A test method, system, device and medium for remotely controlling a power supply

Abstract

A test method, system, device and medium for remotely controlling a power supply, wherein the method comprises: performing initial detection on the power supply to be detected based on a first detection instruction sequence to obtain a first response behavior sequence; obtaining function parameters of the power supply to be detected and environmental data of a location where the power supply to be detected is located; mapping the environmental data to the first response behavior sequence to obtain a second response behavior sequence; identifying a chained behavior sequence in the second response behavior sequence; analyzing an abnormal coupling response relationship in the chained behavior sequence and calculating a coupling risk value; constructing a third response behavior sequence and determining a detection instruction set corresponding to the third response behavior sequence; calculating a triggering probability of each first instruction group in the detection instruction set triggering the third response behavior sequence; generating a second detection instruction sequence based on the triggering probability; and testing the power supply to be detected based on the second detection instruction sequence to obtain a detection result. The application can improve the detection accuracy of remotely controlling the power supply.

Description

Technical Field

[0001] This application relates to the field of power supply testing technology, specifically to a testing method, system, equipment, and medium for a remotely controlled power supply. Background Technology

[0002] Remote power control technology is a fundamental and critical technology in modern, automated infrastructure management. This technology allows operators to perform basic power status management, such as turning on, off, and restarting, on remote, unattended, or inaccessible equipment via a network. This capability significantly reduces reliance on on-site maintenance personnel, improves fault response speed and maintenance efficiency, and is a crucial cornerstone for achieving centralized and automated management of large-scale equipment clusters. Therefore, remote power control technology is widely used in various scenarios, including communication base stations, industrial automated production lines, smart buildings, and large-scale data centers.

[0003] In related technologies, a preset sequence of test commands is often used to perform functional tests on server power supplies. Specifically, a set of standard test commands is sent to the power supply, the response data of the power supply is collected by a testing device, and the data is compared with the power supply's basic attribute parameters and a preset normal response template to determine whether its operating status is normal.

[0004] However, existing technologies for testing and analyzing power supply response behavior only analyze and judge individual response behaviors. In practical applications, when the power supply under test exhibits continuous and interconnected abnormal response behaviors within a specific time period, traditional methods may treat these abnormal response behaviors as isolated events, failing to identify the deeper fault modes hidden behind the abnormal response behaviors, such as systemic hardware aging, cross-interference between power supply modules, or chain reactions driven by environmental factors. This analysis method is prone to misjudgment or omission, thus reducing the accuracy of remotely controlled power supply detection. Summary of the Invention

[0005] This application provides a testing method, system, device, and medium for remotely controlled power supplies, which addresses the technical problem that the testing of remotely controlled power supplies relies solely on analyzing and judging a single response behavior, resulting in low accuracy in the detection of remotely controlled power supplies.

[0006] The first aspect of this application provides a method for testing a remotely controlled power supply, applied in a server. The method includes: sending a first detection command sequence to a detection device; controlling the detection device to perform initial detection on the power supply to be tested to obtain a first response behavior sequence of the power supply to be tested, the first response behavior sequence including multiple first response behaviors, each first response behavior corresponding to a detection command in the first detection command sequence; acquiring functional parameters of the power supply to be tested and environmental data of the location of the power supply to be tested; mapping the environmental data to the first response behavior sequence to obtain a second response behavior sequence; performing sliding window analysis on the second response behavior sequence to identify chained behavior sequences with temporal correlation among the second response behaviors in the second response behavior sequence; and analyzing the... In a chain-like behavior sequence, an abnormal coupling response relationship exists between adjacent second response behaviors, and the coupling risk value of the response behavior pair with the abnormal coupling response relationship is calculated. The response behavior pair consists of two adjacent second response behaviors. Based on the coupling risk value, a third response behavior sequence is constructed, and the detection instruction set corresponding to the third response behavior sequence is determined. The third response behavior sequence consists of multiple response units. Based on the first detection instruction sequence and the first response behavior sequence, and combined with the functional parameters, the trigger probability of each first instruction group in the detection instruction set triggering the third response behavior sequence is calculated. A second detection instruction sequence is generated based on the trigger probability. The power supply to be tested is tested based on the second detection instruction sequence to obtain the detection result of the power supply to be tested.

[0007] Optionally, a sliding window analysis is performed on the second response behavior sequence to identify chain behavior sequences with temporal correlation among the second response behaviors in the second response behavior sequence. Specifically, this includes: setting the first second response behavior in the second response behavior sequence as the starting point to construct a candidate chain behavior sequence; calculating the environmental data change between the first target response behavior and the second target response behavior, wherein the first target response behavior and the second target response behavior are any two adjacent second response behaviors in the second response behavior sequence, and the first target response behavior precedes the second target response behavior; inputting the environmental data change into a preset environmental suppression function to obtain an environmental suppression factor between the first target response behavior and the second target response behavior; multiplying the environmental suppression factor by a preset time correlation threshold to obtain a target time correlation threshold; and traversing each second response behavior in the second response behavior sequence based on the target time correlation threshold and the candidate chain behavior sequence to obtain the chain behavior sequence.

[0008] Optionally, based on the target time-related threshold and the candidate chained behavior sequence, traversing each second response behavior in the second response behavior sequence to obtain the chained behavior sequence specifically includes: starting from the starting point, traversing each second response behavior in the second response behavior sequence, determining whether the time interval between the second target response behavior and the first target response behavior is less than the target time-related threshold; if the time interval is less than the target time-related threshold, adding the second target response behavior to the end of the candidate chained behavior sequence; if the time interval is greater than or equal to the target time-related threshold, taking the first target response behavior as the last response behavior in the candidate chained behavior sequence to obtain the chained behavior sequence, and setting the second target response behavior with the time interval greater than or equal to the target time-related threshold as a new starting point, continuing to construct the candidate chained behavior sequence, and continuing to traverse the response behaviors until all second response behaviors in the second response behavior sequence have been detected.

[0009] Optionally, analyzing whether there is an abnormal coupling response relationship between adjacent second response behaviors in the chained behavior sequence, and calculating the coupling risk value of the response behavior pair with the abnormal coupling response relationship, specifically includes: obtaining the actual voltage fluctuation value corresponding to the first target response behavior and the actual current fluctuation value corresponding to the second target response behavior in the chained behavior sequence; inputting the actual voltage fluctuation value into a preset response benchmark model to obtain the current change range of the second target response behavior; if the actual current fluctuation value is not within the current change range, then combining the first target response behavior and the second target response behavior into the response behavior pair; calculating the response deviation value of the response behavior pair based on the interval value of the current change range and the actual current fluctuation value; inputting the response deviation value, the time interval of the response behavior pair, and the environmental inhibition factor into a preset risk assessment function for calculation to obtain the coupling risk value.

[0010] Optionally, constructing a third response behavior sequence based on the coupling risk value and determining the detection instruction set corresponding to the third response behavior sequence specifically includes: combining adjacent response behavior pairs whose coupling risk value is greater than a preset association threshold into a response unit, and combining the response units into a third response behavior sequence according to the time order of the response units; aligning the third response behavior sequence with the first detection instruction sequence in time, and determining the detection instruction set corresponding to the third response behavior sequence, wherein the detection instruction set is composed of the first instruction group corresponding to each response unit.

[0011] Optionally, based on the first detection instruction sequence and the first response behavior sequence, and in conjunction with the functional parameters, the trigger probability of each first instruction group in the detection instruction set triggering the third response behavior sequence is calculated. Specifically, this includes: determining target instruction groups whose instruction types are the same as those of the first detection instruction sequence and the first instruction groups in the detection instruction set, and counting the total number of times each target instruction group is repeatedly executed within the limits of the functional parameters; determining the response behavior groups obtained from each execution of the target instruction group in the first response behavior sequence, with each response behavior group corresponding to a response record, and each response record including the first execution order and duration of the first response behavior in the response behavior group; matching the response record with the response units in the third response behavior sequence, and when the first execution order is the same as the second execution order of the second response behavior in the response unit, and the time deviation between the duration and the preset duration is within a preset range, the response matching is successful, and the total number of triggers is determined; calculating the ratio of the total number of triggers to the total number of executions to obtain the trigger probability.

