Darkroom high-frequency test data analysis system and method based on the Internet of Things

Abstract

The present invention discloses a darkroom high-frequency test data analysis system and method based on the Internet of Things, and relates to the field of darkroom test technology. The system comprises a configuration connection debugging module, a test recording module, a data analysis module and an adjustment execution module which are executed in sequence; the technical key points are as follows: the system can predict the required shielding amount according to the difference between the first abnormal fluctuation point and the standard fluctuation range, and configure a corresponding number of shielding covers under darkroom conditions, and ensure that the signal strength of the device to be tested is within the standard fluctuation range by gradually increasing the shielding covers; according to the device impact estimation index and the initial distance value, the system generates an isolation range estimation value, and prompts the engineer to set the installation position of other devices according to the estimation value to ensure the minimum safe distance between them and the device to be tested, thereby reducing the possibility of electromagnetic interference and ensuring the stability and reliability of the overall system.

Description

Technical Field

[0001] The present invention relates to the technical field of darkroom testing, and in particular to a darkroom high-frequency testing data analysis system and method based on the Internet of Things. Background Art

[0002] Darkroom high-frequency test data usually refers to the result data of high-frequency band electromagnetic interference and immunity tests conducted in an electromagnetic shielding darkroom (commonly referred to as a "darkroom") during the electromagnetic compatibility test process. These data are very important for determining whether electronic equipment complies with relevant standards and specifications in the high-frequency band; the test steps are: equipment placement and connection: place the device to be tested in the darkroom and connect the test equipment; frequency scanning: scan within the required frequency range and record the emission or immunity data at each frequency point; data recording and analysis: analyze the test results and evaluate the electromagnetic compatibility of the equipment.

[0003] The document with the existing application publication number CN116321260A discloses a radio frequency performance test system and method for a substation Internet of Things sensor, wherein the system comprises: a control turntable, arranged at a first predetermined position in an anechoic chamber, for carrying the substation Internet of Things sensor to be tested and realizing 360° rotation; an Internet of Things controller, arranged at a second predetermined position in the anechoic chamber, for sending a request for establishing communication to the substation Internet of Things sensor to be tested in the anechoic chamber, so as to realize the sending of test data of the substation Internet of Things sensor to be tested in the anechoic chamber; a logarithmic periodic antenna, arranged at a predetermined position around the substation Internet of Things sensor to be tested in the anechoic chamber, for measuring the radiation intensity and transmission frequency of the substation Internet of Things sensor to be tested in the anechoic chamber during the communication process; a test receiver, connected to the output end of the logarithmic periodic antenna in the anechoic chamber, for analyzing and storing data on the radiation intensity and transmission frequency of the substation Internet of Things sensor to be tested in the anechoic chamber during the communication process, but it does not provide accurate guidance based on the problems found in the test.

[0004] In combination with the above documents and prior art, the normal testing process is: when the device under test is placed in a darkroom for testing, electromagnetic waves are generated by a signal generator, and then the response of the device under test is recorded by a spectrum analyzer, and the data feedback from the response is analyzed to identify the abnormal points or fault points of the device under test, and give general guidance based on the analysis results. For example, if it is identified that the device under test is abnormal at a certain frequency point, a warning signal is issued, and shielding suggestions are given, and a shielding cover needs to be provided, but specific shielding measures are not given, and multiple manual debugging or testing is still required, which is extremely inconvenient. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the deficiencies in the prior art, the present invention provides a darkroom high-frequency test data analysis system and method based on the Internet of Things. The system can predict the required shielding amount according to the difference between the first abnormal fluctuation point and the standard fluctuation range, and configure a corresponding number of shielding covers under darkroom conditions. By gradually adding shielding covers, the signal strength of the device under test is ensured to be within the standard fluctuation range. According to the device impact estimation index and the initial distance value, the system generates an isolation range estimation value. According to the estimation value, the engineer is prompted to set the installation position of other devices to ensure their minimum safe distance from the device under test, thereby reducing the possibility of electromagnetic interference, ensuring the stability and reliability of the overall system, and solving the problems raised in the background technology.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] The IoT-based darkroom high-frequency test data analysis system includes:

[0010] Configure the connection debugging module, and when the test instrument and the device under test in the darkroom are connected, start the test instrument and the device under test, so that both the test instrument and the device under test are connected to the test recording module through the Internet of Things technology; if the test recording module obtains the data sent by the test instrument and the device under test at the start-up time, a response action is taken, otherwise, a prompt signal is issued, and a connection debugging operation is performed;

