Power distribution internet-of-things terminal testing method of power distribution network

Abstract

The invention relates to the technical field of power distribution network testing, and discloses a power distribution internet of things terminal testing method for a power distribution network, which comprises the following steps: constructing a digital twin according to a power distribution network model, a terminal type of a power distribution internet of things terminal and a testing demand, determining virtual testing environment parameters in the digital twin based on the testing demand, and testing the virtual testing environment parameters in the digital twin. The digital twinborn body can receive operation data of a power distribution internet-of-things terminal of the power distribution network, then obtain current power data of the power distribution network and calculate a dynamic coupling degree, if the dynamic coupling degree does not meet a preset range, the power data of the virtual test environment parameters are adjusted and recalculated until the dynamic coupling degree meets the preset range, and then the power distribution network is started. According to the method, the dynamic coupling degree is determined, then the task urgency degree is determined according to the dynamic coupling degree, finally, the cloud or edge node is selected as the execution node according to the task urgency degree, the corresponding test task is executed in the digital twin, the test result is obtained, and the technical bottlenecks of a traditional test method in the aspects of efficiency, real-time feedback, environment construction and task scheduling are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of power distribution network testing technology, and in particular to a method for testing distribution IoT terminals in power distribution networks. Background Technology

[0002] With the rapid development of smart grids, the distribution IoT, as a key component, directly impacts the stable operation of the power grid through the performance and reliability of its distribution IoT terminals. Currently, distribution IoT terminals encompass smart meters, smart switches, and smart sensors, connecting to distribution automation systems via communication networks to achieve data acquisition, monitoring, and control functions. However, against the backdrop of large-scale distributed energy access and smart distribution grid upgrades, the number and types of distribution IoT terminals are experiencing explosive growth, significantly increasing functional complexity. Existing testing methods face severe challenges—centralized testing platforms suffer from cumbersome testing processes, low efficiency, and a lack of real-time feedback, making them unsuitable for rapid testing of large-scale terminals; in terms of environment construction, traditional static test case models cannot dynamically adjust parameters according to the real-time state of the power grid, resulting in a detection rate of less than 65% for complex conditions such as intermittent grounding faults; and at the task scheduling level, existing systems either suffer from insufficient real-time performance due to over-reliance on cloud computing leading to an average latency exceeding 200ms, or lack the ability to simulate complex scenarios due to relying solely on edge computing, failing to balance real-time performance and simulation requirements. Summary of the Invention

[0003] Based on this, it is necessary to address the above problems by proposing a testing method for distribution IoT terminals in distribution networks. This method not only improves the real-time adaptability of test parameters (significantly increasing the detection rate of complex operating conditions), but also enhances the simulation capability of complex scenarios while ensuring real-time performance through a cloud-edge collaborative scheduling strategy driven by task urgency. This achieves high efficiency, real-time performance, and dynamic adaptability in large-scale distribution IoT terminal testing, effectively solving the technical bottlenecks of traditional testing methods in terms of efficiency, real-time feedback, environment construction, and task scheduling.

[0004] To achieve the above objectives, the present invention provides, in a first aspect, a method for testing distribution IoT terminals in a distribution network, the method comprising: Obtain the distribution network model and the terminal types of the distribution IoT terminals, as well as the testing requirements; Based on the power distribution network model and the terminal type, construct a digital twin; Based on the test requirements, the virtual test environment parameters in the digital twin are determined. The digital twin is used to collect the operating data of the distribution IoT terminals of the distribution network in real time. Obtain the current electrical data of the distribution network, and determine the dynamic coupling degree based on the current electrical data; If the dynamic coupling degree does not meet the preset range, the electrical data of the virtual test environment parameters in the digital twin is adjusted according to the dynamic coupling degree, and the dynamic coupling degree is re-determined according to the adjusted electrical data until the dynamic coupling degree meets the preset range. The task urgency is then determined according to the dynamic coupling degree. Based on the urgency of the task, a cloud or edge node is selected as the execution node, and the execution node is commanded to execute the test task corresponding to the execution node in the digital twin to obtain the test results.

[0005] Optionally, the current electrical data includes the current voltage deviation, current active power fluctuation, current total harmonic distortion rate, and current frequency deviation. Determining the dynamic coupling degree based on the current electrical data includes: Using formula Determine the dynamic coupling degree; The adjusted electrical data includes the adjusted voltage deviation, the adjusted active power fluctuation, and the adjusted total harmonic distortion rate. The step of redetermining the dynamic coupling degree based on the adjusted electrical data includes: Using formula Determine the dynamic coupling degree; in, The dynamic coupling degree, For voltage weighting coefficients, The current voltage deviation, Nominal voltage, For power weighting coefficients, This refers to the current active power fluctuation. Nominal active power For harmonic weighting coefficients, The current total harmonic distortion (THD) is... The maximum allowable total harmonic distortion (THD) of the system. For frequency weighting coefficients, The current frequency deviation, Nominal frequency, The adjusted voltage deviation, This refers to the adjusted active power fluctuation. The adjusted total harmonic distortion rate is denoted as .

[0006] Optionally, the electrical data includes voltage deviation, active power fluctuation, and total harmonic distortion rate. The step of adjusting the electrical data for the virtual test environment parameters in the digital twin based on the dynamic coupling degree includes: If the dynamic coupling degree is less than the lower limit of the preset range, the voltage deviation, the active power fluctuation and the total harmonic distortion rate are adjusted according to the first preset adjustment rule. If the dynamic coupling degree is greater than the upper limit of the preset range, the voltage deviation, the active power fluctuation and the total harmonic distortion rate are adjusted according to the second preset adjustment rule.

[0007] Optionally, the first preset adjustment rule includes increasing the voltage deviation by a first preset percentage, increasing the active power fluctuation by a second preset percentage, and taking the minimum value between the total harmonic distortion rate increased by a third preset percentage and the maximum total harmonic distortion rate allowed by the system. The second preset adjustment rule includes reducing the voltage deviation by the first preset percentage, reducing the active power fluctuation by the second preset percentage, and reducing the total harmonic distortion by the third preset percentage.

[0008] Optionally, determining the task urgency based on the dynamic coupling degree includes: Obtain the current network latency of the power distribution network; Based on the test requirements, determine the maximum response time and the number of associated terminals; The urgency of the task is determined based on the maximum response time, the number of associated terminals, the current network latency, and the dynamic coupling degree.

[0009] Optionally, determining the task urgency based on the maximum response time, the number of associated terminals, the current network latency, and the dynamic coupling degree includes: Using formula Determine the urgency of the task; in, The urgency level of the task. , , and These are the first preset weight coefficient, the second preset weight coefficient, the third preset weight coefficient, and the fourth preset weight coefficient, respectively. The maximum response time, The maximum allowable response time for the system. The number of associated terminals, The dynamic coupling degree, This represents the maximum number of management terminals for the edge nodes. The current network latency, This represents the maximum network latency allowed by the system.

