Charging pile multi-dimensional dynamic noise reduction control method based on space-time perception

Abstract

The invention relates to the technical field of charging pile system design and software control, and discloses a charging pile multi-dimensional dynamic noise reduction control method based on space-time perception, and the method comprises the following steps: obtaining a physical address of a charging pile, and analyzing an environment type; determining a spatial weight in combination with crowd density and cell distribution, and dynamically adjusting a noise control threshold; double time dimension weights of time and seasons are introduced, and the rotating speed is flexibly adjusted; based on a fuzzy weight distribution mechanism of space-time parameters, time segmentation and seasonal parameter dynamic adjustment rotating speed coefficients are fused, and a decision is made in combination with an environment perception layer. According to the method, space factors such as crowd density and cell distribution are combined, a noise control threshold value is dynamically adjusted, and one-cell-one-value is achieved; double time dimension parameters of time and seasons are introduced, the rotating speed is flexibly adjusted, meanwhile, the heat dissipation efficiency of equipment is preferentially guaranteed within the noise control range at the high temperature in summer, and one value at a time is achieved.

Description

Technical Field

[0001] This invention relates to the fields of charging pile system design and software control technology, specifically to a multi-dimensional dynamic noise reduction control method for charging piles based on spatiotemporal awareness. Background Technology

[0002] With the rapid popularization of new energy vehicles, the number of charging stations has surged, and the environmental problems arising from their operation have become increasingly prominent. Taking noise pollution as an example, the low-frequency noise generated by the continuous operation of the charging system's cooling fans, especially at night near residential areas, easily leads to complaints from residents, hindering station operations and reducing user satisfaction. This negative social effect caused by location characteristics has become a key factor restricting the sustainable operation of charging stations.

[0003] The limitations of traditional noise control strategies are mainly reflected in three aspects: First, insufficient spatial adaptability, using a uniform fan speed threshold without distinguishing between commercial areas, residential areas, or differences in population density around the site; second, crude time management, adjusting the speed only through preset time periods, lacking dynamic response to changes in day and night duration and residents' activity patterns; third, imbalance in target coordination, where simply reducing speed may reduce noise but may sacrifice charging efficiency, creating a conflict between user experience and equipment performance. This rigid model is difficult to meet the needs of refined operation.

[0004] The current contradiction reflects a common pain point in the industry—how to achieve a win-win situation of environmental friendliness and commercial benefits through technological means against the backdrop of rapid expansion of new energy infrastructure. This requires noise management to move beyond a one-size-fits-all approach and upgrade towards intelligent spatial perception, refined temporal granularity, and multi-objective dynamic optimization. Summary of the Invention

[0005] To address existing problems, this invention aims to provide a multi-dimensional dynamic noise reduction control method for charging piles based on spatiotemporal awareness. This method obtains the physical address of the charging pile through network connectivity and analyzes the environmental type. Combining spatial factors such as population density and community distribution, it dynamically adjusts the noise control threshold to achieve "one value per area." By introducing dual time-dimensional parameters of time and season, it flexibly adjusts the rotation speed. Simultaneously, during high summer temperatures, it prioritizes ensuring the heat dissipation efficiency of the equipment within the noise control range, achieving "one value per time." Based on a fuzzy weight allocation mechanism using spatiotemporal parameters, it adaptively optimizes multiple objectives related to noise, heat dissipation, and energy consumption.

[0006] To achieve the above objectives, the present invention provides the following technical solution.

[0007] This invention provides a multi-dimensional dynamic noise reduction control method for charging piles based on spatiotemporal awareness, which obtains the physical address of the charging pile and parses the environment type; Spatial weights are determined by combining population density and community distribution, and noise control thresholds are dynamically adjusted. By introducing dual time-dimensional weights of time and season, the rotation speed can be flexibly adjusted; A fuzzy weight allocation mechanism based on spatiotemporal parameters is used to dynamically adjust the rotation speed coefficient by integrating time segmentation and seasonal parameters, and to make decisions in conjunction with the environmental perception layer.