[0012] Optionally, generating a second detection instruction sequence based on the trigger probability specifically includes: comparing the trigger probability of each first instruction group in the detection instruction set with a preset matching degree threshold, and selecting a second instruction group whose trigger probability is greater than the preset matching degree threshold; determining a baseline function parameter value corresponding to the second instruction group based on the test function parameters corresponding to the test instructions in the first detection instruction sequence; generating multiple function detection parameter values ​​according to a preset parameter step size, centered on the baseline function parameter value, within a preset operation range of the function parameters, and combining the function detection parameter values ​​into a test parameter set; combining each second instruction group with the test parameter set corresponding to the second instruction group to generate multiple test units, and splicing all the test units in a preset order to obtain the second detection instruction sequence.

[0013] A second aspect of this application provides a test system for a remotely controlled power supply, comprising: a response module, configured to send a first detection command sequence to a detection device, controlling the detection device to perform initial detection on the power supply under test to obtain a first response behavior sequence of the power supply under test, the first response behavior sequence including multiple first response behaviors, each first response behavior corresponding to a detection command in the first detection command sequence; an acquisition module, configured to acquire functional parameters of the power supply under test and environmental data of the location of the power supply under test; a mapping module, configured to map the environmental data to the first response behavior sequence to obtain a second response behavior sequence; a first analysis module, configured to perform sliding window analysis on the second response behavior sequence to identify a chain of behavior sequences with temporal correlation among the second response behaviors in the second response behavior sequence; and a second analysis module, configured to analyze the chain of behavior sequences. The system determines whether there is an abnormal coupling response relationship between adjacent second response behaviors in the column, and calculates the coupling risk value of response behavior pairs with such abnormal coupling response relationships, wherein the response behavior pair consists of two adjacent second response behaviors; a construction module is used to construct a third response behavior sequence based on the coupling risk value, and determine the detection instruction set corresponding to the third response behavior sequence, wherein the third response behavior sequence consists of multiple response units; a calculation module is used to calculate, based on the first detection instruction sequence and the first response behavior sequence, and in combination with the functional parameters, the control of the detection device to trigger the third response behavior sequence by each first instruction group in the detection instruction set; a generation module is used to generate a second detection instruction sequence based on the control of the detection device; and a detection module is used to test the power supply to be tested based on the second detection instruction sequence to obtain the detection result of the power supply to be tested. In a third aspect of this application, an electronic device is provided, including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method described in any of the above.

[0014] A fourth aspect of this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any of the preceding descriptions.

[0015] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: 1. By sending a first detection command sequence and collecting the corresponding first response behavior sequence, and combining it with the power supply's functional parameters and environmental data of its location, a second response behavior sequence with environmental awareness is constructed. This achieves dynamic modeling of the power supply's response behavior under different environmental conditions, overcoming the limitation of traditional methods in being unable to identify environmental modulation effects. Based on this, by performing sliding window analysis on the second response behavior sequence, the system can identify multiple chain-like response behaviors with temporal continuity and logical dependence. Furthermore, through coupling analysis and risk calculation between behavior pairs, potential chain-like response anomalies caused by factors such as power supply aging, electromagnetic interference, or coordinated failure of power supply modules are revealed. Compared to traditional isolated behavior judgment methods, this application can more comprehensively capture systemic fault modes, significantly improving the accuracy and depth of anomaly identification. In addition, the system constructs a third response behavior sequence based on coupling risks and generates an optimized second detection command sequence based on the trigger probability of triggering the third response behavior sequence. This sequence is used for targeted retesting of the power supply, improving the accuracy of remote control power supply fault detection.

[0016] 2. By introducing environmental inhibition factors to participate in the dynamic regulation of time correlation thresholds, the chain behavior sequence identification process not only considers the temporal relationship between response behaviors, but also fully integrates the potential impact of environmental data changes on the temporal relationship of response behaviors. This effectively solves the problem of interference caused by environmental disturbances to the correlation analysis of response behaviors in complex operating scenarios, significantly improves the accuracy and robustness of chain behavior sequence identification, and also lays a more reliable data foundation for subsequent abnormal coupling response relationship identification and risk assessment. This further enhances the ability to mine and identify potential systemic failure modes of remote control power supplies in multivariate complex environments.

[0017] 3. By deeply analyzing adjacent second response behaviors in the chain-like behavior sequence, the actual voltage fluctuation value corresponding to the first target response behavior and the actual current fluctuation value corresponding to the second target response behavior are obtained. This voltage fluctuation value is then input into a preset response benchmark model to obtain the current change range of the second target response behavior, thereby establishing the expected response correlation between voltage and current. When the actual current fluctuation value deviates from this current change range, an unexpected correlation is determined between the first and second target response behaviors, thus forming a response behavior pair. Further, based on the deviation between the actual current fluctuation value and the current change range of this response behavior pair, a response deviation value is calculated. Combined with the time interval between the response behavior pairs and the previously obtained environmental inhibition factor, these parameters are input into a preset risk assessment function to quantify the coupling risk value of this abnormal coupling response relationship. This method can identify response behavior pairs with potential response coupling relationships from the chain-like behavior sequence and can also perform multi-dimensional quantitative assessment of coupling risk based on response deviation, time interval, and environmental influencing factors, effectively improving the modeling ability of the inherent causal relationship between complex power supply response behaviors. Especially when voltage anomalies have a nonlinear and asynchronous effect on current response, this method can accurately capture the potential risks of abnormal coupling between power modules. It overcomes the limitations of traditional single-behavior analysis modes, which cannot reflect the coupling characteristics between cross-time-series and multivariable response behaviors. This significantly enhances the accuracy of remote-controlled power supply testing methods in identifying systemic fault symptoms and assessing latent risks. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the system architecture of an embodiment of a test method or test system for a remotely controlled power supply applied in this application. Figure 2 This is a flowchart illustrating a test method for a remotely controlled power supply according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a test system for a remotely controlled power supply according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of the electronic device in the embodiments of this application.

[0019] Explanation of reference numerals in the attached figures: 301, Response module; 302, Acquisition module; 303, Mapping module; 304, First analysis module; 305, Second analysis module; 306, Construction module; 307, Calculation module; 308, Generation module; 309, Detection module; 401, Processor; 402, Communication bus; 403, User interface; 404, Network interface; 405, Memory. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0021] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0022] Figure 1 An exemplary system architecture 100 is shown, representing an embodiment of a test method for a remotely controlled power supply or a test system for a remotely controlled power supply to which this application can be applied.

[0023] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105 deployed with the remote control power supply testing method. The network 104 provides a communication link between the terminal devices 101, 102, and 103 and the server 105, supporting the remote issuance of detection commands and the return of response data in the detection method. The network 104 may include various communication methods, such as wired communication links, wireless communication links, or fiber optic communication links.

[0024] Terminal devices 101, 102, and 103 can be server devices deployed in data centers or edge nodes, used to carry the power supply module under test and communicate with server 105. These terminal devices can integrate testing equipment to receive the first sequence of testing commands issued by server 105, collect the first sequence of responses from the power supply under test, and acquire environmental data related to its operating environment in real time. The terminal devices may also include functional modules such as a local data acquisition module, an interface control module, and a data upload module to support standardized sampling and uploading of testing data.

[0025] The terminal device can be a hardware device, such as a rack-mounted server or a power supply unit for communication equipment, or a software system or control program running on the aforementioned hardware device. The terminal device can also integrate video acquisition or sensing equipment to assist in acquiring environmental variables related to power supply operation, such as temperature, humidity, air pressure, and electromagnetic interference levels.

[0026] Server 105 is used to: issue a first detection command sequence to the terminal device; receive and parse the first response behavior sequence returned by the terminal device; acquire functional parameters and environmental data and perform behavior mapping to generate a second response behavior sequence; identify chained behavior sequences based on the sliding window algorithm and analyze the abnormal coupling response relationship between adjacent second response behaviors; calculate the coupling risk value based on response deviation, time interval, and environmental inhibition factor; construct a third response behavior sequence and determine the detection command set; generate a second detection command sequence based on the trigger probability and issue it again to the terminal device for testing; comprehensively analyze the test results, form a final detection report, and send it back to the relevant management platform or terminal device. Server 105 can be implemented as a single server or as a distributed cluster system composed of multiple servers, supporting parallel detection and management of large-scale power supply devices. Server 105 can also interface with a cloud platform to realize centralized control and remote operation and maintenance capabilities for power supply detection strategies. It should be noted that... Figure 1 The number of terminal devices, networks, and servers shown is for illustrative purposes only. In actual deployment, the number can be flexibly expanded according to the power detection scale and system performance requirements.

