculating airflow adjustment rate; the resistance-related parameter refers to a parameter related to the resistance of the ventilation regulation component to airflow, such as the duct resistance coefficient of the fan, the resistance (unit: Pascal, Pa) of the filter screen, etc. This parameter reflects the degree of hindrance of the component to airflow, and the greater the resistance, the more obvious the change in airflow speed when passing through, which is an important factor in calculating the airflow adjustment rate; the influence coefficient refers to a coefficient for measuring the influence of each component performance parameter on the airflow adjustment rate, generally ranging from 0 to 1, and different component performance parameters will be assigned different influence coefficients due to their different effects on airflow adjustment; the parameter measured value refers to the real measurement value of the component state or environmental data collected by the sensor in real time, for example: the measured value of the fan wind pressure is 2000 Pa displayed by the current operating wind pressure sensor; the measured value of the environmental temperature is 303 K (30℃) detected by the temperature sensor inside the charging pile; the measured value of the filter screen resistance is 50 Pa fed back by the pressure difference sensor before and after the filter screen.

[0095] S3, detecting the dust prevention index corresponding to the airflow adjustment rate, calculating the ventilation energy consumption ratio corresponding to the target charging pile based on the dust prevention index and the real-time load index of the target charging pile, and constructing a multi-mode regulation architecture corresponding to the target charging pile based on the ventilation energy consumption ratio.

[0096] The application can judge the interception efficiency of the filter screen, whether the airflow can effectively block dust, and timely find hidden troubles such as filter screen blockage, so as to facilitate targeted maintenance, protect the internal cleanliness of the charging pile, avoid faults caused by dust accumulation, prolong the service life of the equipment, and ensure stable and reliable operation of the equipment.

[0097] The dustproof index refers to a quantitative index for measuring the dust blocking and filtering capacity of the ventilation control component under a specific airflow regulation rate. The higher the value, the better the dustproof effect. For example, in a specific charging pile ventilation system, when the airflow regulation rate is 5 m / s, if the dustproof index is 80%, it means that the system can intercept 80% of the dust of a specific particle size. The dustproof performance can be intuitively reflected. Optionally, the detection of the dustproof index corresponding to the airflow regulation rate can be realized by aerosol kinetics analysis, such as using particle image velocimetry (PIV) combined with a dust concentration sensor to quantify the particle deposition rate under different wind speeds, thereby obtaining the dustproof index.

[0098] Further, based on the dustproof index and the real-time load index of the target charging pile, the ventilation energy consumption ratio corresponding to the target charging pile is calculated, which can realize precise energy consumption management, facilitate optimization of ventilation strategy, avoid excessive ventilation energy consumption, reduce energy consumption cost under the premise of ensuring dustproof and equipment heat dissipation, and improve charging pile operation efficiency.

[0099] The real-time load index refers to a quantitative value reflecting the current workload of the target charging pile. It can intuitively show the real-time running state of the charging pile based on parameters such as charging pile output current and voltage. For example, when the real-time load index of a charging pile is 80%, it means that the current output power reaches 80% of the rated power, indicating that it is in a high load working state at this time. The ventilation energy consumption ratio refers to a quantitative index for measuring the energy consumption of the ventilation system and the comprehensive benefits obtained under a specific operating state of the target charging pile. It is calculated by correlating the dustproof index, real-time load index, ventilation energy consumption, and noise cost. The higher the value, the stronger the dustproof effect and load protection capability achieved per unit of energy consumption. For example, a ventilation energy consumption ratio of 50 means that 1 unit of electrical energy is consumed to achieve the equivalent of 50 units of dustproof and load performance comprehensive benefits.

[0100] As an embodiment of the application, the calculation of the ventilation energy consumption ratio corresponding to the target charging pile based on the dustproof index and the real-time load index of the target charging pile comprises:

[0101] The ventilation energy consumption ratio corresponding to the target charging pile is calculated using the following formula:

[0102]

[0103] E rrepresents the ventilation energy consumption ratio corresponding to the target charging pile, p represents the dust prevention index weight, DI represents the dust prevention index, s represents the load index weight, LI represents the real-time load index, g represents the energy consumption weight coefficient, t1 and t2 respectively represent the starting time and the ending time of the ventilation energy consumption period, P(t) represents the ventilation energy consumption function, d represents the noise weight coefficient, and NI represents the noise index.

