Temperature control methods, devices, marine antennas, and storage media for marine antennas

Abstract

This invention discloses a method, device, antenna, and storage medium for constant temperature control of a marine antenna. By inputting historical operating data of each execution unit of the marine antenna and historical ambient temperature outside the radome into the antenna temperature prediction model, a first temperature inside the radome and its confidence level are obtained when the temperature regulator is not activated in the next temperature control cycle. When the confidence level is greater than a confidence threshold, a temperature correction coefficient is determined based on the confidence levels of the first temperature predicted over multiple temperature control cycles to correct the first temperature and obtain a second temperature. In the next temperature control cycle, the temperature regulator is controlled to operate according to the second temperature and a preset target temperature to adjust the temperature inside the radome to the target temperature. This invention enables the prediction of the marine antenna temperature in the next temperature control cycle based on historical data and the correction of the predicted temperature. It allows for the advance determination of the temperature in the next temperature control cycle and timely temperature adjustment, keeping the radome in a constant temperature state and ensuring the performance of the marine antenna.

Description

Technical Field

[0001] This invention relates to the field of marine antenna control technology, and in particular to a method, apparatus, marine antenna, and storage medium for constant temperature control of a marine antenna. Background Technology

[0002] Since ships need to sail far from the shore and cannot communicate with mobile base stations, they need to communicate via satellite, meaning they need to be equipped with a marine antenna to communicate with satellites.

[0003] like Figure 1 The diagram shows the structure of a marine antenna. The reflector 1, feed 2, orthogonal mode coupler 3, frequency converter 4, antenna mainboard 5, pose motor 6, and pose determination device 7 are integrated inside the radome 8. The marine antenna is mounted on a ship in a complex marine environment. The temperature of the marine antenna has a significant impact on its electrical and mechanical performance. For example, in terms of electrical performance, high temperature can cause frequency shift and decreased directivity, affecting communication quality. In terms of mechanical performance, high or low temperature can cause deformation of the first reflector 1, and low temperature can also cause the plastic and metal in the antenna to become brittle.

[0004] In existing technologies, a temperature sensor is usually installed inside the radome to detect the temperature. When the detected temperature exceeds the range, the temperature is adjusted to keep the marine antenna in a constant temperature environment inside the radome. However, temperature adjustment by detecting the temperature with a temperature sensor is a reactive adjustment behavior and cannot be controlled in advance to maintain a constant temperature. This results in the marine antenna inside the radome being in an environment with large temperature fluctuations, which affects the performance of the marine antenna. Summary of the Invention

[0005] This invention provides a method, device, antenna, and storage medium for constant temperature control of a marine antenna, which can predict the temperature of the marine antenna in advance and perform constant temperature control in a timely manner to reduce temperature fluctuations inside the antenna cover.

[0006] In a first aspect, the present invention provides a method for constant temperature control of a marine antenna, wherein multiple actuators of the marine antenna are disposed within an antenna radome and are equipped with a temperature regulator, comprising:

[0007] The historical operating data of multiple actuators of the marine antenna and the historical ambient temperature outside the antenna radome are obtained. The historical operating data includes the operating data of each actuator in the current temperature control cycle and multiple historical temperature control cycles before the current temperature control cycle.

[0008] The historical ambient temperature and the historical operating data are input into a pre-trained antenna temperature prediction model to obtain the first temperature inside the radome and the confidence level of the first temperature when the temperature regulator is not activated in the next temperature control cycle.

[0009] Determine whether the confidence level is greater than a preset confidence threshold;

[0010] If so, the temperature correction factor is determined based on the confidence level of the first temperature predicted by multiple temperature control cycles;

[0011] The second temperature is obtained by correcting the first temperature based on the temperature correction coefficient.

[0012] During the next temperature control cycle, the temperature regulator is controlled to operate according to the second temperature and the preset target temperature to adjust the temperature inside the radome to the target temperature.

[0013] Optionally, the execution unit of the marine antenna includes a reflector, a feed source, an orthogonal mode coupler, a frequency converter, an antenna mainboard, a pose motor, and a pose determination device, acquiring historical operating data of multiple execution units of the marine antenna and historical ambient temperature outside the radome, including:

[0014] Obtain the pitch angle curve and angular velocity curve of the reflecting surface;

[0015] Obtain the voltage curve, current curve, and power curve of the feed source;

[0016] Obtain the temperature profile of the orthogonal mode coupler;

[0017] Obtain the power curve, input frequency curve, and output frequency curve of the frequency converter;

[0018] Obtain the power curve of the antenna motherboard;

[0019] Obtain the power curve and speed curve of the posture motor;

[0020] Obtain the power curve of the pose determination device;

[0021] Obtain the historical ambient temperature curve outside the radome.

[0022] Optionally, the antenna temperature prediction model includes a feature extraction network, a feature fusion network, an antenna operating state matching network, and a temperature prediction network connected in sequence. The historical operating data is input into the pre-trained antenna temperature prediction model. The antenna temperature prediction model predicts the first temperature inside the radome and the confidence level of the first temperature when the temperature regulator is not activated in the next temperature control cycle, including:

[0023] The historical operating data of the reflector, feed, orthogonal mode coupler, frequency converter, antenna motherboard, posture motor, and posture determination device, as well as the historical ambient temperature curve outside the radome, are input into the feature extraction network.

[0024] The feature extraction network extracts features from the historical operating data and the historical ambient temperature curve to obtain the first operating feature map and the first ambient temperature feature map of the reflector, feed, orthogonal mode coupler, frequency converter, antenna motherboard, posture motor and posture determination device.

[0025] The first running feature map and the first ambient temperature feature map are fused in the feature fusion network to obtain a first fused feature map;

[0026] In the antenna operating state matching network, the first fused feature map is matched with feature map samples in a preset state feature library to obtain feature pairs and the matching degree of the feature pairs. The feature pairs include the first fused feature map and the first feature map sample, where the first feature map sample is a feature map whose antenna operating state has been determined.

[0027] The antenna operating state associated with the first feature map sample in the feature pair is determined as the target operating state of the marine antenna in the next temperature control cycle;

[0028] In the temperature prediction network, based on the target operating state, the first temperature inside the radome and the probability of the first temperature are predicted when the temperature regulator is not activated in the next temperature control cycle.

[0029] The confidence level of the first temperature is calculated as the weighted sum of the probability of the first temperature and the probability of the target operating state.

[0030] Optionally, a temperature correction factor is determined based on the confidence level of the first temperature predicted over multiple temperature control cycles, including:

[0031] Acquire multiple third temperatures inside the radome detected during the current temperature control cycle and multiple historical temperature control cycles;

[0032] The temperature regulation deviation rate of the temperature regulator in multiple temperature control cycles is calculated based on multiple third temperatures and a preset target temperature.

[0033] The weighted average of the confidence levels of the first temperature predicted by multiple temperature control cycles and multiple temperature adjustment deviation rates is used as the temperature correction coefficient.

[0034] Optionally, correcting the first temperature based on the temperature correction coefficient to obtain the second temperature includes:

[0035] The product of the first temperature and the temperature correction coefficient is calculated as the temperature compensation value;

[0036] The second temperature is obtained by summing the temperature compensation value with the first temperature.

