Method and system for automated test flow control of a satellite navigation signal simulator

Abstract

This invention discloses an automated testing process control method and system for a satellite navigation signal simulator, belonging to the field of satellite navigation testing technology. The method includes: receiving test commands input by a user; parsing the test commands to generate control commands containing signal parameters and test scenario parameters; calling a preset template or dynamically generating test scenario simulation data based on the test scenario parameters; generating corresponding satellite navigation simulated signals based on the signal parameters and test scenario simulation data, and sending the satellite navigation simulated signals to the test object according to a predefined process logic to execute the test, while simultaneously controlling the test process based on the test scenario simulation data; and collecting the positioning performance data and system operating status parameters of the test object in real time, analyzing and evaluating them, and outputting the test results. This invention achieves fully automated execution of the testing task through integrated process control, helping to reduce manual intervention and improve testing efficiency and result consistency.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation testing technology, and specifically to an automated testing process control method and system for a satellite navigation signal simulator. Background Technology

[0002] Satellite navigation systems (such as GPS, BeiDou, and Galileo) are widely used in key fields such as national defense, transportation, and communications. As terminal equipment, the positioning accuracy and reliability of satellite navigation receivers are of paramount importance. Satellite navigation signal simulators are used to simulate on-orbit satellite signals and propagation environments in a laboratory setting, and are fundamental equipment for receiver research, development, testing, and verification.

[0003] In satellite navigation receiver testing, open-loop testing is a fundamental and common testing mode. In this mode, the signal simulator outputs excitation data such as satellite signals, navigation messages, power, and timing to the terminal under test (DUT) according to preset test cases. The DUT completes signal reception, processing, and response output without feedback to the simulator, forming an open-loop test link. Traditional open-loop testing processes heavily rely on manual operation: testers must manually configure various simulator parameters, continuously monitor the terminal output, manually interpret test results, and record data during the test. This method is not only time-consuming, but the accuracy and consistency of the test results also largely depend on the operator's experience level.

[0004] The limitations of existing satellite navigation signal simulator testing methods are mainly reflected in the following aspects: 1. The testing process relies on manual operation, which limits efficiency and consistency. Testers need to manually configure parameters such as satellite constellation, signal power, and user trajectory, and continuously monitor receiver output and manually interpret performance indicators during the test. This method makes it difficult to compress the test cycle, and differences in operation between different test rounds or different personnel can easily introduce human error, affecting the comparability and repeatability of test results.

[0005] 2. The automation level of constructing and executing complex test scenarios is low. For dynamic and complex scenarios such as urban canyons, high-speed movement, and ionospheric delay, testers need to manually combine multiple environmental model parameters and signal parameters based on the scenario characteristics. This process requires a high level of professional knowledge and the configuration steps are cumbersome; when switching scenarios, the test needs to be interrupted and manually reconfigured, making it difficult to achieve continuous automatic execution and batch processing of test tasks, thus limiting the test coverage and overall efficiency.

[0006] It is evident that existing satellite navigation signal simulator testing methods still have shortcomings in terms of process automation, intelligent integration of scenario construction and execution. How to achieve automatic parsing of test intent, intelligent generation of test environments, and automatic control of the entire test process to reduce manual intervention and improve testing efficiency and standardization is a technical problem that needs to be solved in this field. Summary of the Invention

[0007] This invention proposes an automated testing process control method and system for satellite navigation signal simulators, aiming to overcome the problems of cumbersome manual operation, low efficiency, complex test scenario construction and disconnection from process execution, and difficulty in standardizing and automating the testing process in traditional satellite navigation signal simulator testing methods.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: As a first aspect of the present invention, an automated testing process control method for a satellite navigation signal simulator is provided, comprising the following steps: Receive test commands input by the user; The test command is parsed to generate a control command that includes signal parameters and test scenario parameters; Based on the test scenario parameters in the control instructions, corresponding test scenario simulation data is dynamically generated from a preset scenario template library or using an artificial intelligence model. Based on the signal parameters in the control command and the test scenario simulation data, a corresponding satellite navigation simulation signal is generated; According to the predefined test process logic, the satellite navigation simulation signal is sent to the object under test, and the execution of the test process is controlled according to the test scenario simulation data; During the test, the positioning performance data output by the object under test is collected in real time, and the status parameters of the system operation are also collected in real time. The positioning performance data and the system's operating status parameters are analyzed and evaluated to obtain test results.

[0009] As a second aspect of the present invention, an automated testing process control system for a satellite navigation signal simulator is provided, comprising: The instruction parsing module is used to receive test instructions input by the user, parse the test instructions, and generate control instructions that include signal parameters and test scenario parameters; The scene editing module is used to dynamically generate test scene simulation data by calling from a preset scene template library or through its integrated artificial intelligence model, based on the test scene parameters in the control instructions. The signal generation module is used to generate corresponding satellite navigation simulation signals based on the signal parameters in the control command and the test scenario simulation data provided by the scene editing module; The process control module is used to send the satellite navigation simulation signal to the object under test to perform the test according to the predefined test process logic, and to control the execution of the test process according to the test scenario simulation data. The data acquisition module is used to collect the positioning performance data output by the object under test in real time during the test, and to collect the status parameters of the system operation in real time. The analysis and evaluation module is used to analyze and evaluate the positioning performance data and the system operation status parameters to obtain test results.

