5G repeater performance detection system and detection method

Abstract

The present application discloses a 5G repeater performance detection system and detection method, which relates to the field of communication technology and solves the technical problem that the current 5G repeater performance detection has insufficient adaptability, which makes it impossible to timely discover potential or sudden problems of the repeater, resulting in a decrease in the working efficiency of the repeater; the system and detection method include: a data acquisition module: obtaining a number of detection data; a test planning module: obtaining a number of historical data of the repeater through a database; generating an influence coefficient of each detection item according to the historical data; obtaining project characteristic data corresponding to each detection item, and generating a correlation score between each detection item according to the project characteristic data; generating a project detection plan according to the correlation score and the influence coefficient; a performance testing module: testing each detection item according to the project detection plan, and generating a detection result of the corresponding detection item according to the detection data; effectively improving the efficiency of performance detection and ensuring the working efficiency of the repeater.

Description

Technical Field

[0001] This application belongs to the field of communication technology and relates to communication technology, specifically a 5G repeater performance detection system and detection method. Background Art

[0002] With the rapid development of 5G communication technology, 5G repeaters, as key devices for signal amplification and transmission, are crucial for the stability and reliability of their performance throughout the 5G network. During operation, 5G repeaters are affected by a variety of factors, such as equipment aging, parts repair, ambient temperature, humidity, and electromagnetic interference, all of which can cause performance changes.

[0003] Prior art (patented under patent number CN114374443B) discloses a repeater signal monitoring device, system, and method. These devices, which fall within the field of repeater technology, monitor signal transmission at each stage during communication between a base station transmitter and a terminal receiver via a repeater. This solution enables rapid and accurate guidance on signal transmission anomalies during the communication process. To avoid wasting resources, signal transmission anomaly monitoring is performed sequentially from the transmitter to the receiver. This prevents signal transmission anomaly monitoring from continuing to the subsequent stages if an anomaly in one intermediate stage occurs, leading to ineffective resource waste.

[0004] The above-mentioned repeater signal detection method detects the RF transmission signal and RF reception signal of the repeater; the detection items are single, and the overall evaluation of the repeater is poor; the existing method of conducting overall evaluation of the repeater is often to conduct comprehensive performance evaluations on a regular basis; most evaluation results are normal, resulting in most evaluation work being wasted; at the same time, due to the high transmission frequency, the penetration ability of 5G signals is relatively weak, resulting in the need for more repeaters to enhance signal coverage; in actual applications, due to the significant differences in the operating environment and operating status of 5G repeaters at different times, these differences have different impacts on the performance of the repeaters; the traditional strategy of regular comprehensive performance testing is inefficient; it is difficult to promptly detect potential or sudden problems of the repeater, which may lead to a decrease in the working efficiency of the repeater and even affect the stability and reliability of the entire 5G network. Summary of the Invention

[0005] The present application aims to solve at least one of the technical problems existing in the prior art; to this end, the present application proposes a 5G repeater performance detection system and detection method to solve the technical problem that the current 5G repeater performance detection has insufficient adaptability, which makes it impossible to timely detect potential or sudden problems of the repeater, resulting in a decrease in the working efficiency of the repeater.

[0006] To achieve the above-mentioned purpose, the first aspect of the present application provides a 5G repeater performance detection system, including: a data acquisition module, a test planning module, a performance testing module and a database;

[0007] The data acquisition module is configured to acquire a number of test data according to a project test plan through a data acquisition device connected thereto;

[0008] The test planning module: obtains a number of historical data of the repeater through the database; generates an influence degree coefficient of each test item based on the historical data; obtains project characteristic data corresponding to each test item, generates a correlation score between each test item based on the project characteristic data; generates a project test plan based on the correlation score and the influence degree coefficient;

[0009] The performance testing module is configured to test each test item according to the test plan, obtain test data, and generate test results for the corresponding test item based on the test data.

[0010] The present application obtains several historical data of the repeater station, and generates the influence coefficient of each detection item based on the historical data; the detection items with the influence score greater than the set influence threshold are marked as items to be detected; the project characteristic data corresponding to each item to be detected are obtained, and the correlation score between each item to be detected is generated based on the project characteristic data; the project detection plan is generated based on the correlation score and the influence coefficient of each item to be detected; each detection item is detected according to the project detection plan; the detection data is obtained, and the detection results of the corresponding detection items are generated based on the detection data; it realizes adaptive detection of the performance of the repeater station according to the actual use environment and working status of the repeater station, so that potential or sudden problems of the repeater station can be discovered in time, which effectively improves the efficiency of performance detection and improves the working efficiency of the repeater station.