[0037] Optionally, during the next temperature control cycle, the temperature regulator is controlled to operate based on the second temperature and a preset target temperature to adjust the temperature inside the radome to the target temperature, including:

[0038] Calculate the difference between the second temperature and the target temperature, and take the absolute value of the difference to obtain the absolute value of the temperature difference;

[0039] Determine whether the difference is greater than the value 0;

[0040] If so, the target cooling capacity is calculated using the absolute value of the temperature difference and the preset volume of the radome, and the temperature regulator is controlled to cool according to the target cooling capacity;

[0041] If not, the target heat output is calculated using the absolute value of the temperature difference and the preset volume of the radome, and the temperature regulator is controlled to heat the device based on the target heat output.

[0042] Optionally, the antenna temperature prediction model is trained through the following steps:

[0043] Obtain a training dataset. Each data sample in the training dataset includes operating curve samples of the reflector, feed, orthogonal mode coupler, frequency converter, antenna motherboard, posture motor, posture determination device, and temperature curve samples outside the radome. Each data sample is labeled with the actual operating status and actual temperature.

[0044] Construct a state feature library, which includes feature map samples and the antenna operating states associated with the feature map samples;

[0045] An antenna temperature prediction model is constructed, consisting of a feature extraction network, a feature fusion network, an antenna operating state matching network, and a temperature prediction network connected in sequence.

[0046] The extracted data samples are input into the antenna temperature prediction model. The feature extraction network extracts features from the data samples to obtain the second operating feature map and the second ambient temperature feature map of the reflector, feed, orthogonal mode coupler, frequency converter, antenna motherboard, posture motor, and posture determination device.

[0047] The second running feature map and the second ambient temperature feature map are fused in the feature fusion network to obtain a second fused feature map;

[0048] In the antenna operating state matching network, the second fused feature map is matched with the feature map samples in the state feature library to obtain the predicted feature pairs and the matching degree of the predicted feature pairs. The predicted feature pairs include the second fused feature map and the target feature map sample. The target feature map sample is a feature map whose antenna operating state has been determined.

[0049] The antenna operating state associated with the target feature map sample in the feature pair is determined as the predicted operating state of the marine antenna in the next temperature control cycle;

[0050] In the temperature prediction network, based on the predicted operating status, the predicted temperature inside the radome is predicted when the temperature regulator is not activated in the next temperature control cycle, and the probability of the predicted temperature is predicted.

[0051] The total loss value is calculated based on the predicted temperature, the probability, the actual temperature, the predicted operating state, the actual operating state, and the matching degree of the predicted feature pairs.

[0052] Determine whether the conditions for stopping training are met;

[0053] If so, confirm that the antenna temperature prediction model has completed training;

[0054] If not, adjust the network parameters of the feature fusion network, antenna operating status matching network, and temperature prediction network using the total loss value, and return to the step of extracting data samples and inputting them into the antenna temperature prediction model.

[0055] Secondly, the present invention provides a constant temperature control device for a marine antenna, wherein multiple actuators of the marine antenna are disposed within an antenna radome and are equipped with a temperature regulator, comprising:

[0056] The historical operation data and ambient temperature acquisition module is used to acquire historical operation data of multiple execution units of the marine antenna and historical ambient temperature outside the antenna radome. The historical operation data includes the operation data of each execution unit in the current temperature control cycle and multiple historical temperature control cycles before the current temperature control cycle.

[0057] The temperature prediction module is used to input the historical ambient temperature and the historical operating data into a pre-trained antenna temperature prediction model to obtain the first temperature inside the antenna cover and the confidence level of the first temperature when the temperature regulator is not activated in the next temperature control cycle.

[0058] The confidence level determination module is used to determine whether the confidence level is greater than a preset confidence level threshold. If so, the temperature correction coefficient determination module is executed.

[0059] The temperature correction coefficient determination module is used to determine the temperature correction coefficient based on the confidence level of the first temperature predicted by multiple temperature control cycles.

[0060] A temperature correction module is used to correct the first temperature based on the temperature correction coefficient to obtain a second temperature;

[0061] A temperature regulation module is used to control the temperature regulator to operate according to the second temperature and the preset target temperature during the next temperature control cycle, so as to adjust the temperature inside the radome to the target temperature.

[0062] Thirdly, the present invention provides a marine antenna, the marine antenna comprising:

[0063] At least one processor; and

[0064] A memory communicatively connected to the at least one processor; wherein,

[0065] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the constant temperature control method for a marine antenna according to any one of the first aspects of the present invention.

[0066] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a processor to execute and implement the constant temperature control method for a marine antenna according to any one of the first aspects of the present invention.

[0067] This invention, through obtaining historical operating data of each actuator of the marine antenna inside the radome and historical ambient temperature outside the radome, inputs these data into an antenna temperature prediction model to obtain a first temperature inside the radome and its confidence level when the temperature regulator is not activated in the next temperature control cycle. When the confidence level is greater than a preset confidence threshold, a temperature correction coefficient is determined. Based on this coefficient, the first temperature is corrected to obtain a second temperature. In the next temperature control cycle, the temperature regulator is controlled to operate according to the second temperature and a preset target temperature to adjust the temperature inside the radome to the target temperature. This achieves the ability to predict the temperature of the marine antenna in the next temperature control cycle in advance using historical data, and to correct and adjust the temperature accordingly. It enables the early determination of the marine antenna temperature in the next temperature control cycle and timely temperature adjustment, resulting in minimal temperature fluctuations and a constant temperature state inside the radome. In other words, each actuator of the marine antenna is in a constant temperature environment, ensuring the performance of the marine antenna.

[0068] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0069] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0070] Figure 1 This is a schematic diagram of the structure of a marine antenna;

[0071] Figure 2 This is a flowchart of a constant temperature control method for a marine antenna provided in Embodiment 1 of the present invention;

[0072] Figure 3 This is a flowchart of a constant temperature control method for a marine antenna provided in Embodiment 2 of the present invention;

[0073] Figure 4 This is a schematic diagram of the network structure of the antenna temperature prediction model;

[0074] Figure 5 This is a schematic diagram of the structure of a constant temperature control device for a marine antenna provided in Embodiment 3 of the present invention;

[0075] Figure 6 This is a schematic diagram of the structure of the marine antenna provided in Embodiment 4 of the present invention. Detailed Implementation

[0076] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0077] Example 1

[0078] Figure 2 This is a flowchart of a temperature control method for a marine antenna according to Embodiment 1 of the present invention. This embodiment is applicable to adjusting the temperature inside the radome of a marine antenna, so that each execution unit of the marine antenna inside the radome is in a constant temperature environment. This method can be executed by a temperature control device for the marine antenna, which can be implemented in hardware and / or software and can be configured in the marine antenna, such as in the processor of the marine antenna. Figure 2 As shown, the temperature control method for this marine antenna includes:

[0079] S201. Obtain historical operating data of multiple actuators of the marine antenna and historical ambient temperature outside the radome. The historical operating data includes the operating data of each actuator in the current temperature control cycle and multiple historical temperature control cycles before the current temperature control cycle.

