Signal multi-angle transceiving mechanism based on 5G base station

Abstract

The invention is suitable for the technical field of signal transmitting and receiving, and provides a signal multi-angle transmitting and receiving mechanism based on a 5G base station, which comprises an electric lifting rod, a telescopic part of the electric lifting rod is rotatably connected with a support frame, the support frame is fixedly connected with a signal receiving-transmitting device, and a driving assembly is arranged between the support frame and the electric lifting rod. The driving assembly can drive the supporting frame to rotate relative to the electric lifting rod, the center of the signal receiving-transmitting device is fixedly connected with a signal antenna, the signal receiving-transmitting device is connected with a dustproof mechanism used for filtering dust impurities, and the self-adaptive dust removing system is further included. According to the method, relevance judgment of the filter screen blockage degree and equipment performance attenuation is achieved, and adhesive dust is effectively removed by adjusting the vibration frequency and the duration time in the sand and dust and high-humidity composite environment.

Description

Technical Field

[0001] This invention belongs to the field of signal transceiver technology, and particularly relates to a multi-angle signal transceiver mechanism based on a 5G base station. Background Technology

[0002] With the rapid advancement of 5G network construction, base station signal transceivers, as key equipment for network coverage, directly impact communication quality. Traditional base station signal transceivers typically employ fixed-angle installations or simple mechanical adjustments, making it difficult to achieve flexible multi-angle coverage. While some improved solutions utilize electric lifting rods and drive components to achieve rotational adjustment of the support frame, these mechanical structures exhibit significant drawbacks during long-term outdoor operation: firstly, when the signal receiver / transmitter is exposed to complex environments, dust accumulation leads to signal attenuation and reduced heat dissipation efficiency; secondly, existing dustproof mechanisms mostly employ passive filtration designs, failing to dynamically adjust dust removal strategies based on actual operating conditions.

[0003] More importantly, base station signal quality is affected by a combination of factors, including but not limited to: the stability of the transmitter's power output, changes in antenna performance parameters, fluctuations in the receiver's signal sensitivity, and external electromagnetic interference and changes in weather conditions. These parameters have complex coupling relationships, and current technologies lack the ability to coordinate and intelligently analyze them. When equipment performance degrades, maintenance personnel often struggle to quickly pinpoint whether the problem is caused by dust accumulation, mechanical failure, or environmental interference, leading to the implementation of uniform maintenance measures, resulting in resource waste or untimely maintenance.

[0004] Furthermore, existing dust control systems lack data support for maintenance decisions, typically relying on fixed-cycle cleaning or manual observation. This approach fails to respond promptly to sudden increases in dust concentration and may lead to unnecessary maintenance even when dust levels are good. This extensive management not only increases operating costs but can also compromise equipment sealing due to frequent disassembly. Therefore, there is an urgent need for an intelligent solution that can assess equipment operating status, environmental conditions, and filtration system efficiency in real time, and achieve precise dust removal control. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-angle signal transceiver mechanism based on a 5G base station, which aims to solve the problem that the maintenance decision-making of existing dust prevention systems lacks data support, making it impossible to respond in a timely manner when the dust concentration suddenly increases, and may also require unnecessary maintenance when the dust is in good condition.

[0006] This invention is implemented as follows: a multi-angle signal transceiver mechanism based on a 5G base station includes an electric lifting rod, a support frame rotatably connected to the telescopic part of the electric lifting rod, a signal receiver-transmitter fixedly connected to the support frame, a drive assembly between the support frame and the electric lifting rod, the drive assembly capable of driving the support frame to rotate relative to the electric lifting rod, a signal antenna fixedly connected to the center of the signal receiver-transmitter, and a dustproof mechanism for filtering dust and impurities connected to the signal receiver-transmitter. It also includes an adaptive dust removal system, comprising:

[0007] Signal transmission status assessment module: Constructs a signal transmission status assessment model based on transmission power, antenna gain, antenna efficiency, and VSWR, and outputs transmission status assessment coefficients;

[0008] Signal reception status assessment module: Constructs a signal reception status assessment model based on received signal strength, received signal sensitivity, and signal-to-noise ratio, and outputs reception status assessment coefficients;

[0009] Overall institutional status assessment module: Under the influence of the transmission status assessment coefficient and the reception status assessment coefficient, the overall institutional status assessment model is constructed based on the external noise intensity and interference intensity, and the overall institutional status assessment coefficient is output.

[0010] External environment assessment module: Constructs an external environment assessment model based on external wind speed, dust concentration, and air humidity, and outputs external environment assessment coefficients;

[0011] Filtration status control module: Based on the pressure difference on both sides of the dustproof mechanism, the external environment assessment coefficient, and the overall status assessment coefficient of the mechanism, a filtration status control model is constructed, and the filtration status control coefficient is output. When the filtration status assessment coefficient exceeds the preset threshold, a judgment information is generated.

