Charging pile efficient ventilation and dust prevention method and system based on intelligent sensing regulation and control

Abstract

The invention relates to the technical field of intelligent dust prevention, and discloses a charging pile efficient ventilation and dust prevention method and system based on intelligent sensing regulation and control, and the method comprises the steps: firstly obtaining a detection environment in a pile to analyze a ventilation demand, determining a dust prevention priority, adjusting a ventilation regulation and control assembly according to the dust prevention priority, detecting the performance, and calculating the airflow regulation rate; a ventilation energy consumption ratio is calculated by combining a dustproof index and a real-time load index, and a multi-mode regulation and control framework is constructed; regulating and controlling the component logic based on the architecture to obtain component ventilation logic, analyzing an airflow coverage range and querying an optimization path; and finally, monitoring a dust deposition state, dynamically adjusting a wind control index, and identifying an efficient regulation and control node so as to formulate a self-adaptive dust prevention scheme. According to the invention, the reliability and safety of the operation of the charging pile can be improved.

Description

Technical Field

[0001] The present invention relates to a method and system for efficient ventilation and dust prevention of charging piles based on intelligent sensing regulation, belonging to the field of intelligent dust prevention technology. Background Art

[0002] Charging piles are infrastructure equipment that provide electrical energy replenishment for electric vehicles. They are usually divided into AC charging piles (slow charging) and DC charging piles (fast charging). Since charging piles are long-term exposed to complex outdoor environments, the internal electrical components are prone to performance attenuation or failure due to dust accumulation and high temperature.

[0003] Currently, the ventilation and dust prevention of charging piles mostly adopt the method of combining traditional mechanical ventilation fans with filters, and ventilation is carried out by timed operation or manual control. The filters also need to be replaced regularly to maintain the dust prevention effect. However, this method cannot accurately regulate according to the dynamic changes such as the real-time operating temperature of the charging pile and the ambient dust concentration, and there are problems such as low ventilation efficiency, premature blockage of the filter, and energy consumption waste, which may lead to safety hazards caused by untimely heat dissipation of the charging pile. Therefore, a method for efficient ventilation and dust prevention of charging piles based on intelligent sensing regulation is needed to improve the reliability and safety of the operation of charging piles. Summary of the Invention

[0004] The present invention provides a method and system for efficient ventilation and dust prevention of charging piles based on intelligent sensing regulation, and its main purpose is to improve the reliability and safety of the operation of charging piles.

[0005] To achieve the above object, a method for efficient ventilation and dust prevention of charging piles based on intelligent sensing regulation provided by the present invention includes:

[0006] 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;

[0007] Based on the dust prevention priority, adjust the ventilation control components corresponding to the target charging pile, perform performance detection on the ventilation control components to obtain component performance parameters, and calculate the air flow adjustment rate corresponding to the ventilation control components based on the component performance parameters;

[0008] Detect the dust prevention index corresponding to the air flow adjustment rate, calculate the ventilation energy consumption ratio corresponding to the target charging pile based on the dust prevention index combined with the 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;

[0009] Based on the multi-mode regulation architecture, logically regulate the ventilation regulation component to obtain the component ventilation logic, analyze the airflow coverage range corresponding to the component ventilation logic, and query the airflow optimization path within the airflow coverage range;

[0010] Monitor the dust deposition state in the airflow optimization path, dynamically adjust the air control index corresponding to the ventilation regulation component based on the dust deposition state, identify the efficient regulation nodes in the air control index, and formulate an adaptive dust prevention plan for the target charging pile based on the efficient regulation nodes.

[0011] Optionally, determining the dust prevention priority corresponding to the target charging pile according to the ventilation requirement includes:

[0012] Analyze the airflow rate data corresponding to the ventilation requirement;

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

[0014] Collect the dust particle concentration in the dust retention area;

[0015] Analyze the dust accumulation risk index corresponding to the dust particle concentration;

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

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

[0018] Detect the ventilation grid interval corresponding to the target charging pile according to the dust prevention priority;

[0019] Divide the ventilation grid nodes corresponding to the ventilation grid interval;

[0020] Analyze the airflow disturbance amplitude corresponding to the ventilation grid node;

[0021] Screen out the inefficient grid nodes with the airflow disturbance amplitude lower than the preset threshold;

[0022] Adjust the ventilation regulation component corresponding to the target charging pile according to the inefficient grid nodes.

[0023] Optionally, calculating the airflow adjustment rate corresponding to the ventilation regulation component based on the component performance parameters includes:

[0024] Calculate the airflow adjustment rate corresponding to the ventilation regulation component using the following formula:

[0025]

[0026] Among them, V adj represents the air flow regulation 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, and α i represents the weight coefficient of the i-th category of components, P i represents the performance parameter value of the i-th category of components, R i represents the resistance-related parameter of the i-th category of components, m represents the total number of parameters of the component performance parameters, j represents the quantity index of the component performance parameters, and β j represents the influence coefficient corresponding to the j-th component performance parameter, D j represents the measured value of the parameter corresponding to the j-th component performance parameter.

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

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

[0029]

[0030] Among them, 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 start time and end 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, constructing the multi-mode regulation architecture corresponding to the target charging pile based on the ventilation energy consumption ratio includes:

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

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

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

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

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

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

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

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

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

[0041] Integrate the ventilation start-stop time sequence corresponding to the core ventilation nodes;

[0042] Based on the ventilation start-stop time sequence, logically regulate the ventilation regulation component to obtain component ventilation logic.

[0043] Optionally, the querying of the airflow optimization path within the airflow coverage area includes:

[0044] Determine the high-turbulence area corresponding to the airflow coverage area;

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

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

[0047] Analyze the airflow disturbance trajectory corresponding to the airflow disturbance coefficient;

[0048] Based on the airflow disturbance trajectory, query the airflow optimization path within the airflow coverage area.

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

[0050] Analyze 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] To solve the above problems, the present invention also provides a high-efficiency ventilation and dust-proof system for charging piles based on intelligent sensing regulation, and the system includes:

[0056] A priority determination module, configured to obtain the in-pile detection environment corresponding to the target charging pile, analyze the ventilation requirement 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 requirement;

[0057] A rate calculation module, configured to adjust the ventilation control component corresponding to the target charging pile based on the dust prevention priority, perform a performance test on the ventilation control component to obtain component performance parameters, and calculate the air flow adjustment rate corresponding to the ventilation control component based on the component performance parameters;

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

[0059] A path query module, configured to perform logical control on the ventilation control component based on the multi-mode control architecture to obtain the component ventilation logic, analyze the air flow coverage range corresponding to the component ventilation logic, and query the air flow optimization path within the air flow coverage range;

[0060] A solution formulation module, configured to monitor the dust deposition state in the air flow optimization path, dynamically adjust the air flow control index corresponding to the ventilation control component based on the dust deposition state, identify the efficient control nodes in the air flow control index, and formulate an adaptive dust prevention solution corresponding to the target charging pile based on the efficient control nodes.