[0011] Preferably, generating the influence coefficient of each detection item based on historical data includes:

[0012] Obtain historical data of several related components corresponding to the inspection project;

[0013] Extract several environmental project data and work project data corresponding to each recording period in the historical usage data; generate each component influence coefficient based on the several environmental project data and work project data of each recording period corresponding to the project component; generate the influence degree coefficient of the detection item based on the influence coefficient of each component corresponding to the detection item; and obtain the influence degree coefficient of each detection item in turn.

[0014] Preferably, generating the influence coefficient of each component according to a plurality of environmental project data and work project data of each recording period corresponding to the project component includes:

[0015] Mark the environment project data and work project data as HX respectively mni and GX mnj ; n is the number of the recording period, m is the number of the relevant component; i is the number of the environmental project data; j is the number of the work project data;

[0016] According to the environmental project data and work project data, the formula is:

[0017]

[0018] Calculate the component influence coefficient BY corresponding to the relevant component numbered m m Among them, ZH mi DH is the environmental optimum data corresponding to the environmental item data numbered i in the relevant component numbered m; i is the environmental unit data corresponding to the environmental project data numbered i; αi is the proportional coefficient corresponding to the environmental project data numbered i; ZG mj DG is the optimal data of the work item corresponding to the environmental item data numbered j in the relevant component numbered m; j is the work item unit data corresponding to the work item data numbered j; βj is the proportional coefficient corresponding to the work item data numbered j.

[0019] Preferably, generating the influence degree coefficient of the test item according to the influence coefficient of each component corresponding to the test item includes:

[0020] Get the influence coefficient of each component corresponding to the test item BY m ;

[0021] By formula Calculate the influence coefficient XY of the corresponding test item; where, is the weight coefficient corresponding to the relevant component numbered m; m=1, 2, ..., M, where M is the total number of relevant components corresponding to the inspection item.

[0022] Preferably, generating the correlation scores between the various test items based on the item characteristic data includes:

[0023] Obtain project feature data corresponding to several test items, and extract the test environment requirements, test steps, and test equipment requirements from the feature data of each project; input the test environment requirements, test steps, and test equipment requirements corresponding to any two test items into a correlation analysis model to obtain the correlation scores corresponding to the two test items, and obtain the correlation scores between the various test items in turn; the correlation analysis model is obtained through artificial intelligence model training.

[0024] Preferably, the correlation analysis model is obtained through artificial intelligence model training, including:

[0025] Obtaining project characteristic data of several test items and correlation scores between the test items from a database; integrating the several correlation scores and the test environment requirements, test steps, and test equipment requirements in the corresponding two project characteristic data into several sets of training data and test data;

[0026] The artificial intelligence model is trained using training data, and the trained artificial intelligence model is tested using test data, ultimately obtaining a project analysis and detection model whose input is the detection environment requirements, detection steps, and detection equipment requirements in the two detection items, and whose output is the corresponding correlation score; wherein the artificial intelligence model includes a BP neural network model and an RBF neural network model.

[0027] Preferably, generating a project detection plan based on the correlation score and the impact coefficient includes:

[0028] Obtain the impact score of each detection item, and mark the detection items with an impact score greater than a set impact threshold as items to be detected;

[0029] Obtaining correlation scores between the project to be detected with the highest impact score and the remaining projects to be detected; marking the projects to be detected with correlation scores greater than a set correlation threshold as related projects to be detected; integrating the related projects to be detected and the project to be detected with the highest impact score into a first detection project group;

[0030] Obtain the project to be tested with the highest impact score among the remaining projects to be tested, mark the projects to be tested whose correlation scores are greater than a set correlation threshold among the remaining projects to be tested as their corresponding related projects to be tested, and integrate the related projects to be tested and the project to be tested with the highest impact score among the remaining projects to be tested into a second testing project group;

[0031] Acquire several test item groups in sequence; acquire the test items in each test item group and the test plans corresponding to the related test items; and integrate the test plans corresponding to the test items in each test item group into a project test plan in sequence.

[0032] In this application, test items with a high degree of relevance are divided into the same test item group, and the project tests are carried out in the order of the project groups, which saves time in the test process and effectively improves the test efficiency when inexperienced or inexperienced employees perform the test.

[0033] Preferably, generating the test results of the corresponding test items according to the test data includes:

[0034] Extract the test value in the test data and determine whether the test value is within the qualified range corresponding to the test item; if yes, set the test result of the test item to normal; if not, set the test result of the test item to abnormal.