[0012] The control module can receive judgment information from the filter status control module and control the dustproof mechanism to vibrate.

[0013] In a further technical solution, the drive assembly includes a driven wheel, a motor, and a driving wheel;

[0014] The telescopic end of the electric lifting rod is fixedly connected to a driven wheel, the support frame is fixedly connected to a motor, and the output end of the motor is fixedly connected to a driving wheel, which can mesh with the driven wheel.

[0015] In a further technical solution, the dustproof mechanism includes a fixed frame, a dielectric dustproof filter, a spring, and a vibrator;

[0016] The mounting bracket is fixedly connected to the signal receiver-transmitter. A dielectric dustproof filter is slidably connected to the mounting bracket. Several springs are connected between the dielectric dustproof filter and the mounting bracket. A vibrator is fixedly connected to the dielectric dustproof filter.

[0017] A further technical solution involves substituting the transmit power, antenna gain, and VSWR into the maximum-minimum normalization formula for processing, and sequentially generating the transmit power index, antenna gain index, and VSWR index; the signal transmission state evaluation model is as follows:

[0018] ;

[0019] in This is the transmit power weighting coefficient. This is the antenna gain weighting coefficient. This represents the antenna efficiency gain coefficient. The standing wave ratio (SWR) weighting coefficient. ,and , , as well as All greater than ; This refers to the transmit power index. This is the antenna gain index. For antenna efficiency, Dimensionless The voltage standing wave ratio (VSWR) is the exponent. This is the launch status evaluation coefficient.

[0020] A further technical solution involves substituting the received signal strength, received signal sensitivity, and signal-to-noise ratio into the maximum-minimum normalization formula for processing, and sequentially generating the received signal strength index, received signal sensitivity index, and signal-to-noise ratio index; the signal reception status evaluation model is as follows:

[0021] ;

[0022] in For received signal strength weighting coefficients, For the received signal sensitivity weighting coefficient, This is the signal-to-noise ratio weighting coefficient. , , as well as All greater than ; The received signal strength index, The sensitivity index for receiving signals. The signal-to-noise ratio index. This is the reception status evaluation coefficient.

[0023] A further technical solution involves substituting the noise intensity and interference intensity into the maximum and minimum value normalization formula for processing, and generating the noise intensity index and interference intensity index in sequence.

[0024] The overall status assessment model for the organization is as follows:

[0025] ;

[0026] .

[0027] in The weighting coefficients are for the impact of noise. To mitigate the impact of interference on the weighting coefficients, ,and as well as All greater than ; Noise intensity index, The interference intensity index. This is the comprehensive evaluation coefficient for noise interference. For receiving status evaluation coefficients, For launch status evaluation coefficients, This is the overall status assessment coefficient for the organization.

[0028] A further technical solution involves substituting external wind speed, dust concentration, and air humidity into the maximum and minimum value normalization formula for processing, and generating wind speed index, dust concentration index, and air humidity index in sequence.

[0029] The external environment assessment model is as follows:

[0030] ;

[0031] in The weighting factor for the influence of wind speed. The weighting coefficient for the influence of dust concentration, The weighting factor for the influence of air humidity. ,and , as well as All greater than , Wind speed index The dust concentration index. The air humidity index. External environment assessment coefficient;

[0032] A further technical solution involves substituting the pressure difference across the dustproof mechanism into a maximum-minimum value normalization formula for processing, and generating a pressure difference index; the filtration state control model is as follows:

[0033] ;

[0034] in For environmental impact weighting coefficients, For the weighting coefficient of institutional influence, ,and , All greater than , The differential pressure index, External environment assessment coefficient, This is the overall status assessment coefficient of the organization. This is the filter state control coefficient.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] This application establishes a correlation between filter clogging and equipment performance degradation. In environments with combined dust and high humidity, it effectively removes adhesive dust by adjusting vibration frequency and duration. Simultaneously, in scenarios with strong electromagnetic interference, it suppresses accidental cleaning operations caused by noise through a comprehensive evaluation coefficient, extending filter lifespan. The coordinated operation of the multi-angle adjustment mechanism and the intelligent cleaning system enables base station equipment to maintain stable signal transmission and reception quality in complex environments, reducing the frequency of manual maintenance.

[0037] This application can quantify the comprehensive impact of noise and interference on equipment operation in real time, and generate dynamic evaluation results by combining transmission and reception performance indicators, providing accurate basis for vibration control of dustproof mechanisms and other maintenance decisions. For example, in scenarios with a sudden increase in electromagnetic interference, the system can quickly identify the overall equipment condition degradation trend and trigger dust removal operations in advance to avoid further degradation of signal quality, thereby improving the stability and reliability of base station operation.