[0010] Optionally, selecting a cloud or edge node as the execution node based on the task urgency includes: If the urgency of the task is less than a preset threshold, the cloud is selected as the execution node. If the urgency of the task is greater than or equal to the preset threshold, the edge node is selected as the execution node.

[0011] Optionally, the method further includes: When the execution node is the cloud, the test task corresponding to the execution node is a virtual simulation and physical verification task; When the execution node is the edge node, the test task corresponding to the execution node is signal injection, real-time monitoring, and anomaly handling.

[0012] Optionally, the signal injected in the signal injection, real-time monitoring, and anomaly handling tasks is a dynamic current, and the method further includes: Obtain the disturbance current of the virtual test environment parameters in the digital twin; The dynamic current is determined based on the disturbance current and the dynamic coupling degree.

[0013] Optionally, the method further includes: Based on the test values ​​of each indicator in the test results, determine the indicator score of each indicator in the test results; A causal analysis was performed on the scores of each indicator in the test results to obtain the causal analysis results; Based on the results of the cause analysis, optimization suggestions are determined. A test evaluation report is generated based on the optimization suggestions, the cause analysis results, and the scores of each indicator in the test results.

[0014] To achieve the above objectives, the present invention provides a distribution IoT terminal testing device for a distribution network in a second aspect, the device comprising: The acquisition module is used to acquire the distribution network model and the terminal type of the distribution IoT terminal, as well as the testing requirements; A construction module is used to construct a digital twin based on the power distribution network model and the terminal type; The determination module is used to determine the virtual test environment parameters in the digital twin according to the test requirements. The digital twin is used to collect the operation data of the distribution IoT terminal of the distribution network in real time. The acquisition and determination module is used to acquire the current electrical data of the distribution network and determine the dynamic coupling degree based on the current electrical data. The judgment and iteration module is used to adjust the electrical data of the virtual test environment parameters in the digital twin according to the dynamic coupling degree when the dynamic coupling degree does not meet the preset range, and redetermine the dynamic coupling degree according to the adjusted electrical data until the dynamic coupling degree meets the preset range, and determine the task urgency according to the dynamic coupling degree. The selection and testing module is used to select a cloud or edge node as an execution node according to the urgency of the task, and command the execution node to execute the test task corresponding to the execution node in the digital twin to obtain the test results.

[0015] To achieve the above objectives, the present invention provides, in a third aspect, a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform a power distribution IoT terminal testing method for a power distribution network as described in any one of the first aspects.

[0016] To achieve the above objectives, the present invention provides a computer device in a fourth aspect, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform a power distribution IoT terminal testing method for a power distribution network as described in any one of the first aspects.

[0017] The present invention provides the following advantages: The method acquires a power distribution network model, the terminal type of the power distribution IoT terminal, and testing requirements, thereby constructing a digital twin. Based on the testing requirements, it determines the virtual testing environment parameters within the digital twin. This digital twin can receive operating data from the power distribution IoT terminals in the power distribution network, then acquire the current electrical data of the power distribution network and calculate the dynamic coupling degree. If the dynamic coupling degree does not meet a preset range, the electrical data of the virtual testing environment parameters is adjusted and recalculated until the preset range is met. Furthermore, the task urgency is determined based on the dynamic coupling degree, and finally, a cloud or edge node is selected as the execution node based on the task urgency. The nodes execute corresponding test tasks and obtain test results within the digital twin; that is, by constructing a digital twin, the virtual test environment and the real power grid are dynamically coupled. Combined with a dynamic coupling degree adjustment mechanism, the real-time adaptability of test parameters is improved (the detection rate of complex operating conditions is significantly improved). Furthermore, through a cloud-edge collaborative scheduling strategy driven by task urgency, the simulation capability of complex scenarios is enhanced while ensuring real-time performance. Ultimately, this achieves high efficiency, real-time performance, and dynamic adaptability in the testing of large-scale distribution IoT terminals, effectively solving the technical bottlenecks of traditional testing methods in terms of efficiency, real-time feedback, environment construction, and task scheduling. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] in: Figure 1 This is a schematic diagram of a power distribution IoT terminal testing method for a power distribution network according to an embodiment of this application; Figure 2 This is a schematic diagram of a power distribution IoT terminal testing device for a power distribution network according to an embodiment of this application; Figure 3 This is a diagram showing the internal structure of a computer device in some embodiments. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] With the rapid development of smart grids, the distribution IoT, as a key component, directly impacts the stable operation of the power grid through the performance and reliability of its distribution IoT terminals. Currently, distribution IoT terminals encompass smart meters, smart switches, and smart sensors, connecting to distribution automation systems via communication networks to achieve data acquisition, monitoring, and control functions. However, against the backdrop of large-scale distributed energy access and smart distribution grid upgrades, the number and types of distribution IoT terminals are experiencing explosive growth, significantly increasing functional complexity. Existing testing methods face severe challenges—centralized testing platforms suffer from cumbersome testing processes, low efficiency, and a lack of real-time feedback, making them unsuitable for rapid testing of large-scale terminals; in terms of environment construction, traditional static test case models cannot dynamically adjust parameters according to the real-time state of the power grid, resulting in a detection rate of less than 65% for complex conditions such as intermittent grounding faults; and at the task scheduling level, existing systems either suffer from insufficient real-time performance due to over-reliance on cloud computing leading to an average latency exceeding 200ms, or lack the ability to simulate complex scenarios due to relying solely on edge computing, failing to balance real-time performance and simulation requirements.

[0022] To address the aforementioned issues, this application proposes a testing method for distribution IoT terminals in power distribution networks. This method not only improves the real-time adaptability of test parameters (significantly increasing the detection rate of complex operating conditions), but also enhances the simulation capability of complex scenarios while ensuring real-time performance through a cloud-edge collaborative scheduling strategy driven by task urgency. This achieves high efficiency, real-time performance, and dynamic adaptability in large-scale distribution IoT terminal testing, effectively solving the technical bottlenecks of traditional testing methods in terms of efficiency, real-time feedback, environment construction, and task scheduling. The specific implementation principle will be described in detail in the following embodiments.

[0023] This application provides a method for testing distribution IoT terminals in a power distribution network in its first aspect.

[0024] Please see Figure 1 This is a schematic diagram of a power distribution IoT terminal testing method for a power distribution network according to an embodiment of this application. The method includes: Step 110: Obtain the distribution network model and the terminal type of the distribution IoT terminal, as well as the testing requirements.

[0025] The testing requirements can be pre-set by the operators according to actual needs.

[0026] In some implementations, a distribution network model can be constructed based on information such as the topology of the distribution network.

[0027] Regarding the terminal types of distribution IoT terminals, in some embodiments, the terminal types include, but are not limited to, feeder terminals, distribution transformer terminals, and station terminals.

[0028] Step 120: Construct a digital twin based on the distribution network model and terminal type.

