A radio device spectrum parameter testing system and method

Abstract

The embodiment of the present specification provides a wireless radio equipment spectrum parameter test system and method, the system comprises a demand analysis module, a planning generation module, a scheme determination module and a scheme recommendation module deployed on a computing unit; wherein the demand analysis module is configured to receive and analyze user demand information to obtain user demand data; the planning generation module is configured to determine at least two recommended test tasks according to the user demand data; the scheme determination module is configured to determine the arrangement scheme of the microwave relay device based on the at least two recommended test tasks; and the scheme recommendation module is configured to send the arrangement scheme to the user terminal of the user.

Description

Technical Field

[0001] This specification relates to the field of microwave communications, and in particular to a system and method for testing the spectrum parameters of wireless equipment. Background Technology

[0002] Microwave relay communication refers to a method of radio communication that utilizes electromagnetic waves in the frequency band above 300MHz to propagate within the line-of-sight range of the troposphere. This communication method is limited by terrain and antenna height; therefore, for long-distance communication, a series of relay stations must be established to convert and amplify the received signals before relaying them to the terminal station. However, there is a lack of rapid testing capabilities for optimal deployment schemes under different communication scenarios.

[0003] Based on this, it is desirable to provide a system, method, apparatus and storage medium for testing the spectrum parameters of wireless equipment, which can be used for microwave relay scheme planning, thereby effectively meeting the user's need for stable communication in specific scenarios. Summary of the Invention

[0004] This specification provides one or more embodiments of a wireless equipment spectrum parameter testing system. The system includes a demand analysis module, a planning generation module, a scheme determination module, and a scheme recommendation module deployed on a computing unit. The demand analysis module is configured to receive and analyze user demand information to obtain user demand data. The planning generation module is configured to determine at least two recommended test tasks based on the user demand data. The scheme determination module is configured to determine a microwave relay device layout scheme based on the at least two recommended test tasks. The scheme recommendation module is configured to send the layout scheme to the user's user terminal.

[0005] This specification provides one or more embodiments of a method for testing the spectrum parameters of a wireless device, implemented based on the wireless device spectrum parameter testing system. The method includes: determining user demand data based on received user demand information; determining at least two recommended test tasks based on the user demand data; determining a microwave relay device layout scheme based on the at least two recommended test tasks; and sending the layout scheme to the user's user terminal.

[0006] This specification provides one or more embodiments of a radio device spectrum parameter testing apparatus, including a processor, the processor being configured to execute the radio device spectrum parameter testing method as described in any of the preceding embodiments.

[0007] This specification provides one or more embodiments of a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes the radio device spectrum parameter testing method as described in any of the preceding embodiments. Attached Figure Description

[0008] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0009] Figure 1 This is a system schematic diagram of a radio equipment spectrum parameter testing system according to some embodiments of this specification;

[0010] Figure 2 This is an exemplary flowchart of a method for testing the spectrum parameters of a wireless device according to some embodiments of this specification;

[0011] Figure 3 This is an exemplary flowchart illustrating the determination of recommended test tasks according to some embodiments of this specification;

[0012] Figure 4 This is an exemplary flowchart illustrating the determination of an arrangement scheme according to some embodiments of this specification. Detailed Implementation

[0013] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0014] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0015] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0016] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0017] Figure 1 This is a system schematic diagram of a radio equipment spectrum parameter testing system according to some embodiments of this specification.

[0018] In some embodiments, the radio equipment spectrum parameter testing system 100 may include a demand analysis module 110, a planning generation module 120, a scheme determination module 130, and a scheme recommendation module 140 deployed on a computing unit.

[0019] A computing unit refers to a computing device that processes instructions, processes data, and performs operations. In some embodiments, a computing unit may include computing devices such as a central processing unit (CPU), a graphics processing unit (GPU), and a server. Some or all of the requirements analysis module 110, the planning generation module 120, the solution determination module 130, and the solution recommendation module 140 may be integrated into the computing unit.

[0020] The requirements analysis module 110 refers to the module that acquires user requirement data. In some embodiments, the requirements analysis module 110 can be configured to receive and analyze user requirement information to acquire user requirement data.

[0021] The planning generation module 120 refers to the module that determines recommended test tasks. In some embodiments, the planning generation module 120 can be configured to determine at least two recommended test tasks based on user requirement data.

[0022] In some embodiments, the planning generation module 120 may be further configured to determine at least two sets of test equipment locations based on communication distance distribution, communication quality requirements, and communication geographical features; obtain candidate test indicators; determine the test indicators corresponding to each set of test equipment locations based on at least two sets of test equipment locations, communication environment distribution, communication geographical features, device model data, and candidate test indicators; and determine at least two recommended test tasks based on at least two sets of test locations and their corresponding test indicators.

[0023] In some embodiments, the planning generation module 120 may be further configured to determine the equivalent equipment deployment corresponding to the test equipment deployment based on the communication environment distribution, communication geographical features, test equipment deployment, and test environment data of the target area; and to determine the equivalent test task based on the equivalent equipment deployment and test indicators corresponding to the test equipment deployment.

[0024] The scheme determination module 130 refers to the module that determines the layout scheme of the microwave relay device. In some embodiments, the scheme determination module 130 can be configured to determine the layout scheme of the microwave relay device based on at least two recommended test tasks.

[0025] In some embodiments, the scheme determination module 130 may be further configured to perform communication tests based on each of at least two recommended test tasks, and obtain actual test data corresponding to each recommended test task; determine the global communication quality based on the actual test data; determine the communication evaluation value corresponding to each recommended test task based on the global communication quality; and determine the deployment scheme based on the communication evaluation value and at least two recommended test tasks.

[0026] The solution recommendation module 140 refers to the module that sends layout solutions. In some embodiments, the solution recommendation module 140 can be configured to send layout solutions to the user's user terminal.

[0027] For more information on the requirements analysis module 110, planning generation module 120, solution determination module 130, and solution recommendation module 140, please refer to the relevant descriptions below.

[0028] It should be noted that the above description of the radio equipment spectrum parameter testing system and its modules is for convenience only and should not be construed as limiting this specification to the scope of the illustrated embodiments. It is understood that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the various modules or construct subsystems connected to other modules without departing from these principles. In some embodiments, Figure 1 The requirements analysis module, planning generation module, solution determination module, and solution recommendation module disclosed herein can be different modules within a single system, or a single module can implement the functions of two or more of the aforementioned modules. For example, the modules can share a single storage module, or each module can have its own separate storage module. Such variations are all within the scope of protection of this specification.