[0038] This application enables dynamic quantitative assessment of the multi-dimensional impacts of the external environment, allowing dust control strategies to adaptively adjust based on real-time changes in wind speed, dust concentration, and humidity. For example, during typhoon weather, the combined effect of high wind speed and humidity indices significantly increases the value, thus triggering dust removal operations earlier to prevent rapid dust accumulation under strong winds. In dry, low-wind-speed environments, the system can reduce the frequency of dust removal to decrease energy consumption. This control method based on comprehensive environmental assessment effectively solves the maintenance lag or over-maintenance problems caused by neglecting the coupling effects between parameters in traditional methods.

[0039] This application implements dynamic correlation analysis between filter clogging degree and environmental factors and equipment operating status. When the equipment is in a high-humidity, low-dust environment, even if the pressure difference reaches the conventional threshold, the system can still delay cleaning to reduce unnecessary mechanical wear; while under critical performance degradation conditions, even if the pressure difference does not reach the threshold, the system can still trigger cleaning in advance to ensure signal quality. This solution reduces the frequency of manual maintenance by approximately 40%, while avoiding damage to the filter structure caused by excessive cleaning. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the structure of the present invention;

[0041] Figure 2 This is a schematic diagram of the driving component in this invention;

[0042] Figure 3 This is a schematic diagram of the dustproof mechanism in this invention;

[0043] Figure 4 This is a schematic diagram of the adaptive dust removal system in this invention.

[0044] In the attached diagram: 1. Electric lifting rod; 2. Support frame; 3. Signal receiver-transmitter; 4. Drive assembly; 41. Driven wheel; 42. Motor; 43. Drive wheel; 5. Signal antenna; 6. Dustproof mechanism; 61. Fixing frame; 62. Dielectric dustproof filter; 63. Spring; 64. Vibrator. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0046] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0047] like Figures 1-4 As shown, an embodiment of the present invention provides a multi-angle signal transceiver mechanism based on a 5G base station with a signal antenna, including an electric lifting rod 1. A support frame 2 is rotatably connected to the telescopic portion of the electric lifting rod 1. A signal receiver-transmitter 3 is fixedly connected to the support frame 2. A drive assembly 4 is provided between the support frame 2 and the electric lifting rod 1, and the drive assembly 4 can drive the support frame 2 to rotate relative to the electric lifting rod 1. A signal antenna 5 is fixedly connected to the center of the signal receiver-transmitter 3, and the signal receiver-transmitter 3 is connected to a dustproof mechanism 6 for filtering dust and impurities. The system also includes an adaptive dust removal system, comprising:

[0048] Signal transmission status assessment module: Constructs a signal transmission status assessment model based on transmission power, antenna gain, antenna efficiency, and VSWR, and outputs transmission status assessment coefficients;

[0049] Signal reception status assessment module: Constructs a signal reception status assessment model based on received signal strength, received signal sensitivity, and signal-to-noise ratio, and outputs reception status assessment coefficients;

[0050] Overall institutional status assessment module: Under the influence of the transmission status assessment coefficient and the reception status assessment coefficient, the overall institutional status assessment model is constructed based on the external noise intensity and interference intensity, and the overall institutional status assessment coefficient is output.

[0051] External environment assessment module: Constructs an external environment assessment model based on external wind speed, dust concentration, and air humidity, and outputs external environment assessment coefficients;

[0052] Filtering status control module: Based on the pressure difference on both sides of the dustproof mechanism 6, the external environment evaluation coefficient, and the overall status evaluation coefficient of the mechanism, a filtering status control model is constructed, and the filtering status control coefficient is output. When the filtering status evaluation coefficient exceeds the preset threshold, judgment information is generated.

[0053] The control module can receive the judgment information from the filter status control module and control the dustproof mechanism 6 to vibrate.

[0054] In this embodiment, the dustproof mechanism 6 includes a multi-layer filter made of dielectric material, specifically a composite structure with a polyimide film coated with a conductive layer, which filters dust while avoiding attenuation of high-frequency signals. Specifically, when the signal receiver-transmitter 3 adjusts its pitch angle under the drive of the support frame 2, the driving wheel 43 of the drive assembly 4 meshes with the driven wheel 41 to generate rotational torque. The dielectric dustproof filter 62 is kept in elastic contact with the fixed frame 61 by the preload of the spring 63, and the vibrator 64 generates high-frequency micro-amplitude vibrations that cause the attached dust to fall off. The signal transmission status assessment module collects the output power of the power amplifier and the antenna reflection coefficient in real time. After normalization to eliminate dimensional differences, the transmission power index, antenna gain index, and VSWR index are input into a linear weighted model. The overall mechanism status assessment module multiplies the average of the receiving status coefficient and the transmitting status coefficient by the noise interference comprehensive assessment coefficient to reflect the overall health of the equipment in a complex electromagnetic environment. When the external environment assessment coefficient shows a high dust concentration and the overall state coefficient of the mechanism is lower than the set value, the filtration state control model automatically increases the weight of the differential pressure monitoring data and triggers the vibration cleaning operation.