[0027] Figure 2 This is a flowchart illustrating a test method for a remotely controlled power supply in an embodiment of this application.

[0028] Please see Figure 2 This application provides a method for testing a remotely controlled power supply, applied in a server. The method includes: S201. Send the first detection instruction sequence to the detection device and control the detection device to perform initial detection on the power supply to be detected to obtain the first response behavior sequence of the power supply to be detected. The first response behavior sequence includes multiple first response behaviors, and one first response behavior corresponds to one detection instruction in the first detection instruction sequence. In step S201, the server equipped with the remote control power supply testing method first generates a preset first detection command sequence and sends this command sequence to the testing device connected to the power supply under test via the network interface module, so that the testing device can perform initial testing on the power supply under test. The first detection command sequence is a set of representative functional commands pre-defined according to the power supply working principle and power supply control interface standard, including voltage regulation commands, current load switching commands, short-time power failure recovery commands, power supply start / stop commands, overload simulation commands, etc., and its function is to trigger the typical response behavior of the power supply under test under different operating states.

[0029] The detection device can be an embedded control unit deployed in a terminal server, capable of exchanging commands, acquiring data, and uploading data through a power control interface. Upon receiving a detection command from the first detection command sequence, the detection device applies corresponding control to the power supply under test, causing real-time changes in parameters such as voltage, current, power factor, and temperature rise within the power supply. The built-in sensing circuit and sampling control module of the detection device acquire data on the power supply's output behavior at a preset sampling frequency after command execution. The acquired response data is then structured and encoded by the data processing module to form the first response behavior. Each detection command corresponds to a first response behavior. Executing multiple detection commands sequentially forms a complete first response behavior sequence. This sequence of first response behaviors is structured coded response behavior data, recording the power supply's response characteristics to each detection command in a unified format. It constitutes a set of temporally related, analyzable digital data sequences for subsequent behavior analysis and pattern recognition.

[0030] S202. Obtain the functional parameters of the power supply under test and the environmental data of the location of the power supply under test; To further enhance the interpretability and contextual awareness of the power supply's response behavior under test, after acquiring the first response behavior sequence, the system needs to simultaneously collect the power supply's functional parameters and environmental data of its location to support subsequent environmental mapping and response behavior modeling analysis. Functional parameters refer to static or semi-static attribute parameters that characterize the power supply's structural configuration and design performance. These typically include rated power, rated voltage, output voltage range, maximum output current, conversion efficiency, operating frequency, power supply type (e.g., switching power supply, linear power supply), circuit topology (e.g., LLC, Buck, Boost), manufacturer model, and production batch. These parameters can be obtained by reading the power supply's electronic tag (e.g., EEPROM), accessing metadata in the device management system, or through standard communication protocols (e.g., PMBus, I...). 2 (C, CAN, etc.) interact with the power control chip to obtain information.

[0031] Unlike functional parameters, environmental data primarily reflects the dynamic state of the physical environment in which the power supply under test is currently operating. This includes, but is not limited to, temperature, humidity, atmospheric pressure, electromagnetic interference levels, dust concentration, wind speed, power supply voltage fluctuations, and the heating status of other devices within the rack. Environmental data acquisition typically relies on environmental sensor arrays deployed in the server rack, server room, or edge nodes. These sensors can transmit data to the monitoring system in real time via communication methods such as Modbus, ZigBee, LoRa, or Ethernet. Some environmental indicators can also be directly obtained from the server's management controller (such as the Baseboard Management Controller, BMC) or the environmental monitoring subsystem.

[0032] S203. Map the environmental data to the first response behavior sequence to obtain the second response behavior sequence; In step S203, the system performs an environmental data mapping operation on the first response behavior sequence to generate a second response behavior sequence. The core of this process is to match each first response behavior with the environmental state corresponding to its occurrence time, and to reconstruct the characteristics of the response behavior under environmental influences, so that the original response behavior not only reflects the direct feedback of the power supply to the detection command, but also incorporates the potential influence of the current environmental variables on its response result.

[0033] Specifically, the system first aligns the sequence of first response actions with environmental data according to timestamps. Each first response action carries the execution time information of its corresponding detection instruction, while environmental data is typically collected continuously in time series format. The system uses methods such as linear interpolation or moving average to obtain the set of environmental feature values ​​within the time point or time window corresponding to each first response action. Environmental feature values ​​include temperature, humidity, electromagnetic interference, power input fluctuations, etc., which originate from the environmental sensor network or management controller acquisition module at the server deployment location.

[0034] After aligning the response behavior with environmental features, the system enhances the modeling of each first response behavior using a set of pre-defined behavior mapping functions. These mapping functions can be based on statistical models, rule engines, or machine learning models (such as multivariate regression, decision trees, neural networks, etc.). By inputting the original response value and the corresponding environmental feature value, they output a fused feature vector, which serves as the structural unit for the second response behavior. The second response behavior includes not only the original detection response signal (such as voltage / current fluctuations, response delay, settling time, etc.) but also parameterized descriptions such as environmental adjustment factors, response offsets, and anomaly weight labels, used to quantify the modulation effect of the environment on the response behavior.

[0035] Through the above mapping process, the resulting second response behavior sequence not only retains the basic response characteristics of the first response behavior, but also incorporates the modulating effect of the environment on the response result, making the subsequent chain behavior recognition and coupled risk assessment more context-sensitive and environmentally adaptable.

[0036] S204. Perform sliding window analysis on the second response behavior sequence to identify chain behavior sequences in which there is a temporal correlation between the second response behaviors in the second response behavior sequence; After constructing the second response behavior sequence, the system needs to further identify temporally related behavioral segments within the sequence to capture potential linked responses that occur continuously under specific environmental or functional conditions. Therefore, in step S204, the system performs a sliding window analysis on the second response behavior sequence to extract multiple chain-like behavior sequences with temporal continuity and logical coupling characteristics from the overall behavior sequence. The sliding window is a sequence analysis method based on the dynamic movement of a time or index window. The system slides a window line by line on the second response behavior sequence, combining environmental change characteristics and time interval information between behaviors to dynamically determine the temporal relationship between behaviors, thereby forming multiple chain-like behavior sequences. These sequences will serve as the basic units for subsequent coupling risk analysis. This may include steps S2041-S2044: S2041. The first second response action in the second response action sequence is set as the starting point to construct a candidate chain action sequence. During the identification of chain action sequences, the system first sets the first second response action in the second response action sequence as the starting point of the candidate chain action sequence, establishing an initial judgment benchmark for the current analysis process. The selection of the starting point directly affects the temporal correlation judgment of subsequent actions; therefore, the earliest responding action is used as the starting point, and an initial candidate chain action sequence is constructed accordingly. Whenever the system finds that a subsequent action meets the preset correlation conditions, it is included in the candidate sequence; otherwise, the current candidate sequence constitutes a complete chain action sequence, and the subsequent action will serve as the starting point for a new round of analysis, restarting the chain action construction process.

[0037] S2042. Calculate the change in environmental data between the first target response behavior and the second target response behavior, wherein the first target response behavior and the second target response behavior are any two adjacent second response behaviors in the second response behavior sequence, and the first target response behavior precedes the second target response behavior. The system pairs any two adjacent second response behaviors in the second response behavior sequence, designating the earlier response behavior as the first target response behavior and the later response behavior as the second target response behavior, and calculates the environmental data change between the first and second target response behaviors. The environmental data change refers to the numerical difference between the corresponding environmental variables (such as temperature, voltage disturbance, humidity, etc.) at the time the first and second target response behaviors occur, used to evaluate the impact of environmental stability on the continuity of the behavior. The calculation of environmental data change typically uses Euclidean distance, weighted difference, or index change values ​​after principal component analysis, which can reflect the fluctuations of macroscopic or microscopic environmental factors within the interval between behavior occurrences.