[0035] Another aspect of the present application provides a 5G repeater performance detection method, comprising the following steps:

[0036] Step 1: Obtain some historical data of the repeater;

[0037] Step 2: Generate the impact coefficient of each test item based on historical data;

[0038] Step 3: Mark the detection items whose impact score is greater than the set impact threshold as items to be detected;

[0039] Step 4: Obtain the project characteristic data corresponding to each item to be tested, and generate the correlation score between each item to be tested based on the project characteristic data;

[0040] Step 5: Generate a project testing plan based on the correlation score and impact coefficient of each project to be tested;

[0041] Step 6: Test each test item according to the project test plan;

[0042] Step 7: Obtain test data and generate test results for corresponding test items based on the test data.

[0043] Preferably, generating the correlation scores between the items to be detected based on the item characteristic data includes:

[0044] Extract the testing environment requirements, testing steps and testing equipment requirements from the characteristic data of each project; input the testing environment requirements, testing steps and testing equipment requirements corresponding to any two projects to be tested into the correlation analysis model to obtain the correlation scores corresponding to the two projects to be tested, and obtain the correlation scores between each project to be tested in turn; the correlation analysis model is obtained through artificial intelligence model training.

[0045] Preferably, generating a project detection plan based on the correlation score and influence coefficient of each project to be detected includes:

[0046] Obtaining correlation scores between the project to be detected with the highest impact score and the remaining projects to be detected; marking the projects to be detected with correlation scores greater than a set correlation threshold as related projects to be detected; integrating the related projects to be detected and the project to be detected with the highest impact score into a first detection project group;

[0047] Obtain the project to be tested with the highest impact score among the remaining projects to be tested, mark the projects to be tested whose correlation scores are greater than a set correlation threshold among the remaining projects to be tested as their corresponding related projects to be tested, and integrate the related projects to be tested and the project to be tested with the highest impact score among the remaining projects to be tested into a second testing project group;

[0048] Acquire several test item groups in sequence; acquire the test items in each test item group and the test plans corresponding to the related test items; and integrate the test plans corresponding to the test items in each test item group into a project test plan in sequence.

[0049] Compared with the prior art, the present invention has the following advantages:

[0050] 1. The present application obtains a number of historical data of a repeater station, and generates an influence coefficient of each detection item based on the historical data; detection items with an influence score greater than a set influence threshold are marked as items to be detected; project characteristic data corresponding to each item to be detected are obtained, and a correlation score between each item to be detected is generated based on the project characteristic data; a project detection plan is generated based on the correlation score and the influence coefficient of each item to be detected; each detection item is detected according to the project detection plan; detection data is obtained, and detection results of the corresponding detection items are generated based on the detection data; adaptive detection of the performance of the repeater station is realized according to the actual use environment and working status of the repeater station, so that potential or sudden problems of the repeater station can be discovered in time, the efficiency of performance detection is effectively improved, and the working efficiency of the repeater station is improved at the same time.

[0051] 2. In this application, the test items with a high degree of relevance are grouped into the same test item group, and the test items are tested in the order of the project groups, which saves time during the test process and effectively improves the test efficiency when inexperienced or inexperienced employees are conducting the test. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0053] Figure 1 This is a module connection diagram of the 5G repeater performance detection system in this application;

[0054] Figure 2 Schematic diagram of the steps of the 5G repeater performance detection method in this application. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions of this application in conjunction with the embodiments. Obviously, the embodiments described are only a part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0056] See also Figure 1 , the first embodiment of the present application provides a 5G repeater performance detection system and detection method, including: a data acquisition module, a test planning module, a performance testing module and a database;

[0057] Data acquisition module: This module acquires test data according to the project test plan through the data acquisition equipment connected to it. The test data is the data obtained when testing a certain performance of the 5G repeater in the project test plan. Different test items have different test data.

[0058] Test planning module: Acquires a number of historical data of the repeater through the database; historical data refers to the environmental and work-related data recorded since the last performance test of the repeater; generates the impact coefficient of each test item based on the historical data. The impact coefficient refers to the change in certain related performance of the repeater caused by equipment aging and parts maintenance during the operation of the repeater. The larger the impact coefficient, the greater the impact of the operation of the repeater on the performance test item, and the need to test the performance test item; obtains the project characteristic data corresponding to each test item, which is the relevant data of the test item test, including test environment requirements, test steps and test equipment requirements; generates the correlation score between each test item based on the project characteristic data. The correlation score is the score generated based on the correlation between each test item. The higher the correlation, the higher the corresponding correlation score; generates the project test plan based on the correlation score and the impact coefficient;

[0059] Performance testing module: Generates detection signals according to the detection plan, notifies relevant personnel to test each detection item according to the project detection plan, obtains detection data, and generates detection results for the corresponding detection items based on the detection data.