[0010] Compared with the prior art, the beneficial effects of the technical solution provided by the present invention are mainly reflected in: First, it helps improve the automation level and execution efficiency of the testing process. The solution of this invention constructs a closed-loop automated testing process by integrating instruction parsing, control instruction generation, scenario and signal driving, process execution control, and data acquisition and analysis. This process can replace the traditional testing steps that rely on manual parameter configuration, manual operation, and real-time monitoring, thereby helping test tasks to be executed faster and more standardized, and reducing the impact of differences in human operation.

[0011] Secondly, it provides the ability to simulate more complex and realistic test scenarios. This invention not only supports calling standard test scenarios from preset templates, but also dynamically generates test scenario simulation data containing propagation characteristics such as multipath effects and signal obstruction based on artificial intelligence models, and automatically controls the synchronous execution of the test process accordingly. This helps to reproduce various dynamic and complex real-world navigation scenarios in a laboratory environment, thus providing richer test conditions for a comprehensive evaluation of receiver performance.

[0012] Third, it enhances the monitorability and manageability of the testing system itself. This invention integrates real-time acquisition and monitoring of the operational status parameters of each functional module within the system into the automated process, such as confirming availability through heartbeat signals. This mechanism provides an information foundation for ensuring the stable execution of the testing process, and also provides a basis for system resource management and multi-task concurrent scheduling, helping to improve the resource utilization efficiency and maintainability of the testing platform. Attached Figure Description

[0013] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 A flowchart illustrating an automated testing process control method for a satellite navigation signal simulator provided in an embodiment of the present invention; Figure 2This is a schematic diagram of the structure of an automated testing process control system for a satellite navigation signal simulator provided in an embodiment of the present invention. Detailed Implementation

[0014] The technical solutions in the embodiments of the present invention will now be clearly and completely described with reference to the accompanying drawings. The described embodiments are only a part of the implementation of the present invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0015] In the testing phase of satellite navigation equipment development, satellite navigation signal simulators are a key tool for evaluating receiver performance. Current testing methods typically require testers to manually configure various parameters of the signal simulator (such as satellite constellation, signal power, etc.) according to test cases, and observe and record the receiver's output data during the test. When testing requirements involve multiple dynamic scenarios or complex propagation conditions, this highly manual operation mode can easily lead to lengthy testing procedures, errors in parameter configuration, and difficulty in efficiently and accurately building and executing complex simulation test environments.

[0016] To address the aforementioned problems, this invention proposes an automated testing process control scheme. This scheme constructs a standardized, automatically running testing process by sequentially connecting test instruction parsing, test scenario simulation data generation or retrieval, satellite navigation simulation signal generation, automated testing process execution and status monitoring, and test data collection, analysis, and evaluation. Its core lies in achieving fully automated execution and collaborative control of the entire process from test intent input to result output, aiming to significantly reduce manual intervention and improve testing efficiency, consistency, and the ability to simulate complex scenarios. The specific implementation methods of this invention will be described in detail below.

[0017] Example 1: An automated testing process control method for a satellite navigation signal simulator As a first aspect of the present invention, Embodiment 1 provides an automated testing process control method for a satellite navigation signal simulator. This method aims to complete the entire testing process from test command input to result output through an automated sequence of steps. See also... Figure 1 The method includes the following steps: S1: Receives test commands input by the user.

[0018] Users input test commands through a human-machine interface. These commands can be in the form of natural language descriptions, such as "test the dynamic positioning performance of the receiver in an urban canyon environment," or they can be in a structured command format that conforms to predefined syntax rules.

[0019] S2: Parse the test command and generate a control command that includes signal parameters and test scenario parameters.

[0020] The system parses the received test instructions. Specifically, it can use natural language processing technology to understand the semantics of text instructions, or parse structured instructions according to predefined grammar rules. The purpose of parsing is to extract and clarify the signal parameters required to execute the test (such as one or more of the following: satellite constellation type, signal frequency, signal power, signal modulation method, signal on / off state, number of signal channels, and satellite elevation angle) and the type of test scenario (such as urban canyon, high-speed movement, etc.).

[0021] S3: Based on the test scenario parameters in the control command, call from the preset scenario template library or dynamically generate the corresponding test scenario simulation data using an artificial intelligence model.

[0022] Based on the test scenario parameters determined in step S2, the system obtains the corresponding test scenario simulation data. For common scenarios, predefined template data can be directly called from the preset scenario template library. For more complex or specific scenarios, preset artificial intelligence models (such as models trained based on generative adversarial networks) can be called to dynamically generate simulation data to simulate complex propagation characteristics such as multipath effects and signal occlusion.

[0023] S4: Generate corresponding satellite navigation simulation signals based on the signal parameters in the control command and the test scenario simulation data.