[0104] In detail, the dust prevention index is a quantitative value reflecting the dust particle interception and filtration capability of the ventilation control assembly, usually presented in percentage form (0-100), which is calculated based on the dust concentration data inside and outside the charging pile. For example, by comparing the dust concentration outside the charging pile with the detected dust concentration inside, if the external dust concentration is 400 g / m 3 and the internal is 80 g / m 3The dust prevention index is (1-80 / 400) x 100 = 80, indicating that the ventilation system can intercept 80% of the dust; the load index weight refers to a coefficient for adjusting the importance of the real-time load index in the calculation of the ventilation energy consumption ratio, and the value range is between 0 and 1, and the sum of the dust prevention index weight (a) is 1, in the high load running scene, such as the charging pile in the high-power fast charging stage, the load index weight can be increased, and the heat dissipation demand is focused on; in the low load scene, the weight can be appropriately reduced; the energy consumption weight coefficient refers to the proportion of the ventilation system energy consumption in the calculation of the ventilation energy consumption ratio, which is usually a value greater than 0, which reflects the importance of energy consumption cost in the overall evaluation, and the greater the value, the greater the influence of energy consumption on the ventilation energy consumption ratio, for example, in the scene sensitive to energy consumption cost, the value of gamma can be increased to make the system preferentially reduce energy consumption; the ventilation energy consumption period refers to the time interval selected when calculating the ventilation energy consumption, which is determined by t1 and t2 in the formula, and the period can be set according to actual needs, a short period (such as 10 minutes) is suitable for quickly monitoring the real-time energy consumption change of the system; a long period is used to analyze the energy consumption trend and average energy consumption level of the charging pile in a long time running; the ventilation energy consumption function refers to the cumulative energy consumption calculation method of the ventilation control component in the ventilation energy consumption period, which takes time as the variable, and the real-time power P(t) of each component (such as the fan and the shutter motor) at each time point is integrated to obtain the total energy consumption in the period, for example, the speed change of the fan at different times causes the power fluctuation, and the function can accurately calculate the actual power consumption; the noise weight coefficient refers to a parameter for evaluating the influence of the running noise of the ventilation system on the ventilation energy consumption ratio, and the value is greater than or equal to 0, in the environment sensitive to noise (such as the charging pile near the residential area and the office area), the coefficient can be increased, so that the ventilation energy consumption ratio is reduced when the noise is high, thereby prompting the system to optimize the running parameters to reduce the noise; the noise index refers to an index for quantifying the noise level of the ventilation system in running, and the value range is 0-100, and the higher the value, the greater the noise, which is calculated by comparing the measured noise value with the environmental background noise and the noise limit, for example, the measured noise of a charging pile ventilation system is 65dB(A), the environmental background noise is 30dB(A), and the noise limit is 70dB(A), and the noise index is (65-30) / (70-30) x 100 = 87.5.

[0105] Based on the ventilation energy consumption ratio, the multi-mode control architecture corresponding to the target charging pile is constructed, the ventilation mode can be dynamically switched according to the energy consumption ratio, the energy consumption is reduced when the energy consumption is high, and the function is enhanced when the dust prevention or heat dissipation is insufficient, which can balance the energy consumption, dust prevention and heat dissipation of the charging pile in running, and improve the running efficiency and stability of the equipment.

[0106] The multi-mode regulation architecture is built based on multi-mode closed-loop data, and can switch multiple ventilation regulation modes according to the operating state of the charging pile. For example, it can switch to a powerful heat dissipation mode when the load is high and the temperature is high, and switch to a high-efficiency dust prevention mode when the dust is high. By integrating different modes, intelligent and accurate regulation and control of the ventilation of the charging pile is achieved.

[0107] As an embodiment of the present application, the multi-mode regulation architecture corresponding to the target charging pile is constructed based on the ventilation energy consumption ratio, including: extracting the dynamic fluctuation component in the ventilation energy consumption ratio; calculating the heat dissipation demand threshold corresponding to the target charging pile according to the dynamic fluctuation component; analyzing the fan speed gradient corresponding to the heat dissipation demand threshold; collecting multi-mode closed-loop data corresponding to the target charging pile according to the fan speed gradient; and constructing the multi-mode regulation architecture corresponding to the target charging pile based on the multi-mode closed-loop data.

[0108] The dynamic fluctuation component refers to the change part of the ventilation energy consumption ratio deviating from its mean value within a certain time, which reflects the real-time variation of the energy consumption ratio. For example, if the ventilation energy consumption ratio is originally stable at 50, and fluctuates between 45-55, the value change of 45-50 and 50-55 deviating from the mean value 50 is the dynamic fluctuation component, which is used to capture the dynamic characteristics of the energy consumption ratio. The heat dissipation demand threshold is a critical value calculated based on the dynamic fluctuation component of the ventilation energy consumption ratio, which measures the degree of heat dissipation demand of the target charging pile. When the temperature of the charging pile approaches or reaches this value, the heat dissipation needs to be strengthened. For example, if the heat dissipation demand threshold is calculated to be 40℃, when the internal temperature of the charging pile reaches 40℃, it means that the heat dissipation demand is urgent and the heat dissipation measures need to be improved. The fan speed gradient refers to the rate of change of the fan speed corresponding to the heat dissipation demand threshold. It represents the speed of the fan speed increase with the increase of the heat dissipation demand. For example, if the heat dissipation demand threshold increases by 1℃, the fan speed increases by 50 revolutions per minute, which is the fan speed gradient, reflecting the relationship between the speed and the heat dissipation demand. The multi-mode closed-loop data refers to a cycle data set formed by the feedback of the system operating state under multiple ventilation regulation modes, including fan speed, temperature, energy consumption, dust prevention index and other information. For example, in different ventilation modes, the real-time collected fan speed is 1000 revolutions per minute, the temperature is 35℃, the energy consumption is 100 watts, and the dust prevention index is 70%. The multi-mode closed-loop data is used for regulation and control architecture optimization.

[0109] Further, the extracting the dynamic fluctuation component in the ventilation energy consumption ratio can be achieved by a signal decomposition algorithm, such as: using empirical mode decomposition (EMD) or wavelet transform to separate the time-frequency characteristics of the energy consumption signal, so as to obtain the dynamic fluctuation component; the calculating the heat dissipation demand threshold corresponding to the target charging pile can be achieved by a heat balance equation model, such as: establishing a mathematical model containing charging power, environmental temperature and heat dissipation efficiency based on the law of conservation of energy, so as to obtain the heat dissipation demand threshold; the analyzing the fan speed gradient corresponding to the heat dissipation demand threshold can be achieved by optimal control theory, such as: applying Pontryagin minimum principle to solve the speed regulation curve of optimal energy consumption, so as to obtain the fan speed gradient; the collecting the multi-mode closed-loop data corresponding to the target charging pile can be achieved by an Internet of Things sensing network, such as: deploying Zigbee wireless sensor nodes to synchronously collect temperature, wind speed and energy consumption data, so as to obtain the multi-mode closed-loop data; the constructing the multi-mode regulation and control architecture corresponding to the target charging pile can be achieved by a hierarchical control strategy, such as: designing a three-level control framework containing a bottom execution layer, an intermediate control layer and an upper decision layer, so as to obtain the multi-mode regulation and control architecture.

