Emergency equipment communication test method, device, equipment, medium and product

Abstract

The application provides an emergency equipment communication test method, device, equipment, medium and product, relates to the technical field of communication, and the method comprises the following steps: determining input data of a signal transmission influence model based on test scene information, inputting the input data into the signal transmission influence model, and the signal transmission influence model is used for simulating the influence of the test scene information on signal transmission; acquiring a sending signal of a to-be-tested equipment, processing the sending signal based on output data of the signal transmission influence model to obtain a processed signal; optimizing the signal transmission influence model based on signal difference information corresponding to the test scene information, and the signal difference information reflects the difference between the sending signal and the processed signal; sending the processed signal to a receiving module, the receiving module is determined based on the signal type of the to-be-tested equipment, and a test result of the to-be-tested equipment under the test scene information is generated based on the received signal of the receiving module. The application can reduce the communication test cost of the emergency equipment.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to communication testing methods, apparatus, equipment, media and products for emergency equipment. Background Technology

[0002] Radio frequency (RF) distress and rescue equipment, as a type of emergency equipment, is a core communication carrier in distress scenarios such as maritime search and rescue, mountain rescue, and aviation emergency response. Its operating environment is often complex, such as in complex geographical environments or extreme weather conditions. Variables such as meteorological conditions (e.g., rainfall intensity, fog concentration), geographical environment (terrain type, material of obstructions), and carrier speed directly affect signal transmission. In existing technologies, to ensure the communication capabilities of emergency equipment in complex environments, field testing is often conducted. Field testing requires manual deployment of equipment, data recording, and result analysis, resulting in long cycles. Furthermore, field testing is dependent on the natural environment. To test emergency equipment in specific scenarios, a significant amount of time is required to find or wait for suitable outdoor environments, leading to high communication testing costs for emergency equipment. Summary of the Invention

[0003] This application provides methods, apparatus, equipment, media, and products for testing communication of emergency equipment, in order to address the high cost of communication testing of emergency equipment in the prior art and achieve the effect of reducing the cost of communication testing of emergency equipment.

[0004] This application provides a communication testing method for emergency equipment, including:

[0005] The input data for the signal transmission impact model is determined based on the test scenario information, and the input data is input into the signal transmission impact model. The test scenario information reflects the application scenario of the device under test, and the signal transmission impact model is used to simulate the impact of the test scenario information on signal transmission. The test scenario information includes the environmental information and weather information of the application scenario, as well as the carrier information of the device under test.

[0006] The transmitted signal of the device under test is obtained through a standardized interface, and the transmitted signal is processed based on the output data of the signal transmission influence model to obtain a processed signal.

[0007] Based on the signal difference information corresponding to the test scenario information, the signal transmission impact model is optimized, and the signal difference information reflects the difference between the transmitted signal and the processed signal.

[0008] The processed signal is sent to the receiving module, which is determined based on the signal type of the device under test. Based on the received signal from the receiving module, the test result of the device under test under the test scenario information is generated.

[0009] According to the emergency equipment communication testing method provided in this application, the step of optimizing the signal transmission impact model based on the signal difference information corresponding to the test scenario information includes:

[0010] Multiple standard signal transmission mathematical models are acquired, and signal difference labels corresponding to the test scenario information are generated based on the test scenario information and the standard signal transmission mathematical models.

[0011] The signal transmission impact model is trained based on the loss corresponding to each sample data in the sample library. The sample library includes multiple first sample data and multiple second sample data. The first sample data includes input data corresponding to the test scenario information and signal difference labels corresponding to the test scenario information. The second sample data includes input data corresponding to the real scenario information and signal difference labels corresponding to the real scenario information. The loss corresponding to the first sample data reflects the difference between the signal difference information and the signal difference label corresponding to the test scenario information. The loss corresponding to the second sample data reflects the difference between the signal difference information and the signal difference label corresponding to the real scenario information.

[0012] According to the emergency equipment communication testing method provided in this application, after generating the test results of the device under test under the test scenario information, the method further includes:

[0013] Based on the test results of the device under test under multiple test scenario information, test optimization data of the device under test is generated, and the test optimization data includes at least one target test scenario information.

[0014] According to the emergency equipment communication testing method provided in this application, the step of generating test optimization data for the device under test based on the test results of the device under test under multiple test scenario information includes:

[0015] Similarity is calculated between the various test scenario information to obtain multiple first similarity scores;

[0016] The similarity of the test results of the device under test under each of the test scenario information is calculated to obtain multiple second similarities;

[0017] The test optimization data is generated based on the first similarity and the second similarity corresponding to the same test scenario information.

