A monitoring receiver test system

Abstract

The present invention discloses a monitoring receiver test system, including a parameter monitoring module for real-time monitoring of several component parameters inside the monitoring receiver; an aging calculation module for calculating the equipment aging efficiency of the monitoring receiver according to the parameters of each component, so as to train and obtain an equipment aging prediction model; a prediction module for screening each test parameter set to obtain several preferred data sets, so as to input the equipment aging prediction model to obtain the predicted aging efficiency; a function processing unit for processing the predicted aging efficiency and the equipment aging efficiency to obtain an objective function value; an introduction unit for introducing a firefly algorithm; an algorithm optimization unit for updating the light absorption coefficient and the random term coefficient in the firefly algorithm; a solution unit for iteratively updating the position of the firefly individual, and obtaining the data in the firefly individual when the objective function value is the maximum as the optimal parameter set; and an environment simulation module for adjusting the test environment in each test sub-cavity according to the optimal parameter set. The present invention improves the efficiency of the sea surface simulation test of the monitoring receiver.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring receivers, and in particular to a monitoring receiver testing system. Background Art

[0002] A monitoring receiver is a device for receiving and decoding radio frequencies, usually used to receive broadcast signals, satellite communications, radio communications, etc. The main function of a monitoring receiver is to receive radio signals within a specific frequency range and convert them into digital or analog signals for analysis and processing. Monitoring receivers play an important role in the fields of radio monitoring, communication, and spectrum analysis. When a monitoring receiver is used on the sea surface, the radio signal will be affected by the sea surface weather, wireless transmission distance, and signal strength during the process of receiving the radio signal. In addition, due to the influence of the humid and low pressure of the sea surface environment, the service life and signal reception accuracy of the detection receiver will be seriously affected. In the prior art, in order to simulate the use scenario of the monitoring receiver on the sea surface, the monitoring receiver is generally subjected to an aging test in a closed laboratory environment. However, the current aging test is to perform various tests step by step according to the standard test process. Although this method can measure a more comprehensive monitoring receiver in various environmental scenarios Test parameters, it takes too long, resulting in low efficiency of the sea surface simulation test of the monitoring receiver. Summary of the invention

[0003] In view of the deficiencies in the prior art, an object of the present invention is to provide a monitoring receiver testing system for improving the efficiency of sea surface simulation testing of monitoring receivers.

[0004] To achieve the above object, the present invention provides the following technical solution: a monitoring receiver test system, configured with a test cavity, the interior of the test cavity is pre-divided into a plurality of test sub-cavities, a monitoring receiver is arranged in the test sub-cavity, and the monitoring receiver test system comprises:

[0005] A storage module, used for storing a number of sea surface environment data;

[0006] An environmental simulation module, arranged in the test sub-cavity and connected to the storage module, for randomly generating a plurality of test parameter sets according to the sea surface environment data, and adjusting the test environment in each of the test sub-cavities according to the plurality of test parameter sets;

[0007] A parameter monitoring module, used for real-time monitoring of several component parameters inside the monitoring receiver;

[0008] An aging calculation module, connected to the parameter monitoring module, for calculating the equipment aging efficiency of the monitoring receiver in each test sub-cavity per unit time according to the parameters of each component;

[0009] A training module, connected to the environmental simulation module and the aging calculation module respectively, for taking each of the test parameter sets as input and the corresponding equipment aging efficiency as output, and retraining a preset initial prediction model to obtain an equipment aging prediction model;

[0010] A prediction module, connected to the training module, for screening each of the test parameter sets to obtain a plurality of preferred data sets, and inputting each of the preferred data sets into the equipment aging prediction model to predict the predicted aging efficiency;

[0011] A parameter optimization module, connecting the prediction module and the environment simulation module, includes:

[0012] A function processing unit, used for processing the predicted aging efficiency and the actual equipment aging efficiency to obtain an objective function value;

[0013] An introduction unit, connected to the function processing unit, used to introduce a firefly algorithm, taking each of the preferred data sets as a firefly individual, and taking the objective function value as the firefly luminescence brightness;

[0014] An algorithm optimization unit, connected to the introduction unit, for updating the light absorption coefficient and the random term coefficient in the position update formula of the firefly algorithm according to a preset absorption coefficient update formula and a preset random term coefficient update formula respectively;

[0015] A solving unit, connected to the algorithm optimization unit, for continuously iteratively updating the position of the firefly individuals according to the optimized position update formula, and calculating the objective function value corresponding to each firefly individual, and finally obtaining the data of the firefly individuals when the objective function value is the maximum through iterative updating as the optimal parameter set;

[0016] The environmental simulation module centrally adjusts the test environment in each of the test sub-cavities according to the optimal parameters.