[0011] The test instrument includes a spectrum analyzer and a signal generator; the characteristics are:

[0012] The test recording module presets the electromagnetic wave frequency range {M, S} generated by the signal generator, and applies the electromagnetic field every 10MHz starting from M, and simultaneously uses a spectrum analyzer to record the response of the device under test and the evaluation data of the device under test at each frequency point;

[0013] The data analysis module extracts the signal strength in the evaluation data, generates a fluctuation curve diagram for each frequency point within the electromagnetic wave frequency range {M, S}, and analyzes whether there are abnormal fluctuation points in the fluctuation curve diagram. If so, the difference between the first abnormal fluctuation point and the standard fluctuation range is obtained, and a first-level warning signal is triggered; the frequency offset value and bandwidth occupancy at each frequency point in the evaluation data are extracted and generated. The device impact estimation index is generated. When the device impact estimation index exceeds the preset evaluation threshold, a second-level warning signal is triggered. Otherwise, no response is made;

[0014] The adjustment execution module executes the shielding adjustment mechanism when a first-level warning signal is obtained; and executes the isolation reference prompt mechanism when a second-level warning signal is obtained.

[0015] Furthermore, in the electromagnetic wave frequency range {M, S}, S>>M, and S and M are both positive integers, and the evaluation data includes signal strength, frequency offset value and bandwidth occupancy; the frequency offset value represents the difference between the actual frequency of the signal and the expected frequency, and the bandwidth occupancy represents the width occupied by the signal of the device under test in the frequency spectrum, and both the frequency offset value and the bandwidth occupancy are acquired by using a spectrum analysis algorithm.

[0016] Furthermore, the process of analyzing whether there are abnormal fluctuation points in the fluctuation curve diagram is as follows: compare the signal strength of the corresponding device under test at the current frequency point with the preset standard fluctuation range. If the signal strength of the device under test at a certain frequency point exceeds the standard fluctuation range, it means that the current device under test is abnormal.

[0017] Furthermore, when generating the device impact estimation index, the formula is as follows:

[0018]

[0019] In formula (1), pl i Indicates the actual signal frequency of the device under test at the current frequency point, pl u Indicates the expected frequency at the current frequency point, |pl i -pl u | represents the frequency offset value, kz represents the bandwidth occupancy, I t Indicates the device impact stability index corresponding to each frequency point, weight coefficient: 0≤a1≤1, 0≤a2≤1, and a1+a2=1;

[0020] In formula (2), I t Where t represents the number of the corresponding frequency point, and t = 1, 2, ..., n, n represents the number of frequency points in the electromagnetic wave frequency range {M, S}, I avg It represents the average value of the stability index of n devices, and Yuz represents the device impact estimation index.

[0021] Furthermore, the content of the shielding adjustment mechanism includes:

[0022] Build a rule engine, obtain and generate a shielding amount prediction value based on the difference between the first abnormal fluctuation point and the standard fluctuation range, and start the corresponding number of shielding covers in the darkroom according to the shielding amount prediction value to completely cover the device under test. When the device under test is completely covered, the corresponding frequency at the current abnormal fluctuation point is emitted again to observe whether the signal strength of the device under test is within the standard fluctuation range. If so, no response is made. If not, a secondary adjustment mechanism is executed.

[0023] Furthermore, the secondary adjustment mechanism includes: adding a shielding cover on the basis of the original number of shielding covers, and continuing to observe until the signal strength of the device under test is within the standard fluctuation range.

[0024] Furthermore, the formula for generating the predicted shielding value is as follows:

[0025]

[0026] Wherein, Sy represents the predicted value of shielding amount, int is the rounding function, xq represents the signal strength corresponding to the first abnormal fluctuation point, G represents the error correction value, and the value range of G is 1 to 2.

[0027] Furthermore, the content of the isolation reference prompt mechanism executed is:

[0028] According to the device impact estimation index, a calculation model is built, and combined with the initial distance value, an isolation range estimation value is generated, indicating that the minimum distance between the installation location of other devices and the device under test is the isolation range estimation value.

[0029] The quarantine range estimate is calculated based on the following formula:

[0030] Gz=Cj+C*(Yuz-Bz);

[0031] In the formula, Gz represents the estimated value of the isolation range, Cj represents the initial distance value, the actual set fixed distance value, C represents the constant correction coefficient, and the value range of C is 0 to 1, and Bz represents the preset evaluation threshold.

