A testing method and system for the wireless communication performance of a 5G terminal

Abstract

The present invention relates to the technical field of 5G testing, and discloses a testing method and system for the wireless communication performance of a 5G terminal, including: initializing a target terminal and generating matching data to train an association model; determining the blocked antennas of the target terminal within a target time period based on the association model; within the target time period, shielding the unblocked antennas of the target terminal, collecting the communication performance index data of the target terminal, and calculating a communication performance evaluation value of the target terminal; by replicating extreme occlusion scenarios that may occur during the actual use of the terminal, and being able to truly collect key communication performance indexes such as throughput, latency, and bit error rate under non-ideal working conditions, a communication performance evaluation value is obtained to reveal hidden problems caused by the adaptive adjustment of the terminal in traditional testing methods.

Description

Technical Field

[0001] The present invention relates to the technical field of 5G testing, and more specifically, it relates to a method and system for testing the wireless communication performance of 5G terminals. Background Art

[0002] In the actual application of 5G terminals, due to the dynamic change of the user's holding posture with the application scenario, the antenna may be blocked in real time. For example, the vertical screen single-handed holding during a video call may block the bottom antenna, and the two-handed holding during a horizontal screen game may block the two side antennas, thus directly affecting the stability of wireless communication. Existing terminals use system-level AI to build an "application program - antenna available state" association model, predict the antenna occlusion situation by analyzing features such as application resource occupancy and terminal posture, and accordingly switch the working state of each antenna in advance. However, traditional testing methods only cover typical scenarios, and it is easy for the terminal AI to identify the test mode using training data and actively adapt, resulting in a phenomenon of overestimated evaluation results; at the same time, due to the lack of verification of extreme scenarios where the model prediction completely deviates from the actual occlusion situation, the antenna strategy of the terminal may fail and the performance may drop suddenly in a real complex environment. Existing testing methods are difficult to effectively evaluate the robustness of the system and need to be improved urgently. Summary of the Invention

[0003] The present invention provides a method and system for testing the wireless communication performance of 5G terminals to solve the technical problems raised in the background art.

[0004] The present invention provides a method for testing the wireless communication performance of 5G terminals, including:

[0005] Step 1, initialize the target terminal and generate matching data to train an association model;

[0006] Step 2, based on the association model, determine the occluded antennas of the target terminal within the target time period;

[0007] Step 3, within the target time period, shield the non-occluded antennas of the target terminal, collect the communication performance index data of the target terminal, and calculate the communication performance evaluation value of the target terminal.

[0008] Further, generating the matching data includes:

[0009] Perform real number coding on M antennas of the target terminal to obtain M antenna codes;

[0010] Allocate N occlusion sequences to N application programs of the target terminal respectively, and each occlusion sequence includes m non-repeating antenna codes; where 1 < m < M, m is a positive integer, and the matching error between any two occlusion sequences is greater than the first error threshold.

[0011] Further, training the association model includes:

[0012] Within a first preset time period, establish an occlusion unit and a program control unit;

[0013] Step 11, start the nth application of the target terminal based on the program control unit; where 1 ≤ n ≤ N and n is a positive integer;

[0014] Step 12, in response to the start instruction of the nth application, obtain m antenna encodings in the occlusion sequence of the nth application, and shield the corresponding m antennas through the occlusion unit;

[0015] Step 13, repeatedly execute Step 11 and Step 12 until:

[0016] Within a test time period, respectively obtain the status information of M antennas of the target terminal; where the status information includes: working status and standby status;

[0017] Based on the program control unit, sequentially traverse N applications, and respectively obtain the status information of M antennas in response to the start instruction of each application;

[0018] If the status information is the working status, mark the corresponding antenna as 1, otherwise mark the corresponding antenna as 0, and extract the antennas marked as 1;

[0019] Determine the set of antennas marked as 1 in response to the start instruction of the nth application, and calculate the matching error between the antenna set and the occlusion sequence corresponding to the nth application;

[0020] If the matching errors of all N applications are less than the second error threshold, the association model converges, stop executing Step 11 and Step 12, otherwise repeat executing Step 11 and Step 12.