[0024] This step transforms abstract control commands and environmental models into radio frequency (RF) or intermediate frequency (IF) signals that the object under test (satellite navigation chip, module, or receiver, etc.) can receive. In practice, the system generates high-fidelity satellite navigation analog signals based on the signal parameters specified in the control commands and the propagation conditions (such as delay and attenuation) defined by the simulation data of the test scenario, using digital signal processing technology.

[0025] S5: According to the predefined test process logic, the satellite navigation simulation signal is sent to the object under test, and the execution of the test process is controlled according to the test scenario simulation data.

[0026] The system controls the generation of analog signals to be sent to the object under test according to a predefined logical sequence, and starts the test. At the same time, the system coordinates the advancement and execution of different stages (such as signal parameter switching and data acquisition triggering) during the test process based on the scene timing or conditions contained in the test scenario simulation data (e.g., changes in motion trajectory, trigger points of interference events).

[0027] S6: During the test, the positioning performance data output by the object under test is collected in real time, and the status parameters of the system operation are collected in real time.

[0028] During the test, the system simultaneously collected two types of data: first, performance data such as the location coordinates and timestamps output by the tested object; and second, operational status parameters used to monitor the system's own health, such as the availability status of each functional unit obtained through a heartbeat mechanism. This provides a data foundation for subsequent comprehensive analysis.

[0029] S7: Analyze and evaluate the positioning performance data and the system operation status parameters to obtain test results.

[0030] The system processes and calculates the collected data to quantitatively evaluate the performance of the satellite navigation target. The system's operational status parameters are used to help determine whether performance anomalies stem from deviations in the analog signal itself.

[0031] By implementing the above steps, the method of this invention realizes a standardized automated testing process, which helps reduce manual intervention during testing and improves testing efficiency and result consistency. This method replaces traditional manual parameter configuration and process control through automatic instruction parsing and execution; it enhances the ability to construct complex testing environments by supporting template calls or dynamic generation; and it achieves automatic evaluation of test results through integrated data acquisition and analysis functions.

[0032] It should be noted that the method of this invention supports both functional testing and performance testing modes, adapting to diverse testing needs ranging from basic functional verification to complex performance evaluation. In functional testing mode, the system sends stimulus signals to the object under test (DUT) according to predefined test logic and collects responses. The DUT completes signal reception and output without feedback to the signal simulator, verifying whether its basic functions meet expectations. In performance testing mode, the system can dynamically adjust subsequent test parameters based on real-time collected positioning performance data. By iteratively adjusting parameters within a preset index range, the performance of the DUT approaches the set minimum or maximum threshold value to evaluate its extreme capabilities or nominal index compliance.

[0033] In some implementations, parsing the test instructions includes: parsing the natural language instructions into structured instructions using a natural language processing model; or, identifying and extracting structured instructions that conform to a predetermined format.

[0034] The process of parsing the test instructions can be achieved through two technical approaches. One approach involves the system calling a pre-trained natural language processing model to semantically understand the instruction text when the user inputs a natural language description. This model can identify key entities and intentions within the instruction, such as extracting scenario and test target keywords like "urban canyon" and "location accuracy," and mapping them to standardized parameter identifiers and operation commands defined within the system, thereby outputting machine-executable structured instructions. This approach is suitable for scenarios where users issue test tasks using descriptions close to everyday language. For example, a pre-trained language model based on the Transformer architecture can be used and fine-tuned on a domain-specific test instruction dataset to improve its accuracy in parsing technical terms and context.

[0035] Another parallel technical approach is to directly process structured instructions that conform to a predefined format. These instructions typically follow predefined syntax rules and data structures, such as specific JSON, XML formats, or custom scripting languages. The system uses a built-in parser to perform lexical and syntactic analysis on the instruction file according to established rules, identifying and extracting signal parameter values ​​and test scenario identifiers corresponding to predefined tags or fields. Structured instructions are characterized by fixed formats and clear meanings, making them suitable for describing test cases with numerous parameters or for scenarios where they are generated and called in batches by upper-level automated scripts. In practical implementation, the system can verify the compliance of instructions and extract necessary control parameters based on a complete instruction pattern specification and a corresponding parsing engine.

[0036] By providing both natural language and structured command parsing methods, the system can handle different forms of command input. Users can choose the appropriate command input method based on task requirements, skill background, or system collaboration. This provides compatibility for the automated execution of subsequent testing processes.

[0037] In some implementations, dynamically generating corresponding test scenario simulation data using an artificial intelligence model includes: calling a scenario generation model based on a generative adversarial network model; inputting the test scenario parameters and associated constraints into the scenario generation model to generate test scenario simulation data simulating at least one of the characteristics of multipath effect, signal blockage, ionospheric delay, or tropospheric delay.