[0029] Figure 2 This is an exemplary flowchart of a method for testing the spectrum parameters of a wireless device according to some embodiments of this specification. Figure 2 As shown, process 200 includes steps 210-240 as described below. In some embodiments, process 200 may be executed by a computing unit.

[0030] Step 210: Determine user demand data based on the received user demand information.

[0031] User requirement information refers to information related to the communication needs that a user requires. In some embodiments, user requirement information may include communication distance, etc. For example, user requirement information may be that a user needs to achieve communication between two locations at a distance of 1 km.

[0032] A computing unit or radio equipment spectrum parameter testing system can determine user requirement information based on methods such as obtaining user input at a user terminal. A user terminal refers to the terminal device used by the user to receive deployment plans. In some embodiments, a user terminal may include a smartphone, laptop, etc.

[0033] User demand data refers to data related to user demand information. In some embodiments, user demand data can be determined by analyzing the received user demand information. For example, if the user demand information is that a user needs to achieve communication between two locations with a distance of 1km, the user demand data can be "achieving communication over a distance of 1km".

[0034] Step 220: Based on user demand data, determine at least two recommended test tasks.

[0035] A recommended test task refers to a scheme for conducting microwave relay testing based on user requirements. In some embodiments, a recommended test task may include test equipment deployment and test indicators. For example, test equipment deployment may include the number of microwave relay devices to be deployed and the deployment locations of each microwave relay device.

[0036] Test indicators may include test transmission parameters, etc. Test transmission parameters refer to the transmission parameters of the microwave relay devices involved in the deployment of test equipment. A microwave relay device refers to a device used for microwave relay testing. In some embodiments, the microwave relay device may be an electromagnetic parameter tester, a radar target simulator, etc.

[0037] In some embodiments, different recommended test tasks may have different test equipment placement and test metrics.

[0038] In some embodiments, recommended test tasks can be determined based on user demand data through various methods. For example, the computing unit can determine recommended test tasks based on user demand data by querying a first preset lookup table. The first preset lookup table can include the correspondence between various types of user demand data and test equipment deployment and test indicators. The first preset lookup table can be pre-constructed based on historical communication data and / or experimental data with communication quality exceeding a preset threshold.

[0039] In some embodiments, the computing unit can determine at least two sets of test equipment deployment locations based on communication distance distribution, communication quality requirements, and communication geographical features; obtain candidate test indicators; determine the test indicators corresponding to each set of test equipment deployment locations based on at least two sets of test equipment deployment locations, communication environment distribution, communication geographical features, device model data, and candidate test indicators; and determine at least two recommended test tasks based on at least two sets of test deployment locations and their corresponding test indicators. For more information on this section, please refer to [link to relevant documentation]. Figure 3 Related descriptions.

[0040] In some embodiments, the computing unit can determine the equivalent equipment deployment corresponding to the test equipment deployment based on the communication environment distribution, communication geographical features, test equipment deployment, and test environment data of the target area; and determine the equivalent test task based on the equivalent equipment deployment and test indicators corresponding to the test equipment deployment. For more information on this part, please refer to [link to relevant documentation]. Figure 3 Related descriptions.

[0041] Step 230: Determine the layout scheme of the microwave relay device based on at least two recommended test tasks.

[0042] A deployment scheme refers to a plan for deploying microwave relay devices. In some embodiments, the deployment scheme may include the number of microwave relay devices, distribution parameters, and deployment parameters. The distribution parameters of the microwave relay devices may include the distance and positional relationship between the devices. The deployment parameters of the microwave relay devices refer to the transmission parameter configurations that need to be set for the deployed microwave relay devices. In some embodiments, the transmission parameters may include the center frequency, output power, modulation method, and bandwidth of the transmitted signal.

[0043] In some embodiments, the layout scheme can be determined based on multiple methods. For example, the computing unit can determine the layout scheme based on the recommended test task that has the fewest microwave relay devices among the determined recommended test tasks.

[0044] In some embodiments, the computing unit may perform communication tests based on each of at least two recommended test tasks to obtain actual test data corresponding to each recommended test task; determine the global communication quality based on the actual test data; determine the communication evaluation value corresponding to each recommended test task based on the global communication quality; and determine the deployment scheme based on the communication evaluation value and at least two recommended test tasks. For more information on this part, please refer to [link to relevant documentation]. Figure 4 Related descriptions.

[0045] Step 240: Send the layout plan to the user's user terminal.

[0046] Once the user terminal receives the layout plan, it can display or push it to the user.

[0047] In some embodiments of this specification, recommended test tasks are determined by user demand information, and the layout scheme of the microwave relay device is determined based on the recommended test tasks. This can effectively meet the user's communication needs and improve the rationality of the layout scheme.

[0048] Figure 3 This is a flowchart illustrating an exemplary process 300 for determining a recommended test task according to some embodiments of this specification.

[0049] In some embodiments, process 300 may be executed by a computing unit.

[0050] like Figure 3 As shown, in some embodiments, process 300 includes steps 310 to 340.

[0051] Step 310: Based on communication distance distribution, communication quality requirements, and communication geographical characteristics, determine the deployment of at least two sets of test equipment.

[0052] For more information on the deployment of testing equipment, please see [link / reference]. Figure 2 And its related descriptions.

[0053] In some embodiments, user requirement information may also include communication distance distribution, communication environment distribution, communication geographical features, and communication quality requirements.

[0054] Communication distance distribution refers to the distance distribution between multiple communication locations that need to communicate with each other. For example, when a user needs to achieve communication between three locations with a distance of (D1, D2, D3) between each other, the communication distance distribution can be (D1, D2, D3).

[0055] Communication environment distribution refers to the distribution of environmental characteristics of the communication locations that require communication. These environmental characteristics include ambient temperature and humidity. For example, when both locations conducting communication are located near the equator with a long-term temperature of 35°C and a humidity of 5%, the communication environment distribution could be (35°C, 5%).