[0038] S2043. Input the change in environmental data into a preset environmental inhibition function to obtain the environmental inhibition factor between the first target response behavior and the second target response behavior. Multiply the environmental inhibition factor by a preset time correlation threshold to obtain the target time correlation threshold. To further quantify the degree of interference of environmental changes on the temporal correlation of behaviors, in step S2043, the system inputs the aforementioned environmental data change amount into a preset environmental suppression function to obtain an environmental suppression factor between the first target response behavior and the second target response behavior. The environmental suppression function is a mathematical model used to simulate the ability of environmental disturbances to suppress the continuity of behavior. For example, it can use an exponential decay function, a sigmoid function, or a regression model trained based on empirical data. The output value is usually between 0 and 1; the smaller the value, the more drastic the environmental change and the weaker the temporal correlation of the behaviors. The system multiplies this environmental suppression factor by a preset time correlation threshold to obtain the target time correlation threshold for the current behavior pair, thereby achieving dynamic adjustment of the original time interval judgment standard and making the chain behavior recognition process environmentally adaptable.

[0039] For example, in a test, the system detected that after a power supply received two different load switching commands in succession, its output voltage fluctuated by 0.4V and 0.6V respectively, with a time interval of 2 seconds. If the environmental changes are small at this time (such as a temperature change of only 0.5℃ and an input voltage fluctuation of less than 1%), the corresponding environmental suppression factor is large, the target time correlation threshold increases, and the system judges that the two have a strong temporal correlation, so it classifies them into the same chain behavior sequence. Conversely, if the temperature suddenly rises by 5℃ and the electromagnetic interference level changes by 2 levels between the two, the environmental suppression factor decreases significantly, the target time correlation threshold decreases, and even if the time interval remains unchanged, the system may determine that they are not correlated, thereby breaking the chain behavior and improving the overall recognition accuracy and robustness.

[0040] S2044. Based on the target time correlation threshold and the candidate chain behavior sequence, traverse each second response behavior in the second response behavior sequence to obtain the chain behavior sequence.

[0041] After calculating the target time-related threshold, the system enters the behavior traversal phase. Using a sliding window, it traverses the entire second response behavior sequence, sequentially determining whether the time interval between any two adjacent second response behaviors is less than the corresponding target time-related threshold. If the condition is met, it indicates a sufficiently strong temporal correlation between the current behaviors, and subsequent behaviors will be added to the candidate chain behavior sequence. If the condition is not met, it indicates a significant temporal and environmental disconnect between behaviors, and the candidate sequence terminates. The system then uses the previous behavior as the end point of the current chain behavior sequence and the next behavior as the new starting point, repeating the above judgment process until the entire sequence has been traversed.

[0042] Specifically, the steps include: starting from the starting point, traversing each second response behavior in the second response behavior sequence, and determining whether the time interval between the second target response behavior and the first target response behavior is less than the target time association threshold; if the time interval is less than the target time association threshold, the second target response behavior is added to the end of the candidate chain behavior sequence; if the time interval is greater than or equal to the target time association threshold, the first target response behavior is taken as the last response behavior in the candidate chain behavior sequence, resulting in a chain behavior sequence, and the second target response behavior with a time interval greater than or equal to the target time association threshold is set as the new starting point, and the candidate chain behavior sequence is continued to be constructed, and the response behaviors are continued to be traversed until all second response behaviors in the second response behavior sequence are detected.

[0043] In the specific implementation process, the system compares the current time interval with the target time correlation threshold corresponding to the behavior. The target time correlation threshold has been dynamically calculated based on the environmental inhibition factor in the previous steps, representing the maximum allowable range of behavior time delay under the influence of the current environmental disturbance. If the current time interval is less than the target time correlation threshold, it indicates that the two behaviors are correlated in the time dimension, and environmental fluctuations have not affected their response connection logic. Therefore, the system appends the second target response behavior to the end of the current candidate chain behavior sequence and continues to traverse and judge the next behavior.

[0044] However, if the current time interval is greater than or equal to the target time correlation threshold, the system considers the temporal continuity between the second target response behavior and the previous behavior to be disrupted. This is highly likely due to factors such as environmental disturbances, system interruptions, or the insertion of independent events, resulting in a lack of causal linkage between the behaviors. Therefore, the system takes the current first target response behavior as the terminating behavior of the current candidate chain behavior sequence and outputs the complete candidate sequence as a valid chain behavior sequence. At the same time, the system sets the current second target response behavior as the new starting point, continues to construct a new candidate chain behavior sequence, and continues to traverse backwards until the traversal process of the entire second response behavior sequence is completed.

[0045] Through the above traversal and judgment mechanism, the system can extract multiple chain response behavior sequences with strong correlation characteristics in segments while ensuring the rationality of the timing. Each sequence reflects the dynamic response process of the power supply under the continuous detection command over a period of time, providing a stable and high-quality data foundation for subsequent identification of abnormal coupling response relationships.

[0046] For example, suppose a second response behavior sequence contains behaviors A, B, C, and D, with time intervals of 1.2 seconds, 0.8 seconds, and 3.5 seconds respectively, and target time correlation thresholds of 2.0 seconds, 2.5 seconds, and 2.2 seconds respectively. The system determines that the time interval between A and B is 1.2 seconds, less than 2.0 seconds, and adds B to the candidate chain sequence; the interval between B and C is 0.8 seconds, less than 2.5 seconds, and C is also added; however, the interval between C and D is 3.5 seconds, exceeding 2.2 seconds, so the system determines C as the end point of the current chain sequence, forming chain sequence ABC, and sets D as the starting point of the next chain sequence, entering a new round of analysis. This approach not only maintains the temporal consistency between behaviors but also effectively avoids misjudgments caused by environmental fluctuations.

[0047] S205. Analyze whether there is an abnormal coupling response relationship between adjacent second response behaviors in the chain behavior sequence, and calculate the coupling risk value of response behavior pairs with abnormal coupling response relationships. The response behavior pair consists of two adjacent second response behaviors. After identifying the chain-like behavior sequence, it is necessary to mine potential response linkages from the chain-like behavior sequence and quantitatively assess their risk levels. The system executes step S205 to analyze whether there is an abnormal coupling response relationship between any two adjacent second response behaviors in the chain-like behavior sequence, and calculates the coupling risk value of the related response behavior pair. An abnormal coupling response relationship refers to the fact that the change trend of the previous response behavior has a non-independent influence on the subsequent behavior in terms of physical or logical aspects. The previous behavior may have an unexpected amplification or inhibition effect on the subsequent behavior. Combining the previous response behavior with the subsequent behavior that has an abnormal coupling response relationship creates a response behavior pair. Identifying this relationship is of great value for discovering hidden nonlinear interaction characteristics, abnormal propagation paths, or potential fault chains in power supply systems. To achieve this objective, the system adopts an analysis mechanism based on response feature modeling and risk function calculation to judge the coupling between adjacent response units in the chain-like behavior pair by pair and output the corresponding risk indicators. The process may include the following steps: obtaining the actual voltage fluctuation value corresponding to the first target response behavior and the actual current fluctuation value corresponding to the second target response behavior in the chained behavior sequence; inputting the actual voltage fluctuation value into a preset response benchmark model to obtain the current change range of the second target response behavior; if the actual current fluctuation value is not within the current change range, then combining the first target response behavior and the second target response behavior into a response behavior pair; calculating the response deviation value of the response behavior pair based on the interval value of the current change range and the actual current fluctuation value; inputting the response deviation value, the time interval of the response behavior pair, and the environmental inhibition factor into a preset risk assessment function for calculation to obtain the coupling risk value.

[0048] In the specific implementation process, the system first acquires the key response indicators for each group of adjacent second response behaviors in the chained behavior sequence, especially the actual voltage fluctuation value corresponding to the previous behavior (i.e., the first target response behavior) and the actual current fluctuation value corresponding to the subsequent behavior (i.e., the second target response behavior). These two parameters are the basic data for measuring whether there is a logical linkage between the power supply's voltage disturbance and current response. They are usually directly acquired by the detection equipment through a high-frequency sampling module after the command is triggered, and then normalized to ensure the comparability of data under different test scenarios.

