Low-temperature environment electric energy meter test method and processing device based on digital twinning

Abstract

The application provides a low-temperature environment electric energy meter test method and processing equipment based on digital twinning, and relates to the technical field of electric energy meters. The method is applied to a processing equipment in a low-temperature environment test system. The processing equipment has an electric energy meter model corresponding to an intelligent electric meter generated based on digital twinning. The method comprises the following steps: sending a work instruction to the intelligent electric meter to control the intelligent electric meter to enter a working state; controlling the inside of a high-low temperature box to gradually decrease to a preset low-temperature temperature and the humidity to be within a preset humidity range according to a preset temperature decreasing curve; collecting working data of the intelligent electric meter; updating the working data of the electric energy meter model according to the working data; and generating a simulation test result based on the electric energy meter model. The application realizes the automation of the test process by constructing a digital twinning model dynamically synchronized with the physical intelligent electric meter in the processing equipment, and effectively solves the problems of low-temperature test relying on manual subsequent analysis, low efficiency and insufficient accuracy in the prior art.

Description

Technical Field

[0001] This application relates to the field of electricity meter technology, and more specifically, to a method and processing equipment for testing electricity meters in low-temperature environments based on digital twins. Background Technology

[0002] As a key terminal device in the smart grid, the stability and reliability of smart meters under various harsh environments are crucial. Low-temperature environments are one of the key factors affecting the metering accuracy, display function, communication capabilities, and component lifespan of smart meters. Therefore, rigorous low-temperature testing of smart meters is an essential step in the product development and factory inspection stages.

[0003] Existing low-temperature testing methods for smart meters typically involve placing a batch of meters in a high-low temperature test chamber, setting one or more fixed low-temperature points (e.g., -25℃ or -40℃), and after the temperature stabilizes, powering on and loading the meters, recording key indicators such as metering error and communication success rate, and finally determining whether they are qualified.

[0004] However, existing methods mainly rely on subsequent manual judgment and analysis of test results, which is inefficient and lacks comparison, and also affects the accuracy of the test. Summary of the Invention

[0005] The purpose of this application is to address the shortcomings of the prior art by providing a low-temperature environment electricity meter testing method and processing equipment based on digital twins. This solves the problem that the low-temperature testing of smart meters in the prior art mainly relies on subsequent manual judgment and analysis of the test results, which is inefficient, lacks comparison, and also affects the accuracy of the test.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0007] In a first aspect, embodiments of this application provide a method for testing electricity meters in low-temperature environments based on digital twins, applied to a processing device in a low-temperature environment testing system. The low-temperature environment testing system includes: a processing device, a high-low temperature chamber, and a smart meter; the smart meter is placed inside the high-low temperature chamber, and the processing device is communicatively connected to both the high-low temperature chamber and the smart meter. The processing device contains an electricity meter model corresponding to the smart meter generated based on a digital twin. The method includes:

[0008] Sending a working instruction to the smart meter to control the smart meter to enter the working state, the working instruction including: simulated load parameters;

[0009] According to the preset cooling curve, the temperature inside the high and low temperature chamber is gradually reduced to the preset low temperature, and the humidity inside the high and low temperature chamber is controlled within the preset humidity range;

[0010] The working data of the smart meter is collected and acquired, including status data and metering data.

[0011] The working data of the electricity meter model is updated based on the working data, and simulation test results are generated based on the electricity meter model.

[0012] As one possible implementation, the method further includes:

[0013] Obtain the design parameters of the smart meter, including the physical structure parameters, device physical characteristic parameters, and software processing logic of the smart meter;

[0014] Based on the design parameters, a digital twin-based energy meter model is generated for the smart meter.

[0015] A virtual connection is established between the electricity meter model and the smart meter to ensure dynamic synchronization.

[0016] As one possible implementation, updating the working data of the electricity meter model based on the working data and generating simulation test results based on the electricity meter model includes:

[0017] Update the working data of the electricity meter model based on the working data;

[0018] An analysis engine is used to compare and analyze the working data with the historical health data of the smart meter to determine whether there are any anomalies in the working data;

[0019] If an anomaly is found, an anomaly report will be generated and displayed.

[0020] As one possible implementation, the method further includes:

[0021] The real-time dynamic information of the smart meter is generated based on the energy meter model and displayed on the user's visualization interface. The real-time dynamic information includes: a three-dimensional visualization model of the energy meter model, a visualization curve corresponding to the working data, and abnormal events.

[0022] The three-dimensional visualization model is labeled with the real-time temperature of each region; the visualization curves corresponding to the working data include one or more of the following: curves of measurement data changing over time, curves of temperature changing over time for key components, curves of voltage / current changing over time, and curves of power consumption changing over time.

[0023] As one possible implementation, after updating the working data of the electricity meter model based on the working data, the method further includes:

[0024] Based on the working data and the pre-trained risk assessment model, fault warning information of the smart meter is generated. The risk assessment model is trained using a large number of change trends from normal working data to fault working data collected in the history of the smart meter.