[0032] The method for analyzing high-frequency test data in a darkroom based on the Internet of Things includes the following steps:

[0033] S1. When the test instrument and the device under test in the darkroom are connected, start the test instrument and the device under test, so that both the test instrument and the device under test are connected to the test recording module through the Internet of Things technology; if the test recording module obtains data sent by the test instrument and the device under test at the start-up time, a response action is performed; otherwise, a prompt signal is issued and a connection debugging operation is performed;

[0034] The test instrument includes a spectrum analyzer and a signal generator; the characteristics are:

[0035] S2. The electromagnetic wave frequency range {M, S} generated by the preset signal generator is applied once every 10 MHz starting from M, and the response of the device under test is recorded by a spectrum analyzer simultaneously, and the evaluation data of the device under test at each frequency point is recorded;

[0036] S3. Extract the signal strength in the evaluation data, generate a fluctuation curve diagram at each frequency point within the electromagnetic wave frequency range {M, S}, and analyze whether there is an abnormal fluctuation point in the fluctuation curve diagram. If so, obtain the difference between the first abnormal fluctuation point and the standard fluctuation range, and trigger a first-level warning signal; extract and generate a device impact estimation index based on the frequency offset value and bandwidth occupancy at each frequency point in the evaluation data. When the device impact estimation index exceeds the preset evaluation threshold, a second-level warning signal is triggered. Otherwise, no response is made;

[0037] S4. When a first-level warning signal is obtained, a shielding adjustment mechanism is executed; when a second-level warning signal is obtained, an isolation reference prompt mechanism is executed.

[0038] (III) Beneficial effects

[0039] The present invention provides a darkroom high-frequency test data analysis system and method based on the Internet of Things, which has the following beneficial effects:

[0040] Use a signal generator to gradually apply an electromagnetic field within a set frequency range, and the spectrum analyzer monitors the response of the device under test in real time. This can identify the signal strength, frequency offset value, and bandwidth occupancy of the device at different frequencies, thereby evaluating its performance in the electromagnetic environment. When significant signal enhancement or abnormal waveform is found at a specific frequency point, an alarm can be automatically generated and marked to help engineers quickly locate and solve potential electromagnetic interference problems, which improves the degree of automation of the test and the accuracy of preliminary data analysis to a certain extent;

[0041] By timely detecting and alerting abnormal fluctuation points, adverse reactions of the equipment in the electromagnetic environment can be effectively prevented, thereby improving the reliability and stability of the product in practical applications. By generating equipment impact estimation indicators, the potential interference of the equipment on the communication of other surrounding equipment can be identified, and then the equipment design can be optimized or shielding measures can be added to improve its anti-interference ability.

[0042] The system can predict the required shielding amount based on the difference between the first abnormal fluctuation point and the standard fluctuation range, and configure the corresponding number of shielding covers under darkroom conditions. By gradually adding shielding covers, it ensures that the signal strength of the device under test is within the standard fluctuation range, thereby improving the device's anti-interference ability and stability in complex electromagnetic environments; based on the device impact estimation index and the initial distance value, the system generates an isolation range estimation value, and based on the estimation value, prompts engineers to set the installation location of other devices to ensure their minimum safe distance from the device under test, thereby reducing the possibility of electromagnetic interference and ensuring the stability and reliability of the overall system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1It is a modular schematic diagram of the darkroom high-frequency test data analysis system based on the Internet of Things in the present invention;

[0044] Figure 2 It is a fluctuation curve diagram reflecting the relationship between frequency points and signal strength in the present invention;

[0045] Figure 3 It is a schematic diagram of the overall process of the darkroom high-frequency test data analysis method based on the Internet of Things in the present invention. DETAILED DESCRIPTION

[0046] 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.

[0047] Embodiment 1:

[0048] See also Figure 1 to Figure 2 ,This embodiment provides a darkroom high-frequency test data analysis system based on the Internet of Things;

[0049] The specific concept and application of the system are as follows:

[0050] Place the device to be tested at a designated location in the darkroom and properly connect it to the detection instruments (such as spectrum analyzers and signal generators). Through the Internet of Things technology, the device to be tested is wirelessly connected to the data processing center to ensure real-time data transmission and monitoring. The signal generator is then set to a specific frequency within the required frequency range, and these frequency points are scanned step by step, and an electromagnetic field is applied at each frequency point. During the test, the spectrum analyzer monitors the response of the device to be tested in real time, and records the emission or immunity data of each frequency point. The collected and recorded data is sent to the data processing center through the Internet of Things protocol, and specific analysis, anomaly detection and evaluation are performed at the data processing center. According to the analysis results, corresponding adjustments are made. For example, according to the anti-interference ability of the device itself obtained by analyzing the device to be tested, it is decided how many layers of shielding covers to add to ensure that the device to be tested can work normally.