[0018] According to the emergency equipment communication testing method provided in this application, the test results include protocol test results; the step of generating test results for the device under test under the test scenario information based on the received signal of the receiving module includes:

[0019] Based on the transmitted signal, the transmitted message data is determined; based on the received signal, the received message data is determined.

[0020] Based on the transmitted message data and the received message data, the key parameters of the communication protocol of the device under test are determined;

[0021] The key parameters are compared with the protocol template of the communication protocol of the device under test to determine the protocol test result.

[0022] According to the emergency equipment communication testing method provided in this application, the test results include link test results, and the step of generating test results for the device under test under the test scenario information based on the received signal of the receiving module includes:

[0023] The transmitted signal, the processed signal, and the received signal are sampled at the transmitting end output of the device under test, the transmitting end output of the processed signal, and the receiving end input of the receiving module to obtain the sampling results;

[0024] The link test result is determined based on the transmitted signal, the received signal, and the sampling result.

[0025] This application also provides an emergency equipment communication testing device, comprising:

[0026] The scenario input module is used to determine the input data of the signal transmission influence model based on the test scenario information, and input the input data into the signal transmission influence model. The test scenario information reflects the application scenario of the device under test. The signal transmission influence model is used to simulate the influence of the test scenario information on signal transmission. The test scenario information includes the environmental information and weather information of the application scenario, as well as the carrier information of the device under test.

[0027] The signal processing module is used to acquire the transmitted signal of the device under test through a standardized interface, and process the transmitted signal based on the output data of the signal transmission influence model to obtain a processed signal.

[0028] The optimization module is used to optimize the signal transmission impact model based on the signal difference information corresponding to the test scenario information, wherein the signal difference information reflects the difference between the transmitted signal and the processed signal.

[0029] The test result output module is used to send the processed signal to the receiving module, which is determined based on the signal type of the device under test. Based on the received signal of the receiving module, the test result of the device under test under the test scenario information is generated.

[0030] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the emergency device communication test method described above.

[0031] This application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the emergency equipment communication test method as described above.

[0032] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the emergency equipment communication testing method as described above.

[0033] The emergency equipment communication testing method, apparatus, equipment, medium, and product provided in this application determine the input data of the signal transmission influence model based on test scenario information, simulate the impact of test scenario information on signal transmission using the signal transmission influence model, thereby processing the transmitted signal to obtain a processed signal, which is then sent to the receiving module. Based on the received signal from the receiving module, the test results of the device under test under the test scenario information are generated. Furthermore, the signal transmission influence model is further optimized based on the signal difference information corresponding to multiple test scenario information, so that the signal transmission influence model can more accurately simulate the impact of test scenario information on signal transmission. This allows communication testing of the device under test in specific complex scenarios to be carried out in a laboratory environment, reducing the communication testing cost of emergency equipment. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart illustrating the emergency equipment communication testing method provided in this application.

[0036] Figure 2 This is a schematic diagram of the overall architecture of the emergency equipment communication testing method provided in this application.

[0037] Figure 3 This is a schematic diagram of the emergency equipment communication testing device provided in this application.

[0038] Figure 4 This is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0040] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0041] It should also be understood that the terminology used in this application specification is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this application specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0042] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0043] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrases "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0044] The following is combined Figure 1 Describe the communication testing method for emergency equipment provided in this application. For example... Figure 1 As shown, the communication testing method for this emergency equipment includes the following steps:

[0045] S110. Determine the input data for the signal transmission influence model based on the test scenario information, and input the input data into the signal transmission influence model. The test scenario information reflects the application scenario of the device under test. The signal transmission influence model is used to simulate the influence of the test scenario information on signal transmission. The test scenario information includes the environmental information of the application scenario, weather information, and carrier information of the device under test.

[0046] S120. Obtain the transmitted signal of the device under test through a standardized interface, process the transmitted signal based on the output data of the signal transmission influence model, and obtain the processed signal.

[0047] S130. Based on the signal difference information corresponding to the test scenario information, optimize the signal transmission impact model. The signal difference information reflects the difference between the transmitted signal and the processed signal.

[0048] S140. The processed signal is sent to the receiving module. The receiving module is determined based on the signal type of the device under test. Based on the received signal from the receiving module, the test result of the device under test under the test scenario information is generated.