[0110] Specifically, to further intuitively understand the multi-mode regulation and control architecture of the target charging pile in the present application, reference can be made to the image shown in Figure 2 The image shown is a multi-mode regulation and control architecture diagram provided by the present application. It should be noted that in the present application, Figure 2 The architecture diagram presented is only used to show the hierarchical structure corresponding to the multi-mode regulation and control architecture of the target charging pile, wherein the hierarchical structure can be divided into a bottom execution layer, an intermediate control layer and an upper decision layer. The bottom execution layer contains parameters such as fan speed and deflector angle, which affect the power loss caused by ventilation energy consumption. The intermediate control layer receives bottom power loss information and calculates energy efficiency coefficient in combination with temperature field index. The upper decision layer makes decision instructions such as adjusting energy consumption distribution based on energy efficiency coefficient and other information from the intermediate control layer. The levels in this architecture diagram are closely related to the content of constructing the multi-mode regulation and control architecture based on ventilation energy consumption ratio described above, intuitively presenting the regulation and control process described above, and are not limited to the relationship analysis of monitoring the multi-mode regulation and control architecture in actual different application scenarios.

[0111] S4, based on the multi-mode regulation and control architecture, logically regulating the ventilation regulation and control component to obtain component ventilation logic, analyzing the airflow coverage range corresponding to the component ventilation logic, and querying the airflow optimization path in the airflow coverage range.

[0112] The application can realize intelligent switching and accurate matching of the ventilation strategy by logically regulating the ventilation regulation component based on the multi-mode regulation architecture to obtain component ventilation logic, and can dynamically balance the heat dissipation, dust prevention and energy consumption demand of the charging pile by analyzing the regulation data under different modes, positioning the core ventilation node and optimizing the start-stop timing, to ensure stable and efficient operation of the charging pile in multiple scenarios.

[0113] The component ventilation logic refers to the overall planning of the operation rules of the ventilation regulation component based on the ventilation regulation mode, mode regulation data, etc. For example, when the charging pile is in a low-load and low-dust environment, the fan runs at low speed and the filter screen is cleaned regularly according to the energy-saving ventilation logic; when the load is high, the strong heat dissipation logic is switched to enhance ventilation and heat dissipation.

[0114] As an embodiment of the application, the logical regulation of the ventilation regulation component based on the multi-mode regulation architecture to obtain component ventilation logic includes: analyzing the ventilation regulation mode corresponding to the multi-mode regulation architecture; querying the mode regulation data under the ventilation regulation mode; positioning the core ventilation node in the ventilation regulation component based on the mode regulation data; integrating the ventilation start-stop timing corresponding to the core ventilation node; logically regulating the ventilation regulation component based on the ventilation start-stop timing to obtain component ventilation logic.

[0115] The ventilation regulation mode refers to different ventilation strategy types preset in the multi-mode regulation architecture, for example, the strong heat dissipation mode is used to cope with high-load heating of the charging pile; the efficient dust prevention mode is used in a dusty environment, and the charging pile is switched between different modes according to real-time conditions such as temperature, load index, and dust prevention index, to ensure stable operation of the equipment; the mode regulation data refers to a set of specific parameter information corresponding to each ventilation regulation mode, for example, in the strong heat dissipation mode, the data includes the maximum speed of the fan and the best angle of the guide vane; in the efficient dust prevention mode, the data includes the cleaning period of the filter screen and the suitable flow rate of the airflow, which guides the ventilation regulation component to accurately operate according to the corresponding mode; the core ventilation node refers to a component or position in the ventilation regulation component that plays a key role in ventilation effect. For example, the fan is a key component that provides airflow, and the ventilation port is an important position for airflow to enter and exit, both of which are core ventilation nodes, and the working state of the core ventilation node will be adjusted accordingly in different ventilation regulation modes; the ventilation start-stop timing refers to the time sequence and time interval arrangement of the core ventilation node under a specific ventilation regulation mode, for example, during the daytime high-temperature period, the fan is temporarily stopped for self-checking every 2 hours, which is a kind of ventilation start-stop timing. Reasonable timing can optimize the ventilation effect and reduce energy consumption.

[0116] Further, the parsing of the corresponding ventilation regulation mode of the multi-mode regulation architecture can be achieved by a pattern recognition algorithm, such as using K-means clustering analysis to analyze the characteristic parameter combination in the historical operation data, so as to obtain the ventilation regulation mode; the query of the mode regulation data under the ventilation regulation mode can be achieved by time series database retrieval, such as using InfluxQL query language to extract time series data such as temperature and wind speed under a specific mode from InfluxDB, so as to obtain the mode regulation data; the positioning of the core ventilation node in the ventilation regulation component can be achieved by a graph theory analysis method, such as constructing a directed graph model of the ventilation network based on the NetworkX library, and calculating the node betweenness centrality, so as to obtain the core ventilation node; the integration of the ventilation start-stop time sequence corresponding to the core ventilation node can be achieved by discrete event simulation, such as using the SimPy framework to simulate the start-stop event sequence of different nodes, and optimizing the time window overlap degree, so as to obtain the ventilation start-stop time sequence; the logical regulation of the ventilation regulation component can be achieved by a programmable logic controller, such as using PLC ladder diagram programming to realize the interlocking control logic based on sensor feedback, so as to obtain the component ventilation logic.