[0061] Compared with the problems described in the background art, the present invention can obtain the detection environment inside the target charging pile, and can real-time master key information such as the internal temperature, humidity, and dust concentration of the charging pile, accurately identify potential overheating and dust accumulation risks, provide data support for dynamically adjusting the ventilation and dust prevention strategies, significantly improve the ventilation efficiency, reduce energy consumption, and can also early warn of the performance attenuation or fault hidden dangers of electrical components. Based on the dust prevention priority, the present invention adjusts the ventilation control component corresponding to the target charging pile, which can achieve the precise adaptation of dust prevention measures, thereby improving the dust prevention effect, and can also avoid the energy consumption waste caused by excessive intervention, ensuring that the charging pile can operate efficiently and safely in different dust environments. Further, the present invention can judge the filter interception efficiency and whether the air flow can effectively block dust by detecting the dust prevention index corresponding to the air flow regulation rate, timely discover hidden dangers such as filter blockage, facilitate targeted maintenance, ensure the cleanliness inside the charging pile, avoid failures caused by dust accumulation, extend the service life of the equipment, and ensure its stable and reliable operation. Further, the present invention can realize the intelligent switching and precise matching of ventilation strategies by logically controlling the ventilation control component based on the multi-mode control architecture to obtain the component ventilation logic, and by analyzing the control data in different modes, locate the core ventilation nodes and optimize the start-stop timing, which can dynamically balance the heat dissipation, dust prevention and energy consumption requirements of the charging pile, and ensure the stable and efficient operation of the charging pile in multiple scenarios. Finally, the present invention can judge problems such as filter load and air flow dead angles by monitoring the dust deposition state in the optimized air flow path, which is convenient for timely cleaning and maintenance or adjusting the path parameters, thereby avoiding dust from blocking key components, improving the dust prevention performance of the charging pile, extending the equipment life and ensuring the stable operation of the charging pile in a high-dust environment. Therefore, the method and system for efficient ventilation and dust prevention of a charging pile based on intelligent sensing control provided by the embodiments of the present invention can improve the reliability and safety of the operation of the charging pile. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 FIG. is a schematic flow chart of a method for efficient ventilation and dust prevention of a charging pile based on intelligent sensing control provided by an embodiment of the present invention;

[0063] Figure 2 FIG. is a schematic architecture diagram corresponding to a multi-mode control architecture in a method for efficient ventilation and dust prevention of a charging pile based on intelligent sensing control provided by an embodiment of the present invention;

[0064] Figure 3 FIG. is a schematic module diagram of a system for realizing the efficient ventilation and dust prevention of a charging pile based on intelligent sensing control provided by an embodiment of the present invention.

[0065] The realization, functional characteristics and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] It should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

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

[0068] Embodiment 1:

[0069] Refer to Figure 1 As shown, it is a schematic flowchart of a method for efficient ventilation and dust prevention of a charging pile based on intelligent sensing and regulation provided by an embodiment of the present invention. In this embodiment, the method for efficient ventilation and dust prevention of the charging pile based on intelligent sensing and regulation includes:

[0070] S1. 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.

[0071] By obtaining the in-pile detection environment corresponding to the target charging pile, the present invention can timely master key information such as the internal temperature, humidity, and dust concentration of the 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, reduce energy consumption, and can also give early warnings of the performance attenuation or fault hidden dangers of electrical components.

[0072] Among them, the target charging pile refers to a specific charging pile individual that needs to be regulated for ventilation and dust prevention, and can be a single or multiple charging pile devices in different scenarios (such as outdoor public areas, industrial parks, etc.). For example, a DC fast charging pile that is charging an electric vehicle in a specific highway service area needs to be used as a target charging pile for targeted monitoring and regulation due to heat generation during high-power operation and the outdoor dust environment; the in-pile detection environment refers to the real-time physical environment data inside the charging pile collected by various sensors, including but not limited to parameters such as the temperature of key points (such as power modules, cable joints), air humidity, dust concentration, and air flow rate. For example, using a temperature sensor to measure the temperature of the IGBT module of a charging pile is 65°C, and a laser dust sensor detects that the internal dust accumulation reaches 80 μg / m 3, These data together constitute the in-pile detection environment of the pile. Optionally, obtaining the in-pile detection environment corresponding to the target charging pile can be achieved through environmental perception technology, such as: real-time collection of the internal environment data of the charging pile through hardware devices such as temperature sensors, humidity sensors, and smoke sensors.

[0073] Furthermore, based on the in-pile detection environment, the present invention analyzes the ventilation requirements corresponding to the target charging pile, can accurately match the real-time operation state of the charging pile, and dynamically plans the ventilation intensity and method according to data such as internal temperature and dust concentration. It can not only avoid potential safety hazards caused by insufficient heat dissipation in high-temperature environments, but also reduce unnecessary ventilation energy consumption in low-dust environments.

[0074] Among them, the ventilation requirements refer to the ventilation strategy parameters required for the target charging pile to maintain safe and stable operation, which are analyzed based on the in-pile detection environment (such as temperature field distribution, dust concentration, equipment load, etc.). The ventilation requirements include ventilation intensity (such as fan speed, air flow rate), ventilation direction (such as the area of directional purging), ventilation duration, and dust prevention measures (such as filter cleaning frequency), etc. For example, when it is detected that the temperature of the power module inside the charging pile reaches 70°C and the dust concentration is low, the ventilation requirement is to start a high-speed fan for strong convection heat dissipation in the high-temperature area; if the dust concentration exceeds the standard, the ventilation path is preferentially adjusted to reduce dust accumulation, and at the same time, the self-cleaning function of the filter is triggered. Optionally, analyzing the ventilation requirements corresponding to the target charging pile can be achieved through experimental testing methods, such as: deploying a temperature and humidity sensor array in a simulated environment to monitor the actual ventilation effect, so as to obtain the ventilation requirements.

[0075] Furthermore, the present invention determines the dust prevention priority corresponding to the target charging pile according to the ventilation requirements, can dynamically balance the resource allocation between heat dissipation and dust prevention, avoid the rapid accumulation of dust affecting the equipment performance, and reduce the obstruction of dust prevention measures to the ventilation efficiency, thereby improving the adaptability of the charging pile in different scenarios.

[0076] Among them, the dust prevention priority refers to the hierarchical determination of the urgency and importance of the dust prevention work of the target charging pile by comprehensively considering information such as air flow rate data, dust retention area, dust particle concentration, and dust accumulation risk index. The higher the priority, the more necessary it is to take dust prevention measures such as strengthening filter filtration, starting the self-cleaning function, and adjusting the ventilation strategy in priority to ensure the safe and stable operation of the charging pile.

[0077] As an embodiment of the present invention, determining the dust prevention priority corresponding to the target charging pile according to the ventilation requirement includes: analyzing the air flow rate data corresponding to the ventilation requirement; dividing the dust retention area corresponding to the target charging pile based on the air flow rate data; collecting the dust particle concentration in the dust retention area; analyzing the dust accumulation risk index corresponding to the dust particle concentration; and determining the dust prevention priority corresponding to the target charging pile based on the dust accumulation risk index.

[0078] Among them, the air flow rate data refers to the flow velocity data of the air flow in each area inside the target charging pile collected by devices such as a wind speed sensor, and the unit is usually meters per second (m / s). It reflects the circulation state of the air inside the charging pile. For example, the air flow rate is relatively high near the ventilation opening, while it is relatively low at the corner. It is the key basis for judging the ventilation efficiency and air flow distribution. The dust retention area refers to a specific area inside the charging pile where dust particles are likely to deposit and stay due to low air flow rate and poor air mobility. For example, corners inside the charging pile, gaps between heat sinks, and recesses at wire joints. In these areas, it is difficult for the air flow to effectively wash away the dust, and the dust is more likely to adhere and accumulate, affecting the normal operation of the equipment. The dust particle concentration refers to the number or mass of dust particles contained in a unit volume of air in the dust retention area. Common units include particles per cubic meter or micrograms per cubic meter, and it is detected by devices such as a laser particle sensor. The higher the value, the more serious the dust pollution in this 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 through a specific algorithm based on data such as the dust particle concentration, combined with factors such as the sensitivity of the internal components of the charging pile to dust and the operating environment, and is used to evaluate the risk degree 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 airflow rate data corresponding to the parsed ventilation demand can be achieved through hydrodynamic simulation methods. For example, use ANSYS Fluent software to simulate the airflow field distribution inside the charging pile to obtain the airflow rate data; the dust retention area corresponding to the target charging pile can be divided through a particle trajectory tracking algorithm. For example, adopt the Lagrangian particle tracking method combined with the CFD simulation results to identify the low-speed airflow area to obtain the dust retention area; the dust particle concentration in the dust retention area can be collected through laser scattering detection technology. For example, use a TSIDustTrak aerosol monitor to measure the real-time particulate matter concentration to obtain the dust particle concentration; the dust accumulation risk index corresponding to the dust particle concentration can be analyzed through a weighted scoring model. For example, combine parameters such as particulate matter concentration, residence time, and equipment sensitivity to construct a risk assessment matrix to obtain the dust accumulation risk index; the dust prevention priority corresponding to the target charging pile can be determined through a multi-criteria decision analysis method. For example, adopt the AHP (Analytic Hierarchy Process) to comprehensively evaluate factors such as dust accumulation risk and maintenance cost to obtain the dust prevention priority.