[0017] Further, the component parameters include antenna parameters, filter parameters, frequency converter parameters, amplifier parameters, and digital-to-analog converter parameters;

[0018] The aging calculation module comprises:

[0019] A first calculation unit, used for respectively subtracting each of the component parameters from the corresponding standard parameters to obtain a component aging deviation value;

[0020] The second calculation unit is connected to the first calculation unit and is used to bring the aging deviation value of each component into a preset equipment aging calculation formula to calculate the equipment aging efficiency.

[0021] Furthermore, the component aging deviation value includes an antenna aging deviation value, a filter aging deviation value, a frequency converter aging deviation value, an amplifier aging deviation value, and a digital-to-analog converter aging deviation value, and the device aging calculation formula is configured as:

[0022]

[0023] ΔA p =|A p -A p0 |

[0024] ΔF p =|F p -F p0 |

[0025] ΔV fd =|V fd -V fd0 |

[0026] ΔA a =|A a -A a0 |

[0027] ΔD ac =|D ac -D a0 |

[0028] Among them, E ad Used to indicate the aging efficiency of the equipment, A p Used to represent antenna aging parameters, A p0 Used to indicate the preset antenna aging standard value, ΔA p Used to represent the antenna aging deviation value, F p Used to represent the filter aging parameter, F p0 Used to indicate the preset filter aging standard value, ΔF p Used to represent the filter aging deviation value, V fd Used to indicate the inverter aging parameters, V fd0 Used to indicate the preset inverter aging standard value, ΔV fd Used to indicate the aging deviation value of the inverter, A a Used to represent the amplifier aging parameter, A a0 Used to indicate the preset amplifier aging standard value, ΔA a Used to represent the amplifier aging deviation value, D ac Used to represent the aging parameter of the digital-to-analog converter, D a0 Used to indicate the preset aging standard value of the digital-to-analog converter, ΔD acIt is used to represent the aging deviation value of the digital-to-analog converter, ρ1, ρ2, ρ3, ρ4, ρ5 are respectively used to represent the preset antenna conversion coefficient, filter conversion coefficient, converter conversion coefficient, amplifier conversion coefficient, filter conversion coefficient and digital-to-analog converter conversion coefficient, and T is used to represent unit time.

[0029] Furthermore, the initial prediction model is a LightGBM model.

[0030] Furthermore, the prediction module includes:

[0031] A preprocessing unit, used for performing data preprocessing on each of the test parameter sets, filtering out some invalid data and forming a plurality of the preferred data sets;

[0032] A prediction unit, connected to the preprocessing unit, configured to input each of the preferred data sets into the equipment aging prediction model, and the equipment aging prediction model outputs the predicted aging efficiency;

[0033] The data in the preferred data set include sea surface air salinity, sea surface temperature, sea surface air pressure and sea surface humidity.

[0034] Further, the processing unit inputs the predicted aging efficiency and the equipment aging efficiency into a preset equipment aging function to obtain the objective function value, and the equipment aging function is configured as:

[0035]

[0036] Among them, f x It is used to represent the objective function value, E(S a ,S t ,S p ,S h ) is used to represent the predicted aging efficiency, S a Used to indicate the salt content of the sea surface air, S t Used to represent the sea surface temperature, S p Used to express the sea surface pressure and S h Used to indicate the sea surface humidity.

[0037] Furthermore, it also includes a plurality of signal transmitting terminals and signal processing modules, wherein the signal transmitting terminals are used to transmit radio signals toward each of the monitoring receivers, wherein the radio signals include valid signals and a plurality of invalid signals, and the signal quality, signal transmission distance, and signal transmission power of the valid signals and the invalid signals are known;

[0038] The signal processing module is connected to each of the signal transmitting terminals and the parameter optimization module, and the signal processing module includes a first processing unit, which is used to input the signal quality, signal transmission distance, signal transmission power of the valid signal and the signal quality, signal transmission distance, and signal transmission power of each of the invalid signals into the absorption coefficient update formula to calculate the updated light absorption coefficient.

[0039] Furthermore, the absorption coefficient update formula is configured as:

[0040]

[0041] Among them, γ' is used to represent the updated light absorption coefficient, γ max Used to indicate the preset maximum value of light absorption coefficient, γ min Used to indicate the preset minimum value of the light absorption coefficient, f avg It is used to represent the average objective function value of all current firefly individuals, f max It is used to represent the maximum objective function value among all firefly individuals at present, S Q It is used to represent the signal quality average value of the valid signal and each invalid signal, S TD It is used to represent the average signal transmission distance of the valid signal and each invalid signal, S TP It is used to represent the average signal transmission power of the valid signal and each invalid signal, and ε1, ε2, and ε3 are used to represent the preset quality conversion coefficient, distance conversion coefficient, and power conversion coefficient, respectively.