[0080] like Figure 1 The diagram shows the structure of a marine antenna. The reflector 1, feed 2, orthogonal mode coupler 3, frequency converter 4, antenna main board 5, attitude motor 6, and attitude determination device 7 are integrated inside the radome 8. Of course, the marine antenna may also include other execution units. This embodiment does not limit the execution units of the marine antenna. In addition, the marine antenna in this embodiment may also include a temperature regulator, which can be set inside the radome 8 for cooling or heating. For example, the temperature regulator may include a heating resistor for heating and a refrigerant circulation system for cooling.

[0081] The temperature control cycle can be the cycle for adjusting the temperature. For example, the duration of the temperature control cycle can be 30 seconds or 1 minute. The current temperature control cycle can be the cycle in which the current time is located. The historical temperature control cycle can be the temperature control cycle before the current temperature control cycle. The next temperature control cycle can be the first temperature control cycle after the current temperature control cycle.

[0082] Historical operating data for each actuator of a marine antenna can be at least one of electrical parameters, temperature, or radio frequency (RF) data. Electrical parameters reflect the thermal state of each actuator, such as operating voltage, current, and power. Temperature and RF data reflect the operating status or mode of each actuator; for example, the antenna's radiation frequency reflects the operating status or mode of the marine antenna. The historical operating data for different actuators may differ. The historical operating data for each actuator can be obtained from recorded operating logs. The historical ambient temperature outside the radome can be detected using a temperature sensor.

[0083] S202. Input historical ambient temperature and historical operating data into the pre-trained antenna temperature prediction model to obtain the first temperature inside the radome and the confidence level of the first temperature when the temperature regulator is not activated in the next temperature control cycle.

[0084] This embodiment can pre-train an antenna temperature prediction model. The antenna temperature prediction model is used to input the historical operating data of each execution unit in the current cycle and the previous cycle, as well as the historical ambient temperature outside the radome, to predict the first temperature inside the radome and the confidence level of the first temperature in the next temperature control cycle. The first temperature is the temperature inside the radome when the temperature regulator is not activated in the next temperature control cycle. The first temperature reflects the total heat generated by each execution unit of the marine antenna when it enters the operating state in the next temperature control cycle.

[0085] The antenna temperature prediction model can be trained using the real ambient temperature outside the radome of the marine antenna, the real operating data of each execution unit, and the real temperature inside the radome. The training method can refer to the supervised training method of neural network models, which will not be detailed here.

[0086] S203. Determine whether the confidence level is greater than the preset confidence level threshold.

[0087] The confidence level represents the reliability of the first temperature predicted by the temperature prediction model to be close to the actual temperature. When the confidence level is greater than the confidence level threshold, it can be determined that the predicted first temperature can be used for temperature regulation, and S204 can be executed. When the confidence level is less than or equal to the confidence level threshold, it is determined that the predicted first temperature may differ significantly from the actual temperature and cannot be used for temperature regulation. In this case, the temperature inside the radome detected in the current temperature control cycle can be determined as the basis for temperature regulation in the next temperature control cycle, i.e., the second temperature.

[0088] S204. Determine the temperature correction factor based on the confidence level of the first temperature predicted by multiple temperature control cycles.

[0089] In one embodiment, the deviation rate between the temperature after adjustment for multiple historical temperature control cycles and the target temperature can be obtained, and a weighted average of the confidence level and deviation rate of the first temperature predicted by multiple temperature control cycles can be calculated as the temperature correction coefficient. Alternatively, a weighted sum can be calculated as the temperature correction coefficient, or the average value of the deviation rate can be directly calculated as the temperature correction coefficient.

[0090] S205. The second temperature is obtained by correcting the first temperature based on the temperature correction coefficient.

[0091] Specifically, the product of the first temperature and the temperature correction coefficient can be calculated, and the sum of this product and the first temperature can be calculated to obtain the second temperature, which is the basis for temperature adjustment in the next temperature control cycle.

[0092] S206. In the next temperature control cycle, the temperature regulator is controlled to work according to the second temperature and the preset target temperature to adjust the temperature inside the radome to the target temperature.

[0093] In one embodiment, the difference between the second temperature and the target temperature can be calculated, and the absolute value of the difference can be taken to obtain the absolute value of the temperature difference. It is then determined whether the difference is greater than 0. If so, it is determined that the temperature inside the radome in the next temperature control cycle may be greater than the target temperature. The target cooling capacity is calculated using the absolute value of the temperature difference and the preset volume of the radome, and the temperature regulator is controlled to cool based on the target cooling capacity. If not, it is determined that the temperature inside the radome in the next temperature control cycle may be lower than the target temperature. The target heating capacity is calculated using the absolute value of the temperature difference and the preset volume of the radome, and the temperature regulator is controlled to heat based on the target heating capacity. This embodiment does not limit the cooling and heating methods of the temperature regulator. It can refer to the cooling and heating methods in the prior art. For example, heating can be achieved through a heating resistor, and cooling can be achieved through refrigerant circulation. The target temperature can be a constant temperature control target temperature.

[0094] This invention, through obtaining historical operating data of each actuator of the marine antenna inside the radome and historical ambient temperature outside the radome, inputs these data into an antenna temperature prediction model to obtain a first temperature inside the radome and its confidence level when the temperature regulator is not activated in the next temperature control cycle. When the confidence level is greater than a preset confidence threshold, a temperature correction coefficient is determined. Based on this coefficient, the first temperature is corrected to obtain a second temperature. In the next temperature control cycle, the temperature regulator is controlled to operate according to the second temperature and a preset target temperature to adjust the temperature inside the radome to the target temperature. This achieves the ability to predict the temperature of the marine antenna in the next temperature control cycle in advance using historical data, and to correct and adjust the temperature accordingly. It enables advance temperature control, ensuring that the temperature inside the radome remains constant, and that each actuator of the marine antenna is in a constant temperature environment, thus guaranteeing the performance of the marine antenna.

[0095] Example 2

[0096] Figure 3 This is a flowchart of a constant temperature control method for a marine antenna provided in Embodiment 2 of the present invention. This embodiment of the present invention is an optimization based on Embodiment 1 described above, such as... Figure 3 As shown, the temperature control method for this marine antenna includes:

[0097] S301. Obtain historical operating data of multiple actuators of the marine antenna and historical ambient temperature outside the radome. The historical operating data includes the operating data of each actuator in the current temperature control cycle and multiple historical temperature control cycles before the current temperature control cycle.

[0098] like Figure 1The diagram shows the structure of a marine antenna. The reflector 1, feed 2, orthogonal mode coupler 3, frequency converter 4, antenna mainboard 5, attitude motor 6, and attitude determination device 7 are integrated within the radome 8. This information can be read from the marine antenna's operation log.

[0099] The pitch angle and angular velocity curves of the reflector, as well as the pitch angle and angular velocity of the reflector within the radome, reflect not only the operational status of the marine antenna (sater search, attitude changes) but also the effect of the air disturbance within the radome on temperature when the reflector rotates at different pitch angles and angular velocities.

[0100] Obtaining the voltage, current, and power curves of the feed source reflects its heating state.

[0101] The temperature profile of the orthogonal mode coupler reflects its heating state.

[0102] The inverter's power curve, input frequency curve, and output frequency curve are shown. The power curve reflects the inverter's heat generation status, while the input and output frequency curves reflect the operating status of the marine antenna (high-frequency, medium-frequency, or low-frequency operation).