[0025] The method further includes:

[0026] After generating the anomaly report, the anomaly report is compared and analyzed with the fault warning information to obtain the analysis results.

[0027] As one possible implementation, the status data in the working data includes at least one of the following: the temperature of key components of the smart meter, the temperature difference between the inside and outside of the smart meter casing, the internal humidity information of the smart meter, the signal strength and bit error rate of the communication module, or the real-time power consumption of the smart meter.

[0028] As one possible implementation, after using an analysis engine to compare and analyze the working data with the historical health data of the smart meter to determine whether there are any anomalies in the working data, the method further includes:

[0029] If no abnormality is found, continue to control the temperature inside the high and low temperature chamber to continue to drop until an abnormality is detected. Record the corresponding abnormal temperature and use the abnormal temperature as the critical temperature value.

[0030] As one possible implementation, if no abnormality is detected, the temperature inside the high and low temperature chamber continues to drop until an abnormality is detected, at which point the corresponding abnormal temperature is recorded, including:

[0031] If no abnormality is found, the temperature inside the high and low temperature chamber is continuously reduced multiple times according to different cooling curves, and the abnormal temperature is recorded each time an abnormality is detected. The different cooling curves are used to indicate different cooling rates and different cooling amplitudes within a unit time.

[0032] Secondly, embodiments of this application provide a processing device, including: a processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the processing device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the low-temperature environment energy meter testing method based on digital twin as described in any of the first aspects above.

[0033] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the low-temperature environment energy meter testing method based on digital twin as described in any of the first aspects above.

[0034] According to the embodiments of this application, a method and electronic device for testing electricity meters in low-temperature environments based on digital twins includes an electricity meter model corresponding to a smart meter generated based on a digital twin in the processing device. The processing device sends working instructions to the smart meter to control it to enter the working state, and controls the temperature inside the high and low temperature chamber to gradually decrease to a preset low temperature according to a preset cooling curve, while controlling the humidity inside the high and low temperature chamber within a preset humidity range. Based on this, the processing device collects and acquires the working data of the smart meter, updates the working data of the electricity meter model based on the smart meter's working data, and generates simulation test results based on the electricity meter model. According to the embodiments of this application, by utilizing digital twins to construct an electricity meter model dynamically synchronized with the physical smart meter in the processing device, this electricity meter model serves as the digital twin model of the smart meter. During the low-temperature test, the status data and metering data of the smart meter are collected in real time, and the working data of the electricity meter model is updated using the status data and metering data of the smart meter. Simulation tests are then performed based on the updated electricity meter model to dynamically generate simulation test results and predict the performance trend and potential faults of the smart meter under extreme operating conditions. In this way, not only is the testing process automated, reducing the workload of manual intervention and post-judgment, but the comparability, repeatability and accuracy of the test are also improved through virtual-real comparison and data closure, thereby significantly improving the efficiency and accuracy of low-temperature environment testing. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This paper shows a schematic diagram of the architecture of a low-temperature environment testing system provided in an embodiment of this application;

[0037] Figure 2 A schematic flowchart of a low-temperature environment energy meter testing method based on digital twin provided in an embodiment of this application is shown.

[0038] Figure 3 The illustration shows a flowchart of a method for establishing a virtual connection between an energy meter model and a smart meter according to an embodiment of this application;

[0039] Figure 4 A schematic diagram of a user visual interface provided in an embodiment of this application is shown;

[0040] Figure 5A schematic diagram of the structure of a processing device provided in an embodiment of this application is shown. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0042] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0043] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0044] Figure 1 A schematic diagram of the architecture of a low-temperature environment testing system provided in an embodiment of this application is shown. (Refer to...) Figure 1 As shown, the low-temperature environment testing system includes: a processing device 12, a high-low temperature chamber 11, and a smart meter 10. The smart meter 10 is placed inside the high-low temperature chamber 11. The processing device 12 is communicatively connected to both the high-low temperature chamber 11 and the smart meter 10. The processing device 12 contains a power meter model 13 corresponding to the smart meter, generated based on a digital twin.

[0045] Optionally, the high and low temperature chamber 11 is used to provide a controllable low temperature environment for the smart meter 10 placed inside it. Specifically, based on the control of the processing equipment 12, the high and low temperature chamber 11 can gradually reduce the internal ambient temperature of the high and low temperature chamber 11 to the required preset low temperature according to a preset cooling curve, and maintain the internal ambient humidity within a preset humidity range, thereby providing accurate testing conditions for the smart meter 10.