[0051] The system is composed of several functional modules, each of which performs its corresponding function, and the system includes a configuration connection debugging module, a test recording module, a data analysis module and an adjustment execution module which are executed in sequence, wherein the data analysis module and the adjustment execution module are used as the core modules of the whole system;

[0052] Configure the connection debugging module, and when the test instrument and the device under test in the darkroom are connected, start the test instrument and the device under test, so that both the test instrument and the device under test are connected to the test recording module through the Internet of Things technology. If the test recording module obtains the data sent by the test instrument and the device under test at the start-up time, it means that the connection is successful and no debugging is required, and the system does not respond. If the test recording module does not obtain the data sent by the test instrument and the device under test at the start-up time, it means that the connection fails and debugging is required, a prompt signal is issued, and the connection debugging operation is performed;

[0053] Among them, the test instruments include spectrum analyzers and signal generators;

[0054] The signal generator is set to a specific frequency within the required frequency range, and these frequency points are scanned step by step, and an electromagnetic field is applied at each frequency point. During the test, the spectrum analyzer monitors the response of the device under test in real time and records the emission or immunity data at each frequency point;

[0055] A spectrum analyzer is an instrument used to measure and display the frequency distribution (spectrum) of a signal;

[0056] Its main functions include: spectrum display: it can display the energy distribution of the signal on the frequency axis as a spectrum diagram, usually expressed as power density or voltage value; spectrum feature analysis: it can analyze the bandwidth, center frequency, harmonics, spurious and other characteristics of the signal to help engineers understand the composition and nature of the signal; interference detection: it can detect and analyze signals from external interference sources or internal interference of the equipment to help locate and solve electromagnetic compatibility (EMC) problems;

[0057] A signal generator is an instrument that produces an electrical signal of a specific waveform and frequency;

[0058] Its main functions include: Waveform generation: various types of waveforms can be generated, such as sine waves, square waves, pulse waves, etc., as well as modulated signals such as AM, FM, etc.; Frequency adjustment: signals in different frequency ranges can be generated, usually covering the range from a few hertz to several gigahertz; Output control: the amplitude (power), phase and other parameters of the signal can be adjusted to simulate different working conditions and test scenarios;

[0059] The connection debugging operation performed is: reconnect the device and the communication connection.

[0060] The test recording module presets the electromagnetic wave frequency range {M, S} generated by the signal generator, and starts from M, gradually increases the frequency, applies the electromagnetic field once every 10MHz, and simultaneously uses a spectrum analyzer to record the response of the device under test, and records the evaluation data of the device under test at each frequency point;

[0061] Among them, the frequency range of electromagnetic waves generated by the preset signal generator is independently set according to the device under test;

[0062] The example scenario is as follows:

[0063] Suppose an automotive electronic equipment manufacturing company tests its newly developed in-vehicle communication module in a darkroom. To ensure that the module will not be affected by external high-frequency electromagnetic interference in actual application, the engineer uses a signal generator to generate electromagnetic waves from 100MHz to 2GHz and gradually scans this frequency range;

[0064] Equipment placement and connection: The vehicle communication module is placed in the darkroom, connected to the spectrum analyzer and signal generator, and connected through the Internet of Things. The data acquisition and processing system monitors the test process in real time;

[0065] Sweep frequency and signal application: The signal generator starts at 100MHz and gradually increases the frequency, applying an electromagnetic field every 10MHz, while the spectrum analyzer records the module's response;

[0066] Data recording and analysis: Data is transmitted to the cloud server, and the response of each frequency point can be analyzed using machine learning algorithms. If the signal strength of the communication module drops significantly or an abnormal waveform appears at certain frequency points, the system will automatically generate an alarm and mark the specific frequency point on the user interface;

[0067] Through these analysis results, engineers can determine which frequencies interfere with the vehicle communication module and further optimize the module design or shielding measures; for example, if significant signal attenuation is found near 1.2GHz, engineers may add additional shielding materials to the module or adjust the circuit design to improve its anti-interference performance;

[0068] In summary, by using a signal generator to apply an electromagnetic field of a specific frequency in high-frequency equipment testing, and combining the Internet of Things technology to collect, transmit and analyze data in real time, the electromagnetic compatibility and immunity of the equipment can be effectively evaluated. This method not only improves the degree of automation of the test and the accuracy of data analysis, but also helps engineers to promptly discover and solve potential problems, ensuring the reliability and stability of the product in actual applications.