[0042] Furthermore, the signal processing module also includes:

[0043] A signal analysis unit, used for performing bandwidth analysis and frequency band analysis on the valid signal and each of the invalid signals, to obtain a signal working bandwidth and a signal working frequency band of the valid signal and each of the invalid signals;

[0044] The second processing unit is connected to the signal analysis unit, and is used to input the signal working bandwidth and signal working frequency band of the valid signal and each invalid signal into the random term coefficient update formula to calculate the updated random term coefficient.

[0045] Further,

[0046] Wherein, α' is used to represent the updated random term coefficient, fb The signal operating frequency band used to represent the effective signal, W b is used to represent the signal working bandwidth of the valid signal, n is used to represent the number of the invalid signals, O iThe signal operating frequency band used to represent the invalid signal, W i is used to represent the signal working bandwidth of the invalid signal, ε4 and ε5 are used to represent the preset frequency band conversion coefficient and bandwidth conversion coefficient respectively, and α ini It is used to represent the initial value of the random term coefficient, α fin It is used to represent the final value of the random term coefficient, t is used to represent the current iteration number of the firefly algorithm, and t max Used to indicate the maximum number of iterations of the firefly algorithm.

[0047] Beneficial effects of the present invention:

[0048] The present invention first randomly generates a test data set in a test cavity according to sea surface environment data to adjust the test environment of each test cavity for random testing, and then obtains a number of component parameters inside the monitoring receiver in each test cavity to obtain the equipment aging efficiency of the monitoring receiver in unit time, and then establishes an equipment aging prediction model to predict the output predicted aging efficiency, establishes an objective function according to the predicted aging efficiency and the actual equipment aging efficiency, and uses the preprocessed preferred data set as firefly individuals, and uses the optimized firefly algorithm to iteratively update to obtain the optimal parameter set when the objective function value is maximum. At this time, the optimal parameter set is the environmental parameter when the gap between the predicted aging efficiency and the actual equipment aging efficiency is the smallest. Therefore, according to the optimal adjustment of the test environment in the test cavity, the test environment in the test cavity can be made infinitely close to the test environment when the aging test efficiency is highest, and it is no longer necessary to adjust the test environment step by step according to the test process for aging testing, which significantly improves the efficiency of the sea surface simulation test of the monitoring receiver. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a schematic diagram of a monitoring receiver test system in the present invention;

[0050] Figure 2 It is a structural schematic diagram of the aging calculation module in the present invention;

[0051] Figure 3 It is a schematic diagram of the structure of the prediction module in the present invention;

[0052] Figure 4 It is a structural schematic diagram of the parameter optimization module in the present invention;

[0053] Figure 5 It is a structural schematic diagram of the signal processing module in the present invention.

[0054] Figure numerals: 1. Storage module; 2. Environment simulation module; 3. Parameter monitoring module; 4. Aging calculation module; 41. First calculation unit; 42. Second calculation unit; 5. Training module; 6. Prediction module; 61. Preprocessing unit; 62. Prediction unit; 7. Parameter optimization module; 71. Function processing unit; 72. Introduction unit; 73. Algorithm optimization unit; 74. Solution unit; 8. Signal transmitting terminal; 9. Signal processing module; 91. First processing unit; 92. Signal analysis unit; 93. Second processing unit. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] It should be noted that when a component is referred to as being "fixed to" another component, it may be directly on the other component or there may also be a component centered. When a component is considered to be "connected to" another component, it may be directly connected to the other component or there may also be a component centered. When a component is considered to be "set on" another component, it may be directly set on the other component or there may also be a component centered. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0058] Please also see Figure 1 and Figure 2 This embodiment provides a monitoring receiver test system, which is equipped with a test cavity, wherein the interior of the test cavity is pre-divided into a plurality of test sub-cavities, and the monitoring receiver is arranged in the test sub-cavity. The monitoring receiver test system includes:

[0059] Storage module 1, used to store some sea surface environment data;

[0060] The environment simulation module 2 is arranged in the test sub-cavity and connected to the storage module 1, and is used to generate a plurality of test parameter sets according to the random matching of various sea surface environment data, and adjust the test environment in each test sub-cavity according to the plurality of test parameter sets;

[0061] The parameter monitoring module 3 is used to monitor the parameters of several components inside the monitoring receiver in real time;

[0062] The aging calculation module 4 is connected to the parameter monitoring module 3 and is used to calculate the equipment aging efficiency of the monitoring receiver in each test sub-cavity per unit time according to the parameters of each component;

[0063] The training module 5 is connected to the environment simulation module 2 and the aging calculation module 4 respectively, and is used to take each test parameter set as input, take the corresponding equipment aging efficiency as output, and retrain the preset initial prediction model to obtain the equipment aging prediction model;

[0064] The prediction module 6 is connected to the training module 5 and is used to screen each test parameter set to obtain a plurality of preferred data sets, and input each preferred data set into the equipment aging prediction model to predict the predicted aging efficiency;