[0046] Optionally, digital twin refers to constructing a virtual model in virtual space that completely corresponds to a physical entity through digital means, realizing real-time mapping, dynamic interaction, and collaborative evolution between the physical and digital worlds. In this application, the electricity meter model 13 is a virtual mapping of the smart meter 10 constructed based on digital twin technology, used to simulate the behavior of the smart meter 10 under various operating conditions. As a digital twin of the smart meter 10, the electricity meter model 13 not only includes the three-dimensional geometric structure and physical behavior characteristics of key components of the smart meter 10, but also integrates the software logic and operating rules of the smart meter 10, ultimately forming a multi-level, multi-physics high-fidelity virtual model. Based on the communication connection between the processing device 12 and the smart meter 10, the electricity meter model 13 dynamically updates its own state by continuously receiving working data from the real smart meter 10, maintaining dynamic synchronization with the entity of the smart meter 10, thereby achieving accurate replication of the performance of the smart meter 10. In addition, the electricity meter model 13 also supports testing and analysis in various scenarios without changing the physical device, improving testing efficiency and flexibility.

[0047] Optionally, the processing device 12 may be an electronic device such as a computer. As the core control and data processing center of the low-temperature environment testing system, the processing device 12 is responsible for sending working instructions to the smart meter 10. These instructions include simulated load parameters, causing the smart meter 10 to operate in a specific state. The processing device 12 is also used to control the operation of the high-low temperature chamber 11, adjusting the temperature and humidity inside the chamber according to a preset cooling curve to ensure the testing environment meets requirements. The processing device 12 has a built-in electricity meter model 13 established and maintained based on digital twin technology. The processing device 12 is also used to collect working data from the smart meter 10 under different working states and update the working data of the electricity meter model 13 in real time using the working data from the smart meter 10 to reflect the actual working status of the smart meter. This achieves two-way data interaction between the physical entity and the virtual model, and generates simulation test results based on the updated working data of the electricity meter model 13 for simulation analysis and optimization feedback.

[0048] Based on this, the low-temperature environment testing system provided in this application, by introducing digital twin technology, realizes virtual-real linkage and closed-loop optimization of smart meters during low-temperature environment testing, significantly improving the accuracy, controllability, and intelligence level of the test. On the one hand, it can accurately replicate the real operating state of the energy meter under complex low-temperature conditions, and identify potential metering deviations or functional abnormalities in advance. On the other hand, with the help of the rapid simulation capability of the virtual model, multi-scenario and multi-parameter testing and verification can be carried out without increasing actual hardware losses, greatly shortening the testing cycle and reducing the testing cost.

[0049] The following is in conjunction with the above. Figure 1The contents described in the illustrated low-temperature environment testing system provide a detailed explanation of the low-temperature environment energy meter testing method based on digital twin provided in the embodiments of this application.

[0050] Figure 2 This illustration shows a flowchart of a digital twin-based method for testing energy meters in low-temperature environments, as provided in an embodiment of this application. The main execution component of this method is the processing equipment within the low-temperature environment testing system. (Refer to...) Figure 2 As shown, the method specifically includes the following steps:

[0051] S201. Send a working command to the smart meter to control the smart meter to enter the working state.

[0052] Optionally, based on the communication connection between the processing device 12 and the smart meter 10, the processing device 12 can send working instructions to the smart meter 10 placed in the high and low temperature chamber 11 via Ethernet, wireless communication, or RS-485 communication to control the smart meter 10 to enter the working state. The working instructions include: simulated load parameters.

[0053] Optionally, the simulated load parameters include parameters used to set the electrical load conditions for the smart meter 10 to enter the working state, such as voltage, current, power factor, etc. The simulated load parameters can be configured by the processing device 12 according to the actual application scenario, thereby realizing the evaluation of the meter performance under different electrical operating conditions.

[0054] Optionally, the smart meter 10 responds to the working instructions sent by the processing device 12. Based on the simulated load parameters contained in the working instructions, by loading a simulated load, it can verify whether the smart meter 10 can still accurately collect and process electrical energy data in a low-temperature environment, and whether there are problems such as metering drift, response delay, or communication interruption. In this way, through the communication interaction between the processing device 12 and the smart meter 10, the processing device 12 can realize the initialization of remote control and automated testing of the smart meter 10 as a physical device.

[0055] S202. According to the preset cooling curve, control the temperature inside the high and low temperature chamber to gradually decrease to the preset low temperature, and control the humidity inside the high and low temperature chamber within the preset humidity range.

[0056] Optionally, the preset cooling curve refers to a curve reflecting the temperature change over time. For example, it gradually cools down to a preset low temperature at a rate of 1°C per minute. This preset low temperature is typically the target temperature under extremely cold conditions, such as -25°C. The specific setting of the preset low temperature can be determined according to the performance of the smart meter 10 or actual usage requirements. Based on this, the processing device 12, through a communication connection with the high and low temperature chamber 11, sends temperature control commands to the chamber in real time, causing the interior of the chamber to gradually cool down to the preset low temperature according to the preset cooling curve. It is worth noting that the processing device 12 controls the temperature inside the high and low temperature chamber 11 to gradually decrease, avoiding thermal shock damage to the smart meter 10 caused by a sudden temperature drop, while also ensuring the safety and accuracy of the testing process.