[0056] For example, when the desired communication involves three locations A, B, and C, the communication environment distribution can be a matrix. Elements in the same row correspond to the same region, and elements in the same column correspond to the same type of environmental feature. As an example, environmental feature types include Environmental Feature 1, Environmental Feature 2, and Environmental Feature 3, and regions include Region A, Region B, and Region C. The communication environment distribution is as follows:

[0057] 20% 40% 40%

[0058] 30% 35% 35%

[0059] 15% 35% 45%

[0060] This indicates that region A experiences environmental characteristic 1 for 20% of the time, environmental characteristic 2 for 40% of the time, and environmental characteristic 3 for 40% of the time; region B experiences environmental characteristic 1 for 30% of the time, environmental characteristic 2 for 35% of the time, and environmental characteristic 3 for 35% of the time; and region C experiences environmental characteristic 1 for 15% of the time, environmental characteristic 2 for 35% of the time, and environmental characteristic 3 for 45% of the time.

[0061] In some embodiments, the distribution of communication environments can be determined by analyzing the historical environmental characteristics of multiple locations that need to communicate.

[0062] Communication geographic features refer to the geographical characteristics of the location where communication needs to be conducted. These geographic features include altitude, terrain type, landform distribution, and building density. Terrain type can be mountains, hills, plains, etc. Landforms can be forests, grasslands, bodies of water, etc., and landform distribution can be a distribution map formed by the landforms.

[0063] Communication geographic features can be represented by topographic maps, satellite topographic maps, etc. For example, communication geographic features can be satellite cloud images that include the altitude, topography, etc. of multiple locations that need to communicate.

[0064] In some embodiments, communication geographic features can be obtained by acquiring satellite maps of the communication locations where communication is required.

[0065] Communication quality requirements refer to a user's communication quality requirements between multiple locations that need to communicate. In some embodiments, communication quality requirements may be a user's requirements for communication latency between multiple locations. For example, a communication quality requirement may be a communication latency of no more than 1 second.

[0066] In some embodiments, communication quality requirements can be set according to user needs.

[0067] In some embodiments, the higher the communication quality requirements, the denser the microwave relay devices need to be deployed.

[0068] The computing unit can determine the deployment of at least two sets of test equipment based on communication distance distribution, communication quality requirements, and communication geographical characteristics through various methods.

[0069] In some embodiments, the computing unit can construct a demand feature vector based on communication distance distribution, communication quality requirements, and communication geographical features, and determine at least two sets of test equipment locations based on the demand feature vector through a first vector database.

[0070] In some embodiments, the first vector database includes multiple sets of reference demand feature vectors and their corresponding reference test equipment locations. For example, the first vector database can construct reference demand feature vectors based on historical communication data or experimental communication data with good communication quality from historical data, and use the test equipment locations corresponding to the communication distance distribution, communication quality requirements, and communication geographical features as the reference test equipment locations corresponding to the reference demand feature vectors.

[0071] In some embodiments, the computing unit may, based on the demand feature vector, determine at least two sets of reference demand feature vectors that meet preset requirements from a first vector database as at least two sets of target vectors, and use the reference test equipment placement points corresponding to the target vectors as test equipment placement points. The preset requirements may include a vector similarity between the reference demand feature vector and the demand feature vector being higher than a preset similarity threshold, wherein the vector similarity may be negatively correlated with the vector distance between the demand feature vector and the reference demand feature vector, and the vector distance may be determined based on cosine distance, etc. The preset similarity threshold is set empirically.

[0072] Step 320: Obtain candidate test metrics.

[0073] Candidate test metrics refer to test metrics that are yet to be determined. For more information on test metrics, please see [link to relevant documentation]. Figure 2 And related content.

[0074] In some embodiments, the calculation unit may randomly select from a preset range of indicators to generate multiple sets of candidate test indicators covering the preset range. The preset range of indicators can be set based on experience.

[0075] For example, when the test parameters include center frequency, output power, modulation method, and bandwidth, the preset parameter range is a center frequency of 1000MHz to 2000MHz, an output power of 10dBm to 20dBm, an AM or BM modulation method, and a bandwidth of 10kHz to 110kHz. Multiple sets of center frequencies (e.g., three segments of 1000MHz, 1500MHz, and 2000MHz) can be generated in 500MHz steps for the center frequency, multiple sets of output power (e.g., three segments of 10dBm, 15dBm, and 20dBm) can be generated in 5dBm steps for the output power, and multiple sets of bandwidth (e.g., three segments of 10kHz, 60kHz, and 110kHz) can be generated in 50kHz steps for the bandwidth. These multiple sets of center frequencies, output power, and bandwidth, combined with a randomly generated modulation method, can then be randomly combined to form multiple sets (e.g., 54 sets) of candidate test parameters.

[0076] Step 330: Based on at least two sets of test equipment deployment locations, communication environment distribution, communication geographical features, device model data, and candidate test indicators, determine the test indicators corresponding to each set of test equipment deployment locations.

[0077] Device model data refers to the model of the microwave relay device. In some embodiments, the device model can be determined by obtaining user input.

[0078] In some embodiments, the test metrics also include test weather data.

[0079] Test weather data refers to weather data from multiple locations where communication needs to be conducted. This includes, for example, the temperature and humidity of these locations. Air humidity and particle size in these locations can affect communication quality. Considering weather data as a test indicator allows us to account for weather conditions as a factor influencing communication quality, thereby improving the accuracy of the final deployment plan.

[0080] In some embodiments, test weather data can be obtained by analyzing historical weather data from multiple locations that need to communicate. For example, the most frequently occurring weather data from the historical weather data of a location that needs to communicate can be used as the test weather data for that location.

[0081] For more information on test metrics, please see [link / reference]. Figure 2 And its related descriptions.

[0082] In some embodiments, the distribution data of communication environments in multiple locations that need to communicate may not change drastically, but test weather data is considered differently, as considering test weather data can make the final results closer to the actual situation.

[0083] The computing unit can determine the test indicators corresponding to each set of test equipment locations through various methods, based on at least two sets of test equipment locations, communication environment distribution, communication geographical features, device model data, and candidate test indicators.

[0084] In some embodiments, the computing unit can determine the predicted test result sequence through a quality prediction model, and determine the test indicators corresponding to each group of test equipment deployment points based on the predicted test result sequence.

[0085] A predicted test result sequence refers to a sequence of test results obtained when multiple sets (e.g., at least two sets) of test equipment are deployed under corresponding candidate test indicators. Each element in the predicted test result sequence corresponds to a predicted test result of a candidate test indicator under a set of test equipment deployments; the predicted test result can refer to the predicted global communication quality.