[0065] The parameter optimization module 7 is connected to the prediction module 6 and the environment simulation module 2, and includes:

[0066] A function processing unit 71 is used to obtain an objective function value according to the predicted aging efficiency and the actual equipment aging efficiency;

[0067] The introduction unit 72 is connected to the function processing unit 71, and is used to introduce the firefly algorithm, taking each preferred data set as a firefly individual, and taking the objective function value as the firefly luminescence brightness;

[0068] The algorithm optimization unit 73 is connected to the introduction unit 72 and is used to update the light absorption coefficient and the random term coefficient in the position update formula of the firefly algorithm according to a preset absorption coefficient update formula and a preset random term coefficient update formula respectively;

[0069] The solving unit 74 is connected to the algorithm optimization unit 73, and is used to continuously iteratively update the position of the firefly individual according to the optimized position update formula, and calculate the objective function value corresponding to each firefly individual, and finally obtain the data of the firefly individual when the objective function value is the maximum through iterative update as the optimal parameter set;

[0070] The environment simulation module 2 centrally adjusts the test environment in each test sub-cavity according to the optimal parameters.

[0071] Specifically, in this embodiment, the storage module 1 can be a non-volatile memory. The environmental simulation module 2 can be installed in the environmental parameter adjustment device in the test sub-chamber, including a temperature and humidity adjustment box, an air pressure adjustment device, a salt spray device and a salt separator. The temperature and humidity adjustment box is used to adjust the temperature and humidity in the test sub-chamber, the air pressure adjustment device is used to adjust the air pressure in the test sub-chamber, the salt spray device is used to spray smoke to increase the salt content in the air, and the salt separator is used to separate and remove salt in the air to reduce the salt content in the air. The salt spray device and the salt separator are used together to adjust the salt content in the air in the test sub-chamber. After the salt spray device sprays smoke, the air humidity increases, and the temperature and humidity adjustment box is used for dehumidification to maintain the air humidity unchanged.

[0072] The parameter monitoring module 3 may be a component detection circuit arranged inside the monitoring receiver, and is used to obtain the component parameters of each circuit inside the monitoring receiver. The component detection circuit is connected to an external intelligent processing terminal through a line, and a number of calculation programs are configured in the intelligent processing terminal. The aging calculation module 4 may be an aging calculation program, and is used to calculate the equipment aging efficiency of each monitoring receiver in a unit time according to the parameters of each component. The training module 5 is a model training program, which first introduces an initial prediction model, and then uses a number of test parameter sets and corresponding equipment aging efficiencies as training data sets of the initial prediction model, and then uses each test parameter set as input, and uses the corresponding equipment aging efficiency as output, and retrains the preset initial prediction model to obtain an equipment aging prediction model, wherein the initial prediction model is a LightGBM model. The prediction module 6 may be a prediction program, which first performs data preprocessing on the test data set to obtain a preferred data set after filtering out some invalid data, and then uses the preferred data set to input the equipment aging prediction model, so that the equipment aging prediction model predicts the predicted aging efficiency. The parameter optimization module 7 may be an environmental parameter optimization program, and its purpose is to find the environmental parameters when the difference between the predicted aging efficiency output by the equipment aging prediction model and the actual equipment aging efficiency is the smallest. The function processing unit 71 first obtains the objective function value according to the predicted aging efficiency and the actual equipment aging efficiency, and then the introduction unit 72 introduces the firefly algorithm, takes each preferred data set as a firefly individual, and takes the objective function value as the firefly luminescence brightness. The purpose of the firefly algorithm is to find the position of the firefly when the firefly luminescence brightness is the brightest, that is, the preferred data set when the objective function value is the maximum is the optimal solution. The algorithm optimization unit 73 optimizes and adjusts the firefly algorithm by updating the light absorption coefficient and the random term coefficient in the position update formula of the firefly algorithm, further improving the efficiency and accuracy of finding the optimal solution. The final solution unit 74 continuously iterates and updates using the optimized firefly algorithm to obtain the firefly individual when the objective function is the maximum, and outputs the preferred data set in the firefly individual as the optimal data set. Finally, the environmental parameter adjustment device dynamically adjusts the test environment in each test sub-cavity according to the data in the optimal data set, which can make the test environment in the test cavity infinitely close to the test environment when the aging test efficiency is the highest. It is no longer necessary to adjust the test environment step by step according to the test process for aging test, which significantly improves the efficiency of the sea surface simulation test of the monitoring receiver.

[0073] Preferably, the component parameters include antenna parameters, filter parameters, frequency converter parameters, amplifier parameters, and digital-to-analog converter parameters;

[0074] Reference Figure 3 As shown, the aging calculation module 4 includes:

[0075] The first calculation unit 41 is used to respectively calculate the difference between each component parameter and the corresponding standard parameter to obtain a component aging deviation value;

[0076] The second calculation unit 42 is connected to the first calculation unit 41 and is used to bring the aging deviation value of each component into a preset equipment aging calculation formula to calculate the equipment aging efficiency.