[0080] S2. Based on the dust prevention priority, adjust the ventilation control components corresponding to the target charging pile, perform performance detection on the ventilation control components to obtain component performance parameters, and calculate the airflow adjustment rate corresponding to the ventilation control components based on the component performance parameters.

[0081] Based on the dust prevention priority of the present invention, adjusting the ventilation control components corresponding to the target charging pile can achieve precise adaptation of dust prevention measures, thereby improving the dust prevention effect, and can also avoid energy consumption waste caused by excessive intervention, ensuring that the charging pile can operate efficiently and safely in different dust environments.

[0082] Among them, the ventilation control components refer to various hardware devices used to adjust the ventilation and dust prevention effects inside the target charging pile, including variable-speed ventilation fans, intelligent louvers, self-cleaning filters, guide vanes, etc. By controlling the operating parameters of these components (such as fan speed, louver opening degree, etc.), the internal airflow distribution can be optimized and the ventilation and dust prevention efficiency can be improved.

[0083] As an embodiment of the present invention, adjusting the ventilation control components corresponding to the target charging pile based on the dust prevention priority 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 out the inefficient grid nodes with the airflow disturbance amplitude lower than the preset threshold; and adjusting the ventilation control components corresponding to the target charging pile according to the inefficient grid nodes.

[0084] Among them, the ventilation grid interval refers to a set of multiple regions obtained by dividing the internal space of the target charging pile according to certain rules, which is used for systematic analysis of the internal air flow distribution and dust movement. For example, the internal space of the charging pile can be equally divided into several cubic regions in 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 within the ventilation grid interval, which is the key point for obtaining air flow data. Just like the coordinate points in a three-dimensional coordinate system, by arranging sensors or performing simulation calculations at these nodes, parameters such as air flow velocity and pressure at this position can be obtained to evaluate the ventilation state of the interval where it is located; the air flow disturbance amplitude refers to the degree of fluctuation of parameters such as air flow velocity and direction at the ventilation grid node relative to the stable state. For example, at a specific node, the air flow velocity changes frequently in a short period of time, and the magnitude of the velocity difference is the air flow disturbance amplitude at this node. The larger the amplitude, the more unstable the air flow, which can affect dust diffusion and sedimentation; the preset threshold refers to a numerical standard preset according to the ventilation requirements for the normal operation of the charging pile, which is used to measure whether the air flow disturbance amplitude meets the standard. When the air flow disturbance amplitude at a node is lower than this threshold, it indicates that the ventilation effect at this node is not good and needs further optimization; the low-efficiency grid node refers to a ventilation grid node whose air flow disturbance amplitude is lower than the preset threshold. Due to unstable air flow or too low flow velocity at such nodes, dust is likely to accumulate and stay here, and the ventilation and heat dissipation and dust prevention functions cannot be effectively realized. It is the area that needs to be focused on improving in ventilation regulation.

[0085] Furthermore, the detection of the ventilation grid interval corresponding to the target charging pile can be achieved through three-dimensional point cloud reconstruction technology. For example, a three-dimensional model of the internal space of the charging pile can be constructed by using a depth camera combined with the SLAM algorithm, so as to obtain the ventilation grid interval; the division of the ventilation grid nodes corresponding to the ventilation grid interval can be achieved through the finite element mesh division method. For example, the ANSYS Meshing tool is used to generate a structured hexahedral mesh, so as to obtain the ventilation grid nodes; the analysis of the air flow disturbance amplitude corresponding to the ventilation grid nodes can be achieved through computational fluid dynamics simulation. For example, based on the k-ε turbulence model, the standard deviation of the velocity fluctuation of each grid node is calculated, so as to obtain the air flow disturbance amplitude; the screening of the low-efficiency grid nodes with air flow disturbance amplitudes lower than the preset threshold can be achieved through image processing technology. For example, the OpenCV library is used to perform threshold segmentation and connected component analysis on the visualization result of the air flow field, so as to obtain the low-efficiency grid nodes; the adjustment of the ventilation control component corresponding to the target charging pile can be achieved through fuzzy logic control. For example, a Mamdani-type fuzzy controller based on air flow velocity and temperature feedback is established, so as to obtain the ventilation control component.

[0086] By performing performance testing on the ventilation control component, the component performance parameters are obtained, which can ensure the stability of the ventilation and dust prevention effect, early warning of equipment failures in advance, reduce the risk of sudden shutdowns, extend the service life of the component, and improve the overall operating efficiency of the charging pile.

[0087] Among them, the component performance parameters refer to the quantitative indicators used to measure the operating status and functional performance of the ventilation control component, covering dimensions such as the physical characteristics of the component, operating parameters, and energy efficiency performance. For example, the performance parameters of a variable-speed ventilation fan include the real-time rotational speed (rpm), air volume (m 3 / h), air pressure (Pa), and power consumption (kW); the parameters of an intelligent louver include the opening percentage, response time (s), and sealing performance index; the parameters of a self-cleaning filter include the filtration efficiency (interception rate of dust with a specific particle size), resistance (Pa), and cumulative usage duration, 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 control strategy is effective. Optionally, the performance testing of the ventilation control component can be achieved through a dynamic response testing method. For example, a step response test combined with a LabVIEW data acquisition system is used to measure the response time and overshoot of the fan speed regulation, thereby obtaining the component performance parameters.

[0088] Furthermore, based on the component performance parameters, the present invention calculates the corresponding air flow regulation rate of the ventilation control component, which can accurately match the real-time ventilation requirements of the charging pile. By dynamically converting the actual air flow velocity through data such as the fan speed and air pressure, it is ensured that a reasonable air volume is obtained in high-temperature or high-dust areas, realizing intelligent and precise control of the charging pile's thermal management and dust prevention.

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

[0090] As an embodiment of the present invention, the calculation of the corresponding air flow regulation rate of the ventilation control component based on the component performance parameters includes:

[0091] Using the following formula to calculate the corresponding air flow regulation rate of the ventilation control component:

[0092]

[0093] Among them, V adjrepresents the air flow regulation 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, α 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 parameters of the component performance parameter, j represents the quantity index of the component performance parameter, β j represents the influence coefficient corresponding to the j-th component performance parameter, D j represents the measured value of the parameter corresponding to the j-th component performance parameter.