[0055] Compared to existing technologies, traditional solutions typically design the mechanical adjustment mechanism and dust control system independently, leading to structural deformation of the filter screen during angle adjustment. This solution, through the rotating connection design of the support frame 2 and the electric lifting rod 1, ensures uniform force on the filter screen during the rotation of the signal receiver-transmitter 3, preventing localized tearing. Existing dust removal control methods are mostly based on timers or single differential pressure thresholds. This solution, by integrating equipment performance parameters and environmental variables, can identify false blockage signals caused by electromagnetic interference, avoiding false triggering of dust removal actions. Compared to traditional fixed-weight evaluation methods, dynamically adjusted environmental and mechanical influence weight coefficients can more accurately reflect the impact of dust agglomeration on dust removal efficiency in high-humidity environments.

[0056] like Figure 2 As shown, in a preferred embodiment of the present invention, the drive assembly 4 includes a driven wheel 41, a motor 42, and a drive wheel 43;

[0057] The electric lifting rod 1 has a driven wheel 41 fixedly connected to its telescopic end, the support frame 2 has a motor 42 fixedly connected to its output end, and a driving wheel 43 fixedly connected to its output end. The driving wheel 43 can mesh with the driven wheel 41.

[0058] In this embodiment, when the motor 42 starts, it drives the driving wheel 43 to rotate. The driving wheel 43 and the driven wheel 41 transmit torque through tooth meshing, driving the driven wheel 41 to rotate around the axis of the electric lifting rod 1. Since the driven wheel 41 is directly fixed to the telescopic end of the lifting rod, its rotational motion is converted into the synchronous rotation of the support frame 2 around the axis of the lifting rod, thereby realizing the angle adjustment of the signal receiver-transmitter 3. The rigid connection between the motor 42 and the support frame 2 avoids changes in gear backlash caused by mechanism displacement during transmission, ensuring the stability of the meshing state. The module and number of teeth of the gear pair can be matched according to the load requirements. For example, a large module gear is used in scenarios requiring high torque, while a small module gear is used in scenarios requiring high speed, thereby adapting to the transmission requirements under different working conditions.

[0059] like Figure 3 As shown, in a preferred embodiment of the present invention, the dustproof mechanism 6 includes a fixing frame 61, a dielectric dustproof filter 62, a spring 63, and a vibrator 64;

[0060] The mounting bracket 61 is fixedly connected to the signal receiver-transmitter 3. A dielectric dustproof filter 62 is slidably connected to the mounting bracket 61. Several springs 63 are connected between the dielectric dustproof filter 62 and the mounting bracket 61. A vibrator 64 is fixedly connected to the dielectric dustproof filter 62.

[0061] In this embodiment, the dielectric dust filter 62 refers to a filter layer made of non-conductive material, specifically polycarbonate or ceramic composite material. Its function is to block dust while avoiding electromagnetic interference to the signal transmission path. The spring 63 refers to a mechanical element with elastic deformation capability, specifically a helical spring 63 or a disc spring 63. Its function is to absorb vibration energy through elastic deformation and provide a restoring force to the filter, preventing rigid collisions between the filter and the mounting bracket 61. The vibrator 64 refers to a driving device capable of generating high-frequency mechanical vibration, specifically an electromagnetic vibration motor or a piezoelectric ceramic vibrator 64. Its function is to remove dust adhering to the filter surface through periodic vibration.

[0062] Compared to existing technologies, traditional dust filters are typically fixed in place, requiring manual disassembly or high-pressure gas backflushing for cleaning, resulting in low maintenance efficiency and potential filter damage. This solution, however, achieves a self-cleaning function by combining a vibrator 64 with an elastic support structure, while utilizing the properties of dielectric materials to avoid negative impacts on signal transmission during the cleaning process.

[0063] In a preferred embodiment of the present invention, the transmit power, antenna gain, and voltage standing wave ratio are respectively substituted into the maximum and minimum value normalization formula for processing, and the transmit power index, antenna gain index, and voltage standing wave ratio index are generated sequentially; the signal transmission state evaluation model is as follows:

[0064] ;

[0065] in This is the transmit power weighting coefficient. This is the antenna gain weighting coefficient. This represents the antenna efficiency gain coefficient. The standing wave ratio (SWR) weighting coefficient. ,and , , as well as All greater than ; This refers to the transmit power index. This is the antenna gain index. For antenna efficiency, Dimensionless The voltage standing wave ratio (VSWR) is the exponent. This is the launch status evaluation coefficient.

[0066] In this embodiment, the maximum and minimum value normalization formula refers to linearly transforming the original parameter values ​​according to preset maximum and minimum values, mapping them to the range of 0 to 1, thus eliminating numerical differences between parameters with different dimensions. The transmit power weighting coefficient is a parameter that quantifies the importance of transmit power in the overall evaluation. It can be determined through expert experience or historical data analysis, for example, taking a value between 0.3 and 0.5, to reflect the degree of influence of transmit power on signal quality. The VSWR index is processed in a "1-N" form, meaning the normalized VSWR value is inverted, specifically implemented using subtraction, to characterize the negative impact of VSWR on signal reflection loss. The constraint that the sum of the weighting coefficients is 1 means that each weighting coefficient must meet the normalization requirement, specifically implemented through a linear constraint algorithm, to ensure the logical rationality of the evaluation model.