[0008] As a further improvement of the present invention, the step of obtaining the physical address of the charging pile and resolving the environment type includes: resolving the latitude and longitude through the public IP of the 4G card in the charging pile, and using the Gaode Map Web Service API to calculate the number of communities N within a 1 square kilometer radius after obtaining the latitude and longitude.

[0009] As a further improvement of the present invention, the determination of spatial weight by combining population density and community distribution includes: spatial weight W s =min(1.0, 0.2×N), where N is the number of cells within 1 square kilometer; when the number of cells N is greater than or equal to 5, N is counted as 5.

[0010] As a further improvement of the present invention, the dynamic adjustment of the noise control threshold includes: when the ambient temperature of the charging pile exceeds the threshold T critical At 75℃, the safety mode is triggered, at which point the reference speed R is forcibly executed. base Reference speed R base This is the system fan operating at 100% maximum speed.

[0011] As a further improvement of the present invention, when the temperature drops to 50°C or below after the safety mode is triggered, the noise reduction control is restarted.

[0012] As a further improvement of the present invention, the restart noise reduction control includes: maintaining the minimum operating speed R of the system fan. min It is 30%.

[0013] As a further improvement of the present invention, the introduction of dual time dimension weights of time and season includes: When tϵ [daytime period], the time weight W t = 0.9, where the daytime hours in summer are 6:00-19:30, the daytime hours in winter are 7:00-17:30, and the daytime hours in spring and autumn are 7:00-18:00; When tϵ [evening period], the time weight W t = 0.7, where the evening hours in summer are 19:30-22:30, the evening hours in winter are 17:30-22:00, and the evening hours in spring and autumn are 18:00-22:00; When tϵ [nighttime period], the time weight W t= 0.6, where the nighttime hours in summer are 22:30-6:00, the nighttime hours in winter are 2:00-7:00, and the nighttime hours in spring and autumn are 22:00-7:00; When time t is in winter, which is from December to February, the seasonal weight S = 0.9; When time t is in summer and winter is from June to August, the seasonal weight S = 1.1; When time t falls in spring or autumn, spring is from March to May and autumn is from September to November, the seasonal weight S = 1.0.

[0014] As a further improvement of the present invention, the fuzzy weight allocation mechanism based on spatiotemporal parameters, which integrates time segmentation and seasonal parameters to dynamically adjust the speed coefficient, includes: when N is greater than or equal to 1, the speed coefficient is W. s *W t *S.

[0015] As a further improvement of the present invention, the decision-making process in conjunction with the environmental perception layer includes: executing the decision that when N=0, free heat dissipation is allowed; when N is between 1 and 5, noise reduction control is initiated, and the rotational speed under noise reduction control is R=max(R min R base *W s *W t *S), where R min It is 30%.

[0016] The present invention also provides a computer storage medium storing a computer-executable program, the computer-executable program being used to execute the aforementioned spatiotemporal awareness-based multidimensional dynamic noise reduction control method for charging piles.

[0017] The present invention has the following beneficial effects: This invention achieves dynamic and precise control of charging pile noise through multi-dimensional spatiotemporal perception and parameter fusion. It effectively reduces the impact of noise on the surrounding environment and rationally adjusts equipment operation status according to different situations, improving energy efficiency and enhancing user experience. By acquiring the physical address of the charging pile through network connectivity and analyzing the environment type, combined with spatial factors such as population density and community distribution, it dynamically adjusts the noise control threshold to achieve "one value per area." By introducing dual time-dimensional parameters of time and season, it flexibly adjusts the rotation speed, while prioritizing heat dissipation efficiency within the noise control range during high summer temperatures, achieving "one value per time." Based on a fuzzy weight allocation mechanism using spatiotemporal parameters, it adaptively optimizes multiple objectives related to noise, heat dissipation, and energy consumption.