[0021] Further, the matching error includes the first matching error between the occlusion sequences and the second matching error between the antenna set and the occlusion sequence;

[0022] The first matching error includes:

[0023] Associate the same antennas in the occlusion sequences;

[0024] If the association is successful, respectively remove the corresponding antennas in the occlusion sequences;

[0025] Obtain the occlusion sequences after the removal is completed, calculate the sum of the number of remaining units in the occlusion sequences, and use the sum value as the matching error between the occlusion sequences;

[0026] The second matching error includes:

[0027] Associate the antenna set with the same antennas in the occlusion sequence;

[0028] If the association is successful, the corresponding antennas are removed from the antenna set and the occlusion sequence respectively;

[0029] Obtain the antenna set and the occlusion sequence after the removal is completed, calculate the sum of the number of remaining units in the antenna set and the occlusion sequence, and use the sum value as the matching error between the antenna set and the occlusion sequence.

[0030] Further, determining the occluded antennas of the target terminal within the target time period based on the association model includes:

[0031] Within the target time period, start the nth application program of the target terminal based on the program control unit;

[0032] Based on the association model, determine the antennas in the occlusion sequence corresponding to the nth application program as the occluded antennas of the target terminal within the target time period.

[0033] Further, shielding the non-occluded antennas of the target terminal includes:

[0034] In response to the start instruction of the nth application program, shield the non-occluded antennas among the M occluded antennas of the target terminal through the occlusion unit.

[0035] Further, the communication performance index data includes:

[0036] Within the target time period, establish a stable communication connection between the target terminal and the target base station;

[0037] In response to the start instruction of the nth application program, the target terminal sends a data packet to the target base station, and collects the communication performance index data of the target terminal, including:

[0038] The number of bits of data sent by the target terminal, the number of bits of data received by the target base station, the number of error bits of data received by the target base station, the timestamp when the target terminal starts to send data, and the timestamp when the target base station finishes receiving data.

[0039] Further, calculating the target communication performance evaluation value of the target terminal includes: based on the communication performance index data, calculating the throughput, delay, and bit error rate of the target terminal, and performing weighted fusion on the throughput, delay, and bit error rate to obtain the target communication performance evaluation value of the target terminal.

[0040] In a second aspect, a test system for the wireless communication performance of a 5G terminal, which is applied to the test method for the wireless communication performance of a 5G terminal described above, includes:

[0041] A training module, configured to initialize a target terminal and generate matching data for training an association model;

[0042] A prediction module, configured to determine an occluded antenna of the target terminal within a target time period based on the association model;

[0043] A testing module, configured to shield non-occluded antennas of the target terminal within the target time period, collect communication performance metric data of the target terminal, and calculate a communication performance evaluation value of the target terminal.

[0044] The beneficial effects of the present invention are as follows: By using the matching data to induce the 5G terminal to autonomously construct an association model, and then quickly identifying the standby antenna and the working antenna of the terminal based on the application start instruction, and artificially shielding the working antenna within the target time period, thereby simulating the communication redundancy performance of the terminal in the case of AI prediction failure. In this way, not only can an extreme occlusion scenario that may occur in the actual use of the terminal be replicated under experimental conditions, but also key communication performance metrics such as throughput, latency, and bit error rate in a non-ideal working state can be truly collected, and a communication performance evaluation value can be obtained to reveal hidden problems caused by terminal adaptive adjustment in traditional testing methods. Description of the Drawings

[0045] Figure 1 is a flowchart of a method for testing the wireless communication performance of a 5G terminal according to the present invention;

[0046] Figure 2 is a module diagram of a method for testing the wireless communication performance of a 5G terminal according to the present invention. Detailed Embodiments

[0047] Now, the subject matter described herein will be discussed with reference to exemplary embodiments. It should be understood that discussing these embodiments is only to enable those skilled in the art to better understand and thus implement the subject matter described herein, and the functions and arrangements of the elements discussed can be changed without departing from the protection scope of the content of this specification. Each example can omit, substitute, or add various processes or components as needed. Additionally, features described relative to some examples can also be combined in other examples.

[0048] As Figures 1 to 2 shown, a method for testing the wireless communication performance of a 5G terminal includes:

[0049] Step 1, initializing the target terminal and generating matching data for training an association model;

[0050] Step 2, determining the occluded antenna of the target terminal within the target time period based on the association model;

[0051] Step 3, within the target time period, block the non-occluded antennas of the target terminal, collect the communication performance index data of the target terminal, and calculate the communication performance evaluation value of the target terminal.