[0029] For digital twins, in some embodiments, digital twins include, but are not limited to, a database of electrical parameters of the device, a disturbance model, and a communication environment. The database of electrical parameters includes, but is not limited to, node voltage amplitude / phase angle, line impedance matrix, and transformer turns ratio / loss. The disturbance model includes, but is not limited to, short-circuit faults (such as A-phase grounding), voltage sags (such as 0.3 pu), and harmonics (such as the 5th and 7th harmonics). The communication environment includes, but is not limited to, network latency (such as 4 ms) and GOOSE packet loss rate (such as 0.1%~1%).

[0030] GOOSE stands for Generic Object Oriented Substation Event.

[0031] Step 130: Based on the testing requirements, determine the virtual test environment parameters in the digital twin. The digital twin is used to collect the operating data of the distribution IoT terminals of the distribution network in real time.

[0032] For testing requirements, in some embodiments, testing requirements include, but are not limited to, test type (such as overcurrent / grounding / reclosing), terminal model, fault type (such as phase-to-phase / grounding), etc.

[0033] In some embodiments, operational data includes, but is not limited to, voltage, current, etc.

[0034] In some embodiments, for the collection of operational data, the digital twin collects operational data of the distribution IoT terminals of the distribution network in real time through a low-latency link.

[0035] In some embodiments, the virtual test environment parameters in a digital twin can be determined based on the test requirements, by identifying the operating scenarios needed for simulation analysis (such as short-circuit faults, voltage sags, harmonic pollution, etc.), and then determining the virtual test environment parameters in the digital twin based on these operating scenarios.

[0036] Furthermore, in some embodiments, it is also necessary to determine some initialization environment parameters of the virtual test environment parameters in the digital twin, such as reference voltage, reference current, short-circuit current, disturbance current, voltage sag parameters, and communication delay parameters, according to test requirements.

[0037] Furthermore, regarding the determination of the reference voltage, reference current, short-circuit current, disturbance current, voltage sag parameter, and communication delay parameter, in some embodiments, the terminal rated voltage, allowable voltage fluctuation percentage, terminal rated current, system equivalent impedance, sag ratio, measured network average delay, and protocol-required minimum interval can be determined according to test requirements. Then, the reference voltage is determined based on the terminal rated voltage and allowable voltage fluctuation percentage; the reference current is determined based on the terminal rated current and load factor; the short-circuit current is determined based on the reference voltage and system equivalent impedance; the disturbance current is determined based on the short-circuit current and safety factor; and the communication delay parameter (i.e., the final GOOSE message interval) is determined based on the terminal rated voltage and sag ratio, voltage sag parameter, and protocol-required minimum interval, measured network average delay, and delay margin factor.

[0038] The load factor, safety factor, and time delay margin factor can all be obtained and preset by the operator based on a large amount of experience, experiments, or statistics. Of course, the operator can also preset them according to actual needs.

[0039] Regarding the values ​​of the load factor, safety factor, and delay margin factor, in some embodiments, this application preferably sets the load factor to 0.8, the safety factor to 0.7, and the delay margin factor to 1.8.

[0040] In some embodiments, the reference voltage can be determined using a formula. Determine the reference voltage; where, As the reference voltage, The terminal rated voltage, Percentage of allowable voltage fluctuation.

[0041] Regarding the method of determining the reference current, in some embodiments, the product between the terminal rated current and the load factor can be used as the reference current.

[0042] In some embodiments, the short-circuit current can be determined using a formula. Determine the short-circuit current; where, This is the short-circuit current. As the reference voltage, This is the system's equivalent impedance.

[0043] Regarding the method of determining the disturbance current, in some embodiments, the product between the short-circuit current and the safety factor can be used as the disturbance current.

[0044] Regarding the method for determining the voltage sag parameter, in some embodiments, the product between the terminal rated voltage and the sag ratio can be used as the voltage sag parameter.

[0045] In some embodiments, the method for determining communication delay parameters can be achieved using formulas. Determine the communication delay parameters; where, For communication delay parameters, To find the maximum value function, The protocol requires a minimum interval. To measure the average network latency, This is the time delay margin coefficient.

[0046] Step 140: Obtain the current electrical data of the distribution network and determine the dynamic coupling degree based on the current electrical data.

[0047] In some embodiments, current electrical data includes, but is not limited to, current voltage deviation, current active power fluctuation, current total harmonic distortion, and current frequency deviation.

[0048] Step 150: If the dynamic coupling degree does not meet the preset range, adjust the electrical data of the virtual test environment parameters in the digital twin according to the dynamic coupling degree, and redetermine the dynamic coupling degree according to the adjusted electrical data until the dynamic coupling degree meets the preset range. Determine the task urgency according to the dynamic coupling degree.

[0049] The preset range can be set in advance by the operator based on a large amount of experience, experiments or statistics. Of course, the operator can also set it in advance according to actual needs.

[0050] It should be noted that if the dynamic coupling degree determined in step 150 still does not meet the preset range, the electrical data of the virtual test environment parameters in the digital twin needs to be adjusted according to the dynamic coupling degree until the dynamic coupling degree meets the preset range before the task urgency can be determined according to the dynamic coupling degree. If the dynamic coupling degree determined in step 140 meets the preset range, the task urgency can be determined directly according to the dynamic coupling degree.

[0051] Regarding the value of the preset range, in some embodiments, this application preferably sets the preset range to [0.25, 0.75].

[0052] In some embodiments, the adjusted electrical data includes, but is not limited to, adjusted voltage deviation, adjusted active power fluctuation, and adjusted total harmonic distortion.

[0053] Step 160: Based on the urgency of the task, select a cloud or edge node as the execution node, and command the execution node to execute the corresponding test task in the digital twin to obtain the test results.

[0054] Regarding the selection method of execution nodes, in some embodiments, cloud or edge nodes can be selected as execution nodes based on the comparison between task urgency and preset threshold. The preset threshold can be obtained and set in advance by the operator based on a large amount of experience, experimentation or statistics. Of course, the operator can also set it in advance according to actual needs.

[0055] In this embodiment, a digital twin is constructed to achieve dynamic coupling between the virtual test environment and the real power grid. Combined with a dynamic coupling degree adjustment mechanism, the real-time adaptability of test parameters is improved (the detection rate of complex operating conditions is significantly improved). Furthermore, through a cloud-edge collaborative scheduling strategy driven by task urgency, the simulation capability of complex scenarios is enhanced while ensuring real-time performance. Ultimately, this achieves high efficiency, real-time performance, and dynamic adaptability in the testing of large-scale distribution IoT terminals, effectively solving the technical bottlenecks of traditional testing methods in terms of efficiency, real-time feedback, environment construction, and task scheduling.