[0060] This embodiment obtains several historical data of the repeater station and generates an influence coefficient of each detection item based on the historical data; the detection items with an influence score greater than a set influence threshold are marked as items to be detected; the project characteristic data corresponding to each item to be detected are obtained, and the correlation score between each item to be detected is generated based on the project characteristic data; the project detection plan is generated based on the correlation score and the influence coefficient of each item to be detected; each detection item is detected according to the project detection plan; the detection data is obtained, and the detection results of the corresponding detection items are generated based on the detection data; it realizes adaptive detection of the performance of the repeater station according to the actual use environment and working status of the repeater station, so that potential or sudden problems of the repeater station can be discovered in time, which effectively improves the efficiency of performance detection and improves the working efficiency of the repeater station.

[0061] Generating an influence coefficient of each inspection item based on historical data, including: obtaining historical data of several relevant components corresponding to the inspection item, the historical data including historical usage data and maintenance data;

[0062] Extract several environmental project data and work project data corresponding to each recording period in the historical usage data, where the environmental project data is the data recorded for a corresponding environmental project, and the environmental projects include relevant environmental data such as temperature, humidity and electromagnetic interference; the work project data is the relevant data recorded when the corresponding component is working, including monitoring data of the relevant components, such as current, voltage and power; generate the influence coefficient of each component according to the several environmental project data and work project data of each recording period corresponding to the project component; the component influence coefficient is the influence of each environmental project in a certain recording period and the working status of the equipment on the equipment performance. When the component influence coefficient is higher, it means that the working environment of the component in the recording period is relatively unsuitable or the working status of the component is unsuitable; the impact on the component is greater; generate the influence degree coefficient of the corresponding detection item according to the influence coefficient of each component corresponding to the detection item; obtain the influence degree coefficient of each detection item in turn.

[0063] Generate the impact coefficient of each component based on several environmental project data and work project data of each recording period corresponding to the project component, including:

[0064] Mark the environment project data and work project data as HX respectively mni and GX mnjn is the number of the recording period, m is the number of the relevant component, i is the number of the environmental item data, and j is the number of the working item data. It can be understood that each environmental item of the same component corresponds to one environmental item data in each recording period; each working item corresponds to one working item data in each recording period. Environmental items include environmental factors such as ambient temperature and ambient humidity that affect the operation of the corresponding equipment in the repeater; working items include items related to the operation of the internal components of the repeater, such as operating voltage and current.

[0065] According to the environmental project data and work project data, the formula is:

[0066]

[0067] Calculate the component influence coefficient BY corresponding to the relevant component numbered m m Among them, ZH mi The optimal environmental data corresponding to the environmental item data of number i in the relevant component numbered m is the environmental value where the relevant component is least affected by the environmental item; DH i is the environmental unit data corresponding to the environmental project data numbered i; the specific value is set according to expert experience, which is used to adjust the comparability between different environmental projects and remove units; αi is the proportional coefficient corresponding to the environmental project data numbered i, and the specific value is set according to experience. The proportional coefficient can be set considering the degree of influence of different environmental projects on the components. The greater the degree of influence, the larger the value of the proportional coefficient should be set; ZG mj DG is the optimal data of the work item corresponding to the environmental item data of number j in the relevant component numbered m. The optimal data of the work item is the working value with the least impact of the relevant component on the work item. j is the work item unit data corresponding to the work item data numbered j; βj is the proportional coefficient corresponding to the work item data numbered j. The specific value is set based on experience. The proportional coefficient can be set considering the degree of influence of different work items on the components. The greater the influence, the larger the value of the proportional coefficient should be set.

[0068] This embodiment calculates the component influence coefficient of the working environment of the component and the working state of the component in each recording period on the aging or performance of the component itself through the above formula; when the relevant component works under inappropriate environmental conditions or when the relevant component works in an inappropriate state, the greater the aging impact it is subjected to, the larger the corresponding component influence coefficient is set; otherwise, the smaller it is.

[0069] It can be understood that when an environmental item has no impact on a related component, the environmental item data corresponding to the related component is 0, and the optimal environmental data is also 0; when a work item has no impact on a related component, the work item data corresponding to the related component is 0, and the optimal work item data is also 0.