[0086] Global communication quality refers to a parameter used to measure the communication effectiveness when recommended test tasks are communicating. Global communication quality can be represented in several ways. For example, when global communication quality is represented numerically, a higher value indicates better communication performance.

[0087] In some embodiments, the global communication quality can be determined by superimposing the single-channel communication quality of each individual channel in the test equipment deployment. For example, if a signal is transmitted from location A and received by locations B and C, the single-channel communication quality from location A to location B is M1, the single-channel communication quality from location A to location C is M2, and the global communication quality is M1+M2.

[0088] In some embodiments, the quality of a single-channel communication can be measured by at least one of the following: the strength of the received signal, the signal-to-noise ratio of the received signal, the bit error rate of the received signal, the packet loss rate of the received signal, and the transmission rate.

[0089] In some embodiments, the strength of the received signal can be obtained by measuring the strength of the received signal using a spectrum analyzer or a signal strength meter. The signal-to-noise ratio (SNR) of the received signal can be obtained by measuring the power ratio of the signal and noise using a spectrum analyzer or a dedicated SNR measurement tool. The bit error rate (BER) of the received signal can be obtained by sending known test data, comparing the received data with the transmitted data, and counting the number of erroneous bits. The packet loss rate of the received signal can be obtained by sending a known sequence of data packets, counting the number of lost data packets, and comparing the results. The transmission rate can be obtained by measuring the time interval from when the signal is sent from the transmitter to when the signal is received from the receiver.

[0090] In some embodiments, the quality of a single-channel communication can be determined by the strength of the received signal, the signal-to-noise ratio of the received signal, the bit error rate of the received signal, the packet loss rate of the received signal, and the transmission rate in the following ways:

[0091] f1=c1*y1+c2*y2+c3*y3+c4*y4+c5*y5 (1)

[0092] Where c1 to c5 are coefficients, y1 to y5 are the received signal strength, received signal-to-noise ratio, received signal bit error rate, received signal packet loss rate, and transmission rate, respectively, and f1 is the single-channel communication quality.

[0093] In some embodiments, c1 to c5 can be set based on experience, wherein c3<0 and c4<0.

[0094] In some embodiments, c1 to c5 can be set according to user needs. For example, the higher the user's requirement for transmission rate, the greater the absolute value of the weight of transmission rate.

[0095] The quality prediction model can be a machine learning model, such as a recurrent neural network (RNN).

[0096] In some embodiments, the input to the quality prediction model may include candidate test indicators, at least two sets of test equipment locations, communication environment distribution, communication geographical features, and device model data. The output of the quality prediction model may include at least two sets of predicted test result sequences corresponding to the candidate test indicators for the test equipment locations.

[0097] In some embodiments, the quality prediction model can be trained using multiple labeled first training samples. Each set of first training samples includes historical test metrics, at least two sets of historical test device locations, historical communication environment distribution, historical communication geographical features, and historical device model data. The corresponding label is a sequence of test results. In some embodiments, the labels of the first training samples can be determined manually. For example, the label can be obtained by using the global communication quality sequence from the first training sample tests, where the global communication quality sequence refers to the sequence composed of the global communication quality corresponding to multiple sets of historical test device locations.

[0098] In some embodiments, the computing unit can input multiple labeled first training samples into the initial quality prediction model, construct a loss function using the labels and the output of the initial quality prediction model, and iteratively update the parameters of the initial quality prediction model based on the loss function using gradient descent or other methods. Model training is complete when preset conditions are met, resulting in a trained quality prediction model. These preset conditions may include loss function convergence, the number of iterations reaching a threshold, etc.

[0099] The calculation unit can determine the test indicators corresponding to each group of test equipment deployment points through various methods based on the estimated test result sequence.

[0100] In some embodiments, the calculation unit may select candidate test indicators whose predicted test result sequences corresponding to candidate test indicators meet preset requirements as test indicators. The preset requirements can be set based on experience. For example, a preset requirement may be that the variance (or standard deviation) of the test results corresponding to each test equipment deployment point in the predicted test result sequence corresponding to the candidate test indicator is greater than a preset threshold, wherein the preset threshold is set based on experience. In some embodiments, the size of the preset threshold may be positively correlated with the number of test equipment deployment points corresponding to the candidate test indicator.

[0101] Step 340: Based on at least two sets of test sites and their corresponding test metrics, determine at least two sets of recommended test tasks.

[0102] In some embodiments, the computing unit can randomly combine at least two sets of test points and their corresponding test metrics to obtain at least two sets of recommended test tasks.

[0103] In some embodiments, recommended test tasks include a recommendation priority, and the number of recommended test tasks in at least two sets of recommended test tasks is positively correlated with the complexity of the user's requirements.

[0104] Requirement complexity is a parameter used to measure the complexity of user requirements.

[0105] In some embodiments, requirement complexity is positively correlated with the total communication distance, the number of communication locations determined based on user requirement information, and the weather and geographical complexity of the communication locations requiring communication. For example, requirement complexity can be determined by the following method:

[0106] f2=a1*w1+a2*w2+a3*w3+a4*w4 (2)

[0107] Where a1 to a4 are coefficients that can be set based on experience. f2 represents the complexity of the requirements, and w1 to w4 represent the total communication distance, the number of communication locations, and the complexity of weather and geographical features of the communication locations, respectively.

[0108] Total communication distance refers to the total physical distance between multiple locations that need to communicate. It can be determined based on the obtained user demand information.

[0109] The number of communication locations refers to the number of locations required for communication by the user. This can be determined based on the obtained user demand information.

[0110] Weather feature complexity refers to the degree of weather complexity at the communication location where communication needs to be conducted. In some embodiments, weather feature complexity can be positively correlated with the weather change characteristics of the communication location where communication needs to be conducted. The weather change characteristics can include temperature variance (or maximum temperature difference), precipitation variance (or maximum precipitation difference), wind speed variance (or maximum wind speed difference), cloud cover variance (or maximum cloud cover difference), visibility variance (or maximum visibility difference), etc., determined based on weather data of the communication location over a preset historical period.

[0111] Geographic feature complexity refers to the degree of geographical complexity of the communication location where communication needs to be conducted. In some embodiments, geographic feature complexity can be determined based on factors such as elevation fluctuations, surface undulations, terrain type variations, vegetation cover fluctuations, and building density of the communication location. For example, greater elevation fluctuations indicate more complex geographic features. Similarly, higher building density also indicates more complex geographic features.