[0077] Specifically, in this embodiment, inside the monitoring receiver, important component parameters that affect the life of the monitoring receiver are mainly antenna parameters, filter parameters, frequency converter parameters, amplifier parameters, and digital-to-analog converter parameters.

[0078] Preferably, the component aging deviation value includes an antenna aging deviation value, a filter aging deviation value, a frequency converter aging deviation value, an amplifier aging deviation value, and a digital-to-analog converter aging deviation value, and the equipment aging calculation formula is configured as:

[0079]

[0080] ΔA p =|A p -A p0 |

[0081] ΔF p =|F p -F p0 |

[0082] ΔV fd =|V fd -V fd0 |

[0083] ΔA a =|A a -A a0 |

[0084] ΔD ac =|D ac -D a0 |

[0085] Among them, E ad Used to indicate the aging efficiency of equipment, A p Used to represent antenna aging parameters, A p0 Used to indicate the preset antenna aging standard value, ΔA p Used to indicate the antenna aging deviation value, F p Used to represent the filter aging parameter, F p0 Used to indicate the preset filter aging standard value, ΔF p Used to indicate the filter aging deviation value, V fd Used to indicate the inverter aging parameters, V fd0 Used to indicate the preset inverter aging standard value, ΔVfd Used to indicate the inverter aging deviation value, A a Used to represent the amplifier aging parameter, A a0 Used to indicate the preset amplifier aging standard value, ΔA a Used to indicate the amplifier aging deviation value, D ac Used to represent the aging parameter of the digital-to-analog converter, D a0 Used to indicate the preset aging standard value of the digital-to-analog converter, ΔD ac It is used to represent the aging deviation value of the digital-to-analog converter, ρ1, ρ2, ρ3, ρ4, ρ5 are used to represent the preset antenna conversion coefficient, filter conversion coefficient, inverter conversion coefficient, amplifier conversion coefficient, filter conversion coefficient and digital-to-analog converter conversion coefficient respectively, and T is used to represent unit time.

[0086] Specifically, in this embodiment, the unit time can be 1 hour. The antenna aging standard value, the filter aging standard value, the inverter aging standard value, the amplifier aging standard value, and the digital-to-analog converter aging standard value are the standard parameters of the components. By subtracting each component parameter from the corresponding standard parameter, the component aging deviation value is obtained. Each aging deviation value can be used to reflect the degree of deviation of the aging of each important component from the standard parameter. The aging degree of each component can be calculated by multiplying the square of the aging deviation value with the relevant conversion coefficient. The aging degree of each component is accumulated and squared, and finally divided by the coefficient average value and the unit time, the equipment aging efficiency of the monitoring receiver in unit time can be obtained.

[0087] Preferably, refer to Figure 4 As shown, the prediction module 6 includes:

[0088] A preprocessing unit 61 is used to perform data preprocessing on each test parameter set, and form several preferred data sets after filtering out some invalid data;

[0089] The prediction unit 62 is connected to the preprocessing unit 61 and is used to input each preferred data set into the equipment aging prediction model, and the equipment aging prediction model outputs the predicted aging efficiency;

[0090] The data in the preferred data set include sea surface air salinity, sea surface temperature, sea surface pressure and sea surface humidity.

[0091] Specifically, in this embodiment, the preprocessing process for each test parameter set includes data inspection and data cleaning, wherein the data inspection process includes checking whether the data has abnormalities, missing and duplicate problems, and the data cleaning process includes deleting abnormal data and duplicate data, filling in some missing data, etc. Through data preprocessing, some invalid data is filtered out, the amount of calculation of subsequent models is reduced, and the overall calculation efficiency is improved.

[0092] Preferably, the function processing unit 71 inputs the predicted aging efficiency and the equipment aging efficiency into a preset equipment aging function to obtain the objective function value. The equipment aging function is configured as:

[0093]

[0094] Among them, f x Used to represent the objective function value, E(S a ,S t ,S p ,S h ) is used to represent the predicted aging efficiency, S a Used to indicate the salt content of sea surface air, S t Used to express sea surface temperature, S p Used to indicate sea surface pressure and S h Used to indicate sea surface humidity.

[0095] Specifically, in this embodiment, the objective function value is inversely proportional to the square of the difference between the predicted aging efficiency and the actual equipment aging efficiency. Therefore, when the objective function value is maximized, the difference between the predicted aging efficiency and the actual equipment aging efficiency is the smallest. At this time, the prediction accuracy of the equipment aging prediction model is the highest.