[0070] Generate the influence coefficient of the inspection item according to the influence coefficient of each component corresponding to the inspection item, including: obtaining the influence coefficient of each component corresponding to the inspection item BY m ;

[0071] By formula Calculate the influence coefficient XY of the corresponding test item; where, is the weight coefficient corresponding to the relevant component numbered m, m=1, 2, ..., M, M is the total number of relevant components corresponding to the detection item; the value of the weight coefficient can be set according to expert experience, specifically referring to the importance of the relevant component to the detection item. The higher the importance, the larger the corresponding weight coefficient is set; it can also be obtained in the following way: obtaining component parameters of several components inside the 5G repeater, and constructing digital twin models corresponding to the corresponding components according to the component parameters to obtain component models; obtaining the working relationship between the various components, and constructing a 5G repeater digital twin model corresponding to the detection item according to the working relationship and the various component models; the 5G repeater digital twin model is a digital twin model that can simulate the operation of the 5G repeater; the 5G repeater digital twin model is simulated to obtain the test value of the corresponding performance of each detection item;

[0072] Lower the gain of a set ratio of a component model in the 5G repeater digital twin model, simulate the 5G repeater digital twin model to obtain the component impact test value of the corresponding performance of each test item; obtain the component impact test value corresponding to each component in turn;

[0073] Obtain the test value corresponding to the inspection item and the component impact test value corresponding to each relevant component in the inspection item; mark the ratio of the component impact test value corresponding to each relevant component to the test value as the project impact degree of the relevant component; obtain the project impact degree corresponding to each relevant item of the inspection item in turn; normalize the project impact degree of each relevant component of the same inspection item, and mark the project impact degree of each relevant component after normalization as the weight coefficient of the corresponding relevant component in the inspection item; obtain the weight coefficient of each project component corresponding to each inspection item in turn; if the inspection item is output power, the corresponding relevant components include power amplifiers, low-noise amplifiers, and filters. In this embodiment, the gains of the relevant components are reduced by 15% in the digital twin model of the 5G repeater station, that is, the gains of the low-noise amplifier and the filter are ensured to remain unchanged, and the power amplifier is adjusted to 85% of the original gain; obtain the output power at this time, and mark the ratio of the output power at this time to the original output power as the project impact degree of the power amplifier; then reduce other relevant components in turn to obtain the project impact degree of each relevant component.

[0074] In this embodiment, the component influence coefficients of each component of the test item are weighted and summed up by the above formula to obtain the comprehensive influence coefficient of each component, which is used to express the degree to which the performance corresponding to the test item is affected. The larger the influence coefficient, the greater the possibility that the corresponding performance is affected and the greater the degree of the impact; the more necessary it is to test the item corresponding to the performance.

[0075] Generate correlation scores between various test items based on project characteristic data, including: obtaining project characteristic data corresponding to several test items, extracting test environment requirements, test steps and test equipment requirements from the feature data of each project; the test environment requirements are the requirements for various environmental factors when testing the performance corresponding to the test item; the test steps are the operating steps when testing the corresponding test items; the test equipment requirements are the test equipment required when testing the test items; the test environment requirements, test steps and test equipment requirements corresponding to any two test items are input into the correlation analysis model to obtain the correlation scores corresponding to the two test items, and the correlation scores between each test item are obtained in turn; the correlation analysis model is obtained through artificial intelligence model training.

[0076] The correlation analysis model is obtained through artificial intelligence model training, including:

[0077] The database is used to obtain project characteristic data of several test items, as well as correlation scores between the test items. The correlation score is an expert's score of the correlation between the two test items based on the test environment requirements, test steps, and duplication and correlation of the test equipment between the two test items. The more similar the test environment requirements, the more duplication of the test steps and test equipment, and the higher the corresponding correlation score. A high correlation score indicates that the two test items can be tested sequentially. Specifically, the test items of the 5G repeater station include the nominal maximum linear output power test, the automatic level control test, the maximum gain and error test, and the frequency error test. The test steps in the project characteristic data corresponding to the nominal maximum linear output power test include:

[0078] S1: Turn off the uplink or downlink of the TDD band for testing. The FDD band is tested in normal mode. Specifically, when measuring the downlink output power, the uplink of the TDD band is turned off; when measuring the uplink output power, the downlink of the TDD band is turned off.

[0079] S2: Set the signal generator to the center carrier frequency within the operating frequency range of the device under test and generate the modulated signals NR-TM3.1a {3500MHz: 100MHz NR signal; 2100MHz: 50MHz NR signal; 800MHz: 15MHz NR signal} and E-TM3.1 {1800MHz: 20MHz LTE signal};

[0080] S3: Set the repeater gain to the maximum gain;

[0081] S4: Adjust the signal source level until the output power of the device under test reaches the ALC control point, back off 1 dB, and then increase the power in 0.2 dB steps to the maximum linear output power. The power displayed on the spectrum analyzer should be within the tolerance range of the maximum output rated power.