[0117] By analyzing the airflow coverage range corresponding to the component ventilation logic, the present application can determine whether the airflow can fully cover the key parts of the charging pile, avoid local overheating or dust accumulation, and optimize the ventilation logic, adjust the fan speed, the angle of the deflector, and other parameters, to ensure reasonable airflow distribution, improve the heat dissipation and dust prevention capability of the charging pile, and ensure stable and efficient operation.

[0118] The airflow coverage range refers to the spatial area that the airflow can effectively reach and act on when the ventilation regulation component is running, which measures the flow breadth and depth of the airflow inside and around the charging pile. For example, in a certain charging pile ventilation system, after the fan is turned on, the airflow can cover 80% of the space inside the charging pile, including key parts such as charging modules and battery modules. This 80% of the space is the airflow coverage range under the ventilation logic. Optionally, the analysis of the airflow coverage range corresponding to the component ventilation logic can be achieved by computational fluid dynamics simulation, such as using ANSYS Fluent software to establish a three-dimensional turbulent flow model and drawing an airflow trajectory distribution map through particle tracking technology, so as to obtain the airflow coverage range.

[0119] Further, by querying the airflow optimization path in the airflow coverage range, the present application can analyze the path resistance, flow velocity distribution, and other parameters, and adjust the ventilation component parameters (such as fan speed and deflector angle) accordingly, to reduce vortex and dead angles, enhance the heat dissipation and dust prevention effect of the charging pile, thereby prolonging the service life of the equipment and ensuring the operation stability.

[0120] The airflow optimization path refers to a flow path designed for an airflow disturbance trajectory, which can reduce turbulence loss and improve airflow uniformity. It is achieved by adjusting the component layout (such as translating the battery module) or parameters (such as increasing the fan speed). For example, after adjusting the angle of the deflector plate in the high turbulence area from 30° to 45°, the airflow disturbance trajectory changes from spiral to straight line, forming a more efficient "Z" shaped optimization path, covering the originally stagnant corner area.

[0121] As an embodiment of the present application, the query of the airflow optimization path in the airflow coverage range includes: determining the high turbulence area corresponding to the airflow coverage range; extracting the vortex intensity index in the high turbulence area; calculating the airflow disturbance coefficient of the high turbulence area based on the vortex intensity index; analyzing the airflow disturbance trajectory corresponding to the airflow disturbance coefficient; and querying the airflow optimization path in the airflow coverage range based on the airflow disturbance trajectory.

[0122] The high turbulence area refers to an area in the airflow coverage range where the airflow flow is turbulent, the speed and direction change dramatically, and is usually caused by obstacles (such as charging modules, line arrangement) or flow rate difference. For example, due to the narrow space behind the battery module inside the charging pile, the airflow is prone to turbulence, and this area is a high turbulence area, which can cause local insufficient heat dissipation or dust accumulation. The vortex intensity index refers to a dimensionless index that quantifies the strength of the airflow vortex (rotating airflow) in the high turbulence area. It is calculated by flow rate gradient and pressure gradient. The larger the value, the stronger the vortex energy and the higher the airflow turbulence. For example, if the vortex intensity index of the high turbulence area is 0.8 (full value 1), it indicates that there is strong vortex in this area, which can cause airflow stagnation and energy loss. The airflow disturbance coefficient refers to a parameter that measures the influence of airflow disturbance on ventilation efficiency by considering factors such as vortex intensity and turbulence scale. The larger the parameter, the more significant the airflow disturbance. For example, when the vortex intensity index is 0.6 and the area ratio is 15%, the airflow disturbance coefficient is 0.09, indicating that this area has a moderate impact on the overall airflow uniformity. The airflow disturbance trajectory refers to the actual movement path of the airflow in the high turbulence area due to disturbance. It usually presents an irregular curve or vortex shape. For example, in the case of unreasonable deflector plate angle, the airflow disturbance trajectory presents a spiral vortex, causing the airflow in the corner area of the charging pile to be unable to reach effectively.

[0123] Further, the determination of the high-turbulence region corresponding to the airflow coverage range can be achieved by a vorticity identification algorithm, such as: using Q-criterion (Q-criterion) to extract the vortex structure of the CFD simulation results, calculating the local vorticity intensity threshold, and thus obtaining the high-turbulence region; the extraction of the vortex intensity index in the high-turbulence region can be achieved by a vortex dynamics analysis method, such as: calculating the eigenvalues of the velocity gradient tensor based on the λ2 criterion, quantifying the rotation intensity of the vortex core region, and thus obtaining the vortex intensity index; the calculation of the airflow disturbance coefficient of the high-turbulence region can be achieved by a turbulence statistical method, such as: using the Reynolds stress decomposition method to analyze the root mean square value of the velocity fluctuation, establishing a turbulence intensity distribution model, and thus obtaining the airflow disturbance coefficient; the analysis of the airflow disturbance trajectory corresponding to the airflow disturbance coefficient can be achieved by Lagrangian particle tracking, such as: using the icoLagrangian particle tracking module in OpenFOAM to simulate the motion path of the tracer particles, and thus obtaining the airflow disturbance trajectory; the query of the airflow optimization path in the airflow coverage range can be achieved by a streamline topological optimization algorithm, such as: applying Dijkstra algorithm to search for the minimum resistance path in the velocity field, combining with the wall distance constraint to generate the optimal streamline, and thus obtaining the airflow optimization path.