[0049] The method provided in this application can be implemented using testing equipment, including but not limited to computers, mobile terminals, and wearable smart devices. The test scenario information reflects the application scenario of the device under test and can be determined based on the device's designed usage scenario. For emergency equipment, the designed usage scenario might be in a mountainous area, at sea, or in extreme weather conditions. Different test scenario information can be generated based on the designed usage scenario to test the communication function of the device under test in different scenarios. The test scenario information includes scenario parameters that may affect the communication function of the device under test, including environmental information, weather information, and the carrier information of the device under test. Environmental information includes parameters describing the geographical environment, such as latitude and longitude, altitude, distance to the nearest communication object, and the height and / or width of geographical barriers (such as mountains or bodies of water) existing between the device and the nearest communication object. Weather information includes parameters describing the weather, such as rainfall, haze concentration, and light intensity. The device under test may be mounted on a carrier, such as an emergency device installed on an airplane, car, or ship. The carrier information reflects the movement of the object carrying the device, such as its speed and direction of movement.

[0050] The test scenario information can be input by the tester based on the test requirements into the test equipment that performs the method provided in this application. For example, the test equipment can provide an input interface, and the tester inputs the test scenario information into the test equipment. The test equipment extracts parameters based on the input test scenario information to obtain the input data of the signal transmission influence model.

[0051] The emergency equipment communication testing method provided in this application determines the input data of the signal transmission influence model based on the test scenario information. The input data is then input into the signal transmission influence model, that is, the scenario parameters that may affect the communication function of the device under test are input into the signal transmission influence model. The signal transmission influence model simulates the impact of the test scenario information on signal transmission and outputs output data that reflects the impact of the test scenario information on signal transmission. Then, the output data is used to process the transmitted signal of the device under test, simulate the transmission of the signal emitted by the device under test in the application scenario, and obtain the processed signal.

[0052] The emergency equipment communication testing method provided in this application determines the input data of the signal transmission influence model based on the test scenario information, uses the signal transmission influence model to simulate the impact of the test scenario information on signal transmission, thereby processing the transmitted signal to obtain a processed signal, which is then sent to the receiving module. Based on the received signal from the receiving module, the test results of the device under test under the test scenario information are generated. Furthermore, the signal transmission influence model is further optimized based on the signal difference information corresponding to multiple test scenario information, so that the signal transmission influence model can more accurately simulate the impact of the test scenario information on signal transmission. This allows communication testing of the device under test in specific complex scenarios to be carried out in a laboratory environment, reducing the communication testing cost of emergency equipment.

[0053] like Figure 2 As shown, the test equipment implementing the emergency equipment communication test method provided in this application may include a multi-means collaborative receiving module. This multi-means collaborative receiving module includes multiple receiving modules deployed with different adaptation technologies. The receiving module corresponding to the target adaptation technology (e.g., enabling the Tiantong satellite adaptation technology when testing a Tiantong satellite terminal) can be selected according to test requirements. During testing, a data connection is first established with the device under test through a standardized receiving port. The transmitted signal from the device under test is then transmitted to an environment simulation layer with a signal transmission impact model for processing. After obtaining the processed signal, it is sent back to the receiving module, which receives the received signal through the target adaptation technology. In existing technologies, designs are mostly for a single communication method (e.g., shortwave only, Beidou only, etc.) or a single device type. Adapting to different devices requires the separate development of test tools, and field testing requires multiple coverages of multiple scenarios, resulting in increased manpower and material costs and further increasing test costs. The method provided in this application improves the compatibility of the test equipment by configuring multiple adaptation technologies. In one possible implementation, the adaptation technologies that the test equipment of the method provided in this application can deploy include:

[0054] BeiDou adaptation technology: Supports dual-mode signal reception of BeiDou first-generation / second-generation / third-generation RDSS / RNSS, and interfaces with BeiDou terminals through standardized interfaces (RS485 / Ethernet / RS422 / RS232) to achieve signal parsing and data interaction;

[0055] Shortwave adaptation technology: Covers the 1.8-30MHz frequency band, supports SSB / AM / FM modulation, is compatible with mainstream shortwave radios, and completes the reception and preprocessing of shortwave signals;

[0056] Ultra-shortwave adaptation technology: Covering the 30-300MHz frequency band, supporting conventional / trunking communication protocols (such as DMR / DSC), enabling real-time reception and protocol identification of ultra-shortwave radio signals and data packets;

[0057] UHF satellite adaptation technology: Supports UHF band (300MHz-3GHz) satellite signal transmission and reception, adapts to low-orbit satellite rescue terminals, and completes satellite signal demodulation and data processing;

[0058] Tiantong satellite adaptation technology: Supports Tiantong-1 satellite S-band (2483.5-2500MHz) signals, adapts to Tiantong satellite handheld terminals, and enables the reception of Tiantong satellite signals and data interaction.