[0103] The power curve of the antenna motherboard reflects the heat dissipation status of the motherboard;

[0104] The power and speed curves of the posture motor reflect the motor's heating state and the operating state of the marine antenna (satirical and attitude changes).

[0105] The power curve of the pose determination device reflects the heat dissipation status of the pose determination device.

[0106] The historical ambient temperature curve outside the radome reflects the temperature changes in the external environment where the marine antenna is located, such as the temperature in different sea areas and at different times.

[0107] S302. Input the historical operating data of the reflector, feed source, orthogonal mode coupler, frequency converter, antenna mainboard, posture motor and posture determination equipment, and the historical ambient temperature curve outside the radome into the feature extraction network.

[0108] like Figure 4 The diagram shows the network structure of the antenna temperature prediction model. In this embodiment, the antenna temperature prediction model includes a feature extraction network, a feature fusion network, an antenna operating state matching network, and a temperature prediction network connected sequentially. The antenna temperature prediction model can be trained through the following steps:

[0109] S1. Obtain the training dataset. Each data sample in the training dataset includes the operating curve samples of the reflector, feed, orthogonal mode coupler, frequency converter, antenna motherboard, posture motor, posture determination device, and temperature curve samples outside the radome. Each data sample is labeled with the actual operating status and actual temperature.

[0110] For various marine antennas, the operating data of each execution unit and the ambient temperature outside the radome can be recorded for multiple temperature control cycles. For example, the operating data of execution units such as reflectors, feed sources, orthogonal mode couplers, frequency converters, antenna motherboards, posture motors, and posture determination devices can be recorded to obtain the operating curves of each execution unit and the temperature curve outside the radome. These can be used as operating curve samples and temperature curve samples. The actual temperature inside the radome can be detected for each temperature control cycle. The actual temperature and the actual operating status of the marine antenna can be labeled on the operating curve samples and temperature curve samples for each temperature control cycle. The operating status can be classified according to the state or mode of the marine antenna.

[0111] S2. Construct a state feature library, which includes feature map samples and the antenna operating states associated with the feature map samples.

[0112] In this embodiment, a state feature library can be pre-configured, which includes feature map samples of marine antennas in different antenna operating states. Each feature map sample is obtained by fusing feature maps extracted from the operating curves of each execution unit and the temperature curve outside the antenna radome in different operating states of the marine antenna.

[0113] S3. Construct an antenna temperature prediction model that includes a feature extraction network, a feature fusion network, an antenna operating state matching network, and a temperature prediction network connected in sequence.

[0114] like Figure 4 As shown, the feature extraction network can include a reflector feature extraction sub-network, a feed feature extraction sub-network, an orthogonal mode coupler feature extraction sub-network, a frequency converter feature extraction sub-network, an antenna motherboard feature extraction sub-network, a pose motor feature extraction sub-network, a pose determination device feature extraction sub-network, and an ambient temperature feature extraction sub-network. The output layer of each feature extraction sub-network is connected to the input layer of the feature fusion network. The feature fusion network, the antenna operating state matching network, and the temperature prediction network are connected in sequence.

[0115] S4. Extract data samples into the antenna temperature prediction model. Extract features from the data samples in the feature extraction network to obtain the second operating feature map and the second ambient temperature feature map of the reflector, feed, orthogonal mode coupler, frequency converter, antenna motherboard, pose motor, pose determination device.

[0116] like Figure 4As shown, the operating curve samples (Curve1, Curve2, Curve3, Curve4, Curve5, Curve6, Curve7) of the reflector, feed, orthogonal mode coupler, frequency converter, antenna mainboard, pose motor, and pose determination equipment are presented. 7) Input the features into the reflector feature extraction subnetwork, feed feature extraction subnetwork, orthogonal mode coupler feature extraction subnetwork, inverter feature extraction subnetwork, antenna mainboard feature extraction subnetwork, pose motor feature extraction subnetwork, and pose determination device feature extraction subnetwork respectively to obtain the second operating feature maps (F1, F2, F3, F4, F5, F6, F7) of the reflector, feed, orthogonal mode coupler, inverter, antenna mainboard, pose motor, and pose determination device. Input the temperature curve sample Curve8 into the ambient temperature feature extraction subnetwork to obtain the second ambient temperature feature map F8. The second operating feature maps (F1, F2, F3, F4, F5, F6, F7) and the second ambient temperature feature map F8 are feature maps with the same scale.

[0117] S5. The second running feature map and the second ambient temperature feature map are fused in the feature fusion network to obtain the second fused feature map.

[0118] Specifically, in the feature fusion network, the second running feature map (F1, F2, F3, F4, F5, F6, F7) and the second ambient temperature feature map F8 can be fused according to the weights of the current neurons to obtain the second fused feature map F.

[0119] S6. In the antenna operating state matching network, the second fused feature map is matched with the feature map samples in the state feature library to obtain the predicted feature pairs and the matching degree of the predicted feature pairs. The predicted feature pairs include the second fused feature map and the target feature map sample. The target feature map sample is the feature map of the determined antenna operating state.

[0120] In one embodiment, the matching degree (e.g., cosine similarity) of the second fused feature map can be calculated with each feature map sample in the state feature library to obtain the target feature map sample with the highest matching degree with the second fused feature map F. The target feature map sample F_sample and the second fused feature map F are then used to generate the predicted feature pair (F, F_sample).

[0121] S7. Determine the antenna operating status associated with the target feature map samples in the feature pair as the predicted operating status of the marine antenna in the next temperature control cycle.

[0122] Specifically, each feature map sample in the state feature library is associated with an antenna operating state. The antenna operating state associated with the target feature map sample in the feature pair can be determined as the predicted operating state of the marine antenna in the next temperature control cycle. That is, the marine antenna will operate in the predicted operating state in the next temperature control cycle.

[0123] S8. In the temperature prediction network, predict the predicted temperature inside the radome and the probability of the predicted temperature when the temperature regulator is not started in the next temperature control cycle based on the predicted operating status.

[0124] Suppose the predicted temperature is T1, and the probability of predicting temperature T1 is P1.

[0125] S9. Calculate the total loss value based on the predicted temperature, probability, actual temperature, predicted operating status, actual operating status, and the matching degree of the predicted feature pairs.

[0126] In one embodiment, a first loss value can be calculated using the predicted operating state and the actual operating state. This first loss value represents the deviation of the feature fusion network and the antenna operating state matching network from the operating state matching. A second loss value can be calculated using the predicted temperature and the actual temperature. This second loss value represents the deviation of the temperature prediction network from the temperature prediction. Then, the first product of the first loss value and the matching degree is calculated, and the second product of the second loss value and the probability is calculated. The sum of the first product and the second product is calculated as the total loss value.

[0127] S10. Determine whether the conditions for stopping training are met.

[0128] The training can be stopped when the total loss is less than a preset value or when the number of training iterations reaches a preset number. If the training stops when the conditions are met, execute S11; otherwise, execute S12.

[0129] S11, confirm that the antenna temperature prediction model has completed training.

[0130] S12, adjust the network parameters of the feature fusion network, antenna operating status matching network, and temperature prediction network using the total loss value, and return to S4.