[0038] Specifically, the system constructs and trains a conditional generative adversarial network (GAN) model. This GAN model comprises a generator and a discriminator. Training data is derived from the fusion of various information sources, including real-world scene mapping data, 3D city models, historical channel measurement data, and high-precision ray tracing simulation results. The generator's input consists of encoded scene type labels and constraint vectors, while its output is parameter fields such as signal attenuation, time delay, and Doppler shift at specific spatial grid points or time series. The discriminator's task is to distinguish between the scene data synthesized by the generator and the real training data. Through iterative training, the generator's parameters are optimized.

[0039] During the deployment phase, when it is necessary to dynamically generate specific scenarios, the system transforms the corresponding scenario semantic description (e.g., "a high-density urban canyon in a specific neighborhood") into a conditional vector that can be recognized by the GAN model, driving the pre-trained generator to output customized test scenario simulation data.

[0040] This GAN model can learn the nonlinear relationships between complex propagation characteristics and factors such as geographical environment, time, and weather. For example, when simulating multipath effects and signal blockage, the GAN model can use the three-dimensional geographic information of the target area and the electromagnetic properties of building surfaces as input conditions to infer multiple propagation paths formed by signal reflection and diffraction, and output the corresponding time delay, attenuation, and phase shift parameters. When simulating ionospheric and tropospheric delays, the GAN model can learn patterns from historical observation data containing time, geographical location, spatial environmental indices, and corresponding delay amounts, and generate corresponding delay data based on the input spatiotemporal coordinates and real-time meteorological parameters.

[0041] Through this dynamic scene generation method based on generative adversarial networks, the system can generate customized, highly realistic test scenarios according to test requirements, thereby providing the ability to handle complex test scenarios.

[0042] In some implementations, controlling the execution of the test process based on the test scenario simulation data includes: triggering corresponding test phase switching, signal parameter switching, or data acquisition commands according to the scenario timing or conditions defined in the test scenario simulation data.

[0043] The test scenario simulation data not only includes static spatial propagation characteristic parameters, but also defines the temporal sequence of scenario evolution over time or preset conditional triggering rules. By parsing this temporal or conditional information, the system coordinates the execution rhythm and content switching of the test in real time. For example, when the simulated motion trajectory reaches a specific position, the system can automatically switch to the next test phase; or when the intensity of the simulated interference signal exceeds a preset threshold, it can trigger the corresponding data acquisition task.

[0044] In practical implementation, the format specifications of the test scenario simulation data include structured tags or metadata that can be used for process control. For example, when defining a vehicle's trajectory, a "Start recording high-precision data" instruction tag can be associated with specific coordinate points; when describing intermittent interference sources, their start and stop time conditions can be defined. A rule engine or state machine is integrated into the system. This engine continuously monitors key variables such as the current test time and simulated position during the test, comparing them with preset time points or conditional expressions in the environmental data. Once a match is successful, the engine issues control commands to the corresponding functional modules in the system, thereby enabling test phase switching, signal parameter adjustment, or the opening of specific data acquisition windows.

[0045] This synchronization mechanism enables automated and coherent execution of multi-stage dynamic scenario testing, ensuring that performance evaluation can be conducted under conditions that simulate real dynamic operating conditions.

[0046] In some implementations, real-time acquisition of system operation status parameters includes: acquiring and confirming the availability status of each functional module in the test system through periodic heartbeat signals.

[0047] This implementation requires the core control unit to establish a regular status confirmation mechanism with other functional units. Each functional unit periodically sends a short "heartbeat" signal to the monitoring center. The monitoring center receives and analyzes the timing and content of these heartbeat signals to determine the status of each component in real time.

[0048] In its implementation, the heartbeat mechanism is typically based on a network communication protocol. Each monitored functional module runs a background service that sends a data packet containing the module ID, timestamp, and status code to a designated monitoring service port at preset time intervals. The monitoring center maintains a table of expected heartbeat statuses for all registered modules. Upon receiving a heartbeat packet, the monitoring center updates the corresponding module's last active timestamp. Simultaneously, a separate monitoring thread periodically scans this status table to check if the time since each module's last heartbeat has exceeded a preset timeout threshold. If a module fails to respond to heartbeat signals on time multiple times consecutively, the monitoring center determines that the module may have failed and triggers a preset exception handling procedure, such as logging, attempting to restart the module, or sending an alarm to the task scheduler.

[0049] Through this heartbeat-based monitoring, the system can perceive the availability status of functional modules. This helps to trigger timely processing flows when a module fails, supports the complete execution of a single test process, and provides status information support for long-term continuous operation or concurrent execution of multiple tasks. The availability data collected by this mechanism can also provide a reference for subsequent system maintenance and analysis.

[0050] In some implementations, analyzing and evaluating positioning performance data and operating status parameters includes: calculating the deviation between the positioning coordinates and preset reference coordinates to evaluate positioning accuracy, and / or evaluating the acquisition speed based on the time from the start of the test to the first successful positioning, and / or analyzing the standard deviation of the positioning data to evaluate tracking stability.