[0082] S5: Record the output level and input power level of the repeater;

[0083] Testing equipment requirements include: signal source, isolator, attenuator, spectrum analyzer;

[0084] Testing environment requirements: An environment with little electromagnetic interference.

[0085] The detection steps in the project characteristic data corresponding to the automatic level control test include:

[0086] S1: Turn off the uplink or downlink of the TDD band for testing. The FDD band is tested in normal mode. Specifically, when measuring the downlink output power, the uplink of the TDD band is turned off; when measuring the uplink output power, the downlink of the TDD band is turned off.

[0087] S2: Set the signal source to the center frequency point within the operating frequency range to generate the modulated signals NR-TM3.1a {3500MHz: 100MHz NR signal; 2100MHz: 50MHz NR signal; 800MHz: 15MHz NR signal} and E-TM3.1 {1800MHz: 20MHz LTE signal};

[0088] S3: Set the repeater gain to the maximum gain;

[0089] S4: Adjust the signal source level until the output power of the repeater reaches the maximum output power;

[0090] S5: Record the output power of the repeater;

[0091] S6: Increase the output signal level of the signal source in 1dB steps until it increases by 10dB. Use a spectrum analyzer to test the output power of the repeater, starting from ALC control until the maximum input power increases by 10dB, and record the carrier output power value.

[0092] S7: Continue to increase the signal generator level to 20 dB (if the signal source power is insufficient, add a power amplifier). Use a 5G NR signal analyzer to measure and record the repeater's output power. It should be kept within ±2 dB of the maximum transmit power or turned off.

[0093] Testing equipment requirements include: signal source, isolator, attenuator, spectrum analyzer;

[0094] Testing environment requirements: An environment with little electromagnetic interference.

[0095] The detection steps in the project characteristic data corresponding to the frequency error test include:

[0096] S1: Set the gain of the device under test to the maximum gain;

[0097] S2: Set the signal generator to the center carrier frequency within the operating frequency band of the device under test and generate the modulated signals NR-TM3.1a {3500MHz: 100MHz NR signal; 2100MHz: 50MHz NR signal; 800MHz: 15MHz NR signal} and E-TM3.1 {1800MHz: 20MHz LTE signal};

[0098] S3: Adjust the level of the signal source until the output power of the device under test reaches the maximum linear output power;

[0099] S4: Test the frequency deviation between the input signal and the output signal.

[0100] Testing equipment requirements include: signal source, isolator, straight-line isolation head, attenuator, and frequency meter;

[0101] Testing environment requirements: An environment with little electromagnetic interference.

[0102] It can be seen that the test environment requirements, test steps, and test equipment repeatability of the nominal maximum linear output power test and the automatic level control test are very high. Therefore, the correlation score between the nominal maximum linear output power test and the automatic level control test is high; while the correlation score between the nominal maximum linear output power test and the frequency error test is low.

[0103] Integrate a number of correlation scores and the corresponding testing environment requirements, testing steps and testing equipment requirements in the two project feature data into a number of sets of training data and test data;

[0104] The artificial intelligence model is trained using training data, and the trained artificial intelligence model is tested using test data, ultimately obtaining a project analysis and detection model whose input is the detection environment requirements, detection steps, and detection equipment requirements in the two detection items, and whose output is the corresponding correlation score; wherein the artificial intelligence model includes a BP neural network model and an RBF neural network model.

[0105] Generate a project detection plan based on the correlation score and the impact coefficient, including: obtaining the impact score of each detection project, marking the detection project with an impact score greater than a set impact threshold as a detection project;

[0106] Obtaining correlation scores between the project to be detected with the highest impact score and the remaining projects to be detected; marking the projects to be detected with correlation scores greater than a set correlation threshold as related projects to be detected; integrating the related projects to be detected and the project to be detected with the highest impact score into a first detection project group;

[0107] Obtain the project to be tested with the highest impact score among the remaining projects to be tested, mark the projects to be tested whose correlation scores are greater than a set correlation threshold among the remaining projects to be tested as their corresponding related projects to be tested, and integrate the related projects to be tested and the project to be tested with the highest impact score among the remaining projects to be tested into a second testing project group;

[0108] Acquire several test item groups in sequence; acquire the test items in each test item group and the test plans corresponding to the related test items; and integrate the test plans corresponding to the test items in each test item group into a project test plan in sequence.

[0109] In this embodiment, the detection items with a high degree of relevance are divided into the same detection item group, and the items are detected in the order of the item groups, which saves time in the detection process and effectively improves the detection efficiency when inexperienced or inexperienced employees perform the detection.