[0131] Specifically, the tiers can be calculated based on the total loss value, and algorithms such as random tier descent and batch tier descent can be used to adjust the fusion weights of each feature map in the feature fusion network, and adjust the network parameters of the antenna operating status matching network and the temperature prediction network.

[0132] In this embodiment, during training, a first loss value is calculated using the predicted operating state and the actual operating state, and a second loss value is calculated using the predicted temperature and the actual temperature. Then, the first product of the first loss value and the matching degree is calculated, and the second product of the second loss value and the probability is calculated. The sum of the first product and the second product is calculated as the total loss value. The calculated total loss value focuses on measuring the deviation of feature fusion, operating state matching, and temperature prediction. Through this total loss value, the model can be constrained to learn the ability to accurately perform feature fusion, operating state matching, and temperature prediction.

[0133] After the antenna temperature prediction model is trained, historical operating data of the reflector, feed, orthogonal mode coupler, frequency converter, antenna mainboard, pose motor, and pose determination equipment, as well as historical ambient temperature curves outside the radome, can be input into the feature extraction network. Figure 4 As shown, the operating curves (Curve1', Curve2', Curve3', Curve4', Curve5', Curve6', and Curve7') of the reflector, feed, orthogonal mode coupler, inverter, antenna mainboard, pose motor, and pose determination device can be input into the reflector feature extraction subnetwork, feed feature extraction subnetwork, orthogonal mode coupler feature extraction subnetwork, inverter feature extraction subnetwork, antenna mainboard feature extraction subnetwork, pose motor feature extraction subnetwork, and pose determination device feature extraction subnetwork, respectively. The temperature curve Curve8 is input into the ambient temperature feature extraction subnetwork.

[0134] S303. The feature extraction network extracts features from the historical operating data and historical ambient temperature curves to obtain the first operating feature map and the first ambient temperature feature map of the reflector, feed, orthogonal mode coupler, frequency converter, antenna motherboard, posture motor and posture determination device.

[0135] Specifically, such as Figure 4 As shown, feature extraction is performed in the corresponding feature extraction sub-networks to obtain the first operating feature maps (F1', F2', F3', F4', F5', F6', F7') of the reflector, feed, orthogonal mode coupler, frequency converter, antenna mainboard, pose motor, and pose determination device, as well as the first ambient temperature feature map F8'. The first operating feature maps (F1', F2', F3', F4', F5', F6', F7') and the first ambient temperature feature map F8' are feature maps of the same scale.

[0136] S304. The first running feature map and the first ambient temperature feature map are fused in the feature fusion network to obtain the first fused feature map.

[0137] Specifically, in the feature fusion network, the first running feature map (F1', F2', F3', F4', F5', F6', F7') and the first ambient temperature feature map F8' can be fused according to the weights of the neurons to obtain the first fused feature map F'.

[0138] S305. In the antenna operating state matching network, the first fused feature map is matched with the feature map samples in the preset state feature library to obtain feature pairs and the matching degree of the feature pairs. The feature pairs include the first fused feature map and the first feature map sample. The first feature map sample is the feature map of the antenna operating state that has been determined.

[0139] Specifically, the matching degree (e.g., cosine similarity) between the first fused feature map F' and each feature map sample in the state feature library can be calculated to obtain the first feature map sample with the highest matching degree with the first fused feature map F'. The first feature map sample F_target and the first fused feature map F' are used to generate a feature pair (F', F_target, match), where match is the matching degree between the first feature map sample F_target and the first fused feature map F'.

[0140] S306. Determine the antenna operating state associated with the first feature map sample in the feature pair as the target operating state of the marine antenna in the next temperature control cycle.

[0141] Specifically, each feature map sample in the state feature library is associated with an antenna operating state. The antenna operating state associated with the first feature map sample in the feature pair can be determined as the target operating state of the marine antenna in the next temperature control cycle. That is, the marine antenna will operate in the target operating state in the next temperature control cycle.

[0142] S307. In the temperature prediction network, predict the first temperature inside the radome and the probability of the first temperature when the temperature regulator is not started in the next temperature control cycle based on the target operating state.

[0143] Assume the first temperature is T0, and the probability of the first temperature being T0 is P0.

[0144] S308. Calculate the weighted sum of the probability of the first temperature and the probability of the target operating state as the confidence level of the first temperature.

[0145] That is, calculate the product of probability P0 and matching degree match.

[0146] S309. Determine whether the confidence level is greater than the preset confidence level threshold.

[0147] The confidence level indicates the reliability of the first temperature predicted by the temperature prediction model to be close to the actual temperature. When the confidence level is greater than the confidence level threshold, it can be determined that the predicted first temperature can be used for temperature regulation, and S310 can be executed. When the confidence level is less than or equal to the confidence level threshold, it is determined that the predicted first temperature may differ greatly from the actual temperature and cannot be used for temperature regulation. In this case, the temperature inside the radome detected in the current temperature control cycle can be determined as the first temperature of the next temperature control cycle.

[0148] S310: Obtain multiple third temperatures inside the radome detected during the current temperature control cycle and multiple historical temperature control cycles.

[0149] In this embodiment, multiple temperatures inside the radome can be detected by a temperature sensor within a temperature control cycle, and the average of these multiple temperatures can be taken as the third temperature of that temperature control cycle. This third temperature is the actual temperature inside the radome after the temperature regulator has adjusted the temperature.

[0150] S311. Calculate the temperature regulation deviation rate of the temperature regulator in multiple temperature control cycles based on multiple third temperatures and preset target temperatures.

[0151] The target temperature can be a pre-set constant temperature, which is the ideal operating temperature of the marine antenna. The difference between the third temperature after adjustment in each temperature control cycle and the target temperature can be calculated, and the ratio of this difference to the target temperature can be calculated to obtain the temperature adjustment deviation rate of the temperature regulator in each temperature control cycle. This temperature adjustment deviation rate may be caused by the combined deviation of the temperature regulator and the antenna temperature prediction model in predicting the first temperature. For example, the first temperature predicted by the antenna temperature prediction model may be too low or too high compared to the actual temperature, resulting in insufficient adjustment by the temperature regulator, and / or the first temperature may be relatively accurate, but the temperature regulator itself may not adjust it properly.

[0152] S312. The weighted average of the confidence level of the first temperature predicted by multiple temperature control cycles and multiple temperature adjustment deviation rates is used as the temperature correction coefficient.

[0153] Since the confidence level of the first temperature reflects the reliability of the first temperature, the weighted average of the confidence levels of the first temperature predicted by multiple temperature control cycles and multiple temperature adjustment deviation rates can be used as the temperature correction coefficient. This temperature correction coefficient can correct the influence of the antenna temperature prediction model on the inaccuracy of the first temperature prediction.

[0154] S313. Calculate the product of the first temperature and the temperature correction coefficient as the temperature compensation value.

[0155] It should be noted that the temperature correction factor can be positive or negative, and the temperature compensation value can be positive or negative.

[0156] S314. Calculate the sum of the temperature compensation value and the first temperature to obtain the second temperature.

[0157] The second temperature may be higher than the first temperature, or it may be lower than the first temperature.