[0059] The method provided in this application acquires the transmitted signals of various devices under test through a standardized interface, and determines the receiving module for processing the signal based on the signal type of the device under test (e.g., Beidou satellite signal or Tiantong satellite signal). Since the receiving module can be configured with various signal adaptation technologies, it can realize the testing of various devices under test, improve testing efficiency, and reduce the adaptation cost of multiple device types and multiple signal types.

[0060] Furthermore, after receiving the transmission signal from the device under test, the signal is transmitted to the environmental simulation layer to simulate the changes in the signal during transmission. For example... Figure 2 As shown, in one possible implementation, the environment simulation layer includes a compatible interface unit that can receive multiple types of signals, and an environment parameter configuration unit that is used to determine the input data of the signal transmission impact model based on the test scenario information.

[0061] The signal transmission impact model deployed in the environment simulation layer simulates the influence of test scenario information on signal transmission based on input data. It outputs data reflecting this influence, and then uses this output data to process the signal transmitted by the device under test, simulating the transmission of the signal in the application scenario to obtain the processed signal. The output data reflecting the influence of test scenario information on signal transmission can include two parts: one describing the interference experienced by the signal, and the other describing the signal attenuation. For example... Figure 2 As shown, a signal interference simulation unit and a signal attenuation control unit can be configured. The signal interference simulation unit can generate typical wireless interference signals such as narrowband interference, broadband interference, and pulse interference, and the interference power can be processed to meet the requirements of complex electromagnetic environment simulation. The signal attenuation control unit can use programmable technology and a continuously adjustable attenuator to process the transmitted signal according to the signal attenuation in the output data, thereby realizing the quantitative simulation of environmental loss.

[0062] Compared with the shortcomings of existing field testing technologies, such as dependence on the natural environment, uncontrollable parameters, and difficulty in reproducing scenarios, the method provided in this application can achieve quantitative configuration of parameters for multiple scenarios, make the environment reproducible, overcome the bottleneck of uncontrollable environment, and improve the repeatability of test results.

[0063] To enable the signal transmission impact model to accurately simulate the effect of test scenario information on signal transmission, one possible implementation is a neural network model. Its structure can adopt existing neural network model structures, such as multilayer perceptrons. Training data can be constructed using signal transmission conditions in the real environment to train the signal transmission impact model. However, as mentioned earlier, the real external environment is uncontrollable and the data collection cost is high, making it impossible to construct a large amount of diverse training data to support the accuracy of the training results, thus failing to achieve high-performance training of the signal transmission impact model.

[0064] In one possible implementation of the method provided in this application, to compensate for the lack of real data, the signal transmission impact model is continuously optimized using signal difference information of the device under test under multiple test scenario information. Specifically, the signal transmission impact model is optimized based on the signal difference information corresponding to each test scenario information, including:

[0065] Multiple standard signal transmission mathematical models are acquired, and signal difference labels corresponding to the test scenario information are generated based on the test scenario information and the standard signal transmission mathematical models.

[0066] The signal transmission impact model is trained based on the loss corresponding to each sample data in the sample library. The sample library includes multiple first sample data and multiple second sample data. The first sample data includes the input data corresponding to the test scenario information and the signal difference label corresponding to the test scenario information. The second sample data includes the input data corresponding to the real scenario information and the signal difference label corresponding to the real scenario information. The loss corresponding to the first sample data reflects the difference between the signal difference information and the signal difference label corresponding to the test scenario information. The loss corresponding to the second sample data reflects the difference between the signal difference information and the signal difference label corresponding to the real scenario information.

[0067] In related technologies, different standard signal transmission mathematical models have been constructed for different scenarios. These standard signal transmission mathematical models often exist in the form of a formula. For example, the International Telecommunication Union (ITU) has provided relevant recommended calculation formulas for attenuation in different scenarios (including attenuation caused by the atmosphere, attenuation caused by precipitation, attenuation caused by clouds and fog, etc.). Due to the complexity of real-world scenarios, these calculation formulas cannot achieve absolutely accurate simulation, but the calculation results of these mathematical formulas still have considerable reliability. In this implementation method of the present application, the sample library used to train the signal transmission impact model includes first sample data constructed based on these mathematical formulas, and also includes second sample data constructed based on real-world scenario test results.