[0057] Optionally, the preset humidity range is a safe variation range of relative humidity within the high and low temperature chamber 11. Humidity control within the chamber 11 is particularly critical during the cooling process to prevent condensation inside the smart meter 10, which could cause circuit breaks or reduced insulation performance, thus affecting test results. Based on this, the processing device 12, through a communication connection with the high and low temperature chamber 11, sends humidity control commands to the chamber 11 in real time, controlling the internal humidity within the chamber 11 to remain within the preset range. In this way, by sending control commands to the high and low temperature chamber 11, the processing device 12 controls the internal temperature and humidity to meet the test conditions, achieving not only the accuracy and controllability of environmental simulation but also ensuring that the test conditions truly reflect the actual application scenario.

[0058] S203. Collect and acquire the working data of the smart meter.

[0059] Optionally, after the low-temperature environment stabilizes, the processing device 12 begins to continuously collect operating data from the smart meter 10. This operating data includes status data and metering data. Status data refers to information reflecting the smart meter 10's own operating status, internal environment, and the performance of key components, primarily used to evaluate the smart meter 10's reliability, stability, and resistance to environmental interference in low-temperature environments. Metering data refers to the measurement and calculation information of electrical energy-related physical quantities by the smart meter 10 during its operating state, primarily used to evaluate the metering accuracy of the smart meter 10 in low-temperature environments.

[0060] Optionally, the status data includes at least one of the following: the temperature of key components of the smart meter, the temperature difference between the inside and outside of the smart meter casing, the internal humidity information of the smart meter, the signal strength and bit error rate of the communication module, or the real-time power consumption of the smart meter. Specifically, the temperature of key components such as the main control chip, power module, and metering chip can be used to analyze heat distribution and device reliability at low temperatures; the temperature difference between the inside and outside of the smart meter casing reflects heat transfer efficiency and insulation performance, and can determine whether there is localized condensation or thermal stress concentration; the internal humidity information of the smart meter reflects whether there is moisture intrusion or poor sealing; the signal strength and bit error rate of the communication module can reflect the impact of low temperature on wireless or wired communication performance; and the real-time power consumption of the smart meter can determine whether there is abnormal power consumption.

[0061] Optionally, the metering data includes, but is not limited to: voltage, current, active / reactive power, cumulative electrical energy, frequency, power factor, etc.

[0062] Optionally, the processing device 12 can acquire data through the data interface reserved on the high and low temperature chamber 11, the communication port of the electricity meter, or an external sensor. Furthermore, the processing device 12 can acquire the operating data of the smart meter 10 by periodically sampling at a certain sampling frequency, such as once per second.

[0063] S204. Update the working data of the electricity meter model according to the working data, and generate simulation test results based on the electricity meter model.

[0064] Optionally, the electricity meter model 13 is constructed based on digital twin technology and can reflect the comprehensive response of the smart meter 10 under different environmental conditions. The processing device 12 updates the working data of the electricity meter model 13 according to the collected working data of the smart meter 10. That is, the status data and metering data collected from the real smart meter 10 are injected into the pre-established electricity meter model 13 in the processing device 12 to keep the state of the electricity meter model 13 consistent with that of the smart meter 10. For example, the power consumption, signal strength and bit error rate of the smart meter 10 are actually measured and assigned to the corresponding virtual components in the electricity meter model 13 to achieve virtual-real synchronization between the virtual model 13 and the physical entity of the smart meter 10.

[0065] Optionally, the processing device 12 updates the operating data of the energy meter model 13 based on the collected operating data of the smart meter 10, thereby achieving accurate replication and dynamic control of the operating status, test data, and fault scenarios of the smart meter 10. Based on this, the processing device 12 can use the updated energy meter model 13 to conduct simulation tests, comparing and analyzing the current operating data with the historical operating data of the smart meter 10 to determine whether the smart meter 10 has any abnormal operating conditions, such as excessively low temperatures of key components, sudden increases in communication error rates, or exceeding metering deviation limits, thereby generating simulation test results.

[0066] Based on this, the low-temperature environment energy meter testing method based on digital twin according to the embodiments of this application constructs an energy meter model dynamically synchronized with the physical smart meter in a processing device using digital twins. This energy meter model serves as the digital twin model of the smart meter. During the low-temperature test, the method collects the status and metering data of the smart meter in real time, updates the working data of the energy meter model using this data, and performs simulation tests based on the updated model to dynamically generate simulation test results, predicting the performance trend and potential faults of the smart meter under extreme operating conditions. This not only automates the testing process, reducing the workload of manual intervention and post-judgment, but also improves the comparability, repeatability, and accuracy of the test through virtual-real comparison and data closure, thereby significantly improving the efficiency and accuracy of low-temperature environment testing.