[0158] For example, suppose that the third temperature of the current temperature control cycle and several previous historical temperature control cycles deviates from the target temperature. This may be due to deviations in the first temperature predicted by several historical temperature control cycles. For instance, if the first temperature predicted by several historical temperature control cycles is too low, and the temperature is still lower than the target temperature after correction, the calculated temperature compensation value will be negative. The second temperature obtained by calculating the sum of the temperature compensation value and the first temperature will be lower than the first temperature. In other words, the predicted temperature of the marine antenna in the next temperature control cycle will be even lower when the temperature regulator is not activated. The temperature difference between the second temperature and the target temperature will be greater, causing the temperature regulator to generate more heat to increase the temperature inside the radome, thereby compensating for the defect that the first temperature predicted by the antenna temperature prediction model is lower than the actual temperature.

[0159] S315. In the next temperature control cycle, the temperature regulator is controlled to work according to the second temperature and the preset target temperature to adjust the temperature inside the radome to the target temperature.

[0160] In one embodiment, the difference between the second temperature and the target temperature can be calculated, and the absolute value of the difference can be taken to obtain the absolute value of the temperature difference. It is then determined whether the difference is greater than 0. If so, it is determined that the temperature inside the radome in the next temperature control cycle may be greater than the target temperature. The target cooling capacity is calculated using the absolute value of the temperature difference and the preset volume of the radome, and the temperature regulator is controlled to cool based on the target cooling capacity. If not, it is determined that the temperature inside the radome in the next temperature control cycle may be lower than the target temperature. The target heating capacity is calculated using the absolute value of the temperature difference and the preset volume of the radome, and the temperature regulator is controlled to heat based on the target heating capacity. The target temperature can be a constant temperature control target temperature.

[0161] This invention, through obtaining historical operating data of each actuator of the marine antenna inside the radome and historical ambient temperature outside the radome, inputs these data into an antenna temperature prediction model to obtain a first temperature inside the radome and its confidence level when the temperature regulator is not activated in the next temperature control cycle. When the confidence level is greater than a preset confidence threshold, a temperature correction coefficient is determined. Based on this coefficient, the first temperature is corrected to obtain a second temperature. In the next temperature control cycle, the temperature regulator is controlled to operate according to the second temperature and a preset target temperature to adjust the temperature inside the radome to the target temperature. This achieves the ability to predict the temperature of the marine antenna in the next temperature control cycle in advance using historical data, and to correct and adjust the temperature accordingly. It enables advance temperature control, ensuring that the temperature inside the radome remains constant, and that each actuator of the marine antenna is in a constant temperature environment, thus guaranteeing the performance of the marine antenna.

[0162] Furthermore, multiple third temperatures detected within the radome during the current temperature control cycle and multiple historical temperature control cycles are obtained. Based on these multiple third temperatures and a preset target temperature, the temperature regulation deviation rate of the temperature regulator during multiple temperature control cycles is calculated. The weighted average of the first temperature predicted during multiple temperature control cycles and the multiple temperature regulation deviation rates is used as the temperature correction coefficient. The product of the first temperature and the temperature correction coefficient is calculated as the temperature compensation value. The sum of the temperature compensation value and the first temperature is calculated to obtain the second temperature. This prompts the temperature regulator to generate more heat to increase the temperature inside the radome, or to generate more cold energy to decrease the temperature inside the radome. This compensates for the defect that the first temperature predicted by the antenna temperature prediction model is too small or too high compared to the actual temperature, making the constant temperature control inside the radome more precise and ensuring that all actuators of the marine antenna inside the radome operate in a constant temperature environment.

[0163] Example 3

[0164] Figure 5 This is a schematic diagram of the structure of a temperature control device for a marine antenna provided in Embodiment 3 of the present invention. Figure 5 As shown, the temperature control device for this marine antenna includes:

[0165] The historical operation data and ambient temperature acquisition module 501 is used to acquire historical operation data of multiple execution units of the marine antenna and historical ambient temperature outside the antenna radome. The historical operation data includes the operation data of each execution unit in the current temperature control cycle and multiple historical temperature control cycles before the current temperature control cycle.

[0166] Temperature prediction module 502 is used to input the historical ambient temperature and the historical operating data into a pre-trained antenna temperature prediction model to obtain the first temperature inside the antenna cover and the confidence level of the first temperature when the temperature regulator is not activated in the next temperature control cycle.

[0167] The confidence level determination module 503 is used to determine whether the confidence level is greater than a preset confidence level threshold. If so, the temperature correction coefficient determination module 504 is executed.

[0168] Temperature correction coefficient determination module 504 is used to determine the temperature correction coefficient based on the confidence level of the first temperature predicted by multiple temperature control cycles.

[0169] Temperature correction module 505 is used to correct the first temperature based on the temperature correction coefficient to obtain a second temperature;

[0170] Temperature regulation module 506 is used to control the temperature regulator to work according to the second temperature and the preset target temperature during the next temperature control cycle, so as to adjust the temperature inside the radome to the target temperature.

[0171] Optionally, the execution unit of the marine antenna includes a reflector, a feed source, an orthogonal mode coupler, a frequency converter, an antenna mainboard, a posture motor, and a posture determination device. The historical operating data and ambient temperature acquisition module 501 includes:

[0172] The reflector operation data acquisition unit is used to acquire the pitch angle curve and angular velocity curve of the reflector.

[0173] The feeder operation data acquisition unit is used to acquire the voltage curve, current curve and power curve of the feeder;

[0174] An orthogonal mode coupler operation data acquisition unit is used to acquire the temperature curve of the orthogonal mode coupler;

[0175] The inverter operation data acquisition unit is used to acquire the power curve, input frequency curve and output frequency curve of the inverter.

[0176] The motherboard operation data acquisition unit is used to acquire the power curve of the antenna motherboard;

[0177] The motor operation data acquisition unit is used to acquire the power curve and speed curve of the posture motor;

[0178] The pose determination device operation data acquisition unit is used to acquire the power curve of the pose determination device.

[0179] The historical ambient temperature acquisition unit is used to acquire the historical ambient temperature curve outside the radome.

[0180] Optionally, the antenna temperature prediction model includes a feature extraction network, a feature fusion network, an antenna operating state matching network, and a temperature prediction network connected in sequence. The historical operating data is input into the pre-trained antenna temperature prediction model. The antenna temperature prediction model predicts the first temperature inside the radome and the confidence level of the first temperature when the temperature regulator is not activated in the next temperature control cycle, including:

[0181] The historical data input unit is used to input the historical operating data of the reflector, feed source, orthogonal mode coupler, frequency converter, antenna motherboard, posture motor and posture determination device and the historical ambient temperature curve outside the antenna radome into the feature extraction network;

[0182] The feature map extraction unit is used to extract features from the historical operating data and the historical ambient temperature curve in the feature extraction network to obtain the first operating feature map and the first ambient temperature feature map of the reflector, feed, orthogonal mode coupler, frequency converter, antenna motherboard, posture motor and posture determination device.

[0183] The feature map fusion unit is used to fuse the first running feature map and the first ambient temperature feature map in the feature fusion network to obtain a first fused feature map;

[0184] The feature matching unit is used in the antenna operating state matching network to match the first fused feature map with feature map samples in a preset state feature library to obtain feature pairs and the matching degree of the feature pairs. The feature pairs include the first fused feature map and the first feature map sample, and the first feature map sample is a feature map whose antenna operating state has been determined.