[0067] Specifically, the raw parameters of transmit power, antenna gain, and voltage standing wave ratio (VSWR) are first input into the normalization module. A linear transformation is performed using preset maximum and minimum values ​​to generate transmit power, antenna gain, and VSWR indices with uniform dimensions. Antenna efficiency, as a dimensionless parameter, is directly included in the calculation. Subsequently, each indices are weighted and summed according to preset weighting coefficients. The VSWR index is processed in a "1-N" form to reflect its negative impact on signal quality. The weighting coefficients are dynamically adjusted according to the actual scenario; for example, in high-temperature environments, the transmit power weight can be increased to prioritize heat dissipation. The final output transmit status evaluation coefficients can reflect the comprehensive status of the equipment's transmit performance in real time, providing a quantitative basis for subsequent maintenance decisions.

[0068] Through the above technical solution, this application solves the problem of transmission status assessment error caused by differences in parameter dimensions, and realizes dynamic fusion analysis of multi-dimensional parameters. The transmission status assessment coefficient can quantify the degree of attenuation of equipment transmission performance in real time, providing a precise threshold judgment basis for triggering the vibration of the dustproof mechanism 6, thereby avoiding maintenance delays or over-maintenance problems caused by inaccurate assessment. For example, when the standing wave ratio index rises abnormally due to dust accumulation, the model can quickly identify and trigger a dust removal operation to ensure stable signal transmission quality.

[0069] In a preferred embodiment of the present invention, the received signal strength, received signal sensitivity, and signal-to-noise ratio are respectively substituted into the maximum-minimum normalization formula for processing, and the received signal strength index, received signal sensitivity index, and signal-to-noise ratio index are generated sequentially; the signal reception state evaluation model is as follows:

[0070] ;

[0071] in For received signal strength weighting coefficients, For the received signal sensitivity weighting coefficient, This is the signal-to-noise ratio weighting coefficient. , , as well as All greater than ; The received signal strength index, The sensitivity index for receiving signals. The signal-to-noise ratio index. This is the reception status evaluation coefficient.

[0072] In this embodiment, the received signal strength index is a standardized indicator reflecting the current received signal energy level. Specifically, it can be generated by normalizing the signal strength measurement value output by the power detection module at the base station receiver. This index is used to characterize the real-time changes in signal coverage strength.

[0073] The receiver signal sensitivity index is a standardized metric reflecting a device's ability to identify weak signals. It is generated by normalizing the minimum identifiable signal threshold test data of a base station receiver. This index is used to quantify the device's performance in weak signal environments.

[0074] The signal-to-noise ratio (SNR) is a standardized metric reflecting the quality of signal transmission. Specifically, it is generated by normalizing the ratio of received signal power to background noise power. This index is used to assess the impact of electromagnetic interference on signal quality.

[0075] The weighting coefficient is a proportional factor that characterizes the contribution of each parameter to the reception status assessment. It can be dynamically adjusted using expert experience or machine learning algorithms. This coefficient is used to optimize the parameter sensitivity of the assessment model according to the needs of the actual scenario; for example, it can increase the signal-to-noise ratio weight in areas with severe electromagnetic interference.

[0076] Specifically, the raw measurement data of received signal strength, sensitivity, and signal-to-noise ratio are first normalized and converted into an exponential form with uniform dimensions. For example, when the measured received signal strength is -80dBm, and its historical maximum value is -60dBm and minimum value is -100dBm, the normalization exponent can be calculated as 0.5. Subsequently, the three exponents are weighted and summed according to preset weighting coefficients. For example, when the weighting coefficients are set to 0.4, 0.3, and 0.3 respectively, the received state evaluation coefficient output by the model will comprehensively reflect the combined impact of signal strength, equipment sensitivity, and environmental noise. This coefficient can dynamically characterize the degree of attenuation of received performance, and when the evaluation coefficient is lower than a preset threshold, it can trigger an equipment maintenance warning.

[0077] Compared to existing technologies, traditional methods typically monitor only a single parameter (e.g., focusing solely on received signal strength) or perform simple threshold judgments on multiple parameters. This proposed solution eliminates the dimensional differences among multiple parameters through normalization and dynamically adjusts the influence of each parameter on the evaluation results using weighting coefficients, enabling the evaluation model to adapt to different environmental conditions. For example, in scenarios with high signal strength but significant interference during the initial deployment of base stations, increasing the signal-to-noise ratio weighting coefficient can make the evaluation results more accurately reflect the actual performance bottlenecks.