[0068] When training the signal transmission impact model using the first sample data, the loss corresponding to the first sample data reflects the difference between the signal difference information and the signal difference label corresponding to the test scenario information. In other words, the input data for the signal transmission impact model is determined based on the test scenario information corresponding to the first sample data. After obtaining the output data of the signal transmission impact model, the signal difference information is determined based on this output data, and the loss is determined based on the difference between this signal difference information and the signal difference label corresponding to the test scenario information. Similarly, when training the signal transmission impact model using the second sample data, the loss corresponding to the second sample data reflects the difference between the signal difference information and the signal difference label corresponding to the real scenario information. In other words, the input data for the signal transmission impact model is determined based on the real scenario information in the second sample data. After obtaining the output data of the signal transmission impact model, the signal difference information is determined based on this output data, and the loss is determined based on the difference between this signal difference information and the signal difference label corresponding to the real scenario information.

[0069] To control costs, it's understandable that the number of second sample data is much smaller than the number of first sample data. To improve training efficiency, one possible approach is to first train the signal transmission impact model multiple times based on multiple first sample data, and then start training the signal transmission impact model based on the second sample data. This allows the first sample data to provide more information in the early stages of training, enabling the signal transmission impact model to be optimized quickly. Since the second sample data is real-collected data, it can be used for later training with more accurate information, improving the utilization rate of real information and achieving more accurate and refined training.

[0070] In the method provided in this application, the signal transmission influence model can be trained first, and then formally applied to generate test results for the device under test. That is, after repeatedly executing the aforementioned steps S110-S120 multiple times, as... Figure 2 As shown, a preliminary sample library is constructed and stored in the intelligent closed-loop optimization system. The signal transmission impact model is trained, and after multiple training iterations, steps S110-S120 and S140 are re-executed to generate test results for the device under test. Simultaneously, signal difference information and test scenario information obtained during the generation of these test results are added to the sample library. Once the number of new samples in the sample library reaches a certain threshold, steps S110-S140 are executed again during the generation of test results for the device under test, enabling continuous optimization of the signal transmission impact model in application.

[0071] like Figure 2 As shown, the process of optimizing the signal transmission impact model can be achieved through an intelligent closed-loop optimization system. This system executes an automated testing process and optimizes the signal transmission impact model based on a sample library. Then, it returns the optimized signal transmission impact model to the environment simulation layer for deployment, thus realizing data-driven intelligent closed-loop technology.

[0072] After receiving the processed signal, it is sent to the receiving module. Based on the received signal, the receiving module generates the test results for the device under test under the test scenario information. In one possible implementation, such as... Figure 2 As shown, the communication function of the device under test can be tested from two dimensions: protocol parsing verification and full-link sampling analysis. Specifically, the test results include protocol test results; based on the received signal of the receiving module, test results of the device under test under the test scenario information are generated, including:

[0073] Based on the transmitted signal, determine the transmitted message data; based on the received signal, determine the received message data.

[0074] Based on the sent and received message data, determine the key parameters of the communication protocol of the device under test;

[0075] The key parameters are compared with the protocol template of the communication protocol of the device under test to determine the protocol test results.

[0076] The protocol parsing and verification process can be implemented through the protocol parsing and verification module. The protocol parsing and verification module performs the above steps and generates protocol test results.

[0077] The method provided in this application constructs a target protocol template through protocol customization technology, extracts interactive message information in real time through message parsing technology, and completes protocol compliance verification and problem location through automatic error correction technology. It outputs a protocol compatibility report, marking the protocol matching degree, error rate, and error correction scheme, thus solving the limitation of existing technologies that only support standardized protocols and lack the ability to adapt to custom protocols.

[0078] Specifically, the method provided in this application allows users to import custom / private protocols from the device under test before testing. It also supports standardized protocols such as BeiDou RDSS, VHF DSC, HF DSC, HF NBDP, Tiantong satellite, and UHF satellite, generating a protocol template containing "field format, verification rules, and transmission rate." During testing, the method receives interactive messages between the device under test and the receiving module in real time, parses the message header, data segments, and checksum, and extracts key protocol parameters. The parsed results are compared with the protocol template to automatically identify common problems such as missing fields, format errors, verification failures, message length non-compliance, and CRC checksum errors, generating protocol test results containing "error location, error type, and correction suggestions."

[0079] Test results may also include link test results, which are generated based on the received signal from the receiving module, showing the test results of the device under test under the test scenario information, including:

[0080] The transmitted signal, processed signal, and received signal are sampled at the output of the transmitting end of the device under test, the output of the transmitting end of the processed signal, and the input of the receiving end of the receiving module to obtain the sampling results.

[0081] The link test results are determined based on the transmitted signals, received signals, and sampling results.