[0056] In addition to the aforementioned beneficial effects, the distribution IoT terminal testing method for this distribution network also has the following advantages: Reduced hardware investment costs: Traditional testing methods may require the construction of a large number of complex actual test environments, involving the layout and maintenance of numerous physical devices and lines, resulting in high costs. This method, however, constructs a digital twin for virtual testing, reducing reliance on a large number of actual physical test devices. Only the necessary computing equipment is required to run the digital twin and execute test tasks, thus significantly reducing hardware procurement, installation, and maintenance costs; Reduced site occupancy costs: Actual test environments often require large spaces to deploy various test equipment and lines, especially for large-scale distribution networks. Testing IoT terminals requires significantly more space. This method, based on digital twins, eliminates the need for large physical sites; simulations are performed within a computer system, saving on site rental or construction costs. This is particularly suitable for regions or enterprises with limited space. Furthermore, it allows for rapid switching between test scenarios: traditional testing often necessitates rearranging physical equipment and adjusting wiring connections, a cumbersome and time-consuming process. This method, using digital twins, allows operators to quickly modify virtual test environment parameters within the computer system, easily switching between different test scenarios, such as rapidly switching from a short-circuit fault scenario to a voltage sag scenario. This greatly improves testing flexibility and... Efficiency is enhanced, enabling more timely fulfillment of diverse testing needs; support for diverse terminal testing: with the continuous development of distribution IoT terminal technology, terminal types and functions are becoming increasingly diversified. The digital twin constructed by this method can flexibly adjust virtual test environment parameters and test tasks according to the characteristics and testing requirements of different types of distribution IoT terminals, facilitating convenient testing of various new terminals without the need to rebuild a dedicated test environment for each terminal, providing strong support for the research and development and promotion of new products; avoidance of actual power grid risks: testing in the actual power grid may pose potential risks to the stable operation of the power grid, especially when conducting extreme operating condition or fault simulation tests. Improper operation could lead to serious power grid accidents. This method conducts virtual testing in a digital twin, which has no impact on the actual distribution network. It can safely simulate various complex operating conditions and fault scenarios, providing testers with a risk-free experimental environment and effectively ensuring the safe and stable operation of the actual power grid. It also ensures the safety of testers: In actual testing environments, testers may face safety risks such as electric shock and equipment failure. This method, through virtual testing, eliminates the need for testers to directly contact actual power equipment and lines. They only need to operate and monitor from a computer, greatly reducing personnel safety risks during the testing process and providing testers with safer working conditions.Comprehensive Data Recording and Analysis: The digital twin can record the operational data of the distribution IoT terminal and the changes in various parameters during the testing process in real time and comprehensively. This detailed data provides rich material for subsequent analysis and research, helping to deeply understand the performance of the terminal under different operating conditions, discover potential problems and patterns, and provide strong data support for product optimization and improvement and power grid planning and operation. Data Traceability and Reproducibility: Since all test data is stored in a computer, this method has good data traceability. Testers can access historical test data at any time to understand the specific situation and results of each test. Furthermore, for some important test cases or problems discovered, [further details can be added]. By resetting the same virtual test environment parameters and test tasks, the test process is reproduced in a digital twin, allowing for further in-depth analysis and verification, providing a reliable basis for problem solving and product improvement. Resource sharing and optimized configuration are achieved: through a cloud-edge collaborative scheduling strategy, this method can rationally allocate computing resources between cloud and edge nodes according to the needs of different test tasks, achieving resource sharing and optimized configuration. When test tasks are busy, the powerful computing capabilities of the cloud can be fully utilized to handle large-scale data and complex simulation tasks. When test tasks are relatively few or have high real-time requirements, more reliance can be placed on edge nodes for rapid response and processing, improving resource utilization efficiency and reducing overall test costs.

[0057] In one feasible implementation, step 140 in the above embodiments, where the current electrical data includes the current voltage deviation, current active power fluctuation, current total harmonic distortion rate, and current frequency deviation, determines the dynamic coupling degree based on the current electrical data, including: Using formula Determine the dynamic coupling degree; Step 150 in the above embodiment, the adjusted electrical data includes the adjusted voltage deviation, the adjusted active power fluctuation, and the adjusted total harmonic distortion rate, and the dynamic coupling degree is re-determined, including: Using formula Determine the dynamic coupling degree; in, For dynamic coupling degree, For voltage weighting coefficients, This represents the current voltage deviation. Nominal voltage, For power weighting coefficients, This represents the current active power fluctuation. Nominal active power For harmonic weighting coefficients, This represents the current total harmonic distortion (THD). The maximum allowable total harmonic distortion (THD) of the system. For frequency weighting coefficients, This represents the current frequency deviation. Nominal frequency, This is the adjusted voltage deviation. This refers to the adjusted active power fluctuation. This is the adjusted total harmonic distortion rate.

[0058] Among them, the voltage weighting coefficient, power weighting coefficient, harmonic weighting coefficient and frequency weighting coefficient can all be obtained and preset by the operator based on a large amount of experience, experiments or statistics. Of course, the operator can also preset them according to actual needs.

[0059] Regarding the values ​​of the voltage weighting coefficient, power weighting coefficient, harmonic weighting coefficient, and frequency weighting coefficient, in some embodiments, this application preferably sets the sum of the four weighting coefficients to 1.

[0060] Furthermore, in some embodiments, optimization algorithms (such as whale optimization algorithm, ant colony optimization algorithm, etc.) can be used to iteratively update the position of individuals in the population until the number of iterations in the current iteration is equal to the preset maximum number of iterations, thereby obtaining the optimal voltage weight coefficient, optimal power weight coefficient, optimal harmonic weight coefficient, and optimal frequency weight coefficient corresponding to the global optimal fitness value of all individuals in all iterations; wherein, the position of each individual in the population includes a set of voltage weight coefficient, power weight coefficient, harmonic weight coefficient, and frequency weight coefficient.

[0061] The maximum number of preset iterations can be set in advance by the operator based on extensive experience, experiments, or statistics. Alternatively, the operator can set it in advance according to actual needs.

[0062] Regarding the value of the preset maximum number of iterations, in some embodiments, this application preferably sets the preset maximum number of iterations to 200.

[0063] In this embodiment, the dynamic coupling degree is determined by a specific formula, which improves the accuracy and flexibility of the dynamic coupling degree determination and thus optimizes the testing process.

[0064] Understandably, by clearly defining the specific parameters included in the current electrical data (current voltage deviation, current active power fluctuation, current total harmonic distortion rate, and current frequency deviation) and providing a method for determining the dynamic coupling degree using a specific formula, the calculation of the dynamic coupling degree has a clear and accurate basis, avoiding errors caused by unclear parameters or ambiguous calculation methods, thus improving accuracy. Furthermore, after adjusting the electrical data, the same formula is used to re-determine the dynamic coupling degree. This consistent calculation method is easy for operators to understand and execute, and allows for flexible adjustment of the electrical data to obtain a suitable dynamic coupling degree according to different situations. This optimizes the entire testing process and lays a solid foundation for subsequent steps such as determining task urgency and executing test tasks based on the dynamic coupling degree.