[0094] Furthermore, the weight coefficient refers to a proportional coefficient (dimensionless, with a value range of 0 - 1) that reflects the relative importance of the ventilation regulation component or environmental parameters in air flow regulation. For example, the fan plays a dominant role in air flow velocity, and its weight coefficient can be set to 0.6; the filter mainly affects the air flow resistance, and the weight coefficient is set to 0.3; the influence weight of the environmental temperature on gas density can be set to 0.1; the performance parameter value refers to the specific value that can reflect the working state and performance of each type of ventilation regulation component. For example, for a fan, it can be the air pressure, air volume, rotational speed, etc.; for a shutter, it can be the opening size; for a filter, it can be the filtration efficiency, air permeability, etc. These values are the basic data for evaluating the component performance and calculating the air flow regulation rate; the resistance-related parameter refers to the parameter related to the resistance generated by the ventilation regulation component to the air flow, such as the duct resistance coefficient of the fan, the resistance of the filter (in Pascal, Pa), etc. This parameter reflects the degree of obstruction of the component to the air flow. The greater the resistance, the more obvious the change in air flow velocity when passing through. It is an important influencing factor in calculating the air flow regulation rate; the influence coefficient refers to the coefficient used to measure the influence degree of each component performance parameter on the air flow regulation rate, and its value range is generally between 0 - 1. And for different component performance parameters, due to their different effects on air flow regulation, different influence coefficients will be assigned; the measured value of the parameter refers to the true measured value of the component state or environmental data collected in real time by sensors. For example: the measured value of the fan air pressure is 2000 Pa shown by the current operating air pressure sensor; the measured value of the environmental temperature is 303 K (30 °C) detected by the internal temperature sensor of the charging pile; the measured value of the filter resistance is 50 Pa feedback by the pressure difference sensor before and after the filter.

[0095] S3. Detect the dust prevention index corresponding to the air flow regulation rate, calculate the ventilation energy consumption ratio corresponding to the target charging pile based on the dust prevention index combined with the real-time load index of the target charging pile, and construct the multi-mode regulation architecture corresponding to the target charging pile based on the ventilation energy consumption ratio.

[0096] By detecting the dust-proof index corresponding to the air flow regulation rate, the present invention can judge the filter screen interception efficiency and whether the air flow can effectively block dust, timely discover potential hazards such as filter screen blockage, facilitate targeted maintenance, ensure the cleanliness inside the charging pile, avoid failures caused by dust accumulation, extend the service life of the equipment, and ensure its stable and reliable operation.

[0097] Among them, the dust-proof index is a quantitative index that measures the ability of the ventilation control component to block and filter dust particles at a specific air flow regulation rate. The higher the value, the better the dust-proof effect. For example, in a specific charging pile ventilation system, when the air flow regulation rate is 5 m / s, if the dust-proof index is 80%, it means that the system can intercept 80% of the dust with a specific particle size, which can intuitively reflect its dust-proof performance. Optionally, the detection of the dust-proof index corresponding to the air flow regulation rate can be achieved through aerosol dynamics analysis. For example, by using particle image velocimetry (PIV) combined with a dust concentration sensor, the particle sedimentation rate at different wind speeds is quantified, so as to obtain the dust-proof index.

[0098] Furthermore, based on the dust-proof index and combined with the real-time load index of the target charging pile, the present invention calculates the ventilation energy consumption ratio corresponding to the target charging pile, which can achieve precise energy consumption management, facilitate the optimization of the ventilation strategy, avoid excessive ventilation energy consumption, reduce the energy consumption cost under the premise of ensuring dust prevention and equipment heat dissipation, and improve the operation efficiency of the charging pile.

[0099] Among them, the real-time load index is a quantitative value that reflects the current working load degree of the target charging pile. Based on parameters such as the output current and voltage of the charging pile, it can intuitively show the real-time operation state of the charging pile. For example, when the real-time load index of a certain charging pile is 80%, it means that its 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 is a quantitative index that measures the energy consumption input and the comprehensive benefits obtained by the ventilation system of the target charging pile under a specific operating state. It is calculated by correlating the dust-proof index, real-time load index with ventilation energy consumption, noise cost, etc. The higher the value, the stronger the dust-proof effect and load guarantee ability achieved per unit energy consumption. For example, when the ventilation energy consumption ratio is 50, it means that for every 1 unit of electric energy consumed, the comprehensive benefits equivalent to 50 units of dust prevention and load performance can be achieved.

[0100] As an embodiment of the present invention, the calculation of the ventilation energy consumption ratio corresponding to the target charging pile based on the dust-proof index and combined with the real-time load index of the target charging pile includes:

[0101] Using the following formula to calculate the ventilation energy consumption ratio corresponding to the target charging pile:

[0102]

[0103] Among them, E rIt 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 start time and end 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.

[0104] Specifically, the dust prevention index refers to a quantitative value reflecting the dust particle interception and filtration ability of the ventilation control component, usually presented in the form of a percentage (0-100), which is calculated based on the dust concentration data inside and outside the charging pile. For example, by comparing the dust concentration in the external environment of the charging pile with the dust concentration detected inside, if the external dust concentration is 400 μg / m 3 , and the internal is 80 μg / m 3, then the dust index is (1-80÷400)×100=80, indicating that the ventilation system can intercept 80% of the dust; the load index weight refers to the coefficient used to adjust 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 load index weight (α) and the dust index weight (α) is 1. In high-load operation scenarios, such as when the charging pile is in the high-power fast charging stage, the load index weight can be increased to focus on ensuring the heat dissipation requirements; in low-load scenarios, the weight can be appropriately reduced; the energy consumption weight coefficient refers to the coefficient used to measure the proportion of the ventilation system energy consumption in the calculation of the ventilation energy consumption ratio. The degree of ratio is usually a value greater than 0, which reflects the importance of energy consumption cost in the overall evaluation. The larger the value, the greater the impact of energy consumption on the ventilation energy consumption ratio. For example, in scenarios that are sensitive to energy consumption costs, the γ value can be increased to encourage the system to give priority to reducing energy consumption; the ventilation energy consumption cycle refers to the time interval selected when calculating the ventilation energy consumption, which is determined by t1 and t2 in the formula. The cycle can be set according to actual needs. A short cycle (such as 10 minutes) is suitable for quickly monitoring the real-time energy consumption changes of the system; a long cycle is used to analyze the energy consumption trend and average energy consumption level of the charging pile during long-term operation; the The ventilation energy consumption function refers to the calculation method of the cumulative energy consumption of the ventilation control components within the ventilation energy consumption cycle. It takes time as a variable and integrates the real-time power consumption P(t) of each component (such as the fan and the shutter motor) at each time point to obtain the total energy consumption within the cycle. For example, the change in the speed of the fan at different times causes power consumption fluctuations. Through this function, the actual power consumption can be accurately counted; the noise weight coefficient refers to a parameter used to evaluate the influence of the operating noise of the ventilation system on the ventilation energy consumption ratio. The value is greater than or equal to 0. In noise-sensitive environments (such as residential areas and office areas near the charging station), the noise weight coefficient is greater than or equal to 0. Electric charging pile), the coefficient can be increased to reduce the ventilation energy consumption ratio when the noise is high, thereby prompting the system to optimize operating parameters to reduce noise; the noise index refers to an indicator that quantifies the noise level when the ventilation system is running, and the value range is 0-100. The higher the value, the greater the noise. It is calculated by comparing the measured noise value with the ambient background noise and the noise limit. For example, the measured noise of a charging pile ventilation system is 65dB(A), the ambient background noise is 30dB(A), and the noise limit is 70dB(A). The noise index is (65-30)÷(70-30)×100=87.5.

[0105] Based on the ventilation energy consumption ratio, the present invention constructs a multi-mode control architecture corresponding to the target charging pile, which can dynamically switch the ventilation mode according to the energy consumption ratio, optimize the strategy to reduce energy consumption when energy consumption is high, and enhance the function in a targeted manner when dust prevention or heat dissipation is insufficient. This can help balance the energy consumption, dust prevention and heat dissipation of the charging pile operation, and improve the operating efficiency and stability of the equipment.