[0112] In some embodiments of this specification, the more recommended test tasks there are, the easier it is to find the optimal solution. Therefore, the higher the complexity of the user's requirements, the more recommended test tasks are needed to reduce the difficulty of finding the optimal solution.

[0113] Recommendation priority is a parameter used to measure the priority of recommendation tasks.

[0114] In some embodiments, recommended test tasks can be preset. The more important a recommended test task is and the more priority it needs to be tested, the higher its recommendation priority can be.

[0115] In some embodiments, the recommendation priority is positively correlated with the similarity between the target vector and the requirement feature vector corresponding to the test equipment deployment and the variance of the test results.

[0116] The variance of the test results refers to the variance of the test results corresponding to each test equipment placement point in the predicted test result sequence.

[0117] In some embodiments of this specification, the number of recommended test tasks is measured by the complexity of user requirements. This allows for effective allocation of computing resources. Higher complexity user requirements allocate more computing resources to find the optimal result from a larger number of recommended test tasks, while lower complexity reduces resource allocation to accelerate the acquisition of the optimal result. Furthermore, by prioritizing the recommended test tasks, testing resources can be effectively allocated, allowing more important test tasks to be tested first, which also facilitates finding the optimal result faster and improves overall efficiency.

[0118] In some embodiments, the recommended test task further includes an equivalent test task, and the test planning generation module is further configured to: determine the equivalent equipment deployment corresponding to the test equipment deployment based on the communication environment distribution, communication geographical features, test equipment deployment, and test environment data of the target area; and determine the equivalent test task based on the equivalent equipment deployment and test indicators corresponding to the test equipment deployment.

[0119] The target region refers to the region where communication needs to be conducted, as specified in the user's requirements information.

[0120] Test environment data refers to the distribution of the communication environment in the test area.

[0121] In some embodiments, due to limitations in testing conditions, the location where the recommended test task can be performed (i.e., the test site) and the communication location (i.e., the target region) involved in the user requirement data may not be the same location. For example, there may be differences in altitude, ambient temperature, etc. between the test site and the target region. In this case, it is necessary to determine the equivalent test task corresponding to the scenario in the target region under the scenario corresponding to the test site, so as to realize the communication effect of performing the recommended test task in the target region in the test site, thereby obtaining effective and accurate results while reducing experimental costs and / or experimental difficulty.

[0122] The process of performing the equivalent test task described above can be considered as performing an equivalent test of the recommended test task in the target area. The aforementioned equivalence can be understood as obtaining the same or similar global communication quality.

[0123] Equivalent testing tasks refer to the testing tasks performed during equivalent testing. Equivalent testing tasks include the placement of equivalent equipment and testing parameters.

[0124] Equivalent equipment deployment includes the number of microwave relay devices required for equivalent testing and the deployment locations of each microwave relay device. Equivalent equipment deployment can be considered as a type of equipment deployment equivalent to the test equipment deployment in the target area.

[0125] It should be noted that when performing equivalent tests on the target area at the test site, the number of microwave relay devices deployed at both locations is the same. The difference lies in the deployment locations of each microwave relay device, specifically the distance between the two microwave relay devices performing signal transmission and reception. In other words, the number of microwave relay devices to be deployed in the equivalent equipment layout is the same as that in the test equipment layout, but the deployment locations of each microwave relay device in the equivalent equipment layout may differ from those in the test equipment layout.

[0126] For example, if the global traffic quality obtained when performing test task A at the test site is the same as the global traffic quality obtained when performing test task B at the target area, and test task A and test task B correspond to the same test indicators, and the number of microwave relay devices deployed in the test equipment layout of test task A and test task B is the same, with only the spacing between the microwave relay devices being different, then test task A at the test site can be considered as an equivalent task of test task B at the target area, and performing test task A at the test site can be considered as performing an equivalent test of test task B at the target area.

[0127] The computing unit can determine the equivalent equipment deployment corresponding to the test equipment deployment in various ways based on the communication environment distribution, communication geographical features, test equipment deployment, and test environment data of the target area.

[0128] In some embodiments, the computing unit can determine an equivalent function based on test environment data, communication environment distribution, and communication geographical features of the target area. The equivalent device placement corresponding to the test device placement is then determined based on the equivalent function.

[0129] The equivalent function is used to calculate the equivalent distance between the target area and the test area. If the distance A between microwave relay devices in the scenario corresponding to the test area is equivalent to the distance B between microwave relay devices in the scenario corresponding to the target area, then the distance A in the test area can be considered as the equivalent distance B in the target area.

[0130] In some embodiments, the computing unit can determine the equivalent function through various methods using test environment data, communication environment distribution, and communication geographical features from historical data.

[0131] In some embodiments, the equivalent function differs when the altitude difference between the target area and the test site falls within different ranges. The following explanation uses the determination of the equivalent function for an altitude difference within a first preset range (e.g., 0-500m) as an example to illustrate how to determine the equivalent function; the determination of the equivalent function for other altitude difference ranges is similar.

[0132] As an example only, the equivalent function can be represented as:

[0133]

[0134] Where b1~b3 are coefficients, b4~b6 are exponents, x1 is the first temperature, x2 is the second temperature, x3 is the first distance, and f3 is the second distance. The first temperature refers to the temperature of the target area in the equivalent test, the second temperature refers to the temperature of the test site in the equivalent test, the first distance is the spacing between microwave relay devices in the target area in the equivalent test, and the second distance is the spacing between microwave relay devices in the test site in the equivalent test.

[0135] The computing unit uses a fitting algorithm to determine the values ​​of b1 to b3 and b4 to b6 in equivalent tests between a large amount of historical data of target areas and test sites, including the first and second temperatures, the first distance, and the second distance, thereby obtaining the equivalent function. The fitting algorithm can be the least squares method, a machine learning model, etc.

[0136] In some embodiments, based on communication geographic features, the computing unit can determine the altitude difference between the target area and the test site, and then select the corresponding equivalent function. Based on the communication environment distribution and test environment data, the computing unit can determine the first temperature and the second temperature in the equivalent function respectively. Based on the test equipment layout, the computing unit can determine the first distance in the equivalent function. Then, based on the equivalent function, the computing unit can determine the second distance, that is, obtain the deployment location of each microwave relay device in the equivalent equipment layout. Combined with the number of microwave relay devices that need to be deployed in the test equipment layout, the equivalent equipment layout corresponding to the test equipment layout can be obtained.