[0185] The target operating state determination unit is used to determine the antenna operating state associated with the first feature map sample in the feature pair as the target operating state of the marine antenna in the next temperature control cycle.

[0186] A temperature prediction unit is used in the temperature prediction network to predict, based on the target operating state, the first temperature inside the radome and the probability of the first temperature when the temperature regulator is not activated in the next temperature control cycle.

[0187] The temperature confidence calculation unit is used to calculate the weighted sum of the probability of the first temperature and the probability of the target operating state as the confidence of the first temperature.

[0188] Optionally, the temperature correction factor determination module 504 includes:

[0189] The radome temperature acquisition unit is used to acquire multiple third temperatures inside the radome detected in the current temperature control cycle and multiple historical temperature control cycles.

[0190] The deviation rate calculation unit is used to calculate the temperature regulation deviation rate of the temperature regulator in multiple temperature control cycles based on multiple third temperatures and a preset target temperature.

[0191] The correction coefficient calculation unit is used to calculate a weighted average as the temperature correction coefficient using the confidence level of the first temperature predicted by multiple temperature control cycles and multiple temperature adjustment deviation rates.

[0192] Optional, the temperature correction module 505 includes:

[0193] A temperature compensation value calculation unit is used to calculate the product of the first temperature and the temperature correction coefficient as the temperature compensation value.

[0194] A temperature summation calculation unit is used to calculate the sum of the temperature compensation value and the first temperature to obtain the second temperature.

[0195] Optionally, the temperature regulation module 506 includes:

[0196] The absolute value of temperature difference calculation unit is used to calculate the difference between the second temperature and the target temperature, and take the absolute value of the difference to obtain the absolute value of the temperature difference;

[0197] The difference judgment unit is used to determine whether the difference is greater than the value 0. If it is, the cooling control unit is executed; if not, the heating control unit is executed.

[0198] A cooling control unit is used to calculate the target cooling capacity using the absolute value of the temperature difference and the preset volume of the radome, and to control the temperature regulator to cool according to the target cooling capacity;

[0199] A heating control unit is used to calculate the target heating capacity using the absolute value of the temperature difference and the preset volume of the radome, and to control the temperature regulator to heat the device according to the target heating capacity.

[0200] Optionally, the antenna temperature prediction model is trained through a training module, which is specifically used for:

[0201] Obtain a training dataset. Each data sample in the training dataset includes operating curve samples of the reflector, feed, orthogonal mode coupler, frequency converter, antenna motherboard, posture motor, posture determination device, and temperature curve samples outside the radome. Each data sample is labeled with the actual operating status and actual temperature.

[0202] Construct a state feature library, which includes feature map samples and the antenna operating states associated with the feature map samples;

[0203] An antenna temperature prediction model is constructed, consisting of a feature extraction network, a feature fusion network, an antenna operating state matching network, and a temperature prediction network connected in sequence.

[0204] The extracted data samples are input into the antenna temperature prediction model. The feature extraction network extracts features from the data samples to obtain the second operating feature map and the second ambient temperature feature map of the reflector, feed, orthogonal mode coupler, frequency converter, antenna motherboard, posture motor, and posture determination device.

[0205] The second running feature map and the second ambient temperature feature map are fused in the feature fusion network to obtain a second fused feature map;

[0206] In the antenna operating state matching network, the second fused feature map is matched with the feature map samples in the state feature library to obtain the predicted feature pairs and the matching degree of the predicted feature pairs. The predicted feature pairs include the second fused feature map and the target feature map sample. The target feature map sample is a feature map whose antenna operating state has been determined.

[0207] The antenna operating state associated with the target feature map sample in the feature pair is determined as the predicted operating state of the marine antenna in the next temperature control cycle;

[0208] In the temperature prediction network, based on the predicted operating status, the predicted temperature inside the radome is predicted when the temperature regulator is not activated in the next temperature control cycle, and the probability of the predicted temperature is predicted.

[0209] The total loss value is calculated based on the predicted temperature, the probability, the actual temperature, the predicted operating state, the actual operating state, and the matching degree of the predicted feature pairs.

[0210] Determine whether the conditions for stopping training are met;

[0211] If so, confirm that the antenna temperature prediction model has completed training;

[0212] If not, adjust the network parameters of the feature fusion network, antenna operating status matching network, and temperature prediction network using the total loss value, and return to the step of extracting data samples and inputting them into the antenna temperature prediction model.

[0213] The constant temperature control device for marine antennas provided in this embodiment of the invention can execute the constant temperature control method for marine antennas provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0214] Example 4

[0215] Figure 6A schematic diagram of the structure of a marine antenna 40 that can be used to implement an embodiment of the present invention is shown. Figure 6 As shown, the marine antenna 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 and a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded from storage unit 48 into the RAM 43. The RAM 43 can also store various programs and data required for the operation of the marine antenna 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0216] Multiple components in the marine antenna 40 are connected to the I / O interface 45, including: an input unit 46, such as a temperature sensor; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, optical disk, etc.; and a communication unit 49, such as a network card, modem, wireless transceiver, etc. The communication unit 49 allows the marine antenna 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0217] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as the temperature control method for a marine antenna.

[0218] In some embodiments, the temperature control method for the marine antenna can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or mounted on the marine antenna 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the temperature control method for the marine antenna described above can be performed. Alternatively, in other embodiments, processor 41 can be configured to perform the temperature control method for the marine antenna by any other suitable means (e.g., by means of firmware).

[0219] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0220] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0221] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0222] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0223] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0224] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0225] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for constant temperature control of a marine antenna, characterized in that, The marine antenna has multiple actuators housed within a radome, and a temperature regulator is installed within the radome, including: The historical operating data of multiple actuators of the marine antenna and the historical ambient temperature outside the antenna radome are obtained. The historical operating data includes the operating data of each actuator in the current temperature control cycle and multiple historical temperature control cycles before the current temperature control cycle. The historical ambient temperature and the historical operating data are input into a pre-trained antenna temperature prediction model to obtain the first temperature inside the radome and the confidence level of the first temperature when the temperature regulator is not activated in the next temperature control cycle. Determine whether the confidence level is greater than a preset confidence threshold; If so, the temperature correction factor is determined based on the confidence level of the first temperature predicted by multiple temperature control cycles; The second temperature is obtained by correcting the first temperature based on the temperature correction coefficient. During the next temperature control cycle, the temperature regulator is controlled to operate according to the second temperature and the preset target temperature to adjust the temperature inside the radome to the target temperature. The temperature correction factor is determined based on the confidence level of the first temperature predicted by multiple temperature control cycles, including: Acquire multiple third temperatures inside the radome detected during the current temperature control cycle and multiple historical temperature control cycles; The temperature regulation deviation rate of the temperature regulator in multiple temperature control cycles is calculated based on multiple third temperatures and a preset target temperature. The weighted average of the first temperature predicted by multiple temperature control cycles and multiple temperature adjustment deviation rates is used as the temperature correction coefficient.