[0106] Among them, the multi-mode regulation architecture refers to a system built based on multi-mode closed-loop data that can switch multiple ventilation regulation modes according to the operating state of the charging pile. For example, it can switch to the strong heat dissipation mode under high load and high temperature, and switch to the high-efficiency dust-proof mode under high dust. By integrating different modes, it realizes intelligent and precise regulation of the charging pile ventilation.

[0107] As an embodiment of the present invention, constructing the multi-mode regulation architecture corresponding to the target charging pile based on the ventilation energy consumption ratio includes: 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 the multi-mode closed-loop data corresponding to the target charging pile according to the fan speed gradient; constructing the multi-mode regulation architecture corresponding to the target charging pile based on the multi-mode closed-loop data.

[0108] Among them, the dynamic fluctuation component refers to the changing part of the ventilation energy consumption ratio that deviates from its mean value within a certain period of time, which reflects the real-time change of the energy consumption ratio. For example, if the ventilation energy consumption ratio was originally stable at 50 and fluctuates between 45 and 55 during a certain period, the numerical changes of 45 - 50 and 50 - 55 that deviate from the mean value of 50 are the dynamic fluctuation components, which are used to capture the dynamic characteristics of the energy consumption ratio; the heat dissipation demand threshold refers to the critical value calculated according to 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, heat dissipation needs to be strengthened. For example, if the calculated heat dissipation demand threshold is 40°C, when the internal temperature of the charging pile reaches 40°C, it means that the heat dissipation demand is urgent and heat dissipation measures need to be improved; the fan speed gradient refers to the rate of corresponding change of the fan speed corresponding to the heat dissipation demand threshold, which represents how fast the fan speed increases as the heat dissipation demand increases. For example, when the heat dissipation demand threshold increases by 1°C, the fan speed increases by 50 revolutions per minute, and this "50 revolutions per minute" is the fan speed gradient, which reflects the relationship between the fan speed and the heat dissipation demand; the multi-mode closed-loop data refers to the cyclic data set formed by the feedback of the system operating state covering multiple modes of ventilation regulation, including information such as fan speed, temperature, energy consumption, and dust-proof index. For example, under different ventilation modes, the real-time collected data such as fan speed of 1000 revolutions per minute, temperature of 35°C, energy consumption of 100 watts, and dust-proof index of 70% constitute the multi-mode closed-loop data, which is used for optimizing the regulation architecture.

[0109] Furthermore, the extraction of the dynamic fluctuation component in the ventilation energy consumption ratio can be achieved through a signal decomposition algorithm. For example, empirical mode decomposition (EMD) or wavelet transform can be used to separate the time-frequency characteristics of the energy consumption signal, thereby obtaining the dynamic fluctuation component. The calculation of the heat dissipation demand threshold corresponding to the target charging pile can be achieved through heat balance equation modeling. For example, a mathematical model including charging power, ambient temperature, and heat dissipation efficiency can be established based on the law of conservation of energy, thereby obtaining the heat dissipation demand threshold. The analysis of the fan speed gradient corresponding to the heat dissipation demand threshold can be achieved through optimal control theory. For example, the Pontryagin minimum principle can be applied to solve the speed regulation curve with optimal energy consumption, thereby obtaining the fan speed gradient. The acquisition of the multi-mode closed-loop data corresponding to the target charging pile can be achieved through an Internet of Things sensing network. For example, Zigbee wireless sensor nodes can be deployed to synchronously collect temperature, wind speed, and energy consumption data, thereby obtaining multi-mode closed-loop data. The construction of the multi-mode regulation architecture corresponding to the target charging pile can be achieved through a hierarchical control strategy. For example, a three-level control framework including a bottom execution layer, an intermediate control layer, and an upper decision layer can be designed, thereby obtaining the multi-mode regulation architecture.

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

[0111] S4. Based on the multi-mode regulation architecture, perform logical regulation on the ventilation regulation component to obtain the component ventilation logic, analyze the airflow coverage range corresponding to the component ventilation logic, and query the airflow optimization path within the airflow coverage range.

[0112] The present invention performs logical control on the ventilation control component based on the multi-mode control architecture to obtain the component ventilation logic, thereby realizing intelligent switching and precise matching of ventilation strategies. Moreover, by analyzing the control data under different modes, the core ventilation nodes are located and the start-stop timing is optimized, so as to dynamically balance the heat dissipation, dust prevention and energy consumption requirements of the charging pile, and ensure the stable and efficient operation of the charging pile in multiple scenarios.

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

[0114] As an embodiment of the present invention, based on the multi-mode control architecture, the ventilation control component is logically controlled to obtain the component ventilation logic, including: parsing the ventilation control mode corresponding to the multi-mode control architecture; querying the mode control data under the ventilation control mode; locating the core ventilation node in the ventilation control component based on the mode control data; integrating the ventilation start and stop timing corresponding to the core ventilation node; based on the ventilation start and stop timing, the ventilation control component is logically controlled to obtain the component ventilation logic.

[0115] Among them, the ventilation control mode refers to the different types of ventilation strategies preset in the multi-mode control architecture. For example, there is a strong heat dissipation mode for coping with high-load heat generation of charging piles; the high-efficiency dust prevention mode is used in dusty environments. According to the real-time status of the charging pile, such as temperature, load index, dust prevention index, etc., switch between different modes to ensure stable operation of the equipment; the mode control data refers to a set of specific parameter information corresponding to each ventilation control mode. For example, in the strong heat dissipation mode, it contains data such as the maximum speed of the fan and the optimal angle of the guide plate; in the high-efficiency dust prevention mode, there are data such as the cleaning cycle of the filter and the appropriate flow rate of the airflow. These data guide the ventilation control components to operate accurately according to the corresponding mode; the core ventilation node refers to the component or position in the ventilation control component that plays a key role in the ventilation effect. For example, the fan is a key component that provides airflow power, and the vent is an important location for airflow in and out. Both belong to core ventilation nodes. Under different ventilation control modes, the working status of the core ventilation nodes will be adjusted in a targeted manner; the ventilation start and stop sequence refers to the time sequence and time interval arrangement of the opening and closing of the core ventilation nodes under a specific ventilation control mode. For example, during the high temperature period during the day, the fan runs for every 2 hours and pauses for 10 minutes for self-inspection. This is a ventilation start and stop sequence. Reasonable timing can optimize ventilation effects and reduce energy consumption.

[0116] Furthermore, the ventilation control mode corresponding to the parsing of the multimode control architecture can be implemented through a pattern recognition algorithm. For example, the K-means clustering is used to analyze the combination of characteristic parameters in the historical operation data to obtain the ventilation control mode. The query of the mode control data under the ventilation control mode can be realized through the retrieval of a time series database. For example, the InfluxQL query language is used to extract time series data such as temperature and wind speed under a specific mode from InfluxDB to obtain the mode control data. The positioning of the core ventilation node in the ventilation control component can be achieved through graph theory analysis methods. For example, a directed graph model of the ventilation network is constructed based on the NetworkX library, and the node betweenness centrality is calculated to obtain the core ventilation node. The integration of the ventilation start-stop time sequence corresponding to the core ventilation node can be realized through discrete event simulation. For example, the SimPy framework is used to simulate the start-stop event sequences of different nodes and optimize the overlap degree of the time window to obtain the ventilation start-stop time sequence. The logical control of the ventilation control component can be achieved through a programmable logic controller. For example, the PLC ladder diagram programming is used to implement the interlock control logic based on the sensor feedback to obtain the component ventilation logic.