[0137] The above is based on determining the equivalent function with equivalent distance as the variable. If the distance (i.e., the spacing between microwave relay devices) is taken as the quantitative factor, the methods for determining the equivalent function such as test environment data, communication environment distribution, and communication geographical features, as well as the methods for determining the equivalent equipment layout based on the corresponding function, are similar.

[0138] In some embodiments, the processor can also acquire communication interference data of the target area, and determine the equivalent equipment deployment based on the communication interference data, communication environment distribution, communication geographical features, test equipment deployment, test environment data, and test interference data.

[0139] Communication interference data refers to signal interference data in a target area (such as a communication location). Test interference data refers to signal interference data in a test location.

[0140] Signal interference data can be obtained by testing the interference signal using measurement equipment, which may include a spectrum analyzer, an electromagnetic interference tester, etc. In some embodiments, signal interference data can be characterized based on interference level. The interference level can be determined based on a positive correlation between the interference power and the electromagnetic field strength.

[0141] In some embodiments, the computing unit can construct an interference feature vector based on the communication environment distribution, communication geographical features, latitude and longitude data, and geomagnetic data. Based on the interference feature vector, communication interference data is determined through a second vector database. The latitude and longitude data includes the longitude and latitude of the target area. The geomagnetic data includes the geomagnetic field distribution of the target area. In some embodiments, the latitude and longitude data and geomagnetic data can be obtained from third-party websites.

[0142] In some embodiments, the second vector database includes multiple sets of reference interference feature vectors and their corresponding reference communication interference data. For example, the second vector database can construct reference interference feature vectors based on communication environment distribution, communication geographical features, latitude and longitude data, and geomagnetic data in historical communication data or experimental communication data, and use the communication interference data corresponding to the communication environment distribution, communication geographical features, latitude and longitude data, and geomagnetic data as the reference communication interference data corresponding to the reference interference feature vectors.

[0143] In some embodiments, the computing unit may determine a reference interference feature vector that meets preset requirements from the second vector database based on the interference feature vector, and use the reference communication interference data corresponding to the target feature vector as the communication interference data. For more information on the preset requirements, please refer to the relevant description in the first vector database section.

[0144] In some embodiments, the computing unit can determine the equivalent equipment layout based on communication interference data, communication environment distribution, communication geographical features, test equipment layout, test environment data, and test interference data through an equivalent point generation model.

[0145] An equivalent point generation model is a model used to determine the layout of equivalent devices. In some embodiments, the equivalent point generation model can be a machine learning model, such as a recurrent neural network (RNN).

[0146] In some embodiments, the input to the equivalent point generation model may include communication interference data, communication environment distribution, communication geographical features, test equipment deployment, test environment data, and test interference data. The output of the equivalent point generation model may be the equivalent equipment deployment.

[0147] In some embodiments, the equivalent point generation model can be trained using multiple labeled second training samples. Each set of second training samples comprises historical communication interference data, historical communication environment distribution, historical communication geographical features, historical test equipment locations, historical test environment data, and historical test interference data, with the corresponding label being the historical equivalent equipment locations. The historical communication interference data, historical communication environment distribution, historical communication geographical features, and historical test equipment locations correspond to a first environment, while the historical test environment data, historical test interference data, and historical equivalent equipment locations correspond to a second environment. The first and second environments are located in different regions (e.g., different altitudes, different climates, etc.).

[0148] In some embodiments, the labels of the second training samples can be determined manually. For example, after the microwave relay device is tested in the first environment and the single-channel communication quality between each device is obtained, the microwave relay device is transferred to the second environment. To ensure that the single-channel communication quality between each device remains unchanged, the distance between the microwave relay devices in the test equipment deployment is continuously adjusted to obtain test equipment deployment points equivalent to those in the first environment. The obtained equivalent test equipment deployment points are then used as the labels of the second training samples. For more information on the training process of the equivalent point generation model, please refer to [link to relevant documentation]. Figure 3 The above describes the training of the quality prediction model.

[0149] In some embodiments of this specification, the equivalent point generation model can efficiently and accurately determine the layout of equivalent devices. Furthermore, the equivalent point generation model can consider the influence of more factors on equivalent testing, which is beneficial for improving the experimental efficiency and accuracy of equivalent testing. By considering communication interference data, the final equivalent device layout can be made closer to the actual situation of the test device layout, making the equivalent test results more closely resemble reality.

[0150] In some embodiments, the input to the equivalent point generation model may also include test weather data, for which see step 330 above.

[0151] In some embodiments of this specification, by taking into account test weather data, the final equivalent equipment layout can be made closer to the actual situation of the test equipment layout, so that the results of the equivalent test are closer to reality.

[0152] In some embodiments, the computing unit can determine the equivalent test task as a combination of the test indicators of the equivalent test points and the test equipment points corresponding to the equivalent test points.

[0153] In some embodiments of this specification, by performing equivalent testing, the experimental cost / difficulty of testing corresponding to user requirement information can be reduced, which is conducive to improving the success rate and efficiency of experiments.

[0154] In some embodiments of this specification, microwave relay communication is easily limited by terrain and antenna height. By comprehensively considering communication distance distribution, communication quality requirements, and communication geographical characteristics, the test planning generation module can more comprehensively evaluate and select test equipment placement, ensuring the scientific and rational nature of the test plan. This allows the generated recommended test tasks to not only better meet the diverse needs of users and improve user satisfaction, but also optimize test resource allocation, avoid unnecessary duplicate tests, and improve resource utilization efficiency. Furthermore, the selection of multiple options increases the flexibility and adaptability of the test, enabling it to better cope with complex and changing test environments and improve the robustness and stability of the test.

[0155] It should be noted that the above description of process 300 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to process 300 under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0156] Figure 4 This is a flowchart of an exemplary process 400 of an arrangement scheme shown in some embodiments of this specification.

[0157] In some embodiments, process 400 may be executed by a computing unit.

[0158] like Figure 4 As shown, in some embodiments, process 400 includes steps 410 to 440.

[0159] Step 410: Perform communication tests based on each of the at least two recommended test tasks to obtain the actual test data corresponding to each recommended test task.