[0124] S5, monitor the dust deposition state in the airflow optimization path, based on the dust deposition state, dynamically adjust the air control index corresponding to the ventilation control component, identify the high-efficiency control node in the air control index, and develop an adaptive dust prevention scheme corresponding to the target charging pile based on the high-efficiency control node.

[0125] The present application can judge the problems such as filter screen load and airflow dead angle by monitoring the dust deposition state in the airflow optimization path, facilitate timely cleaning and maintenance or adjustment of path parameters, thereby avoiding dust blockage of key components, improving the dust prevention performance of the charging pile, prolonging the service life of the equipment and ensuring the stable operation of the charging pile in a high-dust environment.

[0126] The dust deposition state refers to the adhesion and accumulation of dust particles in the airflow optimization path, including deposition position, thickness, particle size distribution and other characteristics. It reflects the actual blocking ability of the ventilation system to dust and the airflow cleaning effect, for example, at the edge of the guide plate downstream of the charging pile filter screen, if an average thickness of 0.5mm of dust accumulation is monitored, and the particle size is mainly 5-10μm, it indicates that this area has formed a deposition hot spot due to the reduction of airflow velocity, and the airflow path needs to be adjusted or the filter screen cleaning frequency needs to be increased. Alternatively, the monitoring of the dust deposition state in the airflow optimization path can be achieved by laser-induced breakdown spectroscopy technology, such as: using a LIBS sensor to analyze the elemental composition of the surface of the key path, identifying the dust deposition thickness through characteristic spectra, and thus obtaining the dust deposition state.

[0127] Further, based on the dust deposition state, the application dynamically adjusts the corresponding air control index of the ventilation regulation component, optimizes the energy efficiency of the ventilation system, reduces unnecessary energy consumption, enhances the adaptability of the charging pile in a complex dust environment, reduces the risk of failure, prolongs the maintenance cycle, and ensures long-term stable operation.

[0128] The air control index refers to a key parameter for adjusting the operating state of the ventilation system, including fan speed, airflow speed, air pressure, filter cleaning frequency, etc. For example, when the dynamic air pressure threshold is triggered, the air control index is adjusted as follows: the fan speed is increased from 1500 rpm to 2000 rpm, the airflow speed is increased from 5 m / s to 8 m / s, and the filter backwashing program is started (once every 2 hours) to remove dust in the high accumulation area.

[0129] As an embodiment of the application, based on the dust deposition state, the dynamic adjustment of the corresponding air control index of the ventilation regulation component includes: analyzing the deposition thickness distribution data corresponding to the dust deposition state; determining the dust accumulation level corresponding to the deposition thickness distribution data; analyzing the ventilation braking gradient corresponding to the dust accumulation level; matching the dynamic air pressure threshold corresponding to the ventilation braking gradient; and dynamically adjusting the corresponding air control index of the ventilation regulation component based on the dynamic air pressure threshold.

[0130] The deposition thickness distribution data refers to a set of quantitative data of dust deposition thickness at each monitoring point in the airflow optimization path, obtained by laser ranging or weighing sensors, for example, the dust thickness of 0.3 mm, 0.5 mm and 0.1 mm is measured on the filter screen, flow guide plate and charging module surface respectively, forming the distribution data of "filter screen > flow guide plate > module", which directly reflects the difference of dust accumulation at different positions; the dust accumulation level refers to the severity level of dust accumulation divided according to the deposition thickness distribution data, which is usually divided into three levels: low (<0.2 mm), medium (0.2-0.5 mm) and high (>0.5 mm), for example, if the deposition thickness of the flow guide plate is 0.6 mm, it is determined as "high accumulation level", which indicates that the dust in this area has significantly affected the airflow circulation and needs emergency intervention; the ventilation braking gradient refers to the airflow velocity decay rate that the ventilation system needs to adjust corresponding to the dust accumulation level, in order to flush the deposited dust, the flow velocity needs to be increased (negative gradient, such as +5 m / s·level) under high accumulation level; the flow velocity can be reduced to save energy (positive gradient, such as -3 m / s·level) under low accumulation level, for example, when the dust accumulation level rises from "medium" to "high", the ventilation braking gradient is +4 m / s·level, that is, the flow velocity needs to be increased by 4 m / s; the dynamic wind pressure threshold refers to the real-time wind pressure critical value set according to the ventilation braking gradient and the pressure bearing capacity of the equipment, which is used to control the power output of the fan, for example, when the dust accumulation level is "high" and the ventilation braking gradient requires the flow velocity to increase to 8 m / s, the calculated dynamic wind pressure threshold is 200 Pa, if the measured wind pressure is lower than the value, the fan automatically increases the power until it meets the standard.

[0131] Further, the analysis of the dust deposition state corresponding to the deposition thickness distribution data can be realized by laser triangulation technology, such as: using LVDT displacement sensor array to scan the surface profile, combined with Kalman filter denoising processing, so as to obtain the deposition thickness distribution data; the determination of the dust accumulation level corresponding to the deposition thickness distribution data can be realized by density clustering algorithm, such as: applying OPTICS algorithm to identify the spatial density characteristics of thickness data, dividing three accumulation areas, so as to obtain the dust accumulation level; the analysis of the ventilation braking gradient corresponding to the dust accumulation level can be realized by computational fluid-particle coupling simulation, such as: simulating the particle resuspension critical value under different wind speeds based on DEM-CFD coupling method, so as to obtain the ventilation braking gradient; the matching of the dynamic wind pressure threshold corresponding to the ventilation braking gradient can be realized by reinforcement learning algorithm, such as: using DDPG algorithm to train the dynamic balance strategy of wind pressure and deposition rate, so as to obtain the dynamic wind pressure threshold; the dynamic adjustment of the wind control index corresponding to the ventilation regulation component can be realized by model predictive control, such as: establishing a MPC controller containing deposition state feedback, optimizing the speed-flow parameter every 10 seconds, so as to obtain the wind control index.