[0069] In the electromagnetic wave frequency range {M, S}, S>>M, and S and M are both positive integers. The electromagnetic field is applied every 10 MHz as mentioned above, and the 10 MHz here can also be set as needed, and it is only necessary to ensure that the interval frequency of each frequency point is the same. Recording the response of the device under test is to record the evaluation data of the device under test at each frequency point;

[0070] Evaluation data includes signal strength, frequency offset value, and bandwidth occupancy;

[0071] The frequency offset value indicates the difference between the actual signal frequency and the expected frequency, that is, the absolute value of the difference between the actual signal frequency and the expected frequency. For example, when testing at a frequency of 1 GHz, the actual signal frequency of the device under test is 1002 MHz, which is 2 MHz offset from the expected frequency. The 2 MHz here is the frequency offset value, which may affect the communication quality and stability of the device.

[0072] The bandwidth occupancy indicates the width of the DUT signal in the spectrum, reflecting the frequency range used by the DUT. For example, when analyzing in the 900MHz frequency band, the DUT signal occupies the frequency range from 890MHz to 910MHz, occupying a bandwidth of 20MHz. The 20MHz here is the bandwidth occupancy. This wide bandwidth occupancy may affect the communication effect of other surrounding devices.

[0073] The frequency offset value and bandwidth occupancy in the above evaluation data are obtained by using the spectrum analysis algorithm, and the signal strength is obtained through signal recognition, which will not be elaborated here.

[0074] By adopting the above technical solution, a signal generator is used to gradually apply an electromagnetic field within a set frequency range, and a spectrum analyzer monitors the response of the device under test in real time. This can identify the signal strength, frequency offset value and bandwidth occupancy of the device at different frequencies, thereby evaluating its performance in the electromagnetic environment. When significant signal enhancement or abnormal waveform is found at a specific frequency point, an alarm can be automatically generated and marked, helping engineers to quickly locate and solve potential electromagnetic interference problems, thereby improving the degree of test automation and the accuracy of preliminary data analysis to a certain extent.

[0075] The data analysis module extracts the signal strength in the evaluation data, generates a fluctuation curve diagram at each frequency point in the electromagnetic wave frequency range {M, S}, and analyzes whether there are abnormal fluctuation points in the fluctuation curve diagram. If not, it means that the anti-interference ability of the device under test meets the standard. If so, the difference between the first abnormal fluctuation point and the standard fluctuation range is obtained, and a first-level warning signal is triggered;

[0076] Extract and generate a device impact estimation index reflecting the interference degree of the device under test on other devices based on the frequency offset value and bandwidth occupancy at each frequency point in the evaluation data. When the device impact estimation index exceeds the preset evaluation threshold, a secondary warning signal is triggered. When the device impact estimation index does not exceed the preset evaluation threshold, no response is given.

[0077] The process of analyzing whether there are abnormal fluctuation points in the fluctuation curve graph is as follows:

[0078] Compare the signal strength of the device under test at the current frequency point with the preset standard fluctuation range. If the signal strength of the device under test at a certain frequency point exceeds the standard fluctuation range, it means that the current device under test is abnormal, that is, it is determined that the device under test at the current frequency has an abnormal fluctuation point;

[0079] It should be noted that if Figure 2 As shown in the figure, in the standard fluctuation range {T1, T2}, this range is called the "normal range" or "normal working range", and this standard fluctuation range is set according to the design specifications of the equipment. From the performance specification table provided by the supplier or manufacturer, the expected value of the signal strength of the equipment under normal working conditions can be determined, and based on the expected value, an upper and lower limit range of allowable fluctuation can be determined, for example: 20dBm±5dBm, then in the standard fluctuation range {T1, T2}, the value of T1 is 15dBm, and the value of T2 is 25dBm;

[0080] The device impact estimation index is generated based on the following formula:

[0081]