[0160] Actual test data refers to the test data obtained after conducting the recommended test tasks. For example, test data may include metrics such as received signal strength, received signal-to-noise ratio, received bit error rate, received packet loss rate, transmission rate, or transmission delay, obtained during communication testing. Another example is test data that may represent the single-channel communication quality of multiple individual channels within a test equipment deployment. For more information, please see [link to relevant documentation]. Figure 3 and its phase

[0161] In some embodiments, actual test data can be obtained manually after the experiment.

[0162] In some embodiments, after determining the equivalent test task, the computing unit can also directly perform communication testing based on the equivalent test task corresponding to each of the at least two recommended test tasks, and obtain the actual test data of each equivalent test task as the actual test data corresponding to the recommended test task. For an explanation of the equivalent test tasks corresponding to the recommended test tasks, see [link to documentation]. Figure 3 The corresponding content.

[0163] Step 420: Determine the global communication quality based on actual test data.

[0164] For more information on how to determine global communication quality, please see [link to relevant documentation]. Figure 3 And its related descriptions.

[0165] Step 430: Based on the global communication quality, determine the communication evaluation value corresponding to each recommended test task.

[0166] Communication evaluation values ​​are parameters used to characterize the quality of a recommended test task. Communication evaluation values ​​can be represented in various forms; in some embodiments, when expressed numerically, a higher value indicates a better test result.

[0167] In some embodiments, the computing unit may determine the communication evaluation value corresponding to each recommended test task based on the positive correlation between the communication evaluation value and the global communication quality.

[0168] In some embodiments, the actual test data also includes anti-interference data and stability data, and the communication evaluation value can also be determined based on anti-interference data, stability data and global communication quality.

[0169] Anti-interference data refers to data used to characterize the anti-interference capability of a recommended test task against external interference. In some embodiments, anti-interference data can be determined based on interference levels and their corresponding quality differences. The interference level can be based on the interference power of the activated interference device; the higher the interference power, the higher the level. The quality difference corresponding to the interference level refers to the difference between the global communication quality before the introduction of interference from the interference device (such as a signal generator or electromagnetic jammer) and the global communication quality after the introduction of interference.

[0170] For example, when the anti-interference data is ((Level 1, a), (Level 2, b)), it represents that when the interference level is Level 1, the quality difference is a, and when the interference level is Level 2, the quality difference is b. For a more detailed description of global communication quality, please refer to [link to relevant documentation]. Figure 3 And its related descriptions.

[0171] In some embodiments, anti-interference data can be obtained experimentally. For example, tests with different levels of interference can be conducted for a recommended test task to obtain the quality differences under different levels of interference.

[0172] Stability data refers to data used to characterize the stability of global communication quality in the event of equipment failure / unexpected events during a recommended test task. In some embodiments, stability data includes the quality difference corresponding to the shutdown of n microwave relay devices, where the quality difference refers to the difference between the global communication quality before and after the shutdown of n devices, and n is a natural number. For example, stability data ((1, c), (2, d)) indicates that the quality difference is c when one microwave relay device is shut down, and the quality difference is d when two microwave relay devices are shut down.

[0173] In some embodiments of this specification, anti-interference data is considered, taking into account the quality differences under different interference levels. This effectively measures the recommended test task's resistance to external interference, thus facilitating the evaluation of the recommended test task's interference performance. Simultaneously, considering stability data allows for consideration of the impact of unexpected equipment malfunctions on the entire recommended test task, thereby facilitating the evaluation of the recommended test task's resilience.

[0174] In some embodiments, the computing unit may determine the communication evaluation value corresponding to the recommended test task in the following manner:

[0175] f4=d1*z1+d2*z2+d3*z3 (4)

[0176] Where d1, d2, and d3 are coefficients, z1 is the overall global communication quality, z2 is the anti-interference score, z3 is the stability score, and f4 is the communication evaluation value. d1, d2, and d3 are set empirically. The overall global communication quality characterizes the total communication performance of the recommended test task under multiple sets of test metrics. In some embodiments, the calculation unit can sum the global communication qualities of the recommended test task corresponding to multiple sets of test metrics as the overall global communication quality of the recommended test task.

[0177] The robustness score is a parameter used to measure robustness data. In some embodiments, the robustness score z2 corresponding to the recommended test task can be determined based on the following method:

[0178]

[0179] Among them, g1, g2, ..., g n1 Let l1 be the quality difference in global communication quality when the interference level is level 1, and l2 be the quality difference in global communication quality when the interference level is level 2. n1 G1 represents the quality difference in global communication quality when the interference level is n1. <g2<…….<g n1 .

[0180] Stability scores are used to measure parameters of stability data. In some embodiments, the stability score z3 for the recommended test task can be determined based on the following:

[0181]

[0182] Where k1, k2, ..., k n2 Let m1 be the quality difference in global communication quality when one microwave relay device is shut down, m2 be the quality difference in global communication quality when two microwave relay devices are shut down, and m... n2The quality difference in global communication quality when shutting down n2 microwave relay devices, where k1 <k2<…….<k n2 .

[0183] In some embodiments, the communication evaluation value is related to the complexity of the user requirements. For more information on user requirements and requirement complexity, please refer to [link to relevant documentation]. Figure 2 , Figure 3 And related content.

[0184] In some embodiments, the communication evaluation value is positively correlated with the complexity of the user's requirements. The higher the complexity of the user's requirements, the larger d2 and d3 in formula (4) are.

[0185] In some embodiments of this specification, personalized evaluations are conducted based on the user's specific needs (such as anti-interference capability and stability), making the evaluation results more in line with the user's actual needs, improving the relevance and effectiveness of the evaluation, and thus making the determined deployment scheme more in line with customer requirements. Furthermore, by increasing the weighting of anti-interference capability and stability, deployment schemes that perform better in complex environments can be selected, ensuring that the selected deployment scheme maintains good communication quality under various conditions and reducing problems such as bit error rate and communication latency.

[0186] Step 440: Determine the deployment plan based on the communication evaluation value and at least two recommended test tasks.

[0187] For more information on the layout plan, please see [link / reference]. Figure 2 And its related descriptions.