2. The method according to claim 1, characterized in that, The actuation unit of the marine antenna includes a reflector, a feed source, an orthogonal mode coupler, a frequency converter, an antenna mainboard, a pose motor, and a pose determination device. It acquires historical operating data of multiple actuation units of the marine antenna and historical ambient temperature outside the radome, including: Obtain the pitch angle curve and angular velocity curve of the reflecting surface; Obtain the voltage curve, current curve, and power curve of the feed source; Obtain the temperature profile of the orthogonal mode coupler; Obtain the power curve, input frequency curve, and output frequency curve of the frequency converter; Obtain the power curve of the antenna motherboard; Obtain the power curve and speed curve of the posture motor; Obtain the power curve of the pose determination device; Obtain the historical ambient temperature curve outside the radome.

3. The method according to claim 2, characterized in that, The antenna temperature prediction model includes a feature extraction network, a feature fusion network, an antenna operating state matching network, and a temperature prediction network connected in sequence. The historical operating data is input into the pre-trained antenna temperature prediction model. The model predicts the first temperature inside the radome and the confidence level of the first temperature when the temperature regulator is not activated in the next temperature control cycle. This includes: The historical operating data of the reflector, feed, orthogonal mode coupler, frequency converter, antenna motherboard, posture motor, and posture determination device, as well as the historical ambient temperature curve outside the radome, are input into the feature extraction network. The feature extraction network extracts features from the historical operating data and the historical ambient temperature curve to obtain the first operating feature map and the first ambient temperature feature map of the reflector, feed, orthogonal mode coupler, frequency converter, antenna motherboard, posture motor and posture determination device. The first running feature map and the first ambient temperature feature map are fused in the feature fusion network to obtain a first fused feature map; In the antenna operating state matching network, the first fused feature map is matched with feature map samples in a preset state feature library to obtain feature pairs and the matching degree of the feature pairs. The feature pairs include the first fused feature map and the first feature map sample, where the first feature map sample is a feature map whose antenna operating state has been determined. The antenna operating state associated with the first feature map sample in the feature pair is determined as the target operating state of the marine antenna in the next temperature control cycle; In the temperature prediction network, based on the target operating state, the first temperature inside the radome and the probability of the first temperature are predicted when the temperature regulator is not activated in the next temperature control cycle. The confidence level of the first temperature is calculated as the weighted sum of the probability of the first temperature and the probability of the target operating state.

4. The method according to claim 1, characterized in that, The second temperature is obtained by correcting the first temperature based on the temperature correction coefficient, including: The product of the first temperature and the temperature correction coefficient is calculated as the temperature compensation value; The second temperature is obtained by summing the temperature compensation value with the first temperature.

5. The method according to claim 1, characterized in that, During the next temperature control cycle, the temperature regulator is controlled to operate according to the second temperature and the preset target temperature to adjust the temperature inside the radome to the target temperature, including: Calculate the difference between the second temperature and the target temperature, and take the absolute value of the difference to obtain the absolute value of the temperature difference; Determine whether the difference is greater than the value 0; If so, the target cooling capacity is calculated using the absolute value of the temperature difference and the preset volume of the radome, and the temperature regulator is controlled to cool according to the target cooling capacity; If not, the target heat output is calculated using the absolute value of the temperature difference and the preset volume of the radome, and the temperature regulator is controlled to heat the device based on the target heat output.

6. The method according to claim 3, characterized in that, The antenna temperature prediction model is trained through the following steps: Obtain a training dataset. Each data sample in the training dataset includes operating curve samples of the reflector, feed, orthogonal mode coupler, frequency converter, antenna motherboard, posture motor, posture determination device, and temperature curve samples outside the radome. Each data sample is labeled with the actual operating status and actual temperature. Construct a state feature library, which includes feature map samples and the antenna operating states associated with the feature map samples; An antenna temperature prediction model is constructed, consisting of a feature extraction network, a feature fusion network, an antenna operating state matching network, and a temperature prediction network connected in sequence. In the antenna temperature prediction model, the extracted data samples are input into the feature extraction network to extract features from the data samples, thereby obtaining the second operating feature map and the second ambient temperature feature map of the reflector, feed, orthogonal mode coupler, frequency converter, antenna motherboard, posture motor, and posture determination device. The second running feature map and the second ambient temperature feature map are fused in the feature fusion network to obtain a second fused feature map; In the antenna operating state matching network, the second fused feature map is matched with the feature map samples in the state feature library to obtain the predicted feature pairs and the matching degree of the predicted feature pairs. The predicted feature pairs include the second fused feature map and the target feature map sample, and the target feature map sample is the feature map with the determined antenna operating state. The antenna operating state associated with the target feature map sample in the feature pair is determined as the predicted operating state of the marine antenna in the next temperature control cycle; In the temperature prediction network, based on the predicted operating status, the predicted temperature inside the radome is predicted when the temperature regulator is not activated in the next temperature control cycle, and the probability of the predicted temperature is predicted. The total loss value is calculated based on the predicted temperature, the probability, the actual temperature, the predicted operating state, the actual operating state, and the matching degree of the predicted feature pairs. Determine whether the conditions for stopping training are met; If so, confirm that the antenna temperature prediction model has completed training; If not, adjust the network parameters of the feature fusion network, antenna operating status matching network, and temperature prediction network using the total loss value, and return to the step of extracting data samples and inputting them into the antenna temperature prediction model.

7. A constant temperature control device for a marine antenna, characterized in that, The marine antenna has multiple actuators housed within a radome, and a temperature regulator is installed within the radome, including: The historical operation data and ambient temperature acquisition module is used to acquire historical operation data of multiple execution units of the marine antenna and historical ambient temperature outside the antenna radome. The historical operation data includes the operation data of each execution unit in the current temperature control cycle and multiple historical temperature control cycles before the current temperature control cycle. The temperature prediction module is used to input the historical ambient temperature and the historical operating data into a pre-trained antenna temperature prediction model to obtain the first temperature inside the antenna cover and the confidence level of the first temperature when the temperature regulator is not activated in the next temperature control cycle. The confidence level determination module is used to determine whether the confidence level is greater than a preset confidence level threshold. If so, the temperature correction coefficient determination module is executed. The temperature correction coefficient determination module is used to determine the temperature correction coefficient based on the confidence level of the first temperature predicted by multiple temperature control cycles. A temperature correction module is used to correct the first temperature based on the temperature correction coefficient to obtain a second temperature; A temperature regulation module is used to control the temperature regulator to operate according to the second temperature and the preset target temperature during the next temperature control cycle, so as to adjust the temperature inside the radome to the target temperature. The temperature correction factor determination module includes: The radome temperature acquisition unit is used to acquire multiple third temperatures inside the radome detected in the current temperature control cycle and multiple historical temperature control cycles. The deviation rate calculation unit is used to calculate the temperature regulation deviation rate of the temperature regulator in multiple temperature control cycles based on multiple third temperatures and a preset target temperature. The correction coefficient calculation unit is used to calculate a weighted average as the temperature correction coefficient using the confidence level of the first temperature predicted by multiple temperature control cycles and multiple temperature adjustment deviation rates.

8. A marine antenna, characterized in that, The marine antenna includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the constant temperature control method for the marine antenna according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the constant temperature control method for the marine antenna as described in any one of claims 1-6.

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