[0082] In formula (1), pl i Indicates the actual signal frequency of the device under test at the current frequency point, pl u Indicates the expected frequency at the current frequency point, |pl i -pl u | represents the frequency offset value, kz represents the bandwidth occupancy, I t Indicates the device impact stability index corresponding to each frequency point, weight coefficient: 0≤a1≤1, 0≤a2≤1, and a1+a2=1;

[0083] In formula (2), I t Where t represents the number of the corresponding frequency point, and t = 1, 2, ..., n, n represents the number of frequency points in the electromagnetic wave frequency range {M, S}, I avg It represents the average value of the stability index of n devices, and Yuz represents the estimated index of device impact;

[0084] It should be noted that the smaller the frequency offset value, the higher the stability and quality of the device's communication, and the higher the intensity of the impact on other surrounding devices. The larger the bandwidth occupancy, the higher the intensity of the device's impact on the communication effect of other surrounding devices. Therefore, the device impact stability index is inversely proportional to the frequency offset value, and the device impact stability index is directly proportional to the bandwidth occupancy. The device impact stability index reflects the intensity of the impact of the device under test on other surrounding devices.

[0085] By adopting the above technical solution, by timely detecting and warning abnormal fluctuation points, the adverse reaction of the equipment in the electromagnetic environment can be effectively prevented, thereby improving the reliability and stability of the product in practical applications. By generating the device impact estimation index, the potential interference of the device to the communication of other surrounding devices can be identified, and then the device design can be optimized or shielding measures can be added to improve its anti-interference ability.

[0086] In summary, this technical solution not only solves the technical challenges of electromagnetic compatibility assessment and equipment immunity assessment, but also effectively improves the accuracy and response speed of the test, providing important support and guarantee for the development and application of high-frequency equipment.

[0087] The adjustment execution module executes the shielding adjustment mechanism when a first-level warning signal is obtained, and executes the isolation reference prompt mechanism when a second-level warning signal is obtained;

[0088] The shielding adjustment mechanism includes:

[0089] Build a rule engine, obtain and generate a predicted shielding value based on the difference between the first abnormal fluctuation point and the standard fluctuation range, start a corresponding number of shielding covers in a darkroom based on the predicted shielding value to completely cover the device under test, and in the state of completely covering the device under test, send out the corresponding frequency at the current abnormal fluctuation point again to observe whether the signal strength of the device under test is within the standard fluctuation range. If so, do not respond. If not, execute a secondary adjustment mechanism to add a shielding cover on the basis of the original number of shielding covers, and continue to observe until the signal strength of the device under test is within the standard fluctuation range;

[0090] The formula used to generate the shielding prediction is as follows:

[0091]

[0092] In the formula, Sy represents the predicted value of the shielding amount, int is the rounding function, xq represents the signal strength corresponding to the first abnormal fluctuation point, G represents the error correction value, and the value range of G is 1 to 2. The specific value can be adjusted and set by the user, or generated by the analysis function fitting;

[0093] The thickness of a single shielding cover is a standard thickness. For example, the thickness of a single shielding cover is 0.5 cm. If the shielding amount prediction value Sy is calculated to be 3, then the number of activated shielding covers is 3 layers, and the 3 layers of shielding covers are arranged in a nesting doll-like manner. Several shielding covers are mutually nested to form a whole shielding assembly, and the movement of each shielding cover is driven by a configured cylinder.

[0094] The content of the isolation reference hint mechanism implemented is:

[0095] According to the device impact estimation index, a calculation model is built, and combined with the initial distance value, an isolation range estimation value is generated, indicating that the minimum distance between the installation location of other devices and the device under test is the isolation range estimation value.

[0096] The quarantine range estimate is calculated based on the following formula:

[0097] Gz=Cj+C*(Yuz-Bz);

[0098] In the formula, Gz represents the estimated isolation range, Cj represents the initial distance value, the actual fixed distance value, C represents the constant correction coefficient, the specific value of which can be adjusted and set by the user, or generated by the analysis function fitting, and the value range of C is 0 to 1, which can be adjusted according to the results of historical actual tests. Bz represents the preset evaluation threshold, which is used to determine whether it is necessary to trigger the secondary warning signal.

[0099] By adopting the above technical solution, the system can predict the required shielding amount according to the difference between the first abnormal fluctuation point and the standard fluctuation range, and configure the corresponding number of shielding covers under darkroom conditions. By gradually adding shielding covers, the signal strength of the device under test is ensured to be within the standard fluctuation range, thereby improving the anti-interference ability and stability of the device in a complex electromagnetic environment; according to the device impact estimation index and the initial distance value, the system generates an isolation range estimation value, and based on the estimation value, the engineer is prompted to set the installation position of other devices to ensure their minimum safe distance from the device under test, thereby reducing the possibility of electromagnetic interference and ensuring the stability and reliability of the overall system.