[0052] It should be noted that the target terminal is defined as a 5G mobile phone in this application. A 5G mobile phone usually includes multiple antennas, and each antenna dynamically switches its working state according to its own occlusion situation. Existing AI, such as deepseek, is intelligently integrated into the system bottom layer of the 5G mobile phone by mobile phone manufacturers to build a personalized experience for users. For example, users may have various grasping postures of the mobile phone, including: vertical screen grasping (reading) and horizontal screen grasping (playing games), so the occluded antennas change dynamically based on the change of the application. When the system-level AI obtains the association between a large amount of occlusion information and the startup instructions of the application, it will actively build an association model to immediately switch the working state of the antenna when obtaining the startup instructions of the application for the first time to ensure a smooth experience for the user.

[0053] Therefore, initialize (format) the 5G terminal to be tested to reset the association model of the 5G terminal. Induce the AI of the 5G terminal to reconstruct the association model based on strict startup instructions and fixed antenna occlusion. For example, when starting application A, manually block antenna 135. When starting application B, manually block antenna 246. Based on a large number of repeated operations for a long time, force the AI of the 5G terminal to actively build an association model that meets the expectations. That is, when starting any application of the 5G terminal, the association model will actively control the previously manually blocked antennas to be switched to the standby state, and the antennas that were not manually blocked before to be switched to the working state, then the association model is determined.

[0054] After obtaining the association model, start application A, then manually block 246 to simulate an extreme situation where the prediction of the association model fails, and then obtain the corresponding communication performance data and calculate the communication performance evaluation value of the 5G mobile phone. This communication performance evaluation value can reflect the communication performance redundancy ability of the 5G mobile phone.

[0055] In an embodiment of the present invention, generating matching data includes:

[0056] Perform real-number encoding on the M antennas of the target terminal to obtain M antenna encodings;

[0057] Allocate N occlusion sequences to the N applications of the target terminal respectively. Each occlusion sequence includes m non-repeating antenna encodings; where 1 < m < M, m is a positive integer, and the matching error between any two occlusion sequences is greater than the first error threshold.

[0058] Specifically, real - number encoding is performed on M antennas in a 5G mobile phone, and occlusion sequences are respectively assigned to different applications. Each occlusion sequence consists of multiple non - repeating antenna encodings, and a first error threshold is set to ensure significant differences between different sequences. The significance of this scheme is to provide a structured and highly distinguishable data basis for subsequently inducing the mobile phone AI to reconstruct the association model, ensuring that the antenna occlusion states in different application scenarios can be clearly distinguished during the training process, thereby laying a key data support for simulating AI prediction failure and accurately evaluating communication redundancy performance.

[0059] In an embodiment of the present invention, a target 5G mobile phone with system - level AI built - in is selected as the test object. This mobile phone contains 6 independent antennas (i.e., M = 6). Real - number encoding is performed on these 6 antennas respectively, and encoding values 1, 2, 3, 4, 5, and 6 are respectively assigned. Subsequently, occlusion sequences are respectively generated for 4 applications (i.e., N = 4) preset in the 5G mobile phone. Each sequence contains 3 non - repeating antenna encodings (where m = 3, satisfying 1 < m < 6), and it is ensured that there is an obvious distinction between sequences by setting the matching error between each sequence to be greater than 0. For example, the occlusion sequence of application A can be set as , the sequence of application B can be set as , the sequence of application C can be set as , and the sequence of application D can be set as .

[0060] In an embodiment of the present invention, an association model is trained, including:

[0061] In the first preset time period, an occlusion unit and a program control unit are established;

[0062] Step 11, based on the program control unit, start the nth application of the target terminal; where 1 ≤ n ≤ N, and n is a positive integer;

[0063] Step 12, in response to the start instruction of the nth application, obtain m antenna encodings in the occlusion sequence of the nth application, and shield the corresponding m antennas through the occlusion unit;

[0064] Step 13, repeatedly execute Step 11 and Step 12 until:

[0065] During the test time period, respectively obtain the status information of M antennas of the target terminal; where the status information includes: working status and standby status;

[0066] Based on the program control unit, sequentially traverse N applications, and respectively obtain the status information of M antennas in response to the start instruction of each application;

[0067] If the status information is the working status, mark the corresponding antenna as 1; otherwise, mark the corresponding antenna as 0, and extract the antennas marked as 1.