[0067] Figure 3 This illustration shows a flowchart of a method for establishing a virtual connection between an energy meter model and a smart meter, according to an embodiment of this application. (Refer to...) Figure 3 As shown, the method specifically includes the following steps:

[0068] S301. Obtain the design parameters of the smart meter.

[0069] Optionally, design parameters refer to a series of technical indicators and configuration information defined during the product design and manufacturing process of the smart meter 10, reflecting the physical structure, functional characteristics, and operating logic of the smart meter 10. For example, design parameters include the physical structure parameters of the smart meter, the physical characteristic parameters of the components, and the software processing logic. The physical structure parameters of the smart meter include geometric and structural information such as the overall dimensions, casing material, internal layout, heat dissipation structure, sealing level, and terminal block location. These parameters can be used to construct the three-dimensional geometry of the energy meter model. The physical characteristic parameters of the components include the technical specifications and physical characteristics of key components in the smart meter, such as the sampling accuracy and temperature coefficient of the metering chip, the frequency stability and temperature drift curve of the crystal oscillator, and the response time and contrast variation of the LCD screen at low temperatures. These parameters can be directly used to construct the energy meter model, ensuring that the behavior of the components in the final energy meter model is consistent with the behavior of the components in a real smart meter. The software processing logic includes the algorithmic logic of the internal firmware of the smart meter, such as the energy metering algorithm, communication protocol stack, and data storage mechanism. The software processing logic is used to construct the behavioral rules and state transition logic of the energy meter model.

[0070] Optionally, the design parameters of the smart meter 10 can be extracted from the product design document of the smart meter, or the design parameters of the smart meter 10 can be pre-stored in a database, and the processing device 12 can call the API interface to automatically import the design parameters from the database.

[0071] S302. Based on the design parameters, generate the energy meter model corresponding to the smart meter using digital twin.

[0072] Optionally, the design parameters of a smart meter include its physical structure parameters, device physical characteristic parameters, and software processing logic, reflecting information about the smart meter in three dimensions: geometry, physical behavior, and rules. Correspondingly, based on the design parameters of the smart meter, the energy meter model generated using digital twins can also include three levels of modeling: the construction of a geometric model, a physical behavior model, and a rule model.

[0073] For example, based on the acquired physical structure parameters of the smart meter, a geometric model of the meter is constructed using CAD modeling tools or 3D reconstruction technology, achieving a high-precision 3D visualization of the meter's appearance and internal structure. Then, combining the physical characteristic parameters of the components, a mathematical and simulable physical behavior model is established for key components such as crystal oscillators, LCD screens, and metering chips. By introducing dynamic characteristics such as temperature-frequency drift curves and low-temperature response time functions, the model supports the reproduction of the smart meter's real physical behavior under different operating conditions in a simulation environment. Simultaneously, based on software processing logic and safety specifications, a rule model is constructed, transforming performance thresholds, fault determination conditions, and other rules into executable logical rules. Finally, the geometric model, physical behavior model, and rule model at these three levels are integrated and encapsulated in a digital twin platform, stored in a standardized data format, and interfaced with processing device 12 to generate a complete digital twin of the smart meter that can be dynamically driven and supports real-time simulation and state mapping, achieving automated construction and system-level integration from design parameters to a virtual model. Thus, based on the acquired design parameters of the smart meter 10, the processing device 12 constructs a virtual energy meter model 13 corresponding to the smart meter 10. This energy meter model 13 is a digital twin of the smart meter 10, realizing the transformation of the smart meter from a physical entity to a virtual mapping.

[0074] S303. Establish a virtual connection between the electricity meter model and the smart meter to ensure dynamic synchronization.

[0075] Optionally, establishing a virtual connection between the electricity meter model 13 and the smart meter 10 means establishing a real-time data channel between the virtual model of the electricity meter model and the physical entity of the smart meter, ensuring that the states of the electricity meter model 13 and the smart meter 10 are consistent, and realizing the virtual-physical linkage of the electricity meter.

[0076] Optionally, a bidirectional data link can be established in the processing device 12 via a communication network, such as Ethernet, RS-485 communication, or Wi-Fi. This bidirectional data link includes a forward channel and a reverse channel. The forward channel is used by the processing device 12 to send instructions to the smart meter 10, while the reverse channel is used to collect working data from the smart meter 10 in real time and transmit the working data to the energy meter model 13 for working data updates.

[0077] Optionally, by establishing a virtual connection between the electricity meter model 13 and the smart meter 10, dynamic synchronization between the electricity meter model 13 and the smart meter 10 is ensured, enabling the electricity meter model 13 to reflect the status of the smart meter 10 in real time. The dynamic synchronization mechanism includes timestamp alignment, data mapping, and model refresh frequency. Timestamp alignment means that all collected working data is timestamped to ensure that the updates of the electricity meter model 13 are synchronized with the physical events of the smart meter 10. Data mapping means that the working data is automatically matched to the corresponding virtual components in the electricity meter model 13. The model refresh frequency means that the update cycle is set according to the test requirements, such as updating once per second, to ensure that the model status of the electricity meter model 13 is highly consistent with that of the smart meter 10.