[0049] The system inputs the actual voltage fluctuation value of the first target response behavior into a preset response benchmark model to obtain the theoretical current variation range of the second target response behavior. This response benchmark model is a voltage-current response mapping model constructed based on a large amount of historical test data, typically trained using regression analysis or machine learning methods (such as support vector machines, random forests, or neural networks). Its function is to estimate the possible current response range of the system under normal coupling conditions based on specific voltage disturbance conditions. This current variation range reflects the reasonable current response expectation of the power supply under the drive of this voltage change, and its upper and lower limits are used to define the judgment criteria.

[0050] The system then compares the actual current fluctuation value of the second target response behavior with the current change range mentioned above. If the actual value falls outside the range, the system determines that the behavior pair has an abnormal coupling, indicating that the previous behavior may have an unexpected amplification or suppression effect on the subsequent behavior, thus forming a response behavior pair that requires further quantification of its coupling risk. At this point, the system marks the behavior pair and proceeds to the subsequent risk calculation process. To quantify the degree of abnormal coupling of this response behavior pair, the system calculates the response deviation value based on the deviation between the upper and lower limits of the current change range and the actual value. The response deviation value can be expressed using normalized difference, relative error, or Z-score, reflecting the degree to which the current current response deviates from the theoretical model. The larger the response deviation, the more unstable the system response and the higher the potential risk.

[0051] Finally, the system inputs the response deviation value, the time interval of the behavior pair, and the environmental inhibition factor previously calculated from environmental changes into a preset risk assessment function to calculate the coupling risk value of the response behavior pair. The following is a representative formula for the risk assessment function: Where Risk is the coupling risk value; ΔR is the response deviation value, representing the magnitude of the deviation between the actual current fluctuation and the reference range; R max ΔT is the response deviation normalization factor used for normalization, which is the maximum tolerance deviation value of the system; τ is the time interval between response behaviors; E is the time sensitivity constant, which controls the degree of influence of the time interval on risk; E is the environmental inhibition factor, ranging from [0, 1], the larger the value, the more stable the environment; α, β, and γ represent the weighting coefficients of response deviation, time interval, and environmental disturbance on the overall risk, respectively, satisfying α + β + γ = 1, which can be modified according to the actual scenario.

[0052] For example, if there are two adjacent actions in a chain of actions, and the first action generates a voltage fluctuation of 0.6V under the load switching command, the system calculates the theoretical current change range as 0.3A to 0.5A using the response benchmark model. However, the actual measured current fluctuation is 0.68A, exceeding the upper limit of the range. Based on this, the system calculates a response deviation of 0.18A. Combining the time interval between the two actions (e.g., 1.5 seconds) and the environmental suppression factor (e.g., 0.7), the system finally outputs a coupling risk value of 0.82 (out of 1) through the risk assessment function. This indicates that there is a high degree of abnormal coupling relationship between the actions, which may be the result of an unstable power supply response mechanism or amplified external disturbances. This requires close attention in subsequent steps.

[0053] S206. Construct a third response behavior sequence based on the coupled risk value, and determine the detection instruction set corresponding to the third response behavior sequence. The third response behavior sequence consists of multiple response units. In step S206, after obtaining the coupling risk values ​​of all response behavior pairs in the chained behavior sequence, the system further filters and structurally integrates these behavior pairs with different degrees of coupling to construct a third response behavior sequence and determine its corresponding detection instruction set. The key to this step is that the system does not include all response behavior pairs in the third response behavior sequence, but only selects those adjacent pairs with coupling risk values ​​higher than a preset association threshold, combining them into structured response units. In this way, the third response behavior sequence retains only the most representative behavior segments most likely to cause system coupling anomalies, thereby achieving a high degree of extraction and focused modeling of complex system response patterns. This may include the following steps: combining adjacent response behavior pairs with coupling risk values ​​greater than a preset association threshold into a response unit, and combining the response units into a third response behavior sequence according to their temporal order; aligning the third response behavior sequence with the first detection instruction sequence in time to determine the detection instruction set corresponding to the third response behavior sequence, whereby the detection instruction set consists of a first instruction group corresponding to each response unit.

[0054] The system first traverses all identified response behavior pairs in the chained behavior sequence and sequentially determines whether the coupling risk value of each behavior pair is greater than a preset association threshold. The preset association threshold can be dynamically set according to the system's risk tolerance requirements, typically between 0.5 and 0.8, representing the system's lower limit for identifying coupling strength. Response behavior pairs exceeding the preset association threshold are considered candidate units with strong coupling relationships. However, to avoid discontinuous splicing between arbitrary behavior pairs, the system further restricts this: only when two response behavior pairs are consecutively adjacent in the original chained behavior sequence can they be combined into a single response unit. "Adjacent" means that the constituent behaviors of the two response behavior pairs overlap or are sequentially connected. For example, response behavior pair 1 consists of response behaviors A and B, and response behavior pair 2 consists of response behaviors B and C; therefore, they are continuous in behavior B and can be merged into a single response unit ABC. If there is a low-risk or threshold-unsatisfied behavior pair between two high-risk behavior pairs, they are not considered consecutively adjacent and must be split into different response units to avoid structural misjudgments.

[0055] The system performs a merging operation on response behavior pairs that meet the conditions, adding them as a complete response unit to the third response behavior sequence. The merging operation not only involves the concatenation of response behaviors but also integrates information such as coupling characteristics, time span, and environmental factors between behavior pairs to comprehensively characterize the dynamic behavior of the response unit. All response units that meet the conditions are sorted according to the timestamp of their initial response behavior, constructing the third response behavior sequence, which serves as the key high-dimensional response structure for subsequent system analysis.

[0056] To determine which detection commands triggered the third response sequence, the system compares the time interval of each response unit with the timestamps of the commands in the first detection command sequence, aligning the corresponding command groups. Each response unit's time range may contain multiple detection commands; the system combines these commands into a complete first command group and establishes a mapping relationship between this command group and the response unit. The set of first command groups corresponding to all response units constitutes the detection command set corresponding to the third response sequence.

[0057] Step S206 systematically reorganizes and refines the scattered, high-risk response behavior fragments identified in the preceding steps, thereby constructing a fault signature representing a specific deep-level fault mode and its corresponding trigger script. Without step S206, the coupled risk values ​​obtained from the preceding analysis would merely be a series of discrete data points, failing to form a macroscopic description of a complete and continuous chain of fault events, and thus failing to provide a clear and actionable target for generating targeted detection sequences. Step S206 filters and concatenates high-risk response behavior pairs by setting association thresholds, aggregating originally independent risk points into one or more response units with inherent logical connections, and further combining them into a third response behavior sequence. This is essentially a modeling process for complex fault phenomena, solidifying fuzzy, continuous abnormal behaviors into a standardized template that can be matched and verified subsequently.

[0058] S207. Based on the first detection instruction sequence and the first response behavior sequence, and combined with the functional parameters, calculate the trigger probability of each first instruction group in the detection instruction set triggering the third response behavior sequence. Step S207 further evaluates the triggering capability of each first instruction group in the detection instruction set for the target response behavior. Specifically, by analyzing historical execution data and combining the constraints of functional parameters, the triggering probability of each first instruction group successfully inducing the response unit in the third response behavior sequence is calculated. The triggering probability not only reflects the causal coupling strength between the instruction and the response, but also provides a quantitative basis for subsequent test case selection and instruction optimization recommendation. This may include the following steps: determining the target instruction group whose instruction type is the same as the first instruction group in the detection instruction set and counting the total number of times each target instruction group is repeatedly executed within the constraints of functional parameters; determining the response behavior group obtained from each execution of the target instruction group in the first response behavior sequence, with each response behavior group corresponding to a response record, and each response record including the first execution order and duration of the first response behavior in the response behavior group; matching the response record with the response unit in the third response behavior sequence, and when the first execution order is the same as the second execution order of the second response behavior in the response unit, and the time deviation of the duration from the preset duration is within the preset range, the response match is successful, and the total number of triggers is determined; calculating the ratio of the total number of triggers to the total number of executions to obtain the triggering probability.