[0096] Preferably, it also includes a plurality of signal transmitting terminals 8 and a signal processing module 9, wherein the signal transmitting terminal 8 is used to transmit radio signals toward each monitoring receiver, wherein the radio signals include a valid signal and a plurality of invalid signals, and the signal quality, signal transmission distance, and signal transmission power of the valid signal and the invalid signal are known;

[0097] The signal processing module 9 is connected to each signal transmitting terminal 8 and the parameter optimization module 7, such as Figure 5 As shown, the signal processing module 9 includes a first processing unit 91, which is used to input the signal quality, signal transmission distance, signal transmission power of the valid signal and the signal quality, signal transmission distance, and signal transmission power of each invalid signal into the absorption coefficient update formula to calculate the updated light absorption coefficient.

[0098] Specifically, in this embodiment, the light absorption coefficient has a huge impact on the performance of the firefly algorithm. The light absorption coefficient can not only automatically divide the entire firefly population into sub-populations for optimization, but also effectively balance the algorithm's global detection and local mining capabilities. The step size of the random term coefficient has a greater impact on the execution efficiency of the algorithm, and the length of the random walk of the random term coefficient. If the value of the random term coefficient is too large, the distance between the newly generated solution and the solution generated by the previous iteration will be too far, causing the algorithm to search too much and jump and lack regularity. If the value is too small, the position change between two iterations will be too small and not easy to be detected, so that the algorithm's search process consumes too much time. When the signal transmitting terminal 8 transmits a radio signal toward the monitoring receiver in each test sub-cavity, the signal transmission power, signal transmission quality and signal transmission distance will affect the electromagnetic radiation and signal interference generated by the signal transmitting terminal 8 to the monitoring receiver, thereby affecting the life of each component inside the monitoring receiver. Therefore, in order to improve the optimization accuracy of the firefly algorithm, the light absorption coefficient is optimized and adjusted according to the signal transmission power, signal transmission quality and signal transmission distance, which can optimize the local mining ability of the firefly algorithm and improve the accuracy and efficiency of the firefly algorithm in finding the optimal solution. Preferably, the absorption coefficient update formula is configured as:

[0099]

[0100] Among them, γ' is used to represent the updated light absorption coefficient, γ max Used to indicate the preset maximum value of light absorption coefficient, γ min Used to indicate the preset minimum value of the light absorption coefficient, f avg It is used to represent the average objective function value of all current firefly individuals, f max It is used to represent the maximum objective function value among all firefly individuals at present, S Q It is used to represent the average signal quality of valid signals and invalid signals, S TD It is used to represent the average signal transmission distance of valid signals and invalid signals, S TP It is used to represent the average signal transmission power of valid signals and invalid signals. ε1, ε2, and ε3 are used to represent the preset quality conversion coefficient, distance conversion coefficient, and power conversion coefficient, respectively.

[0101] Specifically, in this embodiment, the average signal quality, the average signal transmission distance, and the average signal transmission power are respectively multiplied by the quality conversion coefficient, the distance conversion coefficient, and the power conversion coefficient, and then accumulated to obtain a comprehensive coefficient interference value. The comprehensive coefficient interference value can be used to reflect the influence of the signal quality of useful signals and useless signals, the signal transmission distance, and the signal transmission power on the light absorption coefficient. The comprehensive coefficient interference value is multiplied by the minimum light absorption coefficient or the maximum light absorption coefficient to achieve the adjustment of the light absorption coefficient. When the objective function value is less than the average objective function value, it indicates that the current light absorption coefficient is relatively small, which can be represented by the product of the comprehensive coefficient interference value and the minimum light absorption coefficient. When the objective function value is greater than the average objective function value, the light absorption coefficient is too large and needs to be adjusted so that the firefly individuals move closer to a better position area to improve the search efficiency of the optimal solution.

[0102] Preferably, refer to Figure 5 As shown, the signal processing module 9 also includes:

[0103] A signal analysis unit 92, used for performing bandwidth analysis and frequency band analysis on the valid signal and each invalid signal, to obtain the signal working bandwidth and signal working frequency band of the valid signal and each invalid signal;

[0104] The second processing unit 93 is connected to the signal analysis unit 92, and is used to input the signal working bandwidth and signal working frequency band of the valid signal and each invalid signal into the random item coefficient update formula to calculate and obtain the updated random item coefficient.