[0100] Embodiment 2:

[0101] See also Figure 3 Based on Example 1, this embodiment also provides a darkroom high-frequency test data analysis method based on the Internet of Things, including the following specific steps:

[0102] S1. When the test instrument and the device under test in the darkroom are connected, start the test instrument and the device under test, so that both the test instrument and the device under test are connected to the test recording module through the Internet of Things technology; if the test recording module obtains data sent by the test instrument and the device under test at the start-up time, a response action is performed; otherwise, a prompt signal is issued and a connection debugging operation is performed;

[0103] The test instrument includes a spectrum analyzer and a signal generator; the characteristics are:

[0104] S2. The electromagnetic wave frequency range {M, S} generated by the preset signal generator is applied once every 10 MHz starting from M, and the response of the device under test is recorded by a spectrum analyzer simultaneously, and the evaluation data of the device under test at each frequency point is recorded;

[0105] S3. Extract the signal strength in the evaluation data, generate a fluctuation curve diagram at each frequency point within the electromagnetic wave frequency range {M, S}, and analyze whether there is an abnormal fluctuation point in the fluctuation curve diagram. If so, obtain the difference between the first abnormal fluctuation point and the standard fluctuation range, and trigger a first-level warning signal; extract and generate a device impact estimation index based on the frequency offset value and bandwidth occupancy at each frequency point in the evaluation data. When the device impact estimation index exceeds the preset evaluation threshold, a second-level warning signal is triggered. Otherwise, no response is made;

[0106] S4. When a first-level warning signal is obtained, a shielding adjustment mechanism is executed; when a second-level warning signal is obtained, an isolation reference prompt mechanism is executed.

[0107] In the application, the several formulas involved are all calculated by removing the dimensions and taking their numerical values. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The formula is set by technical personnel in this field according to actual conditions.

[0108] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0109] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0110] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. Anechoic chamber high frequency test data analysis system based on the Internet of Things, including: Configure the connection debugging module, and when the test instrument and the device under test in the darkroom are connected, start the test instrument and the device under test, so that both the test instrument and the device under test are connected to the test recording module through the Internet of Things technology; If the test recording module obtains the data sent by the test instrument and the device under test at the startup time, it will respond, otherwise, it will send a prompt signal and perform connection debugging operations; The test instrument includes a spectrum analyzer and a signal generator; the characteristics are: The test recording module presets the electromagnetic wave frequency range {M, S} generated by the signal generator, and applies the electromagnetic field every 10MHz starting from M, and simultaneously uses a spectrum analyzer to record the response of the device under test and the evaluation data of the device under test at each frequency point; The data analysis module extracts the signal strength in the evaluation data, generates a fluctuation curve at each frequency point in the electromagnetic wave frequency range {M, S}, and analyzes whether there are abnormal fluctuation points in the fluctuation curve. If so, the difference between the first abnormal fluctuation point and the standard fluctuation range is obtained, and a first-level warning signal is triggered; the frequency offset value and bandwidth occupancy at each frequency point in the evaluation data are extracted and generated. The device impact estimation index is based on the following formula: In formula (1), pl i Indicates the actual signal frequency of the device under test at the current frequency point, pl u Indicates the expected frequency at the current frequency point, |pl i -pl u | represents the frequency offset value, kz represents the bandwidth occupancy, I t Indicates the device impact stability index corresponding to each frequency point, weight coefficient: 0≤a1≤1, 0≤a2≤1, and a1+a2=1; In formula (2), I t Where t represents the number of the corresponding frequency point, and t = 1, 2, ..., n, n represents the number of frequency points in the electromagnetic wave frequency range {M, S}, I avg It represents the average value of the stability index of n devices, and Yuz represents the estimated index of device impact. When the estimated index of device impact exceeds the preset evaluation threshold, a secondary warning signal is triggered, otherwise, no response is given. The adjustment execution module executes the shielding adjustment mechanism to start the shielding cover to cover the device under test when a first-level warning signal is obtained; and executes the isolation reference prompt mechanism to prompt the minimum distance between other devices and the device under test when a second-level warning signal is obtained.