[0082] The process of end-to-end sampling analysis can be implemented through the end-to-end sampling analysis module. The end-to-end sampling analysis module performs the above steps and generates link test results.

[0083] Specifically, during testing, three sampling nodes are set up at the transmitting end of the device under test, the transmitting end of the signal processing module, and the receiving end of the receiving module. Signals are transmitted via coaxial cable and data is uploaded via Ethernet to achieve synchronous sampling of key nodes throughout the entire link. During sampling, an acquisition unit with an adjustable sampling rate of 1MHz-100MHz is used to simultaneously collect parameters such as signal power, bit error rate, signal-to-noise ratio, and signal distortion at each node. By calculating the differences in parameters at each node, such as the power attenuation difference between the transmitting end and the simulated environment output, and the bit error rate difference between the simulated environment output and the receiving end, faulty nodes at the device end, environment end, and receiving end can be located respectively. This confirms the device's transmission performance, eliminates inherent defects, assesses the impact of the environment on the signal, and verifies the effectiveness of the simulation.

[0084] The method provided in this application synchronously collects data from multiple nodes on a link using node sampling technology, and locates the root cause of the fault by comparing parameter differences, thus solving the problem that existing technologies only measure the two ends and cannot locate faults in the middle nodes.

[0085] This invention generates protocol test results and link test results, which can cover all types of verification for custom and standardized protocols. At the same time, through the three-node full-link sampling technology, it can locate intermediate node faults, improve the efficiency of link fault location, and realize the two-dimensional analysis of the protocol link, avoiding invalid tests that only know the fault but not the cause.

[0086] Based on the test results, optimization solutions can be generated for the device under test and the test process. For example, optimization solutions for the link can be generated based on link test results, and improvement solutions for custom protocols can be generated based on protocol test results. Figure 2 As shown, these optimization solutions can be returned to the preceding technical components to form an intelligent closed loop of "test-analysis-optimization-retest".

[0087] In existing technologies, testing emergency equipment, especially when field testing is required, involves long testing cycles (sometimes 15-30 days) and consequently, longer iteration cycles (3-6 months) based on test results. This application, however, achieves full-process automation through intelligent closed-loop optimization, shortening the testing cycle and significantly reducing manual operation costs. Furthermore, based on a structured sample library and machine learning algorithms, the iteration cycle is reduced to 1-2 months, minimizing reliance on manual experience and significantly improving R&D progress.

[0088] Furthermore, in one possible implementation, after generating the test results of the device under test under the test scenario information, the following is also included:

[0089] Based on the test results of the device under test under multiple test scenario information, test optimization data for the device under test is generated. The test optimization data includes at least one target test scenario information.

[0090] Based on the test results of the device under test under multiple test scenarios, we can determine the specific target test scenarios in which the device under test is sensitive to communication functions. In other words, we can determine in which scenarios the device under test is more likely to experience a decline in communication functions.

[0091] In one possible implementation, the test scenario information with poor test results can be directly used as the target test scenario information. However, since the test scenario information includes multiple parameters, simply using the test scenario information with poor test results as the target test scenario information cannot provide more refined optimization directions. In another possible implementation of the method provided in this application, test optimization data for the device under test is generated based on the test results of the device under test under multiple test scenario information, including:

[0092] Similarity is calculated between information from various test scenarios to obtain multiple first similarity scores;

[0093] The similarity of the test results of the device under test under various test scenario information is calculated to obtain multiple second similarities;

[0094] Test optimization data is generated based on the first similarity and second similarity corresponding to the same test scenario information.

[0095] In this implementation, for the scene information to be tested, two scene pairs can be formed. For each scene pair, a first similarity and a second similarity are calculated. The first similarity is the similarity between the two test scene information pieces included in the scene pair, and the second similarity is the similarity between the test results corresponding to the two test scene information pieces in the scene pair. For each scene pair, the difference between the first similarity and the second similarity is calculated. The test scene information in the scene pair with a difference greater than a threshold is taken as the target test scene information. Specifically, if two test scene information pieces are very similar, but the corresponding test results differ greatly, it indicates that the communication function of the device under test is very sensitive to the small differences between these two test scene information pieces. R&D personnel can focus on optimizing the parameters corresponding to these small differences, providing a more refined optimization direction, which is conducive to improving testing efficiency and product development efficiency.

[0096] Furthermore, test scenario information, protocol test results, link test results, and target test scenarios generated in a single test can be stored in a structured database, supporting multi-dimensional retrieval, data traceability, and can be directly reused in subsequent tests of similar devices, reducing redundant testing and providing data support for industry technology accumulation.