[0078] Through the above technical solution, this application achieves a multi-dimensional dynamic evaluation of base station reception performance. By fusing signal strength, equipment sensitivity, and environmental noise parameters, it can accurately identify performance degradation caused by equipment aging, electromagnetic interference, or weak signal coverage. For example, when the signal-to-noise ratio drops sharply due to sudden interference, even if the signal strength index remains normal, the evaluation coefficient will still decrease significantly, thus reflecting potential problems in a timely manner. This evaluation result provides a quantitative basis for subsequent maintenance decisions, avoiding misjudgments or omissions caused by isolated parameter analysis in traditional methods.

[0079] In a preferred embodiment of the present invention, the noise intensity and the interference intensity are respectively substituted into the maximum and minimum value normalization formula for processing, and the noise intensity index and the interference intensity index are generated in sequence.

[0080] The overall status assessment model for the organization is as follows:

[0081] ;

[0082] .

[0083] in The weighting coefficients are for the impact of noise. To mitigate the impact of interference on the weighting coefficients, ,and as well as All greater than ; Noise intensity index, The interference intensity index. This is the comprehensive evaluation coefficient for noise interference. For receiving status evaluation coefficients, For launch status evaluation coefficients, This is the overall status assessment coefficient for the organization.

[0084] In this embodiment, the noise intensity index refers to a dimensionless parameter obtained by standardizing the noise intensity using a maximum-minimum normalization formula. Specifically, it can be achieved by using a sensor to collect noise intensity data in real time and then linearly mapping it to a preset maximum and minimum noise threshold, thus eliminating the influence of different dimensions on the evaluation results. The interference intensity index refers to a parameter obtained by normalizing the interference intensity using the same method. Specifically, it can be obtained using a spectrum analyzer to acquire the interference signal strength and calculate it using the normalization formula, thus quantifying the impact of external electromagnetic interference on the equipment. The noise interference comprehensive evaluation coefficient is the result of a weighted sum of the normalized noise intensity index and the interference intensity index using noise impact weighting coefficients and interference impact weighting coefficients. Specifically, it can be achieved by dynamically adjusting the weighting coefficients, reflecting the different priorities of noise and interference on the overall state of the equipment. The overall institutional state evaluation coefficient is a comprehensive evaluation value generated by combining the receiving state evaluation coefficient, the transmitting state evaluation coefficient, and the noise interference comprehensive evaluation coefficient. Specifically, it can be obtained by multiplying the average of the transmitting and receiving performance indicators by the noise interference comprehensive evaluation coefficient, thus characterizing the overall operating state of the equipment in a complex electromagnetic environment.

[0085] Specifically, real-time data on noise and interference intensity are collected by sensors and a spectrum analyzer, and then normalized to their maximum and minimum values ​​to convert them into exponential values ​​within the range of 0 to 1, eliminating the interference of dimensional differences on subsequent calculations. The comprehensive noise and interference evaluation coefficient is a weighted sum of the noise intensity index and the interference intensity index using preset weighting coefficients. Both the noise intensity index and the interference intensity index are expressed as 1 minus the normalized value, so that the stronger the noise or interference, the smaller the corresponding exponential value, thus reflecting its negative impact on the equipment status in the model. The average of the receiving status evaluation coefficient and the transmitting status evaluation coefficient is used to balance the equipment's two-way communication performance, and then multiplied by the comprehensive noise and interference evaluation coefficient, so that the constraints of the external electromagnetic environment on equipment performance are dynamically reflected in the final evaluation result. For example, when external noise and interference increase, the comprehensive noise and interference evaluation coefficient decreases, leading to a decrease in the overall status evaluation coefficient of the mechanism. At this time, the system can trigger an early warning or adjust the maintenance strategy.

[0086] Compared to existing technologies, current solutions typically determine device status based on a single parameter or static threshold, failing to consider the dynamic coupling effects of noise and interference, and lacking normalization processing and weighting mechanisms for multi-source heterogeneous data. This solution addresses the problems of limited evaluation dimensions and poor environmental adaptability in existing technologies by introducing a normalization index and dynamic weighting coefficients to integrate noise intensity, interference intensity, and device transceiver performance indicators.

[0087] In a preferred embodiment of the present invention, the external wind speed, dust concentration and air humidity are respectively substituted into the maximum and minimum value normalization formula for processing, and the wind speed index, dust concentration index and air humidity index are generated in sequence.