[0188] In some embodiments, the computing unit can determine the deployment scheme of the recommended test tasks with the highest communication evaluation value and their test indicators. For example, based on the transmission parameters of the recommended test tasks with the highest evaluation value, the deployment parameters of the microwave relay device in the deployment scheme can be determined. Based on the test equipment layout of the recommended test tasks with the highest evaluation value, the layout parameters and distribution parameters of the microwave relay device in the deployment scheme can be determined.

[0189] In some embodiments of this specification, microwave relay communication is easily limited by terrain and antenna height. By conducting actual tests in a test environment, recording the test indicators and overall communication quality of each recommended test task, and comprehensively considering multiple indicators such as received signal strength, signal-to-noise ratio, bit error rate, packet loss rate, and transmission rate, the quality of single-channel communication is comprehensively evaluated, thereby ensuring the accuracy of the overall communication quality. This facilitates long-distance, stable, and reliable communication using this method. Selecting the optimal recommended test task based on the overall communication quality not only optimizes the deployment of test equipment but also allows for adjustments to the weights of various metrics according to user needs, generating a more suitable deployment scheme. This improves the accuracy and reliability of testing, significantly enhances system performance, increases user satisfaction, saves resources and costs, and ensures optimal communication quality in various environments, enabling rapid testing of the preferred deployment scheme.

[0190] It should be noted that the above description of process 400 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to process 400 under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0191] This specification provides one or more embodiments of a radio equipment spectrum parameter testing apparatus, including a processor for implementing a radio equipment spectrum parameter testing method.

[0192] This specification provides one or more embodiments of a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes a method for testing the spectrum parameters of a radio device.

[0193] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0194] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0195] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0196] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0197] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0198] For each patent, patent application, patent application publication, and other material, such as articles, books, specifications, publications, and documents, referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0199] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A wireless equipment parameter testing system, characterized in that, The system includes a demand analysis module, a planning generation module, a solution determination module, and a solution recommendation module deployed on a computing unit; wherein: The demand analysis module is configured to receive and analyze user demand information to obtain user demand data, including communication distance distribution, communication environment distribution, communication geographical features, and communication quality requirements. The planning generation module is configured to determine at least two recommended test tasks based on the user demand data. The recommended test tasks include test equipment deployment and test indicators. The planning generation module is further configured as follows: Based on the communication distance distribution, the communication quality requirements, and the communication geographical features, at least two sets of test equipment locations are determined. Obtain candidate test metrics; Based on the at least two sets of test equipment deployment points, the communication environment distribution, the communication geographical features, device model data, and the candidate test indicators, determine the test indicators corresponding to each set of test equipment deployment points; Based on the deployment of the at least two sets of test equipment and their corresponding test indicators, the at least two recommended test tasks are determined. The scheme determination module is configured to determine the layout scheme of the microwave relay device based on the at least two recommended test tasks, including: Based on each of the at least two recommended test tasks, communication tests are performed to obtain the actual test data corresponding to each recommended test task. The actual test data includes the single-channel communication quality, anti-interference data, and stability data of each single channel in the test equipment deployment. Based on the single-channel communication quality, the global communication quality is determined; Based on the anti-interference score of the anti-interference data, the stability score of the stability data, and the global communication quality, the communication evaluation value corresponding to each recommended test task is determined by multiplying them by the corresponding coefficients. The higher the complexity of the user's requirements, the larger the coefficients corresponding to the anti-interference score and the stability score. Based on the communication evaluation value and the at least two recommended test tasks, a deployment scheme is determined, which includes the number of microwave relay devices, distribution parameters, and deployment parameters. The scheme recommendation module is configured to send the layout scheme to the user's user terminal.

2. The system according to claim 1, characterized in that, The recommended test tasks also include equivalent test tasks; The planning generation module is also configured to: Based on the communication environment distribution, communication geographic features, test equipment deployment, and test environment data of the target area, determine the equivalent equipment deployment corresponding to the test equipment deployment; Based on the equivalent equipment layout corresponding to the test equipment layout and the test indicators, the equivalent test task is determined.

3. A method for testing wireless equipment parameters, implemented based on the wireless equipment parameter testing system as described in claim 1, characterized in that, The method includes: Based on the received user demand information, user demand data is determined, including communication distance distribution, communication environment distribution, communication geographical features, and communication quality requirements. Based on the user demand data, at least two recommended test tasks are determined. These recommended test tasks include test equipment deployment and test metrics. Specifically, determining at least two recommended test tasks based on the user demand data includes: Based on the communication distance distribution, the communication quality requirements, and the communication geographical features, at least two sets of test equipment locations are determined. Obtain candidate test metrics; Based on the at least two sets of test equipment deployment points, the communication environment distribution, the communication geographical features, device model data, and the candidate test indicators, determine the test indicators corresponding to each set of test equipment deployment points; Based on the deployment of the at least two sets of test equipment and their corresponding test indicators, at least two recommended test tasks are determined. Based on the at least two recommended test tasks, determine the layout scheme of the microwave relay device, including: Based on each of the at least two recommended test tasks, communication tests are performed to obtain the actual test data corresponding to each recommended test task. The actual test data includes the single-channel communication quality, anti-interference data, and stability data of each single channel in the test equipment deployment. Based on the single-channel communication quality, the global communication quality is determined; Based on the anti-interference score of the anti-interference data, the stability score of the stability data, and the global communication quality, the communication evaluation value corresponding to each recommended test task is determined by multiplying them by the corresponding coefficients. The higher the complexity of the user's requirements, the larger the coefficients corresponding to the anti-interference score and the stability score. Based on the communication evaluation value and the at least two recommended test tasks, a deployment scheme is determined, which includes the number of microwave relay devices, distribution parameters, and deployment parameters. The layout plan is sent to the user's user terminal.

4. The method according to claim 3, characterized in that, The recommended test tasks also include equivalent test tasks; The step of determining at least two recommended test tasks based on the user demand data also includes: Based on the communication environment distribution, communication geographic features, test equipment deployment, and test environment data of the target area, determine the equivalent equipment deployment corresponding to the test equipment deployment; Based on the equivalent equipment layout corresponding to the test equipment layout and the test indicators, the equivalent test task is determined.

5. A wireless equipment parameter testing apparatus, comprising a processor, the processor being configured to execute the wireless equipment parameter testing method according to any one of claims 3 to 4.

6. A computer-readable storage medium storing computer instructions, wherein when a computer reads the computer instructions in the storage medium, the computer executes the wireless equipment parameter testing method as described in any one of claims 3 to 4.

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