[0068] Determine the set of antennas marked as 1 in response to the start instruction of the nth application program, and calculate the matching error between the antenna set and the occlusion sequence corresponding to the nth application program.

[0069] If the matching errors of the N application programs are all less than the second error threshold, the association model converges, and steps 11 and 12 are stopped; otherwise, steps 11 and 12 are repeatedly executed.

[0070] In an embodiment of the present invention, the occlusion unit may be a plurality of shielding sheets made of metallic iron. After the shielding sheets respond to the start instruction, they are attached to the part of the 5G mobile phone edge where the antennas are located based on the transmission component, so as to achieve signal shielding.

[0071] In an embodiment of the present invention, the program control unit may be a macro command clicker, and the macro command clicker is used to click different areas of the 5G mobile phone at preset time intervals to start different application programs.

[0072] In an embodiment of the present invention, obtaining the status information of M antennas of the target terminal includes:

[0073] After the 5G terminal receives the application program start instruction, it performs radio frequency monitoring on the 5G terminal, and real-time collects key RF parameters of the M antennas, such as transmission power, received signal strength, and S parameters, etc.; by comparing these collected data with the preset detection threshold, if the parameter of a certain antenna exceeds the set standard, it is determined to be in the working status, otherwise it is regarded as the standby status.

[0074] For example, when the start instruction of application program A is obtained, then shield , when the start instruction of application program B is obtained, then shield , and so on.

[0075] In one embodiment of the present invention, the program control unit starts each application program in a preset order (1 ≤ n ≤ N). Each time it starts, steps 11 and 12 are executed. The system immediately collects the current status information of all M antennas of the target terminal. The information includes whether each antenna is in the working state or the standby state. For the status collected each time, the system marks the antennas in the working state as "1" and the antennas in the standby state as "0". During this process, the system simultaneously extracts all the antennas marked as "1" to form a set of working antennas for the current application program. For each application program, the system compares the obtained set of working antennas with the preset occlusion sequence of the application program and calculates the matching error between the two. The matching error reflects whether the current shielding operation conforms to the expected occlusion strategy. The smaller the error value, the higher the degree of matching. When the matching errors for all N application programs are lower than the preset second error threshold, it indicates that during the long-term and multi-round testing process, the antenna state switching operation of the terminal has been stable and tends to be consistent. At this time, it is considered that the built-in association model of the terminal has converged and the testing process terminates; otherwise, the system continues to repeat steps 11 and 12 for further training and data collection until the matching error meets the requirements.

[0076] In one embodiment of the present invention, the matching error includes a first matching error between occlusion sequences and a second matching error between the antenna set and the occlusion sequence;

[0077] The first matching error includes:

[0078] Associate the same antennas in the occlusion sequence with the occlusion sequence;

[0079] If the association is successful, the corresponding antennas are respectively removed from the occlusion sequence and the occlusion sequence;

[0080] Obtain the occlusion sequence and the occlusion sequence after the removal is completed, calculate the sum value of the number of remaining units in the occlusion sequence and the occlusion sequence, and use the sum value as the matching error between the occlusion sequence and the occlusion sequence;

[0081] The second matching error includes:

[0082] Associate the same antennas in the antenna set with the occlusion sequence;

[0083] If the association is successful, the corresponding antennas are respectively removed from the antenna set and the occlusion sequence;

[0084] Obtain the antenna set and the occlusion sequence after the removal is completed, calculate the sum value of the number of remaining units in the antenna set and the occlusion sequence, and use the sum value as the matching error between the antenna set and the occlusion sequence.

[0085] Specifically, the first matching error (matching within the occlusion sequence) includes:

[0086] Compare two preset occlusion sequences, that is, find the same antenna codes that appear in both occlusion sequences one by one. For the successfully matched antenna codes, remove them from their respective occlusion sequences. The remaining part is the unmatched antenna code units, and calculate the sum of the remaining unit numbers in these two sequences after removal as the first matching error. This error reflects the consistency or difference within the preset occlusion sequence, that is, the part that cannot fully correspond between the two occlusion sequences. The lower the value, the higher the matching degree of the two sequences.