[0097] The emergency equipment communication testing apparatus provided in this application is described below. The emergency equipment communication testing apparatus described below can be referred to in correspondence with the emergency equipment communication testing method described above. For example... Figure 3 As shown, the emergency equipment communication testing device provided in this application includes:

[0098] The scenario input module 310 is used to determine the input data of the signal transmission influence model based on the test scenario information, and input the input data into the signal transmission influence model. The test scenario information reflects the application scenario of the device under test. The signal transmission influence model is used to simulate the influence of the test scenario information on signal transmission. The test scenario information includes the environmental information of the application scenario, weather information, and carrier information of the device under test.

[0099] Signal processing module 320 is used to acquire the transmitted signal of the device under test through a standardized interface, and process the transmitted signal based on the output data of the signal transmission influence model to obtain the processed signal;

[0100] The optimization module 330 is used to optimize the signal transmission impact model based on the signal difference information corresponding to the test scenario information. The signal difference information reflects the difference between the transmitted signal and the processed signal.

[0101] The test result output module 340 is used to send the processed signal to the receiving module. The receiving module is determined based on the signal type of the device under test. Based on the received signal of the receiving module, the test result of the device under test is generated under the test scenario information.

[0102] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4As shown, the electronic device may include: a processor 410, a communications interface 420, a memory 430, and a communications bus 440, wherein the processor 410, the communications interface 420, and the memory 430 communicate with each other through the communications bus 440. The processor 410 can call logic instructions in the memory 430 to execute an emergency equipment communication test method. This method includes: determining input data for a signal transmission impact model based on test scenario information; inputting the input data into the signal transmission impact model; the test scenario information reflecting the application scenario of the device under test; the signal transmission impact model simulating the impact of the test scenario information on signal transmission; the test scenario information including environmental information, weather information, and carrier information of the device under test; acquiring the transmitted signal of the device under test through a standardized interface; processing the transmitted signal based on the output data of the signal transmission impact model to obtain a processed signal; optimizing the signal transmission impact model based on the signal difference information corresponding to the test scenario information; the signal difference information reflecting the difference between the transmitted signal and the processed signal; and sending the processed signal to a receiving module, which is determined based on the signal type of the device under test; and generating test results for the device under test under the test scenario information based on the received signal from the receiving module.

[0103] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0104] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the emergency equipment communication testing method provided by the above methods. The method includes: determining the input data of a signal transmission influence model based on test scenario information; inputting the input data into the signal transmission influence model; the test scenario information reflects the application scenario of the device under test; the signal transmission influence model is used to simulate the influence of the test scenario information on signal transmission; the test scenario information includes environmental information, weather information, and carrier information of the device under test; obtaining the transmitted signal of the device under test through a standardized interface; processing the transmitted signal based on the output data of the signal transmission influence model to obtain a processed signal; optimizing the signal transmission influence model based on the signal difference information corresponding to the test scenario information; the signal difference information reflects the difference between the transmitted signal and the processed signal; sending the processed signal to a receiving module, the receiving module being determined based on the signal type of the device under test; and generating the test result of the device under test under the test scenario information based on the received signal of the receiving module.

[0105] Furthermore, this application also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program is implemented to perform the emergency equipment communication testing method provided by the above methods. The method includes: determining input data for a signal transmission influence model based on test scenario information; inputting the input data into the signal transmission influence model; the test scenario information reflecting the application scenario of the device under test; the signal transmission influence model simulating the influence of the test scenario information on signal transmission; the test scenario information including environmental information, weather information, and carrier information of the device under test; obtaining the transmitted signal of the device under test through a standardized interface; processing the transmitted signal based on the output data of the signal transmission influence model to obtain a processed signal; optimizing the signal transmission influence model based on the signal difference information corresponding to the test scenario information; the signal difference information reflecting the difference between the transmitted signal and the processed signal; sending the processed signal to a receiving module, the receiving module being determined based on the signal type of the device under test; and generating test results for the device under test under the test scenario information based on the received signal from the receiving module.