[0110] Generate the test results of the corresponding test items based on the test data, including: extracting the test value in the test data, and judging whether the test value is within the qualified range corresponding to the test item; if yes, set the test result of the test item to normal; if not, set the test result of the test item to abnormal.

[0111] See also Figure 2 Another aspect of the present application provides a 5G repeater performance detection method, comprising the following steps:

[0112] Step 1: Obtain some historical data of the repeater;

[0113] Step 2: Generate the impact coefficient of each test item based on historical data;

[0114] Step 3: Mark the detection items whose impact score is greater than the set impact threshold as items to be detected;

[0115] Step 4: Obtain the project characteristic data corresponding to each item to be tested, and generate the correlation score between each item to be tested based on the project characteristic data;

[0116] Step 5: Generate a project testing plan based on the correlation score and impact coefficient of each project to be tested;

[0117] Step 6: Test each test item according to the project test plan;

[0118] Step 7: Obtain test data and generate test results for corresponding test items based on the test data.

[0119] Generate correlation scores between each item to be tested based on the item characteristic data, including:

[0120] Extract the testing environment requirements, testing steps and testing equipment requirements from the characteristic data of each project; input the testing environment requirements, testing steps and testing equipment requirements corresponding to any two projects to be tested into the correlation analysis model to obtain the correlation scores corresponding to the two projects to be tested, and obtain the correlation scores between each project to be tested in turn; the correlation analysis model is obtained through artificial intelligence model training.

[0121] Generate a project testing plan based on the correlation score and impact coefficient of each project to be tested, including:

[0122] Obtaining correlation scores between the project to be detected with the highest impact score and the remaining projects to be detected; marking the projects to be detected with correlation scores greater than a set correlation threshold as related projects to be detected; integrating the related projects to be detected and the project to be detected with the highest impact score into a first detection project group;

[0123] Obtain the project to be tested with the highest impact score among the remaining projects to be tested, mark the projects to be tested whose correlation scores are greater than a set correlation threshold among the remaining projects to be tested as their corresponding related projects to be tested, and integrate the related projects to be tested and the project to be tested with the highest impact score among the remaining projects to be tested into a second testing project group;

[0124] Acquire several test item groups in sequence; acquire the test items in each test item group and the test plans corresponding to the related test items; and integrate the test plans corresponding to the test items in each test item group into a project test plan in sequence.

[0125] Some of the data in the above formula are calculated by removing the dimensions and taking their numerical values. The formula is a formula that is closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0126] How this application works:

[0127] The present application obtains a number of historical data of the repeater station, and generates an influence coefficient of each detection item based on the historical data; the detection items with an influence score greater than a set influence threshold are marked as items to be detected; the project characteristic data corresponding to each item to be detected are obtained, and the correlation score between each item to be detected is generated based on the project characteristic data; the project detection plan is generated based on the correlation score and the influence coefficient of each item to be detected; each detection item is detected according to the project detection plan; the detection data is obtained, and the detection results of the corresponding detection items are generated based on the detection data; it realizes adaptive detection of the performance of the repeater station according to the actual use environment and working status of the repeater station, so that potential or sudden problems of the repeater station can be discovered in time, which effectively improves the efficiency of performance detection and ensures the working efficiency of the repeater station.

[0128] The above embodiments are only used to illustrate the technical method of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present application.

Claims

1. 5G repeater performance detection system, characterized by: include: Data acquisition module, test planning module, performance testing module and database; The data acquisition module is configured to acquire a number of test data according to a project test plan through a data acquisition device connected thereto; The test planning module: obtains a number of historical data of the repeater through the database; generates the influence degree coefficient of each test item based on the historical data; including: obtaining historical data of a number of related components corresponding to the test items, the historical data including historical usage data and maintenance data; Extracting several environmental project data and work project data corresponding to each recording period in the historical usage data; generating the impact coefficient of each component according to several environmental project data and work project data corresponding to each recording period of the project component; including: marking the environmental project data and work project data as HX mni and GX mnj ; n is the number of the recording period, m is the number of the relevant component; i is the number of the environmental project data; j is the number of the work project data; according to the environmental project data and the work project data, the formula: ； I is the total number of environmental items, J is the total number of work items, and N is the total number of recording periods; calculate the component influence coefficient BY corresponding to the relevant component numbered m m Among them, ZH mi DH is the environmental optimum data corresponding to the environmental item data numbered i in the relevant component numbered m; i is the environmental unit data corresponding to the environmental project data numbered i; αi is the proportional coefficient corresponding to the environmental project data numbered i; ZG mj DG is the optimal data of the work item corresponding to the environmental item data numbered j in the relevant component numbered m; j is the work item unit data corresponding to the work item data numbered j; βj is the proportional coefficient corresponding to the work item data numbered j; Generate the influence degree coefficient of the corresponding test item according to the influence coefficient of each component corresponding to the test item; obtain the influence degree coefficient of each test item in turn; Obtain the project characteristic data corresponding to each test item, generate the correlation score between each test item based on the project characteristic data; generate the project test plan based on the correlation score and the influence degree coefficient; The performance testing module is configured to test each test item according to the test plan, obtain test data, and generate test results for the corresponding test item based on the test data.