[0132] The application can shorten the response time of regulation and control, improve the system sensitivity, reduce the energy waste caused by invalid parameter adjustment, and ensure the efficient and stable operation of the charging pile under complex working conditions by identifying the high-efficiency regulation and control nodes in the air control index.

[0133] The high-efficiency regulation and control node refers to a key parameter or component point that plays a decisive role in dust deposition control and airflow efficiency optimization in the air control index system of the ventilation system. These nodes have the characteristics of high regulation sensitivity and wide influence range. Small adjustments can significantly change the overall ventilation efficiency. For example, when the charging pile filter screen is blocked, the fan speed (directly affecting the wind pressure) and the angle of the main ventilation opening guide plate (changing the airflow direction) are high-efficiency regulation and control nodes. Optionally, the identification of the high-efficiency regulation and control nodes in the air control index can be achieved by graph theory network analysis, such as using the PageRank algorithm to sort the influence of the ventilation network nodes, selecting the top 20% high-influence nodes as the key regulation points, and obtaining the high-efficiency regulation and control nodes.

[0134] Further, based on the high-efficiency regulation and control nodes, the application formulates an adaptive dust prevention scheme corresponding to the target charging pile, which can accurately focus on the key links of the ventilation system and dynamically adjust the fan speed, guide plate angle and other core parameters to respond to dust deposition changes in real time, effectively reduce the risk of filter screen blockage, avoid equipment failures caused by dust accumulation, ensure the stable operation of the target charging pile in different dust environments, and prolong the service life of the equipment.

[0135] The adaptive dust prevention scheme refers to an intelligent dust prevention strategy that automatically adjusts the ventilation parameters based on high-efficiency regulation and control nodes, combines real-time dust deposition status and environmental changes, and dynamically matches dust concentration, particle size and other conditions to accurately control airflow velocity, wind pressure and filter screen cleaning frequency. For example, when a high-dust environment (such as a construction site) is monitored, the scheme automatically increases the fan speed to 2200 rpm, increases the main ventilation opening wind pressure to 250 Pa, and shortens the filter screen backflushing period to once every hour, forming an adaptive mode of "strong airflow flushing + high-frequency cleaning", effectively intercepting more than 90% of dust particles, while avoiding excessive energy consumption in low-dust scenarios. Optionally, the adaptive dust prevention scheme corresponding to the target charging pile can be obtained by using the remaining service life prediction, such as using the ConvLSTM network to analyze historical operation data to predict the filter screen replacement period.

[0136] Compared with the problems described in the background art, the application can obtain the in-pile detection environment corresponding to the target charging pile, can master the key information such as the internal temperature, humidity and dust concentration of the charging pile in real time, can accurately identify the potential overheating and dust accumulation risk, can provide data support for dynamically adjusting the ventilation and dust prevention strategy, can significantly improve the ventilation efficiency and reduce the energy consumption, can early warn the performance degradation or fault hidden danger of electrical components, the application adjusts the ventilation regulation component corresponding to the target charging pile based on the dust prevention priority, can realize the accurate adaptation of the dust prevention measures, thereby improving the dust prevention effect, avoiding the energy consumption waste caused by excessive intervention, ensuring that the charging pile can operate efficiently and safely in different dust environments, further, the application can judge the filter screen interception efficiency and whether the airflow can effectively block dust by detecting the dust prevention index corresponding to the airflow regulation rate, can find the hidden troubles such as filter screen blockage in time, is beneficial to targeted maintenance, ensures the cleanliness inside the charging pile, avoids the fault caused by dust accumulation, prolongs the service life of the equipment, and ensures the stable and reliable operation of the equipment, further, the application obtains the component ventilation logic by logically regulating the ventilation regulation component based on the multi-mode regulation architecture, can realize the intelligent switching and accurate matching of the ventilation strategy, can dynamically balance the heat dissipation, dust prevention and energy consumption demand of the charging pile by analyzing the regulation data in different modes, positioning the core ventilation node and optimizing the start-stop timing, ensures the stable and efficient operation of the charging pile in multiple scenarios, finally, the application can judge the filter screen load and airflow dead angle by monitoring the dust deposition state in the airflow optimization path, is convenient for timely cleaning and maintenance or adjustment of path parameters, thereby avoiding dust blocking of key components, improving the dust prevention performance of the charging pile, prolonging the service life of the equipment and ensuring the stable operation of the charging pile in high dust environment. Therefore, the charging pile efficient ventilation and dust prevention method and system based on intelligent sensing regulation provided by the embodiment of the application can improve the reliability and safety of the operation of the charging pile.

[0137] Embodiment 2:

[0138] As Figure 3 shown, it is a functional module diagram of the charging pile efficient ventilation and dust prevention system based on intelligent sensing regulation of the application.