[0105] Specifically, in this embodiment, since there are multiple signal transmitting terminals 8, each signal transmitting terminal 8 will generate spurious, harmonic, intermodulation and other invalid signals outside the working frequency band while transmitting useful signals. The invalid signals fall into the working frequency band and working bandwidth of other wireless receiving devices, which will interfere with them. When the monitoring receiver receives useful signals, the interference signals falling into the working frequency band and working bandwidth may cause the internal components of the monitoring receiver to be affected, resulting in sensitivity loss and service life changes, affecting the test effect of the monitoring receiver. Therefore, in order to further improve the optimization accuracy of the firefly algorithm, the randomness coefficient is adjusted according to the working bandwidth and the signal working frequency band, and the optimization step length of the firefly algorithm is adjusted. Since the working frequency band and working bandwidth of the useless signal of the signal transmitting terminal 8 may change, the optimization step length of the firefly algorithm will also change accordingly. By setting this way, the optimization step length of the firefly algorithm can be dynamically adjusted, and the overall detection and local mining capabilities of the firefly algorithm can be simultaneously improved, so that the optimal solution searched by the firefly algorithm is closer to the actual situation, so the optimization accuracy is higher.

[0106] Preferably,

[0107] Among them, α' is used to represent the updated random term coefficient, O fb Used to indicate the signal working frequency band of effective signal, W b It is used to indicate the signal working bandwidth of the valid signal, n is used to indicate the number of invalid signals, O i The signal working frequency band used to indicate invalid signals, W i It is used to indicate the signal working bandwidth of the invalid signal. ε4 and ε5 are used to indicate the preset frequency band conversion coefficient and bandwidth conversion coefficient respectively. ini Used to represent the initial value of the random term coefficient, α fin It is used to indicate the final value of the random term coefficient, t is used to indicate the current iteration number of the firefly algorithm, and t max Used to indicate the maximum number of iterations of the firefly algorithm.

[0108] Specifically, in this embodiment, the degree of influence of the signal operating frequency band of the invalid signal on the frequency band of the monitoring receiver is obtained by subtracting the signal operating frequency band of each invalid signal from the signal operating frequency band of the valid signal and then multiplying the result by the frequency band conversion coefficient. The degree of influence of the signal operating bandwidth of the invalid signal on the bandwidth of the monitoring receiver is obtained by subtracting the signal operating bandwidth of each invalid signal from the signal operating bandwidth of the valid signal and then multiplying the result by the bandwidth conversion coefficient. The above formula combines the influence of the frequency band influence degree and the bandwidth influence degree on the optimization step length of the firefly algorithm, thereby further improving the optimization accuracy of the firefly algorithm.

[0109] The above are only preferred embodiments of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A monitoring receiver test system, characterized in that: A test cavity is configured, wherein the interior of the test cavity is pre-divided into a plurality of test sub-cavities, and a monitoring receiver is arranged in the test sub-cavity. The monitoring receiver test system comprises: A storage module, used for storing a number of sea surface environment data; An environmental simulation module, arranged in the test sub-cavity, connected to the storage module, and used to generate a plurality of test parameter sets according to the sea surface environment data in a random manner, and adjust the test environment in each test sub-cavity according to the plurality of test parameter sets; A parameter monitoring module, used for real-time monitoring of several component parameters inside the monitoring receiver; An aging calculation module, connected to the parameter monitoring module, for calculating the equipment aging efficiency of the monitoring receiver in each test sub-cavity per unit time according to the parameters of each component; A training module, connected to the environmental simulation module and the aging calculation module respectively, for taking each of the test parameter sets as input and the corresponding equipment aging efficiency as output, and retraining a preset initial prediction model to obtain an equipment aging prediction model; A prediction module, connected to the training module, for screening each of the test parameter sets to obtain a plurality of preferred data sets, and inputting each of the preferred data sets into the equipment aging prediction model to predict the predicted aging efficiency; A plurality of signal transmitting terminals, wherein the signal transmitting terminals are used to transmit radio signals toward each of the monitoring receivers, wherein the radio signals include valid signals and a plurality of invalid signals, and the signal quality, signal transmission distance, and signal transmission power of the valid signals and the invalid signals are known; A parameter optimization module, connecting the prediction module and the environment simulation module, includes: A function processing unit, used for processing the predicted aging efficiency and the actual equipment aging efficiency to obtain an objective function value; An introduction unit, connected to the function processing unit, used to introduce a firefly algorithm, taking each of the preferred data sets as a firefly individual, and taking the objective function value as the firefly luminescence brightness; An algorithm optimization unit, connected to the introduction unit, is used to update the light absorption coefficient and the random term coefficient in the position update formula of the firefly algorithm according to a preset absorption coefficient update formula and a preset random term coefficient update formula, respectively, and the absorption coefficient update formula is configured as: in, Used to represent the updated light absorption coefficient, Used to indicate the preset maximum value of the light absorption coefficient. Used to indicate the preset minimum value of the light absorption coefficient. It is used to represent the average objective function value of all current firefly individuals. It is used to represent the maximum objective function value among all current firefly individuals. used to represent the signal quality average value of the valid signal and each of the invalid signals, used to represent the average value of the signal transmission distance of the valid signal and each of the invalid signals, used to represent the average value of the signal transmission power of the valid signal and each of the invalid signals, They are used to represent the preset mass conversion factor, distance conversion factor and power conversion factor respectively; The random term coefficient update formula is configured as: in, Used to represent the updated random term coefficient, A signal operating frequency band for indicating the effective signal, is used to represent the signal working bandwidth of the valid signal, n is used to represent the number of the invalid signals, A signal operating frequency band for indicating the invalid signal, The signal operating bandwidth used to represent the invalid signal, They are used to represent the preset frequency band conversion coefficient and bandwidth conversion coefficient respectively. is used to represent the initial value of the random term coefficient, is used to represent the final value of the random term coefficient, Used to indicate the current iteration number of the firefly algorithm. Used to indicate the maximum number of iterations of the firefly algorithm; A solving unit, connected to the algorithm optimization unit, for continuously iteratively updating the position of the firefly individuals according to the optimized position update formula, and calculating the objective function value corresponding to each firefly individual, and finally obtaining the data of the firefly individuals when the objective function value is the maximum through iterative updating as the optimal parameter set; The environmental simulation module centrally adjusts the test environment in each of the test sub-cavities according to the optimal parameters.