[0106] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0107] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An emergency equipment communication test method, characterized by, include: The input data for the signal transmission impact model is determined based on the test scenario information, and the input data is input into the signal transmission impact model. The test scenario information reflects the application scenario of the device under test, and the signal transmission impact model is used to simulate the impact of the test scenario information on signal transmission. The test scenario information includes the environmental information and weather information of the application scenario, as well as the carrier information of the device under test. The transmitted signal of the device under test is obtained through a standardized interface, and the transmitted signal is processed based on the output data of the signal transmission influence model to obtain a processed signal. Based on the signal difference information corresponding to the test scenario information, the signal transmission impact model is optimized, and the signal difference information reflects the difference between the transmitted signal and the processed signal. The processed signal is sent to the receiving module, which is determined based on the signal type of the device under test. Based on the received signal from the receiving module, the test result of the device under test under the test scenario information is generated. The optimization of the signal transmission impact model based on the signal difference information corresponding to the test scenario information includes: Multiple standard signal transmission mathematical models are acquired, and signal difference labels corresponding to the test scenario information are generated based on the test scenario information and the standard signal transmission mathematical models. The signal transmission impact model is trained based on the loss corresponding to each sample data in the sample library. The sample library includes multiple first sample data and multiple second sample data. The first sample data includes input data corresponding to the test scenario information and signal difference labels corresponding to the test scenario information. The second sample data includes input data corresponding to the real scenario information and signal difference labels corresponding to the real scenario information. The loss corresponding to the first sample data reflects the difference between the signal difference information and the signal difference label corresponding to the test scenario information. The loss corresponding to the second sample data reflects the difference between the signal difference information and the signal difference label corresponding to the real scenario information.

2. The emergency equipment communication test method of claim 1, wherein, After generating the test results of the device under test under the test scenario information, the method further includes: Based on the test results of the device under test under multiple test scenario information, test optimization data of the device under test is generated, and the test optimization data includes at least one target test scenario information.

3. The emergency equipment communication test method of claim 2, wherein, The step of generating test optimization data for the device under test based on the test results of the device under test under multiple test scenario information includes: Similarity is calculated between the various test scenario information to obtain multiple first similarity scores; The similarity of the test results of the device under test under each of the test scenario information is calculated to obtain multiple second similarities; The test optimization data is generated based on the first similarity and the second similarity corresponding to the same test scenario information.

4. The emergency equipment communication test method of claim 1, wherein, The test results include protocol test results; generating test results for the device under test under the test scenario information based on the received signal from the receiving module includes: Based on the transmitted signal, the transmitted message data is determined; based on the received signal, the received message data is determined. Based on the transmitted message data and the received message data, the key parameters of the communication protocol of the device under test are determined; The key parameters are compared with the protocol template of the communication protocol of the device under test to determine the protocol test result.

5. The emergency equipment communication testing method according to claim 1, characterized in that, The test results include link test results. Generating the test results for the device under test under the test scenario information based on the received signal from the receiving module includes: The transmitted signal, the processed signal, and the received signal are sampled at the transmitting end output of the device under test, the transmitting end output of the processed signal, and the receiving end input of the receiving module to obtain the sampling results; The link test result is determined based on the transmitted signal, the received signal, and the sampling result.

6. An emergency equipment communication testing device, characterized in that, include: The scenario input module is used to determine the input data of the signal transmission influence model based on the test scenario information, and input the input data into the signal transmission influence model. The test scenario information reflects the application scenario of the device under test. The signal transmission influence model is used to simulate the influence of the test scenario information on signal transmission. The test scenario information includes the environmental information and weather information of the application scenario, as well as the carrier information of the device under test. The signal processing module is used to acquire the transmitted signal of the device under test through a standardized interface, and process the transmitted signal based on the output data of the signal transmission influence model to obtain a processed signal. The optimization module is used to optimize the signal transmission impact model based on the signal difference information corresponding to the test scenario information, wherein the signal difference information reflects the difference between the transmitted signal and the processed signal. The test result output module is used to send the processed signal to the receiving module, which is determined based on the signal type of the device under test. Based on the received signal of the receiving module, the test result of the device under test under the test scenario information is generated. The optimization of the signal transmission impact model based on the signal difference information corresponding to the test scenario information includes: Multiple standard signal transmission mathematical models are acquired, and signal difference labels corresponding to the test scenario information are generated based on the test scenario information and the standard signal transmission mathematical models. The signal transmission impact model is trained based on the loss corresponding to each sample data in the sample library. The sample library includes multiple first sample data and multiple second sample data. The first sample data includes input data corresponding to the test scenario information and signal difference labels corresponding to the test scenario information. The second sample data includes input data corresponding to the real scenario information and signal difference labels corresponding to the real scenario information. The loss corresponding to the first sample data reflects the difference between the signal difference information and the signal difference label corresponding to the test scenario information. The loss corresponding to the second sample data reflects the difference between the signal difference information and the signal difference label corresponding to the real scenario information.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the emergency equipment communication test method as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the emergency equipment communication test method as described in any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the emergency equipment communication test method as described in any one of claims 1 to 5.

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