[0087] Specifically, the second matching error (matching between the observed antenna set and the preset occlusion sequence) includes:

[0088] Compare the current working antenna set collected from the terminal with the preset occlusion sequence, and find the same antenna codes between them. For the successfully matched antenna codes, remove them from both the observed antenna set and the preset occlusion sequence. Calculate the sum of the remaining antenna code units in both after removal, and take this sum as the second matching error. This error is used to reflect the deviation between the actually detected working antenna set and the preset occlusion sequence, that is, whether it is consistent with the expected mode during the switching process of the shielded and unshielded antennas in the system.

[0089] For example, for the same application program, the preset occlusion sequence S = ; and the actually detected working antenna set O = . The calculation process is as follows:

[0090] Match the common antennas: Compare the actual working antenna set O with the preset occlusion sequence S, and the common antennas of the two are antenna 1 and antenna 5.

[0091] Remove the matching elements: After removing antenna 1 and antenna 5 from O and S respectively, we get:

[0092] O remaining = ;

[0093] S remaining = ;

[0094] Calculate the error: Add the number of remaining units, that is, the matching error = 1 (O remaining) + 1 (S remaining) = 2. This error reflects the deviation between the actual detection result and the preset strategy. The lower the error, the more consistent the actual system's occlusion processing is with the preset occlusion sequence.

[0095] In an embodiment of the present invention, determining the occluded antenna of the target terminal within the target time period based on the association model includes:

[0096] Within the target time period, start the nth application of the target terminal based on the program control unit;

[0097] Based on the association model, determine the antenna in the occlusion sequence corresponding to the nth application as the occluded antenna of the target terminal within the target time period,

[0098] In an embodiment of the present invention, shielding the non-occluded antennas of the target terminal includes:

[0099] In response to the start instruction of the nth application, the non-occluded antennas among the M occluded antennas of the target terminal are shielded by the occlusion unit.

[0100] In an embodiment of the present invention, the system uses a previously constructed association model to determine which antennas should be regarded as "occluded antennas" during a specific test time period, so as to verify the communication redundancy ability of the terminal. Start the application: within the target test time period, the program control unit sequentially starts the nth application (1 ≤ n ≤ N) preset in the terminal. Obtain the occlusion sequence: for each application, the terminal internally establishes an "occlusion sequence" based on the association model, which clearly stipulates which antennas should be in the occluded state due to factors such as user holding in this application scenario. The system then determines through this model that for the nth application, the corresponding antennas in the occlusion sequence are regarded as the occluded antennas during the current test. Respond to the start instruction: when the nth application starts, the system executes operations according to the preset strategy through the occlusion unit. Shield the non-occluded antennas: the target terminal has M "occluded antenna" candidate units, and the system determines which antennas do not belong to the current occlusion sequence (that is, the antennas determined by the system to be non-occluded) according to the information. Subsequently, the occlusion unit shields these non-occluded antennas to make them in the shielded state, so that only the antennas that are expected to be in the occluded state are in the active state. The purpose of this method is to simulate an abnormal scenario, that is, in different application scenarios, although the terminal AI predicts which antennas should be occluded through the association model, the non-occluded antennas are artificially shielded, forcing the system to switch to a state different from the normal working mode, and evaluating the redundancy performance of the terminal under the failure of AI prediction or under extreme conditions by collecting its communication performance data. All in all, by starting each application and obtaining the corresponding occlusion sequence according to the association model, the system can accurately identify the occluded antennas in the current application scenario; then use the occlusion unit to shield the remaining (non-occluded) antennas to construct a specific combination of antenna working states, so as to conduct a more realistic and rigorous test and verification of the communication performance.

[0101] In an embodiment of the present invention, the communication performance index data includes:

[0102] Within the target time period, establish a stable communication connection between the target terminal and the target base station;

[0103] In response to the startup instruction of the nth application, the target terminal sends a data packet to the target base station and collects the communication performance index data of the target terminal, including:

[0104] The number of bits of data sent by the target terminal, the number of bits of data received by the target base station, the number of error bits of data received by the target base station, the timestamp when the target terminal starts to send data, and the timestamp when the target base station finishes receiving data.

[0105] It should be noted that to establish a shielding environment: during the target time period, the target terminal and the target base station are usually placed in a specially designed dark room or electromagnetic shielding room. This dark room is composed of high-efficiency wave-absorbing materials and a metal shielding layer, which can effectively isolate external radio waves and interference sources, and prevent external electromagnetic noise from affecting the normal operation of the device under test.