2. The monitoring receiver test system according to claim 1, characterized in that: The component parameters include antenna parameters, filter parameters, frequency converter parameters, amplifier parameters, and digital-to-analog converter parameters; The aging calculation module comprises: A first calculation unit, used for respectively subtracting each of the component parameters from the corresponding standard parameters to obtain a component aging deviation value; The second calculation unit is connected to the first calculation unit and is used to bring the aging deviation value of each component into a preset equipment aging calculation formula to calculate the equipment aging efficiency.

3. The monitoring receiver test system according to claim 2, characterized in that: The component aging deviation values ​​include antenna aging deviation values, filter aging deviation values, inverter aging deviation values, amplifier aging deviation values, and digital-to-analog converter aging deviation values. The equipment aging calculation formula is configured as follows: in, Used to indicate the aging efficiency of the equipment, Used to indicate antenna aging parameters, Used to indicate the preset antenna aging standard value. Used to indicate the antenna aging deviation value, Used to indicate filter aging parameters, Used to indicate the preset filter aging standard value, Used to represent the filter aging deviation value, Used to indicate the inverter aging parameters, Used to indicate the preset inverter aging standard value. Used to indicate the aging deviation value of the inverter, Used to indicate amplifier aging parameters, Used to indicate the preset amplifier aging standard value, Used to represent the amplifier aging deviation value, Used to represent the aging parameters of digital-to-analog converters, Used to indicate the preset aging standard value of the digital-to-analog converter. used to represent the aging deviation value of the digital-to-analog converter, They are used to represent the preset antenna conversion coefficient, filter conversion coefficient, frequency converter conversion coefficient, amplifier conversion coefficient, filter conversion coefficient and digital-to-analog converter conversion coefficient, respectively. Used to represent unit time.

4. The monitoring receiver test system according to claim 1, characterized in that: The initial prediction model is the LightGBM model.

5. The monitoring receiver test system according to claim 3, characterized in that: The prediction module comprises: A preprocessing unit, used for performing data preprocessing on each of the test parameter sets, filtering out some invalid data and forming a plurality of the preferred data sets; A prediction unit, connected to the preprocessing unit, configured to input each of the preferred data sets into the equipment aging prediction model, and the equipment aging prediction model outputs the predicted aging efficiency; The data in the preferred data set include sea surface air salinity, sea surface temperature, sea surface air pressure and sea surface humidity.

6. The monitoring receiver test system according to claim 5, characterized in that: The function processing unit inputs the predicted aging efficiency and the equipment aging efficiency into a preset equipment aging function to obtain the objective function value, and the equipment aging function is configured as follows: in, It is used to represent the objective function value, is used to represent the predicted aging efficiency, Used to indicate the salt content of the sea surface air. is used to represent the sea surface temperature, Used to express the sea surface pressure and Used to indicate the sea surface humidity.

7. The monitoring receiver test system according to claim 6, characterized in that: It also includes a signal processing module, which is connected to each of the signal transmitting terminals and the parameter optimization module. The signal processing module includes a first processing unit, which is used to input the signal quality, signal transmission distance, signal transmission power of the valid signal and the signal quality, signal transmission distance, and signal transmission power of each invalid signal into the absorption coefficient update formula to calculate the updated light absorption coefficient.

8. The monitoring receiver test system according to claim 7, characterized in that: The signal processing module also includes: A signal analysis unit, used for performing bandwidth analysis and frequency band analysis on the valid signal and each of the invalid signals, to obtain a signal working bandwidth and a signal working frequency band of the valid signal and each of the invalid signals; The second processing unit is connected to the signal analysis unit, and is used to input the signal working bandwidth and signal working frequency band of the valid signal and each invalid signal into the random term coefficient update formula to calculate the updated random term coefficient.

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