[0117] By analyzing the airflow coverage range corresponding to the component ventilation logic, the present invention can clarify whether the airflow can fully cover the key parts of the charging pile, avoid local overheating or dust accumulation, facilitate the optimization of the ventilation logic, adjust parameters such as the fan speed and the deflector angle, ensure the reasonable distribution of the airflow, improve the heat dissipation and dust prevention capabilities of the charging pile, and ensure its stable and efficient operation.

[0118] Among them, the airflow coverage range refers to the spatial area where the airflow can effectively reach and have an effect during the operation of the ventilation control component, which measures the breadth and depth of the airflow circulation inside and around the charging pile. For example, in a ventilation system of a certain charging pile, after the fan is turned on, the airflow can cover 80% of the space inside the charging pile, including key parts such as the charging module and the battery module. This 80% of the space is the airflow coverage range under this ventilation logic. Optionally, the analysis of the airflow coverage range corresponding to the component ventilation logic can be realized through computational fluid dynamics simulation. For example, the ANSYS Fluent software is used to establish a three-dimensional turbulent model, and the airflow trajectory distribution map is drawn through particle tracking technology to obtain the airflow coverage range.

[0119] Furthermore, by querying the airflow optimization path within the airflow coverage range, the present invention can analyze the path resistance, flow velocity distribution, etc., and can specifically adjust the ventilation component parameters (such as the fan speed and the deflector angle), reduce eddy currents and dead corners, enhance the heat dissipation and dust prevention effects of the charging pile, thereby extending the service life of the equipment and ensuring the operation stability.

[0120] Among them, the airflow optimization path refers to a flow path designed for the airflow disturbance trajectory, which can reduce turbulent losses and improve airflow uniformity. It is achieved by adjusting component layouts (such as translating battery modules) or parameters (such as increasing the fan speed). For example, after adjusting the deflector angle in the high-turbulence area from 30° to 45°, the airflow disturbance trajectory changes from a spiral shape to a straight line, forming a more efficient "Z"-shaped optimization path that covers the originally stagnant corner area.

[0121] As an embodiment of the present invention, querying the airflow optimization path within 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 within the airflow coverage range based on the airflow disturbance trajectory.

[0122] Among them, the high-turbulence area refers to the area within the airflow coverage range where the airflow is turbulent, and the speed and direction change violently. It is usually caused by obstacles (such as charging modules, line arrangements) or flow velocity differences. For example, due to the narrow space behind the battery module inside the charging pile, the airflow is likely to form turbulence, and this area is the high-turbulence area, which can lead to insufficient local 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 and is calculated through the velocity gradient and pressure gradient. The larger its value, the stronger the vortex energy and the higher the degree of airflow disorder. For example, if the vortex intensity index in the high-turbulence area is 0.8 (full value 1), it indicates that there is a strong vortex in this area, which is likely to cause airflow stagnation and energy loss. The airflow disturbance coefficient refers to a parameter that comprehensively considers factors such as vortex intensity and turbulence scale and measures the impact of airflow disturbance on ventilation efficiency. The larger the parameter, the more significant the airflow disturbance. For example, when the vortex intensity index is 0.6 and the area occupancy ratio is 15%, the airflow disturbance coefficient is 0.09, reflecting that this area has a medium 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, usually presenting an irregular curve or vortex form. For example, when the deflector angle is unreasonable, the airflow disturbance trajectory presents a spiral vortex, resulting in the inability of the airflow in the corner area of the charging pile to reach effectively.

[0123] Further, the determination of the high-turbulence region corresponding to the airflow coverage range can be achieved through a vorticity identification algorithm. For example, the Q-criterion is used to extract the vortex structure from the CFD simulation results, and the local vorticity intensity threshold is calculated to obtain the high-turbulence region. The extraction of the vortex intensity index in the high-turbulence region can be achieved through a vortex dynamics analysis method. For example, based on the λ2 criterion, the eigenvalues of the velocity gradient tensor are calculated to quantify the rotation intensity of the vortex core region, thereby obtaining the vortex intensity index. The calculation of the airflow disturbance coefficient in the high-turbulence region can be achieved through a turbulence statistics method. For example, the Reynolds stress decomposition method is used to analyze the root mean square value of the velocity pulsation, and a turbulence intensity distribution model is established to obtain the airflow disturbance coefficient. The analysis of the airflow disturbance trajectory corresponding to the airflow disturbance coefficient can be achieved through Lagrangian particle tracking. For example, the icoLagrangian particle tracking module in OpenFOAM is used to simulate the motion path of tracer particles to obtain the airflow disturbance trajectory. The query of the airflow optimization path within the airflow coverage range can be achieved through a streamline topology optimization algorithm. For example, the Dijkstra algorithm is applied to search for the minimum resistance path in the velocity field, and the optimal streamline is generated in combination with the wall distance constraint to obtain 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 efficient control nodes in the air control index, and formulate an adaptive dust prevention plan for the target charging pile based on the efficient control nodes.

[0125] By monitoring the dust deposition state in the airflow optimization path, the present invention can judge problems such as filter load and airflow dead angles, facilitating 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, extending the equipment life, and ensuring the stable operation of the charging pile in a high-dust environment.

[0126] Among them, the dust deposition state refers to the adhesion and accumulation of dust particles in the airflow optimization path, including characteristics such as deposition location, thickness, and particle size distribution. It reflects the actual dust blocking ability of the ventilation system and the airflow cleaning effect. For example, at the edge of the deflector downstream of the charging pile filter, if dust accumulation with an average thickness of 0.5 mm is detected and the particle size is mainly 5 - 10 μm, it indicates that a deposition hot spot is formed in this area due to the reduction of the airflow velocity, and it is necessary to adjust the airflow path or increase the filter cleaning frequency accordingly. Optionally, the monitoring of the dust deposition state in the airflow optimization path can be achieved through laser-induced breakdown spectroscopy technology. For example, a LIBS sensor is used to analyze the elemental composition of the surface of the key path, and the dust deposition thickness is identified through characteristic spectra to obtain the dust deposition state.

[0127] Furthermore, based on the dust deposition state, the present invention dynamically adjusts the air control indicators corresponding to the ventilation control components, which can optimize the energy efficiency of the ventilation system, reduce unnecessary energy consumption, enhance the adaptability of the charging pile in a complex dust environment, reduce the failure risk, extend the maintenance cycle, and ensure long-term stable operation.

[0128] Among them, the air control indicators refer to the key parameters for adjusting the operation state of the ventilation system, including fan speed, air flow velocity, air pressure, filter cleaning frequency, etc. For example, when the dynamic air pressure threshold is triggered, the air control indicators are adjusted as follows: the fan speed is increased from 1500 rpm to 2000 rpm, the air flow velocity is increased from 5 m / s to 8 m / s, and at the same time, the filter backwashing program is started (once every 2 hours) to remove the dust in the high-aggregation area.

[0129] As an embodiment of the present invention, the dynamic adjustment of the air control indicators corresponding to the ventilation control components based on the dust deposition state includes: analyzing the deposition thickness distribution data corresponding to the dust deposition state; determining the dust aggregation level corresponding to the deposition thickness distribution data; analyzing the ventilation braking gradient corresponding to the dust aggregation level; matching the dynamic air pressure threshold corresponding to the ventilation braking gradient; and dynamically adjusting the air control indicators corresponding to the ventilation control components based on the dynamic air pressure threshold.