[0059] In the specific implementation process, the system first filters out target instruction groups with the same instruction type from the original first detection instruction sequence based on the first instruction group corresponding to each response unit in the third response behavior sequence. Here, instruction type refers to a combination of detection instructions with the same functional category, parameter structure, or execution logic, such as both load switching instructions or voltage disturbance instructions. To avoid interference introduced by different test conditions, the system also limits the parameters based on the set functional parameters, such as limiting the input voltage, temperature, and load conditions to a certain tolerance range, ensuring that the matched target instruction group is comparable to the currently analyzed first instruction group in terms of functional environment. The system counts the total number of times all target instruction groups that meet the conditions are repeatedly executed in historical data, serving as the basis for subsequent trigger probability calculations.

[0060] Next, the system searches the first response behavior sequence for the response behavior group corresponding to each of the aforementioned target instruction groups. Each response behavior group consists of multiple consecutive first response behaviors, reflecting the actual response process of the system after the instruction execution. To facilitate subsequent matching and analysis, the system organizes each response behavior group into a response record and extracts two key indicators from it: first, the first execution order of each first response behavior in the response behavior group, i.e., the position number of the response in the behavior sequence; and second, its duration, reflecting the time span of the response behavior. The duration is usually obtained by recording the timestamps of the start and end of the behavior, in milliseconds or seconds.

[0061] The system then matches the aforementioned response records with the response units in the third response behavior sequence. The matching rules are primarily based on two dimensions: first, the alignment of the response execution order, requiring that the first execution order of a first response behavior in the response record should be consistent with the second execution order of the second response behavior in the response unit; second, the similarity of the behavior duration, meaning that the deviation between the duration in the record and the corresponding preset duration in the response unit is within a set preset threshold range. The preset range is an acceptable time tolerance interval set by the system based on the response behavior type, sampling precision, and historical statistical patterns. It is used to determine whether the actual response behavior and the target response unit maintain consistency in the time dimension, thereby ensuring the accuracy and robustness of the response matching process. If both conditions are met, the system determines that the execution triggered the target response unit, counting it as a trigger event. The system matches the execution records of all target instruction groups and accumulates the total number of triggers. After completing the statistics, the system calculates the ratio of the total number of triggers to the total number of executions of the target instruction group to obtain the trigger probability of that first instruction group.

[0062] For example, suppose the preceding steps identify a third response behavior sequence: a voltage query triggers a voltage drop, followed by a fan speed adjustment triggering a current spike, and determine the corresponding detection instruction set. The system will scan the complete historical records of the first detection instruction sequence and the first response behavior sequence. First, based on the functional parameter settings, such as focusing only on conditions where the CPU load is above 90%, the system will filter out all target instruction groups executed under this condition that have the same instruction type as the detection instruction set, counting a total of 20 executions, which is the total number of executions. Next, the system will check the response records corresponding to these 20 executions, strictly matching the occurrence order and duration of each response behavior with the standard pattern defined in the third response behavior sequence. For example, the conditions for a successful match are: the voltage drop must occur within 5 milliseconds after the voltage query instruction is issued, and the current spike must occur within 10 milliseconds after the fan speed adjustment instruction is issued. After comparison, it is found that 18 out of 20 times completely match this pattern, which is the total number of triggers. Finally, the system calculates the ratio of the total number of triggers to the total number of executions, i.e., the trigger probability. In this way, the system obtains a very high degree of confidence, confirming that there is a strong correlation between the instruction and the fault, providing a solid data basis for the next step of generating a highly targeted second detection instruction sequence, and avoiding ineffective in-depth testing of low-probability, accidental events.

[0063] S208. Generate a second detection instruction sequence based on the trigger probability; After calculating the matching degree between the detection instruction set and the third response behavior sequence, based on the calculated trigger probability, instruction groups that stably and reliably trigger the target response behavior in historical execution data are selected from the detection instruction set, further constructing a more targeted and valuable second detection instruction sequence. Step S208 not only optimizes and reconstructs the original instruction set but also provides a high-quality, high-confidence instruction input sequence for subsequent functional verification, response regression testing, or fault reproduction. This may include the following steps: comparing the trigger probability of each first instruction group in the detection instruction set with a preset matching degree threshold, and selecting second instruction groups with trigger probabilities greater than the preset matching degree threshold; determining the baseline functional parameter value corresponding to the second instruction group based on the test function parameters corresponding to the test instructions in the first detection instruction sequence; generating multiple functional detection parameter values ​​within a preset operating range of the functional parameters, centered on the baseline functional parameter value, according to a preset parameter step size, and combining the functional detection parameter values ​​into a test parameter set; combining each second instruction group with the corresponding test parameter set to generate multiple test units, and concatenating all test units in a preset order to obtain the second detection instruction sequence.

[0064] In practice, the system first compares the trigger probability of each first instruction group in the detection instruction set with a preset matching threshold. This threshold is typically set between 0.6 and 0.8 to filter out instruction groups with high trigger rates in historical executions. Only when the matching degree of a first instruction group exceeds this threshold is it considered a candidate instruction group with stable triggering capabilities and marked as the second instruction group. This filtering process ensures that the second detection instruction sequence retains only high-confidence instructions, thereby improving overall testing efficiency and response controllability.

[0065] Next, based on the test function parameters corresponding to each second instruction group in the first detection instruction sequence, the system extracts the baseline function parameter values ​​used in historical executions. Function parameters refer to set values ​​related to the execution environment or input conditions, such as input voltage, current load, temperature setpoint, etc., which directly affect the characteristics of response behavior. By extracting these parameters from the trigger records as reference values, the system ensures that the subsequently generated test parameter set has a realistic basis and functional consistency.

[0066] To enrich test coverage and enhance the verification capability of the instruction set under different boundary conditions, the system discretizes each baseline functional parameter value within a preset operating range, using a preset parameter step size, to generate a new set of functional test parameter values. For example, if a baseline voltage value is 12V, the operating range is [11V, 13V], and the step size is 0.5V, then the system generates a parameter set of [11V, 11.5V, 12V, 12.5V, 13V]. All generated functional test parameter values ​​constitute a test parameter set used to describe the test scenarios of the second instruction set under various operating conditions.

[0067] Subsequently, the system combines each second instruction group with its corresponding test parameter set to generate a complete test structure for each parameter value, called a test unit. Each test unit contains a highly matched second instruction group and its corresponding functional parameter configuration, representing an independent executable test task. All generated test units are concatenated according to a preset order set by the system (which can be chronological order, priority order, parameter increment order, etc.) to finally generate a structured second detection instruction sequence.

[0068] For example, suppose the trigger probability of a first instruction group A is 0.84, exceeding the set threshold of 0.75, and the system filters it into the second instruction group. Its historical reference parameter is a voltage of 12V, the set parameter range is [11V, 13V], and the parameter step size is 0.5V, then the generated parameter set is [11.0V, 11.5V, 12.0V, 12.5V, 13.0V]. The system combines instruction group A with these five parameters to generate five test units, and arranges them in ascending voltage order, forming a segment of the second detection instruction sequence. This process is repeated for all second instruction groups, ultimately yielding a second detection instruction sequence with broad coverage, strong response correlation, and high parameter diversity.

[0069] Step S208 automatically filters out high-matching instruction groups from the original detection instruction set that have consistently and efficiently triggered the target response behavior in historical executions. These groups are then combined with functional parameters to generate diverse and comprehensive test units, thereby constructing a second detection instruction sequence with optimized structure and strong response drive. Step S208 is particularly crucial in remote power supply detection methods because remote power supply equipment is typically deployed in environments difficult to debug on-site, such as communication base stations, field monitoring terminals, or unattended substations. Its fault detection and response assessment heavily rely on remotely issued test instructions. Through step S208, the system can filter out truly effective instruction combinations based on historical data and expand them in the parameter dimension to adapt to changes in operating conditions such as voltage, current, and temperature, thereby accurately simulating potential anomalies or boundary responses in a remote environment. For example, when testing the load response capability of a remote communication base station power supply, the system can filter out instruction sets that have historically been able to stably induce load fluctuation responses, and generate test units with different test voltages (such as 11V, 12.5V, etc.) around a typical input voltage (such as 12V). Finally, a second detection instruction sequence covering multiple scenarios is constructed to achieve comprehensive remote verification of power supply stability, response delay and abnormal offset, significantly improving the efficiency and accuracy of automated testing.

[0070] S209. Test the power supply under test based on the second detection command sequence to obtain the detection result of the power supply under test.