[0051] The quantitative evaluation of positioning accuracy is based on a preset reference trajectory. When generating the test scenario, the system simultaneously generates a preset reference coordinate sequence that matches the simulation environment. During the analysis phase, the system compares the positioning point coordinates reported by the receiver in real time with the preset reference coordinates at the same time. By calculating the Euclidean distance between the two in two-dimensional or three-dimensional space, the instantaneous positioning deviation at that moment is obtained. After the test, the system performs statistical analysis on the instantaneous deviations of all sampling points, such as calculating the average value, root mean square value, or calculating the probability that it does not exceed a certain threshold, thereby numerically characterizing the positioning accuracy of the receiver during the test.

[0052] The acquisition speed is evaluated by quantifying the time required for the receiver to output a valid positioning result from the start of signal search. The system uses the moment when the control command begins driving signal generation and test execution as the timing start point and continuously monitors the data stream output by the receiver. The system applies a set of preset criteria to determine the validity of each positioning result. These criteria typically include a valid positioning solution status flag, the number of satellites participating in the solution not being less than a threshold, and quality indicators such as the accuracy attenuation factor being within an acceptable range. When the system first identifies a positioning result that meets all preset criteria, it records this moment, and the difference between this moment and the timing start point is the acquisition time for a single test. By statistically analyzing the acquisition times of multiple tests, the efficiency of receiver initialization can be evaluated.

[0053] The stability assessment analyzes the fluctuation characteristics of the receiver's output during the continuous positioning phase. After the receiver enters a stable tracking state, the system selects a continuous and valid positioning data sequence for processing. The standard deviation of each position coordinate component in the sequence is calculated. The magnitude of the standard deviation reflects the volatility of the receiver's output during that time period. To analyze stability at different time scales, the system can also apply time-domain analysis methods such as Allen's variance to long-term sequences.

[0054] Through the above calculation process, the system transforms the receiver's key performance indicators into a series of calculable numerical results. This evaluation method, based on algorithms and mathematical statistics, provides a unified quantitative basis for performance comparisons between different tests and different devices under test.

[0055] In some implementations, the method of this invention further includes: performing queue management and resource scheduling on multiple concurrently executed test tasks based on collected system operating status parameters.

[0056] When the testing system receives multiple test requests, a central scheduler dynamically arranges the execution order of these test tasks and allocates appropriate system resources based on the load and availability status of key resources such as computing nodes, signal generation hardware, and software licenses, which are monitored in real time.

[0057] In terms of implementation, the system includes a dedicated scheduling and management module. This module maintains a queue of tasks to be executed, with each task in the queue carrying a description of its resource requirements and an optional priority label. Simultaneously, the scheduling and management module communicates with a monitoring service responsible for collecting system operational status parameters, continuously acquiring information such as the occupancy status of each signal generation board, real-time CPU and memory load of the server, network bandwidth usage, and the health status of each software service process. Based on this dynamic information, the scheduling and management module employs specific scheduling strategies to determine the next task to be executed and its allocated resource groups. For example, priority-based scheduling or a load balancing strategy may be used. Before a task begins execution, the scheduling and management module reserves the necessary resources for it and releases these resources after the task is completed, marking them as available for subsequent tasks.

[0058] Through this concurrent task management and scheduling based on real-time system status, the system can handle multiple test tasks. This helps improve the overall utilization of test equipment and computing resources, enabling multiple test tasks to be carried out in an orderly parallel manner.

[0059] Example 2: An automated testing process control system for a satellite navigation signal simulator As a second aspect of the present invention, Embodiment 2 provides an automated testing process control system for a satellite navigation signal simulator. For example... Figure 2 As shown, the system mainly includes an instruction parsing module 21, a scene editing module 22, a signal generation module 23, a flow control module 24, a data acquisition module 25, and an analysis and evaluation module 26. Each functional module connects and interacts with data through an agreed-upon software interface or communication bus. The instruction parsing module 21 is used to receive test instructions input by the user, parse the test instructions, and generate control instructions containing signal parameters and test scenario parameters; The scene editing module 22 is used to call from a preset scene template library or dynamically generate test scene simulation data through its integrated artificial intelligence model according to the test scene parameters in the control command; Signal generation module 23 is used to generate corresponding satellite navigation simulation signals based on the signal parameters in the control command and the test scenario simulation data provided by the scene editing module 22; The process control module 24 is used to send the satellite navigation simulation signal to the object under test to perform the test according to the predefined test process logic, and to control the execution of the test process according to the test scenario simulation data; The data acquisition module 25 is used to collect the positioning performance data output by the object under test in real time during the test, and to collect the status parameters of the system operation in real time. The analysis and evaluation module 26 is used to analyze and evaluate the positioning performance data and the system operation status parameters to obtain test results.

[0060] Specifically, the instruction parsing module 21 receives user test instructions and converts them into executable control instructions for the system. The instruction parsing module 21 can integrate a natural language processing engine to understand free text descriptions, or it can process predefined test cases that follow specific syntax rules (such as JSON, XML, or custom scripts) through a structured instruction parser. The parsing process extracts two types of key information from the input instructions: first, specific signal parameters, such as satellite system type and carrier frequency; and second, abstract test scenario type identifiers, such as "urban canyon," used to guide the subsequent construction of the environment.