[0078] Based on this, this application realizes the comprehensive transformation of smart meter design information into a high-fidelity, simulable digital twin model, and constructs a full lifecycle mapping relationship between physical devices and virtual models. Specifically, by acquiring design parameters and constructing a multi-level energy meter model containing geometric structure, physical behavior, and operating rules, and then establishing a real-time virtual connection between the energy meter model and the smart meter entity, it not only achieves accurate reproduction and visual monitoring of the structural state, performance changes, and fault behavior of smart meters in low-temperature environments, but also supports dynamic synchronization and intelligent analysis based on real data, significantly improving the automation level and predictive capability of the testing process.

[0079] As one possible implementation, step S204 above updates the working data of the electricity meter model based on the working data and generates simulation test results based on the electricity meter model, including:

[0080] The working data of the electricity meter model is updated based on the working data. The analysis engine is used to compare and analyze the working data with the historical health data of the smart meter to determine whether there are any anomalies in the working data. If there are anomalies, an anomaly report is generated and displayed.

[0081] For example, the processing device 12 also includes an analysis engine. The working data of the smart meter 10 is transmitted to the processing device 12 through a communication link. Specifically, the analysis engine in the processing device 12 receives the working data of the smart meter 10 and injects the working data of the smart meter 10 as an input signal into the established electricity meter model 13 to ensure that the state of the electricity meter model 13 is refreshed in real time and remains dynamically consistent with the smart meter 10.

[0082] Optionally, the historical health data of the smart meter refers to reference data showing that the smart meter 10 performed normally during normal temperature or previous low-temperature tests. The analysis engine compares the currently updated operating data of the energy meter model 13 with the historical health data of the smart meter. The comparison methods include, but are not limited to, threshold judgment, trend analysis, and multi-parameter correlation analysis. Threshold judgment directly compares the operating data with the historical health data of the smart meter to determine whether the operating data exceeds the allowable range corresponding to the historical health data. If so, it is determined that the operating data is abnormal. Trend analysis determines whether the changes in specific parameters in the operating data show abnormal trends, such as a rapid increase in the error rate. Multi-parameter correlation analysis combines data from multiple dimensions such as temperature, humidity, and operator ID to comprehensively analyze whether the operating data is abnormal.

[0083] For example, if the analysis engine detects that the error rate of the communication module suddenly rises to 1.5% when the ambient temperature drops to -22°C, exceeding the normal threshold of 1% in the historical health data of the smart meter, the analysis engine can then determine that there is an anomaly in the working data.

[0084] Optionally, if an anomaly is detected, an anomaly report is generated and displayed, or an anomaly command is generated and reported. Continuing with the example above, "When the ambient temperature drops to -22℃, the analysis engine detects that the communication module's bit error rate suddenly rises to 1.5%, exceeding the normal threshold of 1% in the smart meter's historical health data," the analysis engine determines that the working data is abnormal, triggers the generation of an anomaly report, and displays it visually. The anomaly report includes, but is not limited to, the following information: anomaly type (e.g., communication anomaly), occurrence time, specific parameters (e.g., bit error rate = 1.5%), associated environmental conditions (temperature = -22℃), and possible causes (e.g., low temperature causing a decrease in RF module performance).

[0085] For example, the processing device 12 provides a user visualization interface on which anomaly reports can be displayed for testers to view, and can also trigger audible alarms or message notifications. Additionally, the location of the communication module experiencing the anomaly can be highlighted on the 3D visualization model corresponding to the electricity meter model, and color-coded warnings, such as flashing red, can be used to indicate the fault area.

[0086] Based on this, the analysis engine, as the core decision-making unit, continuously compares measured data with historical health data to achieve accurate monitoring and intelligent diagnosis of the smart meter's operating status in low-temperature environments. This significantly improves the automation level, response speed, and predictive ability of the test, effectively solving the problems of low efficiency and easy omission of hidden faults in traditional manual interpretation.

[0087] Furthermore, the method further includes using an analysis engine to compare and analyze the working data with the historical health data of the smart meter to determine whether there are any abnormalities in the working data. If there are no abnormalities, the method continues to control the internal temperature of the high and low temperature chamber to continue cooling until an abnormality is detected. The corresponding abnormal temperature is then recorded and used as the critical temperature value.

[0088] Optionally, after the analysis engine compares and analyzes the real-time collected working data with the historical health data of the smart meter, if it is determined that all parameters are within the normal range and no abnormalities have occurred, the progressive operation of the test process will be automatically executed. For example, the temperature inside the high and low temperature chamber 11 will continue to decrease according to the preset cooling curve, gradually approaching more stringent low temperature conditions. During this process, the analysis engine maintains real-time updates and monitoring of the state of the energy meter model 13. Once it detects that one or more working data exceed the normal threshold, such as a sudden increase in communication error rate, a jump in metering data, or a critical component stopping response, the moment will be immediately marked and the ambient temperature at that time will be recorded as the critical temperature value for triggering the abnormality. This critical temperature value reflects the lowest safe operating temperature that the smart meter can withstand under the current test conditions.