[0018] Preferably, the geographical location information of the charging pile is accurately obtained, and the characteristics of its surrounding environment (distribution of residential areas) are accurately understood, so as to provide basic data for subsequent noise control based on environmental characteristics, making the control strategy more targeted and reasonable.

[0019] Preferably, determining the spatial weight by quantifying the number of cells can intuitively reflect the impact of the spatial characteristics of the charging pile's location on noise control. Setting an upper limit (N is 5) avoids an unreasonable increase in weight due to an excessive number of cells, ensuring that the weight value is within a reasonable range, making noise control more scientific and stable. At the same time, it avoids excessive noise reduction that could lead to excessively low operating speed, affecting heat dissipation performance and thus reducing lifespan.

[0020] Preferably, when the ambient temperature of the charging pile is too high and may affect the safe operation of the equipment, the safety mode is triggered in time and the fan speed is forcibly increased to enhance the heat dissipation capacity, effectively protect the charging pile equipment, avoid equipment damage or failure due to overheating, and extend the service life of the equipment.

[0021] Preferably, after ensuring that the equipment has safely passed the high-temperature danger period, the noise reduction control function is restored in a timely manner. This ensures that the equipment operates at a safe temperature while also taking into account noise control requirements, avoiding excessive noise caused by long-term high-speed operation, and achieving a balance between safety and noise reduction.

[0022] Preferably, setting a minimum operating speed can prevent insufficient heat dissipation caused by excessively low fan speed, and at the same time avoid unstable operation of the fan when the speed is too low, ensuring that the charging pile can still maintain basic heat dissipation performance after noise reduction control is activated, and ensuring normal operation of the equipment.

[0023] Prioritizing the different levels of noise sensitivity among people at different times of day, time-based weighting is rationally allocated. During the day, people are more active and have a relatively higher tolerance for noise, so a larger weight is assigned; at night, people are resting and more sensitive to noise, so a smaller weight is assigned. This allows for flexible adjustment of the charging pile's fan speed at different times of day, achieving more user-friendly noise control. Considering the impact of seasonal temperature differences on the charging pile's heat dissipation and noise, seasonal weighting is also set. In summer, high ambient temperatures require stronger heat dissipation, so a higher weight is assigned; in winter, low ambient temperatures require relatively less heat dissipation, so a lower weight is assigned; and in spring and autumn, with moderate climates, a weight of 1.0 is assigned. This allows for optimization of fan speed based on seasonal characteristics, improving energy efficiency while better controlling noise.

[0024] Preferably, the rotational speed coefficient is dynamically adjusted through a fuzzy weight allocation mechanism that integrates spatial weight, temporal weight, and seasonal weight.

[0025] Preferably, different operational decisions are made based on the number of residential communities surrounding the charging station (reflecting environmental characteristics). When there are no communities nearby, free cooling is used to reduce energy consumption; when there are communities nearby, noise reduction control is activated to reduce noise interference to residents' lives. This decision-making method enables the charging station to intelligently adjust its operating mode according to the actual environment, and can accurately calculate the appropriate fan speed based on the specific spatiotemporal environment of the charging station. Maximum and minimum speed limits are set to ensure that the speed fluctuates within a reasonable range, achieving the best balance between noise control and equipment heat dissipation.

[0026] By storing a computer-executable program, this method can be run and implemented in a computer system as software, which facilitates the promotion and application of the noise reduction control method, improves the operability and practicality of the method, and can be more widely applied to charging pile equipment to achieve large-scale noise control optimization. Attached Figure Description