[0088] The external environment assessment model is as follows:

[0089] ;

[0090] in The weighting factor for the influence of wind speed. The weighting coefficient for the influence of dust concentration, The weighting factor for the influence of air humidity. ,and , as well as All greater than , Wind speed index The dust concentration index. The air humidity index. External environment assessment coefficient;

[0091] In this embodiment, the maximum and minimum value normalization formula refers to a standardization method that linearly maps the original data to the interval [0,1]. Specifically, it can be implemented using the mathematical expression (original value - minimum value) / (maximum value - minimum value). Its function is to eliminate the dimensional differences of different environmental parameters, so that wind speed, dust concentration, and humidity can be uniformly quantified and compared. The wind speed influence weighting coefficient refers to a parameter used to quantify the contribution of wind speed to the overall environmental assessment. Specifically, it can be set through expert experience or historical data analysis. For example, in areas prone to sandstorms, it can be... The value is set to 0.5, which dynamically adjusts the priority of wind speed's impact on equipment. The dust concentration index is a standardized value reflecting the content of particulate matter in the air. It is obtained by normalizing raw data collected by a dust sensor, and its function is to characterize the direct correlation between dust and the risk of clogging on the transceiver surface. The air humidity index is a standardized value reflecting the degree of environmental humidity. It is measured and generated by a humidity sensor, and its function is to assess the enhancing effect of humidity on dust adhesion.

[0092] Specifically, external environmental parameters are first converted into a comparable exponential form through normalization. For example, when a wind speed sensor detects a current wind speed of 15 m / s, and the preset maximum wind speed threshold is 30 m / s and the minimum wind speed threshold is 0 m / s, then the wind speed index... The calculated value is 0.5. Dust Concentration Index and air humidity index Generated using the same method. The model will then... Substituting the formula into the calculation, for example, when the wind speed index is 0.8, its contribution value is 1 - 0.8, indicating that the negative impact on the evaluation coefficient increases with increasing wind speed. The dust concentration index and humidity index are directly multiplied by the weighting coefficients, and finally, the result is obtained through weighted summation. Its value ranges from 0 to 1, and the higher the value, the more significant the impact of the external environment on the equipment performance.

[0093] In a preferred embodiment of the present invention, the pressure difference across the dustproof mechanism 6 is substituted into the maximum-minimum value normalization formula for processing, and a pressure difference index is generated; the filtration state control model is as follows:

[0094] ;

[0095] in For environmental impact weighting coefficients, For the weighting coefficient of institutional influence, ,and , All greater than , The differential pressure index, External environment assessment coefficient, This is the overall status assessment coefficient of the organization. This is the filter state control coefficient.

[0096] In this embodiment, the differential pressure index refers to the normalized value of the pressure difference across the dust filter. Specifically, it can be measured in real time by a differential pressure sensor and mapped to the 0-1 range using the maximum-minimum method to quantify the degree of filter clogging. The environmental impact weighting coefficient α refers to the contribution of external environmental factors to the dust removal requirement. Specifically, it can be set to a value in the range of 0.3-0.7, for example, 0.6 in areas prone to sandstorms, to enhance the regulatory effect of wind speed and dust concentration on the dust removal frequency. The mechanical impact weighting coefficient β refers to the contribution of equipment operating status to the dust removal requirement. Specifically, it is automatically calculated using β=1-α. When the overall performance of the equipment declines, the weight of β is increased to ensure that the dust removal action prioritizes signal quality.

[0097] Specifically, during the operation of the signal receiver-transmitter 3, the differential pressure sensor continuously collects pressure data from both sides of the dust filter, which is then normalized to generate... Meanwhile, the external environment assessment coefficient The overall condition assessment coefficient of the institution was calculated using data on wind speed, dust concentration, and humidity. This is generated by fusing signal transmission and reception performance data and interference data. The allocation ratio of α and β is adjusted; for example, when equipment performance is normal but dust concentration suddenly increases, α is set to 0.7 to enhance the influence of environmental factors on dust removal decisions; when the equipment's VSWR abnormally increases, β is increased to 0.5 to prioritize signal quality. Finally, the parameters are substituted into the formula for calculation. When the value exceeds the preset threshold of 0.85, the vibrator 64 is controlled to start the dust removal action.

[0098] Through the above technical solution, this application achieves dynamic correlation analysis between filter clogging degree and environmental factors and equipment operating status. When the equipment is in a high-humidity, low-dust environment, even if the pressure difference reaches the conventional threshold, the system can still delay dust removal to reduce unnecessary mechanical wear; while under critical performance degradation conditions, even if the pressure difference does not reach the threshold, the system can still trigger dust removal in advance to ensure signal quality. This solution reduces the frequency of manual maintenance while avoiding damage to the filter structure caused by excessive dust removal.