[0065] In one feasible implementation, step 150 in the above embodiments, where the electrical data includes voltage deviation, active power fluctuation, and total harmonic distortion (THD), involves adjusting the electrical data of the virtual test environment parameters in the digital twin based on the dynamic coupling degree. This includes: adjusting the voltage deviation, active power fluctuation, and THD according to a first preset adjustment rule when the dynamic coupling degree is less than the lower limit of a preset range; and adjusting the voltage deviation, active power fluctuation, and THD according to a second preset adjustment rule when the dynamic coupling degree is greater than the upper limit of a preset range.

[0066] The first and second preset adjustment rules can both be preset by the operator based on extensive experience, experiments or statistics. Of course, the operator can also preset them according to actual needs.

[0067] In this embodiment, by adjusting the electrical data according to the relationship between the dynamic coupling degree and the upper and lower limits of the preset range using different rules, the accuracy and adaptability of dynamically adjusting the electrical data are improved, ensuring the stability and effectiveness of the testing process.

[0068] Understandably, by clearly defining the relationship between the dynamic coupling degree and the upper and lower limits of the preset range, different preset adjustment rules are used to adjust voltage deviation, active power fluctuation, and total harmonic distortion rate, making the adjustment of electrical data more targeted. When the dynamic coupling degree is less than the lower limit of the preset range, the first preset adjustment rule is applied to avoid the test from reflecting the real power grid state due to excessively low dynamic coupling degree. When the dynamic coupling degree is greater than the upper limit of the preset range, the second preset adjustment rule is applied to prevent excessively high dynamic coupling degree from causing excessive deviation between the test environment and the real power grid. This precise adjustment method, which adapts to different situations, ensures that the dynamic coupling between the virtual test environment and the real power grid is maintained within a reasonable range during the test, thereby ensuring the stability and effectiveness of the test process.

[0069] In one feasible implementation, the first preset adjustment rule in the above embodiment includes increasing the voltage deviation by a first preset percentage, increasing the active power fluctuation by a second preset percentage, and taking the minimum value between the total harmonic distortion rate (THD) increased by a third preset percentage and the maximum allowed THD of the system; the second preset adjustment rule includes decreasing the voltage deviation by a first preset percentage, decreasing the active power fluctuation by a second preset percentage, and decreasing the THD by a third preset percentage.

[0070] The first, second, and third preset percentages can all be preset by the operator based on extensive experience, experiments, or statistics. Of course, the operator can also preset them according to actual needs.

[0071] It should be noted that when the dynamic coupling degree is less than the lower limit of the preset range, it means that most (e.g., more than 90%) of the distribution IoT terminals have no abnormal operation (i.e., the test is insufficient), and the disturbance intensity needs to be increased. Therefore, it should be adjusted according to the first preset adjustment rule. When the dynamic coupling degree is greater than the upper limit of the preset range, it means that a small number (e.g., 5%~10%) of the distribution IoT terminals have malfunctioned or the components are overheated (i.e., they are in the risk zone), and the disturbance intensity needs to be reduced. Therefore, it should be adjusted according to the second preset adjustment rule.

[0072] Regarding the values ​​of the first preset percentage, the second preset percentage, and the third preset percentage, in some embodiments, this application preferably sets the first preset percentage to 10%, the second preset percentage to 15%, and the third preset percentage to 20%.

[0073] In this embodiment of the application, the accuracy and rationality of dynamic adjustment of electrical data are significantly improved by using clear and targeted preset adjustment rules, which effectively ensures the stability and effectiveness of the testing process.

[0074] Understandably, a clear distinction was made between two different scenarios: dynamic coupling less than the lower limit of the preset range and dynamic coupling greater than the upper limit of the preset range. First and second preset adjustment rules were established for each scenario. When the dynamic coupling is less than the lower limit, the first preset adjustment rule is used to increase the voltage deviation, active power fluctuation, and total harmonic distortion (THD) (taking the minimum value within a reasonable range) to enhance the disturbance intensity and avoid insufficient testing. When the dynamic coupling is greater than the upper limit, the second preset adjustment rule is used to decrease the voltage deviation, active power fluctuation, and THD to reduce the disturbance intensity and prevent excessive deviation between the test environment and the real power grid. This precise adjustment method, adaptable to different situations, ensures that the dynamic coupling between the virtual test environment and the real power grid remains within a reasonable range, thereby guaranteeing the stability and effectiveness of the testing process.

[0075] In one feasible implementation, step 150 in the above embodiment, which determines the urgency of the task based on the dynamic coupling degree, includes: obtaining the current network latency of the distribution network; determining the maximum response time and the number of associated terminals according to the test requirements; and determining the urgency of the task based on the maximum response time, the number of associated terminals, the current network latency, and the dynamic coupling degree.

[0076] The number of associated diagnostics refers to the total number of all power distribution IoT terminals involved in this test.

[0077] In this embodiment, the task urgency is determined by combining the maximum response time, the number of associated terminals, the current network latency, and the dynamic coupling degree. This improves the comprehensiveness and rationality of the task urgency determination, provides a reliable basis for the subsequent selection of execution nodes, and ensures efficient testing.

[0078] Understandably, when determining the urgency of a task, not only was the dynamic coupling degree, a key factor reflecting the coupling state between the virtual test environment and the real power grid, considered, but the current network latency of the distribution network was also obtained. Furthermore, the maximum response time and the number of associated terminals were determined based on the test requirements. Integrating these multiple factors to determine the task urgency makes the determination more comprehensive and accurately reflects the actual urgency of the test task. This comprehensive and reasonable method of determining task urgency provides a reliable basis for subsequently selecting cloud or edge nodes as execution nodes based on task urgency. It helps to enhance the simulation capabilities of complex scenarios while ensuring real-time performance, thereby guaranteeing the efficiency of large-scale distribution IoT terminal testing.

[0079] In one feasible implementation, determining the task urgency based on the maximum response time, the number of associated terminals, the current network latency, and the dynamic coupling degree in the above embodiments includes: Using formula Determine the urgency of the task; in, To assess the urgency of the mission, , , and These are the first preset weight coefficient, the second preset weight coefficient, the third preset weight coefficient, and the fourth preset weight coefficient, respectively. For maximum response time, The maximum allowable response time for the system. For the number of associated terminals, For dynamic coupling degree, This represents the maximum number of management terminals at the edge nodes. For the current network latency, This represents the maximum network latency allowed by the system.

[0080] The first, second, third, and fourth preset weight coefficients can all be preset by the operator based on extensive experience, experiments, or statistics. Of course, the operator can also preset them according to actual needs.

[0081] Regarding the values ​​of the first preset weight coefficient, the second preset weight coefficient, the third preset weight coefficient, and the fourth preset weight coefficient, in some embodiments, this application preferably sets the first preset weight coefficient to 0.4, the second preset weight coefficient to 0.3, the third preset weight coefficient to 0.2, and the fourth preset weight coefficient to 0.1.