2. The 5G repeater performance detection system according to claim 1, characterized in that: Generating the influence degree coefficient of the detection item according to the influence coefficient of each component corresponding to the detection item includes: Get the influence coefficient of each component corresponding to the test item BY m ; By formula Calculate the influence coefficient XY of the corresponding test item; where, is the weight coefficient corresponding to the relevant component numbered m; m=1, 2, ..., M, where M is the total number of relevant components corresponding to the inspection item.

3. The 5G repeater performance detection system according to claim 1, characterized in that: Generating the correlation scores between the various test items according to the item characteristic data includes: Obtain project feature data corresponding to several test items, and extract the test environment requirements, test steps, and test equipment requirements from the feature data of each project; input the test environment requirements, test steps, and test equipment requirements corresponding to any two test items into a correlation analysis model to obtain the correlation scores corresponding to the two test items, and obtain the correlation scores between the various test items in turn; the correlation analysis model is obtained through artificial intelligence model training.

4. The 5G repeater performance detection system according to claim 3, characterized in that: The correlation analysis model is obtained through artificial intelligence model training, including: Obtaining project characteristic data of several test items and correlation scores between the test items from a database; integrating the several correlation scores and the test environment requirements, test steps, and test equipment requirements in the corresponding two project characteristic data into several sets of training data and test data; The artificial intelligence model is trained using training data, and the trained artificial intelligence model is tested using test data, ultimately obtaining a project analysis and detection model whose input is the detection environment requirements, detection steps, and detection equipment requirements in the two detection items, and whose output is the corresponding correlation score; wherein the artificial intelligence model includes a BP neural network model and an RBF neural network model.

5. The 5G repeater performance detection system according to claim 1, characterized in that: The project detection plan is generated according to the correlation score and the impact coefficient, including: Obtain the impact score of each detection item, and mark the detection items with an impact score greater than a set impact threshold as items to be detected; Obtaining correlation scores between the project to be detected with the highest impact score and the remaining projects to be detected; marking the projects to be detected with correlation scores greater than a set correlation threshold as related projects to be detected; integrating the related projects to be detected and the project to be detected with the highest impact score into a first detection project group; Obtain the project to be tested with the highest impact score among the remaining projects to be tested, mark the projects to be tested whose correlation scores are greater than a set correlation threshold among the remaining projects to be tested as their corresponding related projects to be tested, and integrate the related projects to be tested and the project to be tested with the highest impact score among the remaining projects to be tested into a second testing project group; Acquire several test item groups in sequence; acquire the test items in each test item group and the test plans corresponding to the related test items; and integrate the test plans corresponding to the test items in each test item group into a project test plan in sequence.

6. The 5G repeater performance detection system according to claim 1, characterized in that: Generating the test results of the corresponding test items according to the test data includes: Extract the test value in the test data and determine whether the test value is within the qualified range corresponding to the test item; if yes, set the test result of the test item to normal; if not, set the test result of the test item to abnormal.

7. A 5G repeater performance detection method, applied to a 5G repeater performance detection system according to any one of claims 1 to 6; characterized in that: The following steps are involved: Step 1: Obtain some historical data of the repeater; Step 2: Generate the impact coefficient of each test item based on historical data; Step 3: Mark the detection items whose impact score is greater than the set impact threshold as items to be detected; Step 4: Obtain the project characteristic data corresponding to each item to be tested, and generate the correlation score between each item to be tested based on the project characteristic data; Step 5: Generate a project testing plan based on the correlation score and impact coefficient of each project to be tested; Step 6: Test each test item according to the project test plan; Step 7: Obtain test data and generate test results for corresponding test items based on the test data.

8. The 5G repeater performance detection method according to claim 7, characterized in that: Generating the correlation scores between the items to be detected based on the item characteristic data includes: Extract the testing environment requirements, testing steps and testing equipment requirements from the characteristic data of each project; input the testing environment requirements, testing steps and testing equipment requirements corresponding to any two projects to be tested into the correlation analysis model to obtain the correlation scores corresponding to the two projects to be tested, and obtain the correlation scores between each project to be tested in turn; the correlation analysis model is obtained through artificial intelligence model training.

Images