[0061] The scene editing module 22 generates corresponding test scene simulation data based on the test scene parameters in the control instructions. This module maintains a pre-set scene template library for quick access. For more complex or dynamically evolving scenes, the module calls its integrated artificial intelligence model, taking the scene type and additional constraints as input, to dynamically generate environmental data. For example, the scene editing module 22 can be a trained conditional generative adversarial network capable of simulating complex signal propagation effects such as multipath reflection, signal blockage, and atmospheric delay, and outputting a digital environmental description containing these characteristics.

[0062] The signal generation module 23 generates satellite navigation simulation signals based on the signal parameters in the control commands and the test scenario simulation data provided by the scene editing module 22. This signal generation module 23 consists of both software and hardware components working together. The software component is responsible for digital baseband signal synthesis, calculating digital signal samples based on signal parameters and environmental data. The hardware component typically includes radio frequency components such as digital-to-analog converters and up-converters, responsible for converting the digital signal samples into radio frequency signals of a specific frequency band.

[0063] The process control module 24 coordinates the execution of the entire test process. Based on predefined test process logic, it sends instructions to the signal generation module 23 to control signal transmission and coordinate the start of the test. Furthermore, it can parse and execute dynamic logic embedded in the test scenario simulation data. For example, when environmental data describes a moving scenario, the process control module 24 monitors the test progress and automatically triggers corresponding operations, such as switching signal parameters or starting to record high-precision data, when a specific location or time point is reached, thereby synchronizing test execution with scenario evolution.

[0064] During the test, the data acquisition module 25 collects information in real time from two levels. First, it collects positioning performance data output by the satellite navigation receiver under test, including raw observations and processed position, velocity, and time results. Second, it collects the system's own operational status parameters, such as confirming the availability of each software module by polling its "heartbeat" status, reading monitoring values ​​from the signal generation hardware, and obtaining resource load information from the computing server.

[0065] The analysis and evaluation module 26 receives positioning performance data and system status parameters from the data acquisition module 25, performs automated analysis and calculations, and generates a test result report. This module 26 incorporates standardized performance evaluation algorithms. For example, for positioning accuracy, it compares the positioning point reported by the receiver under test with a preset reference trajectory and calculates error statistics; for acquisition time, it analyzes the time interval from the start of the process to the first valid positioning result. Simultaneously, the module references system status parameters to identify whether performance anomalies originate from an anomaly in the test system itself.

[0066] Through the collaborative work of the aforementioned modules, the system of this embodiment constructs a complete automated test execution environment. This system integrates traditionally fragmented operations into a coherent, software-driven process, enhancing the ability to construct complex environments while ensuring the controllability and observability of the testing process, ultimately outputting objective and quantifiable test results. This helps improve the efficiency and consistency of satellite navigation receiver testing.

[0067] In one application example, the above system is applied to an open-loop testing scenario. The user inputs a test command, such as "Execute BDS B1 frequency open-loop test, test duration 30 minutes, sampling interval 1 second". The command parsing module 21 parses the command and generates a control command containing signal parameters (frequency B1, signal power -130dBm, modulation method BPSK) and test scenario parameters (open-air static scenario). The scenario editing module 22 calls a preset open-air scenario template to generate simulation environment data, and the signal generation module 23 generates a B1 frequency simulated signal accordingly. The process control module 24 controls the signal to continuously transmit for 30 minutes according to the open-loop test logic, without receiving feedback from the tested object during this period. The data acquisition module 25 collects the positioning results and system operating status parameters output by the tested terminal throughout the test. After the test, the analysis and evaluation module 26 compares the positioning data with a preset reference trajectory and generates a positioning accuracy evaluation report. This example demonstrates that the present invention can transform traditional open-loop testing, which relies on manual operation, into a fully automated process, significantly reducing the need for personnel on-site supervision during the testing process.

[0068] In some embodiments, the instruction parsing module 21 includes: a natural language processing unit for parsing natural language instructions into structured instructions; and / or a structured instruction parsing engine for recognizing and extracting structured instructions that conform to a predetermined format.

[0069] In some embodiments, the scene editing module 22 includes: A scenario template library is used to store predefined test scenario template data; The intelligent scene generator is used to call a scene generation model based on a generative adversarial network model. Based on the input test scene parameters and constraints, it dynamically generates test scene simulation data that simulates at least one of the characteristics of multipath effect, signal blockage, ionospheric delay, or tropospheric delay.

[0070] In some implementations, the process control module 24 is specifically used to: control the switching of test phases, switching of signal parameters, or sending of data acquisition instructions based on the instructions generated by the scene timing or condition triggers defined in the test scenario simulation data.

[0071] In some embodiments, the data acquisition module 25 includes a heartbeat monitoring unit, used to acquire and confirm the availability status of each functional module in the test system through periodic heartbeat signals.