[0082] In this embodiment, the urgency of a task is determined by a specific formula, which improves the accuracy and comprehensiveness of the determination of the urgency of a task and provides strong support for the efficient execution of test tasks.

[0083] Understandably, by using a specific formula to comprehensively consider multiple key factors such as maximum response time, number of associated terminals, current network latency, and dynamic coupling, and assigning different weight coefficients, the determination of task urgency is no longer limited to a single factor, but rather takes into account multiple actual situations. This allows for a more accurate reflection of the urgency of the test task. This precise and comprehensive method of determining task urgency provides a reliable basis for selecting appropriate execution nodes, helps to enhance the simulation capabilities of complex scenarios while ensuring real-time performance, and thus ensures that large-scale power distribution IoT terminal testing can be carried out efficiently and orderly.

[0084] In one feasible implementation, step 160 in the above embodiment, which selects a cloud or edge node as the execution node based on the task urgency, includes: selecting the cloud as the execution node when the task urgency is less than a preset threshold; and selecting an edge node as the execution node when the task urgency is greater than or equal to the preset threshold.

[0085] Regarding the value of the preset threshold, in some embodiments, this application preferably sets the preset threshold to 0.6.

[0086] In this embodiment, by comparing the urgency of the task with a preset threshold, a cloud or edge node is intelligently selected as the execution node, thereby achieving intelligent and efficient selection of the execution node and ensuring a balance between real-time performance and simulation capability in the test task.

[0087] Understandably, this method intelligently selects either cloud or edge nodes as execution nodes based on a comparison between task urgency and a preset threshold. When task urgency is low, cloud nodes are selected as execution nodes to fully utilize the powerful computing capabilities of the cloud for processing large-scale data and complex simulation tasks. When task urgency is high, edge nodes are selected as execution nodes to quickly respond to and process tasks, ensuring real-time performance. This task urgency-based execution node selection method avoids the limitations of solely relying on cloud or edge computing, achieving a balance between real-time performance and simulation capabilities, thereby ensuring the efficient testing of large-scale power distribution IoT terminals.

[0088] In one feasible implementation, the method in the above embodiments further includes: when the execution node is in the cloud, the test task corresponding to the execution node is a virtual simulation and physical verification task; when the execution node is an edge node, the test task corresponding to the execution node is a signal injection, real-time monitoring and anomaly handling task.

[0089] Among them, virtual simulation and physical verification tasks, as well as signal injection, real-time monitoring and anomaly handling tasks, can all be preset by the operator based on a large amount of experience, experiments or statistics. Of course, the operator can also preset them according to actual needs.

[0090] For virtual simulation and physical verification tasks, in some embodiments, virtual simulation and physical verification tasks include, but are not limited to, calling digital twins to run complex scenarios and physical verification. Calling digital twins to run complex scenarios includes, but are not limited to, collaborative testing of multiple power distribution IoT terminals and long-term reliability simulation (such as 72 hours of continuous operation). Physical verification includes, but is not limited to, issuing key instructions to edge nodes for local verification (such as testing power distribution IoT terminals by injecting them into edge nodes after simulating voltage dips and harmonics in the cloud).

[0091] For signal injection, real-time monitoring and anomaly handling tasks, in some embodiments, signal injection, real-time monitoring and anomaly handling tasks include but are not limited to signal injection, real-time monitoring of power distribution IoT terminal response data, anomaly handling, etc., wherein signal injection includes but is not limited to dynamic current, real-time monitoring of power distribution IoT terminal response data includes but is not limited to protection action time, harmonic distortion rate, anomaly handling includes but is not limited to immediately cutting off the output and reducing it when the current exceeds the limit, switching to the backup channel and updating the network latency when communication is interrupted.

[0092] In this embodiment of the application, virtual simulation and physical verification tasks and signal injection, real-time monitoring and anomaly handling tasks are allocated according to whether the execution node is in the cloud or at the edge, so as to achieve accurate allocation and efficient execution of test tasks and ensure the comprehensiveness and real-time nature of the test.

[0093] Understandably, this method allocates virtual simulation and physical verification tasks and signal injection, real-time monitoring, and anomaly handling tasks based on whether the execution node is in the cloud or at the edge. The cloud performs virtual simulation and physical verification tasks, which can fully leverage its powerful computing capabilities to handle large-scale data and complex simulation scenarios, conduct long-term reliability simulation and physical verification, and ensure the comprehensiveness of the test. The edge node performs signal injection, real-time monitoring, and anomaly handling tasks, which can quickly respond to and process real-time data, perform signal injection and real-time monitoring, and handle anomalies in a timely manner, ensuring the real-time performance of the test. This task allocation method avoids the limitations of a single node, achieves accurate allocation and efficient execution of test tasks, and thus ensures the comprehensiveness and real-time performance of large-scale power distribution IoT terminal testing.

[0094] In one feasible implementation, the signal injected in the signal injection, real-time monitoring and anomaly handling tasks in the above embodiments is a dynamic current. The method further includes: obtaining the disturbance current of the virtual test environment parameters in the digital twin; and determining the dynamic current based on the disturbance current and the dynamic coupling degree.

[0095] In some embodiments, the dynamic current can be determined using a formula. Determine the dynamic current; where, For dynamic current, For disturbance current, This refers to the dynamic coupling degree.

[0096] In this embodiment, the dynamic current is determined by combining the disturbance current and the dynamic coupling degree, thereby improving the accuracy of signal injection and enhancing the reliability and effectiveness of testing.

[0097] Understandably, this method first obtains the disturbance current in the virtual test environment parameters during signal injection, and then determines the dynamic current by combining the dynamic coupling degree. This approach makes the injected signal no longer a fixed value, but dynamically adjusted according to the actual simulated disturbance and the dynamic coupling degree between the power grid and the virtual environment, which is closer to the current changes in the real power grid. In this way, it is possible to more accurately simulate current signals under various complex operating conditions, making the test environment more realistic and reliable, thereby effectively improving the accuracy and reliability of the test results and enhancing the effectiveness of the entire test process.

[0098] In one feasible implementation, the method in the above embodiments further includes: determining the index score of each index in the test results based on the test values ​​of each index in the test results; performing a cause analysis on the index scores of each index in the test results to obtain the cause analysis results; determining optimization suggestion information based on the cause analysis results; and generating a test evaluation report based on the optimization suggestion information, the cause analysis results, and the index scores of each index in the test results.

[0099] In some embodiments, the various indicators in the test results include, but are not limited to, action time, current rise time, communication latency, communication status, injection voltage, injection current, task urgency, and dynamic coupling.