[0027] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of the invention in any way. Furthermore, the shapes and proportions of the components in the drawings are merely schematic to aid in understanding the invention and are not intended to specifically limit the shapes and proportions of the components. In the drawings: Figure 1 This is a flowchart illustrating the steps of a multi-dimensional dynamic noise reduction control method for charging piles based on spatiotemporal awareness in Example 1. Figure 2 This is a diagram illustrating the spatial weight acquisition steps of a spatiotemporal-aware multidimensional dynamic noise reduction control method for charging piles in Example 1. Figure 3 This is a flowchart illustrating the steps of introducing seasonal weights in a spatiotemporal-aware multidimensional dynamic noise reduction control method for charging piles in Example 1. Figure 4 This is the core noise reduction logic architecture of a multi-dimensional dynamic noise reduction control method for charging piles based on spatiotemporal awareness in Example 4. Detailed Implementation

[0028] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0029] It should be noted that when an element is referred to as being "set on" another element, it can be directly on the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only embodiments.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0031] Example 1 like Figure 1 As shown, this embodiment provides a multi-dimensional dynamic noise reduction control method for charging piles based on spatiotemporal awareness, including the following steps: obtaining the physical address of the charging pile and parsing the environment type; Spatial weights are determined by combining population density and community distribution, and noise control thresholds are dynamically adjusted. By introducing dual time-dimensional weights of time and season, the rotation speed can be flexibly adjusted; A fuzzy weight allocation mechanism based on spatiotemporal parameters is used to dynamically adjust the rotation speed coefficient by integrating time segmentation and seasonal parameters, and to make decisions in conjunction with the environmental perception layer.

[0032] Specifically, obtaining the physical address of the charging pile and resolving the environment type includes: resolving the latitude and longitude using the public IP address of the 4G card in the charging pile, and then using the Gaode Map Web Service API to calculate the number of residential areas N within a 1-square-kilometer radius. For example... Figure 2 As shown, firstly, the current location information is obtained by combining IP positioning with the Gaode SDK, and key parameters such as latitude and longitude, search radius, and building type are determined. Next, based on these parameters, relevant APIs are called, the returned response data is parsed, and duplicate data is removed. Finally, based on the parsed data, the number of cells in the area and their corresponding weights are calculated.

[0033] In this embodiment, N is 3.

[0034] Specifically, determining the spatial weights by combining population density and cell distribution includes: spatial weight W s =min(1.0,0.2×N), where N is the number of cells within 1 square kilometer; when the number of cells N is greater than or equal to 5, N is counted as 5. In this embodiment, W... s=min(1.0, 0.2×3)=0.6.

[0035] Specifically, the dynamic adjustment of the noise control threshold includes: when the ambient temperature of the charging pile exceeds the threshold T critical At 75℃, the safety mode is triggered, at which point the reference speed R is forcibly executed. base Reference speed R base This is the system fan operating at 100% maximum speed. The ambient temperature of the charging pile in this embodiment is... As a further improvement of the present invention, when the temperature drops to 50°C or below after the safety mode is triggered, the noise reduction control is restarted.

[0036] Specifically, the restart noise reduction control includes: maintaining the minimum operating speed R of the system fan. min It is 30%.

[0037] Specifically, the introduction of dual time dimension weights for time and season includes: When tϵ [daytime period], the time weight W t = 0.9, where the daytime hours in summer are 6:00-19:30, the daytime hours in winter are 7:00-17:30, and the daytime hours in spring and autumn are 7:00-18:00; When tϵ [evening period], the time weight W t = 0.7, where the evening hours in summer are 19:30-22:30, the evening hours in winter are 17:30-22:00, and the evening hours in spring and autumn are 18:00-22:00; When tϵ [nighttime period], the time weight W t = 0.6, where the nighttime hours in summer are 22:30-6:00, the nighttime hours in winter are 2:00-7:00, and the nighttime hours in spring and autumn are 22:00-7:00; When time t is in winter, which is from December to February, the seasonal weight S = 0.9; When time t is in summer and winter is from June to August, the seasonal weight S = 1.1; When time t falls in spring or autumn, spring is from March to May and autumn is from September to November, the seasonal weight S = 1.0.