[0139] The charging pile efficient ventilation and dust prevention system based on intelligent sensing regulation 200 can be installed in an electronic device. According to the realized function, the charging pile efficient ventilation and dust prevention system based on intelligent sensing regulation can include a priority determination module 201, a rate calculation module 202, an architecture construction module 203, a path query module 204 and a scheme development module 205. The modules of the application can also be called units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0140] In the embodiments of the present application, the functions of each module / unit are as follows:

[0141] The priority determination module 201 is configured to obtain an in-pile detection environment corresponding to a target charging pile, analyze ventilation requirements corresponding to the target charging pile based on the in-pile detection environment, and determine a dust prevention priority corresponding to the target charging pile according to the ventilation requirements.

[0142] The rate calculation module 202 is configured to adjust a ventilation regulation component corresponding to the target charging pile based on the dust prevention priority, perform performance detection on the ventilation regulation component, obtain a component performance parameter, and calculate an air flow regulation rate corresponding to the ventilation regulation component based on the component performance parameter.

[0143] The architecture construction module 203 is configured to detect a dust prevention index corresponding to the air flow regulation rate, calculate a ventilation energy consumption ratio corresponding to the target charging pile based on the dust prevention index in combination with a real-time load index of the target charging pile, and construct a multi-mode regulation architecture corresponding to the target charging pile based on the ventilation energy consumption ratio.

[0144] The path query module 204 is configured to perform logical regulation on the ventilation regulation component based on the multi-mode regulation architecture, obtain a component ventilation logic, analyze an air flow coverage range corresponding to the component ventilation logic, and query an air flow optimization path in the air flow coverage range.

[0145] The scheme formulation module 205 is configured to monitor a dust deposition state in the air flow optimization path, dynamically adjust a wind control index corresponding to the ventilation regulation component based on the dust deposition state, identify an efficient regulation node in the wind control index, and formulate an adaptive dust prevention scheme corresponding to the target charging pile based on the efficient regulation node.

[0146] In detail, the modules in the charging pile efficient ventilation dust prevention system 200 based on intelligent sensing regulation in the embodiments of the present application adopt the same technical means as the charging pile efficient ventilation dust prevention method based on intelligent sensing regulation in the above-mentioned Figure 1 , and can produce the same technical effects, which will not be described here.

[0147] It is obvious for those skilled in the art that the present application is not limited to the details of the above-mentioned exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0148] Finally, it should be noted that the above examples are merely intended to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for efficient ventilation and dust prevention in charging piles based on intelligent sensor control, characterized in that, The method includes: Obtain the internal detection environment of the target charging pile, analyze the ventilation requirements of the target charging pile based on the internal detection environment, and determine the dust prevention priority of the target charging pile according to the ventilation requirements. Based on the dust prevention priority, the ventilation control component corresponding to the target charging pile is adjusted, the performance of the ventilation control component is tested to obtain the component performance parameters, and the airflow regulation rate corresponding to the ventilation control component is calculated based on the component performance parameters. Detect the dust prevention index corresponding to the airflow adjustment rate, and calculate the ventilation energy consumption ratio corresponding to the target charging pile based on the dust prevention index and the real-time load index of the target charging pile. The calculation of the ventilation energy consumption ratio corresponding to the target charging pile based on the dust prevention index and the real-time load index of the target charging pile includes: The ventilation energy consumption ratio corresponding to the target charging pile is calculated using the following formula: ； in, This indicates the ventilation energy consumption ratio corresponding to the target charging pile. Indicates the weight of the dustproof index. This indicates the dustproof index. Indicates the load index weight. This represents the real-time load index. This represents the energy consumption weighting coefficient. and These represent the start and end times of the ventilation energy consumption cycle, respectively. This represents the ventilation energy consumption function. This represents the noise weighting coefficient. The noise level is represented; based on the ventilation energy consumption ratio, a multi-mode control architecture corresponding to the target charging pile is constructed, wherein the construction of the multi-mode control architecture corresponding to the target charging pile based on the ventilation energy consumption ratio includes: Extract the dynamic fluctuation component from the ventilation energy consumption ratio; Based on the dynamic fluctuation components, calculate the heat dissipation demand threshold corresponding to the target charging pile; Analyze the fan speed gradient corresponding to the aforementioned heat dissipation demand threshold; Based on the wind turbine speed gradient, collect multi-mode closed-loop data corresponding to the target charging pile; Based on the multi-mode closed-loop data, a multi-mode control architecture corresponding to the target charging pile is constructed; Based on the multi-mode control architecture, the ventilation control component is logically controlled to obtain the component ventilation logic. The airflow coverage range corresponding to the component ventilation logic is analyzed, and the airflow optimization path within the airflow coverage range is queried. Monitor the dust deposition status in the optimized airflow path, dynamically adjust the wind control index corresponding to the ventilation control component based on the dust deposition status, identify the high-efficiency control node in the wind control index, and formulate an adaptive dust prevention scheme for the target charging pile based on the high-efficiency control node.

2. The efficient ventilation and dust prevention method for charging piles based on intelligent sensor control as described in claim 1, characterized in that, The step of determining the dust prevention priority corresponding to the target charging pile based on the ventilation requirements includes: Analyze the airflow rate data corresponding to the ventilation requirements; Based on the airflow rate data, the dust retention area corresponding to the target charging pile is divided; The concentration of dust particles in the dust retention area was collected; Analyze the dust accumulation risk index corresponding to the dust particle concentration; Based on the dust accumulation risk index, the dust prevention priority corresponding to the target charging pile is determined.

3. The efficient ventilation and dust prevention method for charging piles based on intelligent sensor control as described in claim 1, characterized in that, The method of adjusting the ventilation control component corresponding to the target charging pile based on the dust prevention priority includes: Based on the dust prevention priority, detect the ventilation grid interval corresponding to the target charging pile; The ventilation grid nodes are divided into the ventilation grid intervals. Analyze the airflow disturbance amplitude corresponding to the ventilation grid nodes; Inefficient grid nodes whose airflow disturbance amplitude is below a preset threshold are filtered out; Adjust the ventilation control components corresponding to the target charging pile based on the inefficient grid nodes.