[0100] Regarding the method for determining the index scores, in some embodiments, the index scores of each index in the test results can be determined based on the test values ​​of each index in the test results and a preset multi-index standard value table. The preset multi-index standard value table can be obtained and set in advance by the operator based on a large amount of experience, experiments or statistics. Of course, it can also be set in advance by the operator according to actual needs.

[0101] Furthermore, in some embodiments, the product of the quotient of the indicator and 100 can be used as the indicator score, where the quotient of the indicator is the ratio between the standard value and the test value of the indicator.

[0102] In this embodiment of the application, by comprehensively evaluating the test results, optimization directions are provided to improve test quality and terminal performance.

[0103] Understandably, this method first determines the scores of various indicators based on the test values, which can intuitively present the quality of the test results. Then, it conducts a cause analysis on the indicator scores, which can accurately locate the root cause of the problem. Next, it determines optimization suggestions based on the cause analysis, providing a clear direction for improvement. Finally, it generates a test evaluation report containing optimization suggestions, cause analysis, and indicator scores. This series of operations comprehensively and systematically evaluates the test results, which not only helps operators to understand the test situation in depth, but also enables targeted improvements based on optimization suggestions, thereby improving test quality, ensuring the performance and reliability of distribution IoT terminals, and promoting the stable operation of the smart grid.

[0104] In a second aspect, this application provides a power distribution IoT terminal testing device for a power distribution network.

[0105] Please see Figure 2 This is a schematic diagram of a power distribution IoT terminal testing device for a power distribution network according to an embodiment of this application. The device 210 includes: The acquisition module 211 is used to acquire the distribution network model of the distribution network and the terminal type of the distribution IoT terminal, as well as the test requirements; Module 212 is used to construct a digital twin based on the distribution network model and terminal type; The determination module 213 is used to determine the virtual test environment parameters in the digital twin according to the test requirements. The digital twin is used to collect the operation data of the distribution IoT terminal of the distribution network in real time. The acquisition and determination module 214 is used to acquire the current electrical data of the distribution network and determine the dynamic coupling degree based on the current electrical data; The judgment and iteration module 215 is used to adjust the electrical data of the virtual test environment parameters in the digital twin according to the dynamic coupling degree when the dynamic coupling degree does not meet the preset range, and redetermine the dynamic coupling degree according to the adjusted electrical data until the dynamic coupling degree meets the preset range. The task urgency is determined according to the dynamic coupling degree. The selection and testing module 216 is used to select cloud or edge nodes as execution nodes according to the urgency of the task, and command the execution nodes to execute the corresponding test tasks in the digital twin to obtain test results.

[0106] In this embodiment, the relevant contents of the above-mentioned acquisition module 211, construction module 212, determination module 213, acquisition and determination module 214, judgment and iteration module 215, and selection and testing module 216 can be found in the following references. Figure 1 The contents of the illustrated embodiments will not be repeated here.

[0107] It should be noted that the device 210 of this application also includes other modules. It can be understood that the method of this application and the device 210 have a one-to-one correspondence. Therefore, the other modules of the device 210 of this application are the contents corresponding to the method of this application in the above embodiments.

[0108] In this embodiment, a digital twin is constructed to achieve dynamic coupling between the virtual test environment and the real power grid. Combined with a dynamic coupling degree adjustment mechanism, the real-time adaptability of test parameters is improved (the detection rate of complex operating conditions is significantly improved). Furthermore, through a cloud-edge collaborative scheduling strategy driven by task urgency, the simulation capability of complex scenarios is enhanced while ensuring real-time performance. Ultimately, this achieves high efficiency, real-time performance, and dynamic adaptability in the testing of large-scale distribution IoT terminals, effectively solving the technical bottlenecks of traditional testing devices in terms of efficiency, real-time feedback, environment construction, and task scheduling.

[0109] In addition to the aforementioned beneficial effects, the distribution IoT terminal testing device for this power distribution network also has the following advantages: Reduced hardware investment costs: Traditional testing devices may require the construction of a large number of complex actual testing environments, involving the layout and maintenance of numerous physical devices and lines, resulting in high costs. This device, however, constructs a digital twin for virtual testing, reducing reliance on a large number of actual physical testing devices. Only the necessary computing equipment is required to run the digital twin and execute testing tasks, thus significantly reducing hardware procurement, installation, and maintenance costs; Reduced site occupancy costs: Actual testing environments often require large spaces to deploy various testing equipment and lines, especially for large-scale power distribution networks. Testing IoT terminals requires significantly more space. This device, based on a digital twin, eliminates the need for large physical sites; simulations are performed within a computer system, saving on site rental or construction costs. This is particularly suitable for regions or enterprises with limited space. Rapid switching between test scenarios is another advantage: In traditional testing, changing scenarios often requires rearranging physical equipment and adjusting wiring connections, a cumbersome and time-consuming process. This device, through its digital twin, allows operators to quickly modify virtual test environment parameters within the computer system, easily switching between different test scenarios, such as rapidly switching from a short-circuit fault scenario to a voltage sag scenario. This greatly improves testing flexibility and... Efficiency: It can meet diverse testing needs more promptly; Support for diverse terminal testing: With the continuous development of distribution IoT terminal technology, terminal types and functions are becoming increasingly diversified. The digital twin constructed by this device can flexibly adjust the virtual test environment parameters and test tasks according to the characteristics and testing needs of different types of distribution IoT terminals. It can easily test various new terminals without having to rebuild a dedicated test environment for each terminal, providing strong support for the research and development and promotion of new products; Avoid risks to the actual power grid: Testing in the actual power grid may pose potential risks to the stable operation of the power grid, especially when conducting extreme operating conditions or fault simulation tests. Improper operation could lead to serious power grid accidents. This device performs virtual testing in a digital twin, which will not affect the actual power distribution network. It can safely simulate various complex operating conditions and fault situations, providing testers with a risk-free experimental environment and effectively ensuring the safe and stable operation of the actual power grid. It also ensures the safety of testers: In actual testing environments, testers may face safety risks such as electric shock and equipment failure. This device, through virtual testing, eliminates the need for testers to directly contact actual power equipment and lines. They only need to operate and monitor from a computer, greatly reducing personnel safety risks during the testing process and providing safer working conditions for testers.Comprehensive Data Recording and Analysis: The digital twin can record the operational data of the distribution IoT terminal and the changes in various parameters during the testing process in real time and comprehensively. This detailed data provides rich material for subsequent analysis and research, helping to deeply understand the performance of the terminal under different operating conditions, discover potential problems and patterns, and provide strong data support for product optimization and improvement and power grid planning and operation. Data Traceability and Reproducibility: Since all test data is stored in the computer, this device has good data traceability. Testers can access historical test data at any time to understand the specific situation and results of each test. Furthermore, for some important test cases or problems discovered, [further details can be added]. By resetting the same virtual test environment parameters and test tasks, the test process is reproduced in the digital twin, allowing for further in-depth analysis and verification, providing a reliable basis for problem solving and product improvement. Resource sharing and optimized configuration are achieved: through a cloud-edge collaborative scheduling strategy, this device can rationally allocate computing resources between cloud and edge nodes according to the needs of different test tasks, achieving resource sharing and optimized configuration. When test tasks are busy, the powerful computing capabilities of the cloud can be fully utilized to handle large-scale data and complex simulation tasks. When test tasks are relatively few or have high real-time requirements, more reliance can be placed on edge nodes for rapid response and processing, improving resource utilization efficiency and reducing overall test costs.