[0071] In step S209, the system performs automated testing on the power supply under test based on the second detection command sequence generated in the previous step. This allows the system to obtain the actual response behavior under various input conditions and command combinations, and generate test results to evaluate power supply performance or identify potential faults. Step S209 relies on a remote control interface or embedded test module to send the second detection command sequence to the power control module one by one, while simultaneously monitoring its output data in real time, including parameters such as voltage, current, temperature rise, and response delay. The system compares the collected response behavior with the preset expected response or historical benchmark response, calculates the deviation range, response consistency, and behavior integrity, thereby generating a structured test result report. For example, when testing a remote communication base station power supply, the system sequentially sends command groups containing different combinations of voltage disturbances and load switching, and records the output voltage stabilization time, current fluctuation amplitude, and temperature drift after each group is sent. If the voltage response delay under a command exceeds the preset range, it is marked as an abnormal response point. The final output includes a response quality score, the location of the anomaly, and suggested retest parameters, supporting subsequent fault location and system maintenance.

[0072] Please see Figure 3This is a schematic diagram of the structure of a test system for a remotely controlled power supply provided in an embodiment of this application. The test system 300 specifically includes: a response module 301, used to send a first detection command sequence to a detection device, controlling the detection device to perform initial detection on the power supply under test to obtain a first response behavior sequence of the power supply under test. The first response behavior sequence includes multiple first response behaviors, and each first response behavior corresponds to a detection command in the first detection command sequence; an acquisition module 302, used to acquire the functional parameters of the power supply under test and environmental data of the location of the power supply under test; a mapping module 303, used to map the environmental data to the first response behavior sequence to obtain a second response behavior sequence; a first analysis module 304, used to perform sliding window analysis on the second response behavior sequence to identify chain-like behavior sequences with temporal correlation among the second response behaviors in the second response behavior sequence; and a second analysis module... 305 is used to analyze whether there is an abnormal coupling response relationship between adjacent second response behaviors in the chained behavior sequence, and to calculate the coupling risk value of the response behavior pair with the abnormal coupling response relationship, wherein the response behavior pair consists of two adjacent second response behaviors; 306 is used to construct a third response behavior sequence based on the coupling risk value, and to determine the detection instruction set corresponding to the third response behavior sequence, wherein the third response behavior sequence consists of multiple response units; 307 is used to calculate the trigger probability of each first instruction group in the detection instruction set triggering the third response behavior sequence based on the first detection instruction sequence and the first response behavior sequence, combined with the functional parameters, to obtain the trigger probability; 308 is used to generate a second detection instruction sequence based on the trigger probability; 309 is used to test the power supply to be tested based on the second detection instruction sequence to obtain the detection result of the power supply to be tested.

[0073] Optionally, the first analysis module 304 is specifically used for: setting the first second response behavior in the second response behavior sequence as the starting point to construct a candidate chain behavior sequence; calculating the environmental data change between the first target response behavior and the second target response behavior, wherein the first target response behavior and the second target response behavior are any two adjacent second response behaviors in the second response behavior sequence, and the first target response behavior precedes the second target response behavior; inputting the environmental data change into a preset environmental suppression function to obtain an environmental suppression factor between the first target response behavior and the second target response behavior; multiplying the environmental suppression factor by a preset time association threshold to obtain a target time association threshold; and traversing each second response behavior in the second response behavior sequence based on the target time association threshold and the candidate chain behavior sequence to obtain the chain behavior sequence.

[0074] Optionally, the first analysis module 304 is further specifically configured to: starting from the starting point, traverse each second response behavior in the second response behavior sequence, and determine whether the time interval between the second target response behavior and the first target response behavior is less than the target time association threshold; if the time interval is less than the target time association threshold, add the second target response behavior to the end of the candidate chain behavior sequence; if the time interval is greater than or equal to the target time association threshold, take the first target response behavior as the last response behavior of the candidate chain behavior sequence, obtain the chain behavior sequence, and set the second target response behavior with the time interval greater than or equal to the target time association threshold as a new starting point, continue to construct the candidate chain behavior sequence, continue to traverse the response behaviors, until all second response behaviors in the second response behavior sequence are detected.

[0075] Optionally, the second analysis module 305 is specifically used for: obtaining the actual voltage fluctuation value corresponding to the first target response behavior and the actual current fluctuation value corresponding to the second target response behavior in the chained behavior sequence; inputting the actual voltage fluctuation value into a preset response benchmark model to obtain the current change range of the second target response behavior; if the actual current fluctuation value is not within the current change range, then combining the first target response behavior and the second target response behavior into the response behavior pair; calculating the response deviation value of the response behavior pair based on the interval value of the current change range and the actual current fluctuation value; inputting the response deviation value, the time interval of the response behavior pair, and the environmental inhibition factor into a preset risk assessment function for calculation to obtain the coupling risk value.

[0076] Optionally, the construction module 306 is specifically used to: combine adjacent response behavior pairs whose coupling risk value is greater than a preset association threshold into a response unit, and combine the response units into a third response behavior sequence according to the time order of the response units; time-align the third response behavior sequence with the first detection instruction sequence to determine the detection instruction set corresponding to the third response behavior sequence, wherein the detection instruction set is composed of the first instruction group corresponding to each response unit.

[0077] Optionally, the calculation module 307 is specifically used for: determining target instruction groups whose instruction types are the same as those of the first detection instruction sequence and the first instruction group in the detection instruction set, and counting the total number of times each target instruction group is repeatedly executed within the defined range of the functional parameters; determining the response behavior group obtained by each execution of the target instruction group in the first response behavior sequence, each response behavior group corresponding to a response record, each response record including the first execution order and duration of the first response behavior in the response behavior group; matching the response record with the response unit in the third response behavior sequence, and when the first execution order is the same as the second execution order of the second response behavior in the response unit, and the time deviation between the duration and the preset duration is within a preset range, the response matching is successful, and the total number of triggers is determined; calculating the ratio of the total number of triggers to the total number of executions to obtain the trigger probability.

[0078] Optionally, the detection module 309 is specifically used for: comparing the trigger probability of each first instruction group in the detection instruction set with a preset matching degree threshold, and filtering out a second instruction group whose trigger probability is greater than the preset matching degree threshold; determining the baseline function parameter value corresponding to the second instruction group based on the test function parameters corresponding to the test instructions in the first detection instruction sequence; generating multiple function detection parameter values ​​according to a preset parameter step size, centered on the baseline function parameter value, within a preset operation range of the function parameters, and combining the function detection parameter values ​​into a test parameter set; combining each second instruction group with the test parameter set corresponding to the second instruction group to generate multiple test units, and splicing all the test units in a preset order to obtain the second detection instruction sequence.

[0079] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0080] This embodiment also discloses an electronic device, as shown in the reference. Figure 4The electronic device may include: at least one processor 401, at least one communication bus 402, a user interface 403, a network interface 404, and at least one memory 405. The communication bus 402 is used to enable communication between these components. The user interface 403 may include a display screen and a camera; optionally, the user interface 403 may also include a standard wired interface or a wireless interface. The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0081] The processor 401 may include one or more processing cores. The processor 401 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 405, and by calling data stored in the memory 405. Optionally, the processor 401 may be implemented using at least one hardware form of digital signal processing, field-programmable gate array, or programmable logic array. The processor 401 may integrate one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip, not integrated into the processor 401. The memory 405 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 405 may include a non-transitory computer-readable medium. The memory 405 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 405 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), and instructions for implementing the various method embodiments described above. The data storage area may store data involved in the various method embodiments described above. Figure 4 In the electronic device shown, the user interface 403 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 401 can be used to call an application program stored in the memory 405 for a test method of remote control power supply. When executed by one or more processors 401, the electronic device performs one or more methods as described in the above embodiments.