[0130] Among them, the deposition thickness distribution data refers to a set of quantitative data of the dust deposition thickness at each monitoring point in the optimized air flow path, which is obtained through laser ranging or weighing sensors. For example, dust thicknesses of 0.3 mm, 0.5 mm, and 0.1 mm are measured on the charging pile filter, the deflector, and the surface of the charging module respectively, forming distribution data of "filter > deflector > module", which intuitively reflects the dust accumulation differences at different positions; the dust aggregation level refers to the level of the severity of dust accumulation divided according to the deposition thickness distribution data, usually set to 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 deflector is 0.6 mm, it is determined as "high aggregation level", indicating that the dust in this area has significantly affected the air flow and urgent intervention is required; the ventilation braking gradient refers to the attenuation rate of the air flow velocity that the ventilation system needs to adjust corresponding to the dust aggregation level. At a high aggregation level, in order to wash away the deposited dust, the flow velocity needs to be increased (negative gradient, such as +5 m / s·level); at a low aggregation level, the flow velocity can be reduced to save energy (positive gradient, such as -3 m / s·level). For example, when the dust aggregation 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 by combining the ventilation braking gradient and the equipment pressure-bearing capacity, which is used to control the fan power output. For example, when the dust aggregation level is "high" and the ventilation braking gradient requires the flow velocity to be increased to 8 m / s, the calculated dynamic wind pressure threshold is 200 Pa. If the measured wind pressure is lower than this value, the fan automatically increases the power until it reaches the standard.

[0131] Furthermore, the analysis of the deposition thickness distribution data corresponding to the dust deposition state can be achieved through laser triangulation ranging technology. For example, an LVDT displacement sensor array is used to scan the surface profile and combined with Kalman filter noise reduction processing to obtain the deposition thickness distribution data; the determination of the dust aggregation level corresponding to the deposition thickness distribution data can be achieved through density clustering algorithms. For example, the OPTICS algorithm is applied to identify the spatial density characteristics of the thickness data and divide three aggregation areas to obtain the dust aggregation level; the analysis of the ventilation braking gradient corresponding to the dust aggregation level can be achieved through computational fluid-particle coupling simulation. For example, based on the DEM-CFD coupling method, the critical value of particle resuspension at different wind speeds is simulated to obtain the ventilation braking gradient; the matching of the dynamic wind pressure threshold corresponding to the ventilation braking gradient can be achieved through reinforcement learning algorithms. For example, the DDPG algorithm is used to train the dynamic balance strategy of wind pressure and deposition rate to obtain the dynamic wind pressure threshold; the dynamic adjustment of the air control index corresponding to the ventilation control component can be achieved through model predictive control. For example, an MPC controller including deposition state feedback is established to optimize the rotation speed - deflector parameters every 10 seconds to obtain the air control index.

[0132] By identifying the efficient regulation nodes in the air control indicators, the present invention can shorten the regulation response time, improve the system sensitivity, reduce the energy consumption waste caused by ineffective parameter adjustment, and ensure the efficient and stable operation of the charging pile under complex working conditions.

[0133] Among them, the efficient regulation nodes refer to the key parameters or component points in the air control index system of the ventilation system that play a decisive role in dust deposition control and air flow efficiency optimization. These nodes have the characteristics of "high adjustment sensitivity and wide influence range", and their slight adjustment can significantly change the overall ventilation efficiency. For example, when the filter of the charging pile is blocked, the fan speed (directly affecting the air pressure) and the angle of the main ventilation port deflector (changing the air flow direction) belong to the efficient regulation nodes. Optionally, the identification of the efficient regulation nodes in the air control indicators can be achieved through the graph theory network analysis method. For example, the PageRank algorithm is used to rank the influence of the ventilation network nodes, and the top 20% of the high-influence nodes are selected as the key regulation points, so as to obtain the efficient regulation nodes.

[0134] Furthermore, based on the efficient regulation nodes, the present invention formulates an adaptive dust prevention plan for the target charging pile, which can accurately focus on the key links of the ventilation system. By dynamically adjusting the core parameters such as the fan speed and the angle of the deflector, it can respond to the change of dust deposition in real time, effectively reduce the risk of filter blockage, avoid equipment failures caused by dust accumulation, ensure the stable operation of the target charging pile in different dust environments, and extend the service life of the equipment.

[0135] Among them, the adaptive dust prevention plan refers to an intelligent dust prevention strategy that automatically adjusts the ventilation parameters based on the efficient regulation nodes, combined with the real-time dust deposition state and environmental changes. It accurately controls the air flow velocity, air pressure and filter cleaning frequency by dynamically matching conditions such as dust concentration and particle size. For example, when a high-dust environment (such as around a construction site) is detected, the plan automatically increases the fan speed to 2200 rpm, increases the air pressure of the main ventilation port to 250 Pa, and shortens the filter backwashing cycle to once per hour, forming an adaptive mode of "strong air flow scouring + high-frequency cleaning", effectively intercepting more than 90% of the dust particles, and at the same time avoiding excessive energy consumption in low-dust scenarios. Optionally, the formulation of the adaptive dust prevention plan for the target charging pile can be achieved through the prediction of the remaining service life. For example, the ConvLSTM network is used to analyze the historical operation data to predict the filter replacement cycle, so as to obtain the adaptive dust prevention plan.

[0136] Compared with the problems described in the background art, the present invention can obtain the in-pile detection environment corresponding to the target charging pile, and can grasp key information such as the internal temperature, humidity, and dust concentration of the charging pile in real time, 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, reduce energy consumption, and can also early warn of the performance attenuation or fault hidden danger of electrical components. Based on the dust prevention priority, the present invention adjusts the ventilation control component corresponding to the target charging pile, which can achieve the precise adaptation of dust prevention measures, thereby improving the dust prevention effect and avoiding energy consumption waste caused by excessive intervention, ensuring that the charging pile can operate efficiently and safely in different dust environments. Further, the present invention can judge the dust interception efficiency of the filter screen and whether the air flow can effectively block dust by detecting the dust prevention index corresponding to the air flow adjustment rate, timely discover hidden dangers such as filter screen blockage, facilitate targeted maintenance, ensure the cleanliness inside the charging pile, avoid failures caused by dust accumulation, extend the service life of the equipment, and ensure its stable and reliable operation. Further, the present invention can realize the intelligent switching and precise matching of the ventilation strategy by logically controlling the ventilation control component based on the multi-mode control architecture, and obtain the component ventilation logic. And by analyzing the control data in different modes, positioning the core ventilation nodes and optimizing the start-stop timing, it can dynamically balance the heat dissipation, dust prevention and energy consumption requirements of the charging pile, ensure the stable and efficient operation of the charging pile in multiple scenarios. Finally, the present invention can judge problems such as filter screen load and air flow dead corners by monitoring the dust deposition state in the air flow 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, extending the equipment life and ensuring the stable operation of the charging pile in a high-dust environment. Therefore, the method and system for efficient ventilation and dust prevention of a charging pile based on intelligent sensing control provided by the embodiments of the present invention 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 a high-efficiency ventilation and dust prevention system for a charging pile based on intelligent sensing control of the present invention.

[0139] The high-efficiency ventilation and dust prevention system 200 for a charging pile based on intelligent sensing control of the present invention can be installed in an electronic device. According to the functions achieved, the high-efficiency ventilation and dust prevention system for a charging pile based on intelligent sensing control can include a priority determination module 201, a rate calculation module 202, an architecture construction module 203, a path query module 204, and a solution formulation module 205. The modules of the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by the processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

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

[0141] The priority determination module 201 is configured 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-proof priority corresponding to the target charging pile according to the ventilation requirements;

[0142] The rate calculation module 202 is configured to adjust the ventilation control component corresponding to the target charging pile based on the dust-proof priority, perform performance detection on the ventilation control component to obtain component performance parameters, and calculate the air flow adjustment rate corresponding to the ventilation control component based on the component performance parameters;

[0143] The architecture construction module 203 is configured to detect the dust-proof index corresponding to the air flow adjustment rate, calculate the ventilation energy consumption ratio corresponding to the target charging pile based on the dust-proof index combined with the real-time load index of the target charging pile, and construct a multi-mode control 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 control on the ventilation control component based on the multi-mode control architecture to obtain component ventilation logic, analyze the air flow coverage range corresponding to the component ventilation logic, and query the air flow optimization path within the air flow coverage range;

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

[0146] Specifically, each module in the efficient ventilation and dust-proof system 200 for charging piles based on intelligent sensing control in the embodiments of the present invention adopts the same technical means as the Figure 1 efficient ventilation and dust-proof method for charging piles based on intelligent sensing control described above, and can produce the same technical effects, which will not be elaborated here.