[0110] In a third aspect, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform a power distribution IoT terminal testing method for a power distribution network as described in any of the first aspects.

[0111] This application provides a computer device in a fourth aspect, including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform a power distribution IoT terminal testing method for a power distribution network as described in any of the first aspects.

[0112] Figure 3 The diagram illustrates the internal structure of a computer device in some embodiments. This computer device may specifically be a terminal, a server, or a gateway. Figure 3 As shown, the computer device includes a processor, memory, and network interface connected via a system bus.

[0113] The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When executed by a processor, this computer program causes the processor to perform the steps in the above method embodiments. The internal memory may also store a computer program, which, when executed by a processor, causes the processor to perform the steps in the above method embodiments. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0114] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods.

[0115] Any references to memory, storage, database, or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0116] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0117] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for testing distribution IoT terminals in a distribution network, characterized in that, The method includes: Obtain the distribution network model and the terminal types of the distribution IoT terminals, as well as the testing requirements; Based on the power distribution network model and the terminal type, construct a digital twin; Based on the test requirements, the virtual test environment parameters in the digital twin are determined. The digital twin is used to collect the operating data of the distribution IoT terminals of the distribution network in real time. Obtain the current electrical data of the distribution network, and determine the dynamic coupling degree based on the current electrical data; If the dynamic coupling degree does not meet the preset range, the electrical data of the virtual test environment parameters in the digital twin is adjusted according to the dynamic coupling degree, and the dynamic coupling degree is re-determined according to the adjusted electrical data until the dynamic coupling degree meets the preset range. The task urgency is then determined according to the dynamic coupling degree. Based on the urgency of the task, a cloud or edge node is selected as the execution node, and the execution node is commanded to execute the test task corresponding to the execution node in the digital twin to obtain the test results.

2. The method for testing distribution IoT terminals in a distribution network according to claim 1, characterized in that, The current electrical data includes the current voltage deviation, current active power fluctuation, current total harmonic distortion rate, and current frequency deviation. Determining the dynamic coupling degree based on the current electrical data includes: Using formula Determine the dynamic coupling degree; The adjusted electrical data includes the adjusted voltage deviation, the adjusted active power fluctuation, and the adjusted total harmonic distortion rate. The step of redetermining the dynamic coupling degree based on the adjusted electrical data includes: Using formula Determine the dynamic coupling degree; in, The dynamic coupling degree, For voltage weighting coefficients, The current voltage deviation, Nominal voltage, For power weighting coefficients, This refers to the current active power fluctuation. Nominal active power For harmonic weighting coefficients, The current total harmonic distortion (THD) is... The maximum allowable total harmonic distortion (THD) of the system. For frequency weighting coefficients, The current frequency deviation, Nominal frequency, The adjusted voltage deviation, This refers to the adjusted active power fluctuation. The adjusted total harmonic distortion rate is denoted as .

3. The method for testing distribution IoT terminals in a distribution network according to claim 1, characterized in that, The electrical data includes voltage deviation, active power fluctuation, and total harmonic distortion rate. The electrical data used to adjust the virtual test environment parameters in the digital twin based on the dynamic coupling degree includes: If the dynamic coupling degree is less than the lower limit of the preset range, the voltage deviation, the active power fluctuation and the total harmonic distortion rate are adjusted according to the first preset adjustment rule. If the dynamic coupling degree is greater than the upper limit of the preset range, the voltage deviation, the active power fluctuation and the total harmonic distortion rate are adjusted according to the second preset adjustment rule.

4. The method for testing distribution IoT terminals in a distribution network according to claim 3, characterized in that, The first preset adjustment rule includes increasing the voltage deviation by a first preset percentage, increasing the active power fluctuation by a second preset percentage, and taking the minimum value between the total harmonic distortion rate (THD) increased by a third preset percentage and the maximum allowed THD of the system. The second preset adjustment rule includes reducing the voltage deviation by the first preset percentage, reducing the active power fluctuation by the second preset percentage, and reducing the total harmonic distortion by the third preset percentage.

5. The method for testing distribution IoT terminals in a distribution network according to claim 1, characterized in that, The step of determining the task urgency based on the dynamic coupling degree includes: Obtain the current network latency of the power distribution network; Based on the test requirements, determine the maximum response time and the number of associated terminals; The urgency of the task is determined based on the maximum response time, the number of associated terminals, the current network latency, and the dynamic coupling degree.

6. The method for testing distribution IoT terminals in a distribution network according to claim 5, characterized in that, The step of determining the task urgency based on the maximum response time, the number of associated terminals, the current network latency, and the dynamic coupling degree includes: Using formula Determine the urgency of the task; in, The urgency level of the task. , , and These are the first preset weight coefficient, the second preset weight coefficient, the third preset weight coefficient, and the fourth preset weight coefficient, respectively. The maximum response time, The maximum allowable response time for the system. The number of associated terminals, The dynamic coupling degree, This represents the maximum number of management terminals for the edge nodes. The current network latency, This represents the maximum network latency allowed by the system.

7. The method for testing distribution IoT terminals in a distribution network according to claim 1, characterized in that, The step of selecting a cloud or edge node as the execution node based on the task urgency includes: If the urgency of the task is less than a preset threshold, the cloud is selected as the execution node. If the urgency of the task is greater than or equal to the preset threshold, the edge node is selected as the execution node.

8. The method for testing distribution IoT terminals in a distribution network according to claim 1, characterized in that, The method further includes: When the execution node is the cloud, the test task corresponding to the execution node is a virtual simulation and physical verification task; When the execution node is the edge node, the test task corresponding to the execution node is signal injection, real-time monitoring, and anomaly handling.

9. The method for testing distribution IoT terminals in a distribution network according to claim 8, characterized in that, The signal injected in the signal injection, real-time monitoring, and anomaly handling tasks is a dynamic current, and the method further includes: Obtain the disturbance current of the virtual test environment parameters in the digital twin; The dynamic current is determined based on the disturbance current and the dynamic coupling degree.

10. The method for testing distribution IoT terminals in a distribution network according to claim 1, characterized in that, The method further includes: Based on the test values ​​of each indicator in the test results, determine the indicator score of each indicator in the test results; A causal analysis was performed on the scores of each indicator in the test results to obtain the causal analysis results; Based on the results of the cause analysis, optimization suggestions are determined. A test evaluation report is generated based on the optimization suggestions, the cause analysis results, and the scores of each indicator in the test results.

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