[0038] like Figure 3 As shown, the seasonal weights mentioned above are obtained by matching the original values ​​of the seasonal baseline table in the database. Climate compensation is determined by comparing the monthly average temperature of the year with the annual average temperature. The compensation weights are optimized at the end of the quarter based on multiple objectives such as noise, heat dissipation, and energy consumption.

[0039] In this embodiment, time t is 18:00, which is in the evening period. t= 0.7; t is February 22, 2025, which is winter, S = 0.9.

[0040] Specifically, the fuzzy weight allocation mechanism based on spatiotemporal parameters, which integrates time segmentation and seasonal parameters to dynamically adjust the speed coefficient, includes: when N is greater than or equal to 1, the speed coefficient is W. s *W t *S.

[0041] Specifically, the decision-making process in conjunction with the environmental perception layer includes: executing the decision that when N=0, free heat dissipation is allowed; when N is between 1 and 5, noise reduction control is initiated, and the rotational speed under noise reduction control is R=max(R min R base *W s *W t *S), where R min It is 30%. In this embodiment, R = max(R min R base *W s *W t *S)=max(30%,100%*0.6*0.7*0.9)=37.8%, therefore, in this embodiment, the algorithm controls the fan speed at this moment to be 37.8% of the maximum speed.

[0042] The execution effect of this embodiment is 37.8% rotation speed and ≤50dB noise.

[0043] Example 2 W in this embodiment s =min(1.0, 0.2×5)=1.

[0044] In this embodiment, time t is 14:00, which is during daytime. t = 0.9; t is July 15, 2025, which is summer, S = 1.1.

[0045] R=max(Rmin,R base *W s *W t *S)=max(30%,100%*1*0.9*1.1)=99%, therefore, in this embodiment, the algorithm controls the fan speed to 99% of the maximum speed at this moment.

[0046] The execution effect of this embodiment is 99% rotation speed and approximately 70dB noise.

[0047] Example 3 The temperature monitoring mechanism includes: when the detected temperature is greater than or equal to 75°C, skipping the noise reduction algorithm and directly starting full-speed mode; when the detected temperature is less than or equal to 50°C, re-activating the noise reduction algorithm. The charging pile has a built-in temperature sensor that monitors the internal temperature in real time, ensuring rapid response under high-temperature conditions and preventing equipment damage due to overheating. Simultaneously, when the temperature drops below 50°C, it resumes normal operation mode, reducing unnecessary energy consumption. This mechanism, through dynamic adjustment, ensures system stability and energy efficiency.

[0048] Example 4 This embodiment also provides a computer storage medium storing a computer-executable program. The computer-executable program is used to execute the multi-dimensional dynamic noise reduction control method for charging piles based on spatiotemporal awareness. The program logic architecture stored in the storage medium can be the same as the three-layer logic architecture of the control method described below.

[0049] like Figure 4 As shown, the logical architecture of the control method consists of three control layers: a safety priority layer, which forces full-speed cooling when the maximum temperature of the charging pile exceeds the limit, and only triggers the speed adjustment strategy when the temperature falls below this limit; an environmental perception layer, which identifies the surrounding environment and the number of communities based on IP and map interfaces, and calculates weight values ​​according to community density; and a spatiotemporal decision layer, which dynamically adjusts the speed coefficient by integrating time segments and seasonal parameters, and makes decisions in conjunction with the environmental perception layer. This multi-layered control strategy ensures the stable operation of the charging pile in various environments, improving system reliability and user satisfaction.

[0050] The above embodiments are merely one of the implementation methods for achieving the technical solution of the present invention. The scope of protection claimed by the present invention is not limited to this embodiment, but also includes any variations, substitutions and other implementation methods that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention.