4. The efficient ventilation and dust prevention method for charging piles based on intelligent sensor control as described in claim 1, characterized in that, The step of calculating the airflow regulation rate corresponding to the ventilation control component based on the component performance parameters includes: The airflow regulation rate corresponding to the ventilation control component is calculated using the following formula: ； in, This indicates the airflow regulation rate corresponding to the ventilation control component. This indicates the number of categories corresponding to the ventilation control components. This indicates the category index corresponding to the ventilation control component. Indicates the first The weight coefficient of class components, Indicates the first Performance parameter values ​​of class components, Indicates the first Resistance-related parameters of class components, This represents the total number of parameters that indicate the performance parameters of the component. This represents the index indicating the number of performance parameters of the component. Indicates the first The influence coefficients corresponding to the performance parameters of each component Indicates the first The measured values ​​of the performance parameters corresponding to each component.

5. The efficient ventilation and dust prevention method for charging piles based on intelligent sensor control as described in claim 1, characterized in that, The step of performing logical control on the ventilation control component based on the multi-mode control architecture to obtain the component ventilation logic includes: Analysis of the ventilation control modes corresponding to the multi-mode control architecture; Query the mode control data under the aforementioned ventilation control mode; Based on the mode control data, locate the core ventilation node in the ventilation control component; Integrate the ventilation start / stop sequence corresponding to the core ventilation nodes; Based on the ventilation start-stop timing, the ventilation control component is logically controlled to obtain the component ventilation logic.

6. The efficient ventilation and dust prevention method for charging piles based on intelligent sensor control as described in claim 1, characterized in that, The querying of the optimized airflow path within the airflow coverage area includes: Determine the high turbulence region corresponding to the airflow coverage area; Extract the vortex intensity index in the highly turbulent region; Based on the vortex intensity index, the airflow disturbance coefficient of the highly turbulent region is calculated; Analyze the airflow disturbance trajectory corresponding to the airflow disturbance coefficient; Based on the airflow disturbance trajectory, query the optimized airflow path within the airflow coverage area.

7. The efficient ventilation and dust prevention method for charging piles based on intelligent sensor control as described in claim 1, characterized in that, The dynamic adjustment of the air control index corresponding to the ventilation control component based on the dust deposition state includes: Analyze the deposition thickness distribution data corresponding to the dust deposition state; Determine the dust accumulation level corresponding to the deposition thickness distribution data; Analyze the ventilation braking gradient corresponding to the dust accumulation level; Match the dynamic wind pressure threshold corresponding to the ventilation braking gradient; Based on the dynamic wind pressure threshold, the wind control index corresponding to the ventilation control component is dynamically adjusted.

8. A high-efficiency ventilation and dust prevention system for charging piles based on intelligent sensor control, characterized in that, The system includes: The priority determination module is used to obtain the in-pile detection environment corresponding to the target charging pile, analyze the ventilation requirements corresponding to the target charging pile based on the in-pile detection environment, and determine the dust prevention priority corresponding to the target charging pile according to the ventilation requirements. The rate calculation module is used to adjust the ventilation control component corresponding to the target charging pile based on the dust prevention priority, perform performance testing on the ventilation control component to obtain component performance parameters, and calculate the airflow regulation rate corresponding to the ventilation control component based on the component performance parameters. An architecture construction module is used to detect the dust prevention index corresponding to the airflow adjustment rate, and calculate the ventilation energy consumption ratio corresponding to the target charging pile based on the dust prevention index and the real-time load index of the target charging pile. The calculation of the ventilation energy consumption ratio corresponding to the target charging pile based on the dust prevention index and the real-time load index of the target charging pile includes: The ventilation energy consumption ratio corresponding to the target charging pile is calculated using the following formula: ； in, This indicates the ventilation energy consumption ratio corresponding to the target charging pile. Indicates the weight of the dustproof index. This indicates the dustproof index. Indicates the load index weight. This represents the real-time load index. This represents the energy consumption weighting coefficient. and These represent the start and end times of the ventilation energy consumption cycle, respectively. This represents the ventilation energy consumption function. This represents the noise weighting coefficient. The noise index is represented by the ventilation energy consumption ratio. A multi-mode control architecture corresponding to the target charging pile is constructed based on this ratio. The construction of the multi-mode control architecture based on the ventilation energy consumption ratio includes: Extract the dynamic fluctuation component from the ventilation energy consumption ratio; Based on the dynamic fluctuation components, calculate the heat dissipation demand threshold corresponding to the target charging pile; Analyze the fan speed gradient corresponding to the aforementioned heat dissipation demand threshold; Based on the wind turbine speed gradient, collect multi-mode closed-loop data corresponding to the target charging pile; Based on the multi-mode closed-loop data, a multi-mode control architecture corresponding to the target charging pile is constructed; The path query module is used to perform logical control on the ventilation control component based on the multi-mode control architecture, obtain the component ventilation logic, analyze the airflow coverage range corresponding to the component ventilation logic, and query the optimized airflow path within the airflow coverage range. The scheme formulation module is used to monitor the dust deposition status in the airflow optimization path, dynamically adjust the wind control index corresponding to the ventilation control component based on the dust deposition status, identify the high-efficiency control node in the wind control index, and formulate an adaptive dust prevention scheme corresponding to the target charging pile based on the high-efficiency control node.

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