[0147] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

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

Claims

1. An efficient ventilation and dust prevention method for charging piles based on intelligent sensing regulation, characterized in that, The method includes: Obtaining the in-pile detection environment corresponding to the target charging pile, analyzing the ventilation demand corresponding to the target charging pile based on the in-pile detection environment, and determining the dust prevention priority corresponding to the target charging pile according to the ventilation demand; Adjusting the ventilation control component corresponding to the target charging pile based on the dust prevention priority, performing performance detection on the ventilation control component to obtain component performance parameters, and calculating the air flow adjustment rate corresponding to the ventilation control component based on the component performance parameters; Detecting the dust prevention index corresponding to the air flow adjustment rate, calculating the ventilation energy consumption ratio corresponding to the target charging pile based on the dust prevention index combined with the real-time load index of the target charging pile, and constructing a multi-mode control architecture corresponding to the target charging pile based on the ventilation energy consumption ratio; Performing logical control on the ventilation control component based on the multi-mode control architecture to obtain component ventilation logic, analyzing the air flow coverage range corresponding to the component ventilation logic, and querying the air flow optimization path within the air flow coverage range; Monitoring the dust deposition state in the air flow optimization path, dynamically adjusting the air flow control index corresponding to the ventilation control component based on the dust deposition state, identifying the efficient control nodes in the air flow control index, and formulating an adaptive dust prevention plan corresponding to the target charging pile based on the efficient control nodes.

2. The method for efficient ventilation and dust prevention of a charging pile based on intelligent sensing regulation according to claim 1, wherein, The determining the dust prevention priority corresponding to the target charging pile according to the ventilation demand includes: Analyzing the air flow rate data corresponding to the ventilation demand; Dividing the dust retention area corresponding to the target charging pile based on the air flow rate data; Collecting the dust particle concentration in the dust retention area; Analyzing the dust accumulation risk index corresponding to the dust particle concentration; Determining the dust prevention priority corresponding to the target charging pile based on the dust accumulation risk index.

3. The method for efficient ventilation and dust prevention of a charging pile based on intelligent sensing regulation according to claim 1, characterized in that, The adjusting the ventilation control component corresponding to the target charging pile based on the dust prevention priority 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 air flow disturbance amplitude corresponding to the ventilation grid nodes; Selecting the inefficient grid nodes with the air flow disturbance amplitude lower than the preset threshold; Adjusting the ventilation control component corresponding to the target charging pile according to the inefficient grid nodes.

4. The method for efficient ventilation and dust prevention of a charging pile based on intelligent sensing regulation according to claim 1, wherein The calculating the air flow adjustment rate corresponding to the ventilation control component based on the component performance parameters includes: Calculating the air flow adjustment rate corresponding to the ventilation control component by using the following formula: Among them, V adj represents the air flow regulation 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, and α i represents the weight coefficient of the i-th type of component, P i represents the performance parameter value of the i-th type of component, R i represents the resistance-related parameter of the i-th type of component, m represents the total number of parameters of the component performance parameter, j represents the number index of the component performance parameter, and β j represents the influence coefficient corresponding to the j-th component performance parameter, D j represents the measured value of the parameter corresponding to the j-th component performance parameter.

5. The method for efficient ventilation and dust prevention of a charging pile based on intelligent sensing regulation according to claim 1, characterized in that, The calculating the ventilation energy consumption ratio corresponding to the target charging pile based on the dust prevention index combined with the real-time load index of the target charging pile includes: Calculating the ventilation energy consumption ratio corresponding to the target charging pile by using the following formula: Among them, 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 start time and end 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.

6. The method for efficient ventilation and dust prevention of a charging pile based on intelligent sensing regulation according to claim 1, characterized in that, The constructing the multi-mode control architecture corresponding to the target charging pile based on the ventilation energy consumption ratio includes: 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 the multi-mode closed-loop data corresponding to the target charging pile according to the fan speed gradient; Construct a multi-mode regulation architecture corresponding to the target charging pile based on the multi-mode closed-loop data.

7. The method for efficient ventilation and dust prevention of a charging pile based on intelligent sensing regulation according to claim 1, wherein Based on the multi-mode regulation architecture, perform logical regulation on the ventilation regulation component to obtain the component ventilation logic, including: Analyze the ventilation regulation mode corresponding to the multi-mode regulation architecture; Query the mode regulation data under the ventilation regulation mode; Based on the mode regulation data, locate the core ventilation nodes in the ventilation regulation component; Integrate the ventilation start / stop time sequence corresponding to the core ventilation nodes; Based on the ventilation start / stop time sequence, perform logical regulation on the ventilation regulation component to obtain the component ventilation logic.

8. The method for efficient ventilation and dust prevention of a charging pile based on intelligent sensing regulation according to claim 1, wherein The query of the airflow optimization path within the airflow coverage range includes: Determine the high-turbulence area corresponding to the airflow coverage range; Extract the vortex intensity index in the high-turbulence area; Based on the vortex intensity index, calculate the airflow disturbance coefficient in the high-turbulence area; Analyze the airflow disturbance trajectory corresponding to the airflow disturbance coefficient; Based on the airflow disturbance trajectory, query the airflow optimization path within the airflow coverage range.

9. The method for efficient ventilation and dust prevention of a charging pile based on intelligent sensing regulation according to claim 1, characterized in that, Based on the dust deposition state, dynamically adjust the air control index corresponding to the ventilation regulation component, including: Analyze the deposition thickness distribution data corresponding to the dust deposition state; Determine the dust aggregation level corresponding to the deposition thickness distribution data; Analyze the ventilation braking gradient corresponding to the dust aggregation level; Match the dynamic wind pressure threshold corresponding to the ventilation braking gradient; Based on the dynamic wind pressure threshold, dynamically adjust the air control index corresponding to the ventilation regulation component.

10. An efficient ventilation and dust-proof system for charging piles based on intelligent sensing and regulation, characterized in that, The system includes: A priority determination module, configured 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; A rate calculation module, configured to adjust the ventilation regulation component corresponding to the target charging pile based on the dust prevention priority, perform performance detection on the ventilation regulation component to obtain component performance parameters, and calculate the airflow adjustment rate corresponding to the ventilation regulation component based on the component performance parameters; An architecture construction module, configured to detect the dust prevention index corresponding to the airflow adjustment rate, calculate the ventilation energy consumption ratio corresponding to the target charging pile based on the dust prevention index combined with the 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; A path query module, configured to perform logical regulation on the ventilation regulation component based on the multi-mode regulation architecture to obtain the component ventilation logic, analyze the airflow coverage range corresponding to the component ventilation logic, and query the airflow optimization path within the airflow coverage range; A solution formulation module, configured to monitor the dust deposition state in the airflow optimization path, dynamically adjust the air control index corresponding to the ventilation regulation component based on the dust deposition state, identify the efficient regulation nodes in the air control index, and formulate an adaptive dust prevention solution corresponding to the target charging pile based on the efficient regulation nodes.

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