[0072] In some implementations, the analysis and evaluation module 26 is specifically used to: calculate the deviation between the positioning coordinates and the preset reference coordinates to evaluate the positioning accuracy, and / or, evaluate the acquisition speed based on the time from the start of the test to the first successful positioning, and / or, analyze the standard deviation of the positioning data to evaluate the tracking stability.

[0073] In some embodiments, the system of the present invention further includes: a task scheduling module, connected to the data acquisition module 25 and the process control module 24, for performing queue management and resource scheduling on multiple concurrently executed test tasks based on the system operating status parameters collected in real time by the data acquisition module 25.

[0074] The system embodiments of the present invention are based on the same inventive concept as the foregoing method embodiments. Each functional module in the system corresponds one-to-one with each step in the foregoing method embodiments, and is used to execute the method. The specific implementation of each functional module can be found in the description of the corresponding step in the foregoing method embodiments, and will not be repeated here.

[0075] In summary, the automated testing process control method and system for the satellite navigation signal simulator provided in this embodiment of the invention can achieve the following technical effects: 1) Automated Test Command Parsing. By receiving and parsing user-inputted test commands, control commands containing signal parameters and test scenario types are generated. This process converts the tester's operational intentions into standardized commands recognizable by the system, supporting both natural language semantic understanding and structured script syntax parsing. This technical feature automates the conversion of test task descriptions into specific execution parameters, reducing manual parameter configuration and input errors during the test preparation phase. It also supports the archiving and reuse of test cases in natural language or script format.

[0076] 2) Automated Test Command Parsing. By receiving and parsing user-inputted test commands, control commands containing signal parameters and test scenario types are generated. This process converts the tester's operational intentions into standardized commands recognizable by the system, supporting both natural language semantic understanding and structured script syntax parsing. This technical feature automates the conversion of test task descriptions into specific execution parameters, reducing manual parameter configuration and input errors during the test preparation phase. It also supports the archiving and reuse of test cases in natural language or script format.

[0077] 3) High-fidelity signal generation. Based on signal parameters and simulation test environment data, the module generates corresponding satellite navigation simulation signals. According to parameters such as constellation type, frequency, and power in the control commands, and incorporating effects such as propagation delay, attenuation, and Doppler shift defined by environmental data, the signal generation module outputs a high-fidelity waveform through digital signal processing and radio frequency synthesis technology. This feature ensures that the signal source output is strictly controlled by the upper-level commands and environmental model during the automated testing process, providing expected and repeatable signal excitation conditions for test execution.

[0078] 4) Synchronous Execution of Process and Scene. Simulated signals are sent to the test object according to predefined test process logic to execute the test, and the execution of the test process is controlled based on simulation test environment data. The process control module parses the timing sequences and conditional events embedded in the environmental data, automatically switching signal parameters when the simulated motion trajectory reaches a specific position, or initiating data acquisition when interference conditions are triggered. This technical feature enables the seamless execution of complex dynamic scene tests involving multiple stages and state changes in a fully automated manner, reducing execution delays and connection deviations caused by manual intervention.

[0079] 5) Dual-dimensional data acquisition. During testing, the system continuously acquires positioning performance data and operational status parameters from the tested object in real time. The data acquisition module continuously obtains performance indicators such as positioning coordinates, timestamps, and satellite status through the receiver data interface. Simultaneously, it periodically acquires operational parameters such as heartbeat availability, resource load, and signal generation hardware status of each functional module through the internal monitoring bus. This technical feature provides a synchronous dual-dimensional data foundation for test evaluation and system self-diagnosis, serving performance quantitative assessment and rapid anomaly attribution.

[0080] 6) Automatic Performance Evaluation. Based on positioning performance data and system operating parameters, analysis and evaluation are performed to generate test results. The analysis and evaluation module assesses positioning accuracy by calculating the deviation between the positioning coordinates and the preset reference trajectory, evaluates acquisition speed by statistically analyzing the time interval from test initiation to the first effective positioning, and evaluates tracking stability by analyzing the standard deviation of continuous positioning results. This technical feature transforms receiver performance evaluation into a fully automated quantitative calculation based on explicit algorithms and statistics, improving the comparability and objectivity of test results across different personnel and different test rounds.

[0081] 7) Multi-task resource scheduling. Based on collected system operating status parameters, queue management and resource scheduling are performed on multiple concurrently executing test tasks. The task scheduling module dynamically determines the order of tasks to be executed and the resource allocation scheme according to the priority of each test task, resource requirement description, and the real-time occupancy status of the current signal generation channel, computing node, and software license. This technical feature enables efficient sharing of signal simulator hardware and computing resources in multi-project, multi-user scenarios, shortening the test task queuing period and improving the utilization rate of laboratory equipment and test throughput.

[0082] 8) Complete Automated Testing Framework. The above-mentioned technical features work together to construct a complete automated testing solution, encompassing test task description, environment construction, signal generation, process execution, data acquisition, performance evaluation, and system resource management. This solution reduces manual configuration workload in the test preparation phase, decreases reliance on continuous operator monitoring during the execution phase, provides objective and quantifiable evaluation data in the results output phase, and achieves multi-task concurrent scheduling capabilities at the system level, thereby improving the efficiency, consistency, and scalability of satellite navigation receiver testing.