[0106] Isolate external interference: In addition to the physical dark room shielding measures, the test device is also equipped with a dedicated ground wire and grounding system to ensure the stability of the electromagnetic environment in the dark room; at the same time, filters and anti-interference circuits can also be installed inside. These measures work together to minimize the interference signals from the surrounding environment.

[0107] Verify communication stability: In this environment where external interference is isolated, the signal strength, bit error rate, delay and other parameters of the communication link are monitored in real time through calibration equipment and test instruments to ensure that the connection established between the target terminal and the base station reaches a stable communication state.

[0108] It should be noted that in response to the startup instruction of the nth application, the target terminal starts to send a data packet to the target base station. At the same time, the system collects a number of key communication metrics, including: the number of bits of data sent by the target terminal, which is used to reflect the total amount of data actually transmitted during the sending process by the terminal. The number of bits of data received by the target base station, which is used to reflect the complete amount of data obtained by the base station during the receiving process. The number of error bits of data received by the target base station, which is used to record the amount of error data caused by interference, signal attenuation, etc. during the receiving process by the base station, and is used to evaluate the bit error rate. The timestamp when the target terminal starts to send data, which is used to mark the start time of data packet transmission. The timestamp when the target base station finishes receiving data, which is used to mark the completion time of data packet reception.

[0109] In an embodiment of the present invention, the target communication performance evaluation value of the target terminal is calculated, including: based on the communication performance index data, the throughput, delay and bit error rate of the target terminal are calculated, and the throughput, delay and bit error rate are weighted and fused to obtain the target communication performance evaluation value of the target terminal.

[0110] In an embodiment of the present invention, the throughput includes:

[0111] Based on the data collected when the target terminal sends data packets during the test, the calculation formula is: Throughput = Number of bits of transmitted data ÷ (Receive completion timestamp - Start transmission timestamp); where the unit of throughput is bits per second (bps). The higher the throughput, the stronger the data transmission ability, and it is an important indicator to measure the effective transmission rate of the communication link.

[0112] In one embodiment of the present invention, the latency includes:

[0113] Latency is the time interval during data transmission, and the calculation formula is: Latency = Receive completion timestamp - Start transmission timestamp; where latency is expressed in milliseconds (ms). The lower the latency, the faster the communication response, and it is a key parameter to measure user experience and real-time communication ability.

[0114] In one embodiment of the present invention, the bit error rate includes:

[0115] Using the number of error bits collected by the target base station and the number of bits actually received, as follows: Bit error rate = Number of error bits ÷ Number of bits of received data; the lower the bit error rate, the better the communication link quality; a higher bit error rate usually means poor signal quality.

[0116] In one embodiment of the present invention, weighted fusion includes:

[0117] ;

[0118] Where represents the target communication performance evaluation value, which is used to reflect the communication performance redundancy ability of the target terminal, 、 and represent the first weight, the second weight, and the third weight respectively, 、 and are all not zero, and 、 and The sum value of is 1, represents the normalized throughput, represents the normalized latency, represents the normalized bit error rate.

[0119] A test system for the wireless communication performance of a 5G terminal, which is applied to the test method for the wireless communication performance of the 5G terminal described above, includes:

[0120] A training module, which is used to initialize the target terminal and generate matching data to train and obtain an association model;

[0121] A prediction module, configured to determine the blocked antenna of the target terminal within a target time period based on an association model;

[0122] A testing module, configured to shield the unblocked antennas of the target terminal within the target time period, collect the communication performance index data of the target terminal, and calculate a communication performance evaluation value of the target terminal.

[0123] The above has described the embodiments of this embodiment, but this embodiment is not limited to the above specific implementation manners. The above specific implementation manners are merely illustrative rather than restrictive. Under the inspiration of this embodiment, those of ordinary skill in the art can also make many forms, all of which fall within the protection scope of this embodiment.