[0089] Optionally, if no abnormality is found, the temperature inside the high and low temperature chamber continues to decrease until an abnormality is detected. The corresponding abnormal temperature is then recorded. This includes: if no abnormality is found, the temperature inside the high and low temperature chamber is continuously decreased multiple times according to different cooling curves, and the abnormal temperature recorded each time an abnormality is detected. The different cooling curves indicate different cooling rates and different temperature decreases within a unit of time.

[0090] For example, after comparing and analyzing the real-time collected working data with the historical health data of the smart meter using an analysis engine, if it is determined that all current parameters are within the normal range, the temperature inside the high and low temperature chamber 11 can be continuously reduced using the previously preset cooling curve. Alternatively, the temperature inside the high and low temperature chamber can be continuously reduced multiple times according to different cooling curves. Specifically, the test process, such as from room temperature to extremely low temperature, can be repeatedly executed according to multiple different cooling curves. Each cooling curve differs in its cooling rate or the magnitude of temperature change per unit time; for example, one cooling curve reduces the temperature by 1°C per minute, while another reduces it by 3°C per minute, to simulate different actual usage scenarios or accelerate the aging process. During each test, the analysis engine continuously monitors the status of the electricity meter model 13. Once an anomaly is detected, such as communication interruption or metering inaccuracy, the corresponding ambient temperature is recorded as the abnormal temperature point for this test. By conducting multiple cooling tests at different rates, multiple abnormal temperature data can be obtained. This allows for analysis of the impact of cooling rate on the performance degradation of the electricity meter, identification of the most sensitive environmental change conditions, and comprehensive determination of a more representative minimum operating temperature threshold or critical temperature range.

[0091] Based on this, by performing low-temperature tests multiple times according to different cooling curves and recording the abnormal temperatures each time, the impact of cooling rate on the performance stability of smart meters can be comprehensively evaluated. This not only overcomes the shortcomings of the one-sided test results under a single fixed cooling mode, but also accurately determines the critical temperature range for reliable operation of the meter and its environmental sensitivity through comparative analysis of multiple sets of abnormal temperature data.

[0092] As one possible implementation, the method further includes: generating real-time dynamic information of the smart meter based on the electricity meter model and displaying the real-time dynamic information on a user visualization interface. The real-time dynamic information includes: a three-dimensional visualization model of the electricity meter model, visualization curves corresponding to the working data, and abnormal events.

[0093] For example, the 3D visualization model is labeled with the real-time temperature of each area. As a virtual mapping of the electricity meter model, the 3D visualization model displays the overall structure and key component layout of the electricity meter model in high-precision 3D on the user's visualization interface. It also labels the temperature values ​​at corresponding locations on the surface of the electricity meter model or in each functional area in real time, and supports rotation, scaling, and sectioning operations for easy observation of the internal heat distribution. For instance, when an abnormal temperature rise or low-temperature condensation risk occurs in a certain area, a color-coded heat map can be used, such as red for high temperature and blue for low temperature, to provide a high-level warning. This visual display of heat distribution using different colors facilitates rapid fault location. Furthermore, an internal and external perspective view of the 3D visualization model can be rendered and displayed in the user's visualization interface.

[0094] For example, the visualization curves corresponding to the working data are curves that dynamically reflect the performance change trend of the electricity meter in the form of time-series charts. The visualization curves include one or more of the following: metering data change curves over time, temperature change curves of key components over time, voltage / current change curves over time, and power consumption change curves over time. Among them, the metering data change curves over time, such as cumulative energy and active power curves, are used to determine whether the metering is stable at low temperatures and whether there are jumps or drifts. The temperature change curves of key components over time, such as the temperature response process curves of key components such as metering chips, crystal oscillators, and batteries, are used to analyze the coupling relationship between each key component and the ambient temperature. The voltage / current change curves over time can be used to identify and analyze sampling distortion caused by low temperatures. The power consumption change curves over time reflect changes in energy consumption and are used to assess and analyze the impact of low temperatures on energy consumption.

[0095] For example, abnormal events such as communication interruptions, exceeding bit error rate limits, and exceeding temperature limits can be considered. In this application, a multimodal, hierarchical, and intuitive display of real-time dynamic information from smart meters generated based on electricity meter models can be achieved through a user-visual interface. For example, referring to… Figure 4As shown, the main view on the left side of the user visualization interface displays a 3D visualization model, including details such as the casing, terminals, and display screen. It supports rotation, zooming, and viewing component properties, and displays temperature gradients via a heatmap overlay, with different colors representing different temperature ranges. Real-time temperature values ​​are displayed for key components. Abnormal situations are indicated by flashing red lights and warning labels. The upper right area of ​​the user visualization interface presents multiple time-series graphs, including metering data curves for voltage, current, and power, temperature change curves for key components, and a dual Y-axis graph for power consumption and communication performance. All curves are distinguished by different colors for easy identification. The lower right area of ​​the user visualization interface displays status and alarm information, including a list of recently occurring abnormal events. Additionally, the bottom control bar of the user visualization interface provides function buttons for starting the test, pausing, and continuing, as well as options to switch views and select different curve combinations or meter models.