2. The darkroom high-frequency test data analysis system based on the Internet of Things according to claim 1 is characterized in that: In the electromagnetic wave frequency range {M, S}, S>>M, and both S and M are positive integers, and the evaluation data includes signal strength, frequency offset value, and bandwidth occupancy.

3. The darkroom high-frequency test data analysis system based on the Internet of Things according to claim 2 is characterized in that: The frequency offset value indicates the difference between the actual frequency of the signal and the expected frequency, and the bandwidth occupancy indicates the width occupied by the signal of the device under test in the spectrum. Both the frequency offset value and the bandwidth occupancy are obtained by using the spectrum analysis algorithm.

4. The darkroom high-frequency test data analysis system based on the Internet of Things according to claim 3 is characterized in that: The process of analyzing whether there is an abnormal fluctuation point in the fluctuation curve diagram is as follows: compare the signal strength of the device under test at the current frequency point with the preset standard fluctuation range. If the signal strength of the device under test at a certain frequency point exceeds the standard fluctuation range, it means that the current device under test is abnormal.

5. The darkroom high-frequency test data analysis system based on the Internet of Things according to claim 4 is characterized in that: The content of the shielding adjustment mechanism includes: Build a rule engine, obtain and generate a shielding amount prediction value based on the difference between the first abnormal fluctuation point and the standard fluctuation range, and start the corresponding number of shielding covers in the darkroom according to the shielding amount prediction value to completely cover the device under test. When the device under test is completely covered, the corresponding frequency at the current abnormal fluctuation point is emitted again to observe whether the signal strength of the device under test is within the standard fluctuation range. If so, no response is made. If not, a secondary adjustment mechanism is executed.

6. The darkroom high-frequency test data analysis system based on the Internet of Things according to claim 5 is characterized in that: The secondary adjustment mechanism is as follows: add a shielding cover on the basis of the original number of shielding covers, and continue to observe until the signal strength of the device under test is within the standard fluctuation range.

7. The darkroom high-frequency test data analysis system based on the Internet of Things according to claim 6 is characterized in that: The formula used to generate the shielding prediction is as follows: Wherein, Sy represents the predicted value of shielding amount, int is the rounding function, xq represents the signal strength corresponding to the first abnormal fluctuation point, G represents the error correction value, and the value range of G is 1 to 2.

8. The darkroom high-frequency test data analysis system based on the Internet of Things according to claim 7 is characterized in that: The content of the isolation reference hint mechanism implemented is: According to the device impact estimation index, a calculation model is built, and combined with the initial distance value, an isolation range estimation value is generated, indicating that the minimum distance between the installation location of other devices and the device under test is the isolation range estimation value. The quarantine range estimate is calculated based on the following formula: Gz=Cj+C*(Yuz-Bz); In the formula, Gz represents the estimated value of the isolation range, Cj represents the initial distance value, the actual set fixed distance value, C represents the constant correction coefficient, and the value range of C is 0 to 1, and Bz represents the preset evaluation threshold.

9. A darkroom high-frequency test data analysis method based on the Internet of Things, using any system described in claims 1 to 8, characterized in that: The steps include: S1. When the test instrument and the device under test in the darkroom are connected, start the test instrument and the device under test, so that both the test instrument and the device under test are connected to the test recording module through the Internet of Things technology; If the test recording module obtains the data sent by the test instrument and the device under test at the startup time, it will respond, otherwise, it will send a prompt signal and perform connection debugging operations; The test instrument includes a spectrum analyzer and a signal generator; the characteristics are: S2. The electromagnetic wave frequency range {M, S} generated by the preset signal generator is applied once every 10 MHz starting from M, and the response of the device under test is recorded by a spectrum analyzer simultaneously, and the evaluation data of the device under test at each frequency point is recorded; S3. Extract the signal strength in the evaluation data, generate a fluctuation curve diagram at each frequency point within the electromagnetic wave frequency range {M, S}, and analyze whether there is an abnormal fluctuation point in the fluctuation curve diagram. If so, obtain the difference between the first abnormal fluctuation point and the standard fluctuation range, and trigger a first-level warning signal; extract and generate a device impact estimation index based on the frequency offset value and bandwidth occupancy at each frequency point in the evaluation data. When the device impact estimation index exceeds the preset evaluation threshold, a second-level warning signal is triggered. Otherwise, no response is made; S4. When a first-level warning signal is obtained, a shielding adjustment mechanism is executed; when a second-level warning signal is obtained, an isolation reference prompt mechanism is executed.

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