[0083] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An automated testing process control method for a satellite navigation signal simulator, characterized in that, Includes the following steps: Receive test commands input by the user; The test command is parsed to generate a control command that includes signal parameters and test scenario parameters; Based on the test scenario parameters in the control instructions, corresponding test scenario simulation data is dynamically generated from a preset scenario template library or using an artificial intelligence model. Based on the signal parameters in the control command and the test scenario simulation data, a corresponding satellite navigation simulation signal is generated; According to the predefined test process logic, the satellite navigation simulation signal is sent to the object under test, and the execution of the test process is controlled according to the test scenario simulation data; During the test, the positioning performance data output by the object under test is collected in real time, and the status parameters of the system operation are also collected in real time. The positioning performance data and the system's operating status parameters are analyzed and evaluated to obtain test results.

2. The method according to claim 1, characterized in that, The parsing of the test instructions includes: Natural language processing models are used to parse natural language instructions into structured instructions; or, It identifies and extracts structured instructions that conform to a predetermined format.

3. The method according to claim 1, characterized in that, The method of dynamically generating corresponding test scenario simulation data using artificial intelligence models includes: Invoke the scene generation model based on the generative adversarial network model; The test scenario parameters and associated constraints are input into the scenario generation model to generate test scenario simulation data that simulates at least one of the characteristics of multipath effect, signal blockage, ionospheric delay, or tropospheric delay.

4. The method according to claim 1, characterized in that, The step of controlling the execution of the test process based on the test scenario simulation data includes: Based on the scenario timing or conditions defined in the test scenario simulation data, corresponding test phase switching, signal parameter switching, or data acquisition commands are triggered.

5. The method according to claim 1, characterized in that, The status parameters of the real-time acquisition system include: The availability status of each functional module in the test system is confirmed by periodic heartbeat signal acquisition.

6. The method according to claim 1, characterized in that, The analysis and evaluation of the positioning performance data and the system operating status parameters includes: Calculate the deviation between the positioning coordinates and the preset reference coordinates to evaluate positioning accuracy, and / or evaluate the acquisition speed based on the time from the start of the test to the first successful positioning, and / or analyze the standard deviation of the positioning data to evaluate tracking stability.

7. The method according to claim 1, characterized in that, The method further includes: Based on the collected system operation status parameters, queue management and resource scheduling are performed on multiple concurrently executed test tasks.

8. An automated testing process control system for a satellite navigation signal simulator, characterized in that, include: The instruction parsing module is used to receive test instructions input by the user, parse the test instructions, and generate control instructions that include signal parameters and test scenario parameters; The scene editing module is used to dynamically generate test scene simulation data by calling from a preset scene template library or through its integrated artificial intelligence model, based on the test scene parameters in the control instructions. The signal generation module is used to generate corresponding satellite navigation simulation signals based on the signal parameters in the control command and the test scenario simulation data provided by the scene editing module; The process control module is used to send the satellite navigation simulation signal to the object under test to perform the test according to the predefined test process logic, and to control the execution of the test process according to the test scenario simulation data. The data acquisition module is used to collect the positioning performance data output by the object under test in real time during the test, and to collect the status parameters of the system operation in real time. The analysis and evaluation module is used to analyze and evaluate the positioning performance data and the system operation status parameters to obtain test results.

9. The system according to claim 8, characterized in that, The instruction parsing module includes: A natural language processing unit is used to parse natural language instructions into structured instructions; and / or, The structured instruction parsing engine is used to identify and extract structured instructions that conform to a predefined format.

10. The system according to claim 8, characterized in that, The scene editing module includes: A scenario template library is used to store predefined test scenario template data; The intelligent scene generator is used to call a scene generation model based on a generative adversarial network model. Based on the input test scene parameters and constraints, it dynamically generates test scene simulation data that simulates at least one of the characteristics of multipath effect, signal blockage, ionospheric delay, or tropospheric delay.

11. The system according to claim 8, characterized in that, The process control module is specifically used for: Based on the scenario timing or condition triggering instructions defined in the test scenario simulation data, control the switching of test phases, signal parameter switching, or the sending of data acquisition instructions.

12. The system according to claim 8, characterized in that, The data acquisition module includes: The heartbeat monitoring unit is used to collect and confirm the availability status of each functional module in the test system through periodic heartbeat signals.

13. The system according to claim 8, characterized in that, The analysis and evaluation module is specifically used for: Calculate the deviation between the positioning coordinates and the preset reference coordinates to evaluate the positioning accuracy, and / or, The capture speed is evaluated based on the time from the start of the test to the first successful localization, and / or, Analyze the standard deviation of the positioning data to assess tracking stability.

14. The system according to claim 8, characterized in that, The system also includes: The task scheduling module, connected to the data acquisition module and the process control module, is used to perform queue management and resource scheduling for multiple concurrently executed test tasks based on the system operation status parameters collected in real time by the data acquisition module.

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