[0082] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0083] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0084] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the disclosure in this specification. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A test method for a remotely controlled power supply, characterized in that, When applied to a server, the method includes: A first detection command sequence is sent to the detection device, which controls the detection device to perform initial detection on the power supply under test to obtain a first response behavior sequence of the power supply under test. The first response behavior sequence includes multiple first response behaviors, and each first response behavior corresponds to a detection command in the first detection command sequence. The first detection command sequence is a set of representative functional commands pre-set according to the power supply working principle and power supply control interface standard, including voltage regulation command, current load switching command, short-time power failure recovery command, power supply start / stop command, and overload simulation command. The first response behavior sequence is structured response behavior encoded data, which records the response characteristics of the power supply to each detection command in a unified format, forming a set of digital data sequences with time-series relationships that can be analyzed. Acquire the functional parameters of the power supply under test and the environmental data of the location of the power supply under test; The environmental data is mapped to the first response behavior sequence to obtain the second response behavior sequence; Sliding window analysis is performed on the second response behavior sequence to identify chain behavior sequences in which there is a temporal correlation between the second response behaviors in the second response behavior sequence; Analyze whether there is an abnormal coupling response relationship between adjacent second response behaviors in the chained behavior sequence, and calculate the coupling risk value of the response behavior pair with the abnormal coupling response relationship, wherein the response behavior pair consists of two adjacent second response behaviors; A third response behavior sequence is constructed based on the coupling risk value, and the detection instruction set corresponding to the third response behavior sequence is determined. The third response behavior sequence is composed of multiple response units. Based on the first detection instruction sequence and the first response behavior sequence, the trigger probability of each first instruction group in the detection instruction set triggering the third response behavior sequence is calculated in combination with the functional parameters; A second detection instruction sequence is generated based on the trigger probability; The power supply to be tested is tested based on the second detection command sequence to obtain the detection result of the power supply to be tested.

2. The method according to claim 1, characterized in that, The step of performing sliding window analysis on the second response behavior sequence to identify chain-like behavior sequences with temporal correlations among the second response behaviors specifically includes: The first second response behavior in the second response behavior sequence is set as the starting point to construct a candidate chain behavior sequence; Calculate the change in environmental data between the first target response behavior and the second target response behavior, wherein the first target response behavior and the second target response behavior are any two adjacent second response behaviors in the second response behavior sequence, and the first target response behavior precedes the second target response behavior; The environmental data change is input into a preset environmental suppression function to obtain an environmental suppression factor between the first target response behavior and the second target response behavior. The environmental suppression factor is multiplied by a preset time correlation threshold to obtain a target time correlation threshold. Based on the target time-related threshold and the candidate chain behavior sequence, each second response behavior in the second response behavior sequence is traversed to obtain the chain behavior sequence.

3. The method according to claim 2, characterized in that, The step of traversing each of the second response behaviors in the second response behavior sequence based on the target time-related threshold and the candidate chained behavior sequence to obtain the chained behavior sequence specifically includes: Starting from the starting point, traverse each second response behavior in the second response behavior sequence and determine whether the time interval between the second target response behavior and the first target response behavior is less than the target time association threshold. If the time interval is less than the target time-related threshold, then the second target response behavior is added to the end of the candidate chain behavior sequence; If the time interval is greater than or equal to the target time association threshold, the first target response behavior is taken as the last response behavior of the candidate chain behavior sequence to obtain the chain behavior sequence. The second target response behavior with a time interval greater than or equal to the target time association threshold is set as the new starting point. The candidate chain behavior sequence is constructed and the response behavior is traversed until all the second response behaviors in the second response behavior sequence are detected.

4. The method according to claim 3, characterized in that, The analysis of whether there is an abnormal coupling response relationship between adjacent second response behaviors in the chained behavior sequence, and the calculation of the coupling risk value of response behavior pairs with the abnormal coupling response relationship, specifically includes: Obtain the actual voltage fluctuation value corresponding to the first target response behavior and the actual current fluctuation value corresponding to the second target response behavior in the chained behavior sequence; The actual voltage fluctuation value is input into a preset response benchmark model to obtain the current change range of the second target response behavior; If the actual current fluctuation value is not within the current change range, then the first target response behavior and the second target response behavior are combined into the response behavior pair; The response deviation value of the response behavior pair is calculated based on the interval value of the current change range and the actual current fluctuation value. The response deviation value, the time interval of the response behavior pair, and the environmental inhibition factor are input into a preset risk assessment function for calculation to obtain the coupling risk value.

5. The method according to claim 1, characterized in that, The step of constructing a third response behavior sequence based on the coupled risk value and determining the detection instruction set corresponding to the third response behavior sequence specifically includes: The adjacent response behavior pairs with a coupling risk value greater than a preset association threshold are combined into response units, and multiple response units are combined into a third response behavior sequence according to the time order of the response units. The third response behavior sequence is time-aligned with the first detection instruction sequence to determine the detection instruction set corresponding to the third response behavior sequence. The detection instruction set is composed of the first instruction group corresponding to each response unit.

6. The method according to claim 1, characterized in that, The step of calculating the trigger probability of each first instruction group in the detection instruction set triggering the third response behavior sequence based on the first detection instruction sequence and the first response behavior sequence, combined with the functional parameters, specifically includes: Identify target instruction groups whose instruction types are the same as the first instruction group in the detection instruction set, and count the total number of times each target instruction group is repeatedly executed within the limits of the functional parameters; In the first response behavior sequence, a response behavior group is determined for each execution of the target instruction group. Each response behavior group corresponds to a response record. Each response record includes the first execution order and duration of the first response behavior in the response behavior group. The response record is matched with the response unit in the third response behavior sequence. When the first execution order is the same as the second execution order of the second response behavior in the response unit, and the time deviation between the duration and the preset duration is within the preset range, the response match is successful, and the total number of triggers is determined. The trigger probability is obtained by calculating the ratio of the total number of triggers to the total number of executions.

7. The method according to claim 1, characterized in that, The generation of the second detection instruction sequence based on the trigger probability specifically includes: The trigger probability of each first instruction group in the detection instruction set is compared with a preset matching threshold, and a second instruction group whose trigger probability is greater than the preset matching threshold is selected. The baseline function parameter value corresponding to the second instruction group is determined based on the test function parameters corresponding to the test instructions in the first detection instruction sequence. Within the preset operating range of the functional parameters, multiple functional detection parameter values ​​are generated centered on the benchmark functional parameter value according to the preset parameter step size, and the functional detection parameter values ​​are combined into a test parameter set. Each of the second instruction groups is combined with the test parameter set corresponding to the second instruction group to generate multiple test units, and all the test units are spliced ​​together in a preset order to obtain the second detection instruction sequence.

8. A test system for a remotely controlled power supply, characterized in that, include: The response module is used to send a first detection command sequence to the detection device, and control the detection device to perform initial detection on the power supply under test to obtain a first response behavior sequence of the power supply under test. The first response behavior sequence includes multiple first response behaviors, and each first response behavior corresponds to a detection command in the first detection command sequence. The first detection command sequence is a set of representative functional commands pre-set according to the power supply working principle and power supply control interface standard, including voltage regulation command, current load switching command, short-time power failure recovery command, power supply start / stop command, and overload simulation command. The first response behavior sequence is structured response behavior encoded data, which records the response characteristics of the power supply to each detection command in a unified format, forming a set of digital data sequences with time relationship that can be analyzed. The acquisition module is used to acquire the functional parameters of the power supply under test and the environmental data of the location of the power supply under test; The mapping module is used to map the environmental data to the first response behavior sequence to obtain the second response behavior sequence; The first analysis module is used to perform sliding window analysis on the second response behavior sequence to identify chain behavior sequences in which there is a temporal correlation between the second response behaviors in the second response behavior sequence; The second analysis module is used to analyze whether there is an abnormal coupling response relationship between adjacent second response behaviors in the chained behavior sequence, and to calculate the coupling risk value of the response behavior pair with the abnormal coupling response relationship, wherein the response behavior pair consists of two adjacent second response behaviors; A construction module is used to construct a third response behavior sequence based on the coupling risk value and determine the detection instruction set corresponding to the third response behavior sequence, wherein the third response behavior sequence is composed of multiple response units; The calculation module is used to calculate the trigger probability of each first instruction group in the detection instruction set triggering the third response behavior sequence based on the first detection instruction sequence and the first response behavior sequence, combined with the functional parameters; The generation module is used to generate a second detection instruction sequence based on the trigger probability; The detection module is used to test the power supply under test based on the second detection command sequence and obtain the detection result of the power supply under test.

9. An electronic device, characterized in that, include: One or more processors and memory; The memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on an electronic device, the electronic device causes the electronic device to perform the method as described in any one of claims 1-7.

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