Claims

1. A test method for the wireless communication performance of a 5G terminal, characterized in that Including: Step 1, initialize the target terminal and generate matching data to train an association model; Generating matching data includes: Perform real-number encoding on M antennas of the target terminal to obtain M antenna encodings; Allocate N occlusion sequences to N application programs of the target terminal respectively, each occlusion sequence including m non-repeating antenna encodings; where 1 < m < M, m is a positive integer, and the matching error between any two occlusion sequences is greater than the first error threshold; Among them, the matching error includes the first matching error between occlusion sequences and the second matching error between the antenna set and the occlusion sequence; The first matching error includes: Associate the same antennas in the occlusion sequence with the occlusion sequence; If the association is successful, the corresponding antennas are respectively removed from the occlusion sequence and the occlusion sequence; Obtain the occlusion sequence and the occlusion sequence after the removal is completed, calculate the sum value of the number of remaining units in the occlusion sequence and the occlusion sequence, and use the sum value as the matching error between the occlusion sequence and the occlusion sequence; The second matching error includes: Associate the same antennas in the antenna set with the occlusion sequence; If the association is successful, the corresponding antennas are respectively removed from the antenna set and the occlusion sequence; Obtain the antenna set and the occlusion sequence after the removal is completed, calculate the sum value of the number of remaining units in the antenna set and the occlusion sequence, and use the sum value as the matching error between the antenna set and the occlusion sequence; Training to obtain an association model includes: Establish an occlusion unit and a program control unit within the first preset time period; Step 11, start the nth application program of the target terminal based on the program control unit; where 1 ≤ n ≤ N, n is a positive integer; Step 12, in response to the start instruction of the nth application program, obtain the m antenna encodings in the occlusion sequence of the nth application program, and shield the corresponding m antennas through the occlusion unit; Step 13, repeatedly execute Step 11 and Step 12 until: Within the test time period, respectively obtain the status information of M antennas of the target terminal; where the status information includes: working status and standby status; Based on the program control unit, sequentially traverse N application programs to respectively obtain the status information of M antennas in response to the start instruction of each application program; If the status information is the working status, mark the corresponding antenna as 1, otherwise mark the corresponding antenna as 0, and extract the antennas marked as 1; Determine the antenna set marked as 1 in response to the start instruction of the nth application program, and calculate the matching error between the antenna set and the occlusion sequence corresponding to the nth application program; If the matching errors of N application programs are all less than the second error threshold, the association model converges, stop executing Step 11 and Step 12, otherwise repeatedly execute Step 11 and Step 12; Step 2, based on the association model, determine the occluded antennas of the target terminal within the target time period; Step 3, within the target time period, shield the non-occluded antennas of the target terminal, and collect the communication performance index data of the target terminal, and calculate the communication performance evaluation value of the target terminal.

2. The test method for the wireless communication performance of a 5G terminal according to claim 1, characterized in that Determining the occluded antennas of the target terminal within the target time period based on the association model includes: Within a target time period, start the nth application of the target terminal based on a program control unit; Based on an association model, determine the antenna in the occlusion sequence corresponding to the nth application as the occluded antenna of the target terminal within the target time period.

3. The test method for the wireless communication performance of a 5G terminal according to claim 2, characterized in that, Mask the non-occluded antennas of the target terminal, including: In response to the start instruction of the nth application, mask the non-occluded antennas among the M occluded antennas of the target terminal through an occlusion unit.

4. The test method for the wireless communication performance of a 5G terminal according to claim 3, wherein, Communication performance metric data, including: Within the target time period, establish a stable communication connection between the target terminal and the target base station; In response to the start instruction of the nth application, the target terminal sends a data packet to the target base station and collects the communication performance metric data of the target terminal, including: The number of bits of data sent by the target terminal, the number of bits of data received by the target base station, the number of error bits of data received by the target base station, the timestamp when the target terminal starts sending data, and the timestamp when the target base station finishes receiving data.

5. The test method for the wireless communication performance of a 5G terminal according to claim 4, wherein, Calculate the target communication performance evaluation value of the target terminal, including: based on the communication performance metric data, calculate the throughput, latency, and bit error rate of the target terminal, and perform weighted fusion on the throughput, latency, and bit error rate to obtain the target communication performance evaluation value of the target terminal.

6. A test system for the wireless communication performance of a 5G terminal, which is applied to the test method for the wireless communication performance of a 5G terminal described in any one of claims 1-5, and is characterized in that, Including: A training module for initializing the target terminal and generating matching data to train and obtain an association model; A prediction module for determining the occluded antenna of the target terminal within the target time period based on the association model; A testing module for masking the non-occluded antennas of the target terminal within the target time period, collecting the communication performance metric data of the target terminal, and calculating the communication performance evaluation value of the target terminal.

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