Claims

1. A multi-dimensional dynamic noise reduction control method for charging piles based on spatiotemporal perception, characterized in that, Includes the following steps: Obtain the physical address of the charging station and resolve the environment type; Spatial weights are determined by combining population density and community distribution, and noise control thresholds are dynamically adjusted. By introducing dual time-dimensional weights of time and season, the rotation speed can be flexibly adjusted; A fuzzy weight allocation mechanism based on spatiotemporal parameters is used to dynamically adjust the rotation speed coefficient by integrating time segmentation and seasonal parameters, and to make decisions in conjunction with the environmental perception layer.

2. The multi-dimensional dynamic noise reduction control method for charging piles based on spatiotemporal perception according to claim 1, characterized in that, The process of obtaining the physical address of the charging pile and resolving the environment type includes: resolving the latitude and longitude through the public IP address of the 4G card in the charging pile, and then using the Gaode Map Web Service API to calculate the number of residential communities N within a 1-square-kilometer radius.

3. The multi-dimensional dynamic noise reduction control method for charging piles based on spatiotemporal perception according to claim 2, characterized in that, The determination of spatial weights by combining population density and community distribution includes: spatial weight W s =min(1.0, 0.2×N), where N is the number of cells within 1 square kilometer; when the number of cells N is greater than or equal to 5, N is counted as 5.

4. The multi-dimensional dynamic noise reduction control method for charging piles based on spatiotemporal perception according to claim 1, characterized in that, The dynamically adjusted noise control threshold includes: when the ambient temperature of the charging pile exceeds the threshold T critical At 75℃, the safety mode is triggered, at which point the reference speed R is forcibly executed. base Reference speed R base This is the system fan operating at 100% maximum speed.

5. The multi-dimensional dynamic noise reduction control method for charging piles based on spatiotemporal perception according to claim 4, characterized in that, When the safety mode is triggered, noise reduction control will be activated again when the temperature drops to 50°C or below.

6. The multi-dimensional dynamic noise reduction control method for charging piles based on spatiotemporal perception according to claim 5, characterized in that, The restart noise reduction control includes: the minimum operating speed R of the system fan. min It is 30%.

7. The multi-dimensional dynamic noise reduction control method for charging piles based on spatiotemporal perception according to claim 6, characterized in that, The introduced dual time dimension weights of time and season include: When tϵ [daytime period], the time weight W t = 0.9, where the daytime hours in summer are 6:00-19:30, the daytime hours in winter are 7:00-17:30, and the daytime hours in spring and autumn are 7:00-18:00; When tϵ [evening period], the time weight W t = 0.7, where the evening hours in summer are 19:30-22:30, the evening hours in winter are 17:30-22:00, and the evening hours in spring and autumn are 18:00-22:00; When tϵ [nighttime period], the time weight W t = 0.6, where the nighttime hours in summer are 22:30-6:00, the nighttime hours in winter are 2:00-7:00, and the nighttime hours in spring and autumn are 22:00-7:00; When time t is in winter, which is from December to February, the seasonal weight S = 0.9; When time t is in summer and winter is from June to August, the seasonal weight S = 1.1; When time t falls in spring or autumn, spring is from March to May and autumn is from September to November, the seasonal weight S = 1.

0.

8. The multi-dimensional dynamic noise reduction control method for charging piles based on spatiotemporal perception according to claim 1, characterized in that, The fuzzy weight allocation mechanism based on spatiotemporal parameters, which integrates time segmentation and seasonal parameters to dynamically adjust the speed coefficient, includes the following: when N is greater than or equal to 1, the speed coefficient is W. s *W t *S.

9. The multi-dimensional dynamic noise reduction control method for charging piles based on spatiotemporal awareness according to claim 8, characterized in that, The decision-making process, combined with the environmental perception layer, includes: when N=0, allowing free heat dissipation; when N is between 1 and 5, initiating noise reduction control, with the rotational speed under noise reduction control R=max(R... min R base *W s *W t *S), where R min It is 30%.

10. A computer storage medium storing a computer-executable program, characterized in that, The computer-executable program is used to execute the spatiotemporal awareness-based multidimensional dynamic noise reduction control method for charging piles as described in any one of claims 1-9.

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