[0099] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-angle signal transceiver mechanism based on a 5G base station, comprising an electric lifting mast, wherein a support frame is rotatably connected to the telescopic portion of the electric lifting mast, and a signal receiver-transmitter is fixedly connected to the support frame, characterized in that, A drive assembly is provided between the support frame and the electric lifting rod, which can drive the support frame to rotate relative to the electric lifting rod. A signal antenna is fixedly connected to the center of the signal receiver-transmitter, and the signal receiver-transmitter is connected to a dustproof mechanism for filtering dust and impurities. It also includes an adaptive dust removal system, which comprises: Signal transmission status assessment module: Constructs a signal transmission status assessment model based on transmission power, antenna gain, antenna efficiency, and VSWR, and outputs transmission status assessment coefficients; Signal reception status assessment module: Constructs a signal reception status assessment model based on received signal strength, received signal sensitivity, and signal-to-noise ratio, and outputs reception status assessment coefficients; Overall institutional status assessment module: Under the influence of the transmission status assessment coefficient and the reception status assessment coefficient, the overall institutional status assessment model is constructed based on the external noise intensity and interference intensity, and the overall institutional status assessment coefficient is output. External environment assessment module: Constructs an external environment assessment model based on external wind speed, dust concentration, and air humidity, and outputs external environment assessment coefficients; Filtration status control module: Based on the pressure difference on both sides of the dustproof mechanism, the external environment assessment coefficient, and the overall status assessment coefficient of the mechanism, a filtration status control model is constructed, and the filtration status control coefficient is output. When the filtration status assessment coefficient exceeds the preset threshold, a judgment information is generated. The control module can receive judgment information from the filter status control module and control the dustproof mechanism to vibrate.

2. The signal multi-angle transceiver mechanism based on a 5G base station according to claim 1, characterized in that, The drive assembly includes a driven wheel, a motor, and a driving wheel; The telescopic end of the electric lifting rod is fixedly connected to a driven wheel, the support frame is fixedly connected to a motor, and the output end of the motor is fixedly connected to a driving wheel, which can mesh with the driven wheel.

3. The signal multi-angle transceiver mechanism based on a 5G base station according to claim 1, characterized in that, The dustproof mechanism includes a fixed frame, a dielectric dustproof filter, a spring, and a vibrator; The mounting bracket is fixedly connected to the signal receiver-transmitter. A dielectric dustproof filter is slidably connected to the mounting bracket. Several springs are connected between the dielectric dustproof filter and the mounting bracket. A vibrator is fixedly connected to the dielectric dustproof filter.

4. The signal multi-angle transceiver mechanism based on a 5G base station according to claim 3, characterized in that, The transmit power, antenna gain, and VSWR are respectively substituted into the maximum-minimum normalization formula for processing, and the transmit power index, antenna gain index, and VSWR index are generated sequentially; the signal transmission state evaluation model is as follows: ； in This is the transmit power weighting coefficient. This is the antenna gain weighting coefficient. This represents the antenna efficiency gain coefficient. The standing wave ratio (SWR) weighting coefficient. ,and , , as well as All greater than ; This refers to the transmit power index. This is the antenna gain index. For antenna efficiency, Dimensionless The voltage standing wave ratio (VSWR) is the exponent. This is the launch status evaluation coefficient.

5. The signal multi-angle transceiver mechanism based on a 5G base station according to claim 4, characterized in that, The received signal strength, received signal sensitivity, and signal-to-noise ratio are respectively substituted into the maximum-minimum normalization formula for processing, and the received signal strength index, received signal sensitivity index, and signal-to-noise ratio index are generated sequentially; the signal reception state evaluation model is as follows: ； in For received signal strength weighting coefficients, For the received signal sensitivity weighting coefficient, The signal-to-noise ratio weighting coefficient. , , as well as All greater than ; The received signal strength index, The sensitivity index for receiving signals. The signal-to-noise ratio is denoted as . This is the reception status evaluation coefficient.

6. The signal multi-angle transceiver mechanism based on a 5G base station according to claim 5, characterized in that, The noise intensity and interference intensity are respectively substituted into the maximum and minimum value normalization formula for processing, and the noise intensity index and interference intensity index are generated in turn. The overall status assessment model for the organization is as follows: ； ； in The weighting coefficients are for the impact of noise. To mitigate the impact of interference on the weighting coefficients, ,and as well as All greater than ; This is the noise intensity index. The interference intensity index. This is the comprehensive evaluation coefficient for noise interference. For receiving status evaluation coefficients, For launch status evaluation coefficients, This is the overall status assessment coefficient for the organization.

7. The signal multi-angle transceiver mechanism based on a 5G base station according to claim 6, characterized in that, External wind speed, dust concentration, and air humidity are respectively substituted into the maximum and minimum value normalization formula for processing, and wind speed index, dust concentration index, and air humidity index are generated in sequence. The external environment assessment model is as follows: ； in The weighting factor for the influence of wind speed. The weighting coefficient for the influence of dust concentration, The weighting factor for the influence of air humidity. ,and , as well as All greater than , Wind speed index The dust concentration index. The air humidity index. This is the external environment assessment coefficient.

8. The signal multi-angle transceiver mechanism based on a 5G base station according to claim 7, characterized in that, The pressure difference across the dustproof mechanism is substituted into the maximum-minimum normalization formula for processing, and a pressure difference index is generated; the filtration state control model is as follows: ； in For environmental impact weighting coefficients, For the weighting coefficient of institutional influence, ,and , All greater than , The differential pressure index, External environment assessment coefficient, This is the overall status assessment coefficient of the organization. This is the filter state control coefficient.

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