[0096] Based on this, by deeply integrating the digital twin model with the user visualization interface, a comprehensive, multi-dimensional, and highly perceptive real-time presentation of the smart meter's operating status during low-temperature testing was achieved, significantly improving the transparency of the testing process and the efficiency of human-computer interaction.

[0097] As one possible implementation, after updating the working data of the electricity meter model based on the working data in step S204 above, the method further includes: generating fault warning information for the smart meter based on the working data and a pre-trained risk assessment model.

[0098] Optionally, the risk assessment model is a machine learning model. It is trained using a large amount of historical data collected from the smart meter's normal operation to its failure phases, illustrating the changing trends. This means the model uses historical data as training data, covering the complete evolution of the smart meter from normal operation to various typical failures. This training data includes multi-dimensional time-series information, such as key component temperature, power consumption fluctuations, communication error rates, and ambient temperature and humidity, and labels the corresponding failure types and occurrence times. By employing a Long Short-Term Memory (LSTM) network to learn the dependencies in the time-series information—for example, a small fluctuation in power consumption at low temperatures might indicate an impending failure of the storage module—a risk assessment model is ultimately obtained through iterative training and validation on large-scale data. This model possesses the ability to predict the probability of future failures based on current and historical data.

[0099] For example, after updating the working data of the electricity meter model based on the working data, the working data is input into the trained risk assessment model in a time series. The risk assessment model then outputs the predicted probability of the corresponding fault type. For instance, when a specific fluctuation pattern in the power consumption of a smart meter is detected at -20℃, the risk assessment model determines that the probability of a write error occurring after the smart meter has been running at low temperatures for 48 hours is 75%. At this point, the processing device determines that the risk exceeds a preset threshold and generates a fault warning message. This fault warning message includes, but is not limited to, the following information: fault type, risk level, expected occurrence time, and related parameters. The fault warning message is then pushed to the user's visual interface for display through the analysis engine to achieve early warning.

[0100] Furthermore, after generating the anomaly report, the anomaly report is compared and analyzed with the fault warning information to obtain the analysis results.

[0101] For example, to evaluate the predictive accuracy of the risk assessment model, after generating anomaly reports, the fault type, occurrence time, and environmental conditions in the anomaly reports are compared and analyzed with the fault warning information generated by the risk assessment model. For instance, if the risk assessment model warned of a high risk of write failure within 48 hours, and a storage anomaly does indeed occur after 40 hours in actual testing, this is considered a valid prediction, and the relevant information from this valid prediction is used as an analysis result. In this way, by comparing and analyzing anomaly reports with fault warning information, multiple analysis results can be obtained. These results can then be used to calculate metrics such as the accuracy and recall of the risk assessment model to verify its accuracy.

[0102] Based on this, a risk assessment model is trained by learning from historical working data through machine learning models. The risk assessment model is used to predict potential faults in advance. The accuracy of the risk assessment model is verified by comparing and analyzing anomaly reports and fault warning information. This significantly improves the accuracy of fault identification by the risk assessment model, fundamentally changing the inefficient mode of relying on manual interpretation and post-event analysis. It realizes intelligent diagnosis, risk prevention and control and reliability verification of electricity meters in extreme environment testing.

[0103] This application embodiment also provides a processing device 500, such as... Figure 5The diagram shown is a structural schematic of a processing device 500 provided in an embodiment of this application, including a processor 501 and a memory 502. Optionally, it may also include a bus 503. The memory 502 stores machine-readable instructions executable by the processor 501. When the processing device 500 is running, the processor 501 and the memory 502 communicate via the bus 503. When the machine-readable instructions are executed by the processor 501, the method steps in the low-temperature environment energy meter testing method based on digital twin as described in any of the preceding claims are executed.

[0104] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the method steps of the low-temperature environment energy meter testing method based on digital twin as described in any of the preceding claims.

[0105] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces; the indirect coupling or communication connection of devices or modules can be electrical, mechanical, or other forms.

[0106] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0107] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for testing electricity meters in low-temperature environments based on digital twins, characterized in that, A processing device is used in a low-temperature environment testing system, the low-temperature environment testing system comprising: a processing device, a high-low temperature chamber, and a smart meter; the smart meter is placed inside the high-low temperature chamber, the processing device is communicatively connected to both the high-low temperature chamber and the smart meter, the processing device contains a power meter model corresponding to the smart meter generated based on a digital twin, and the method includes: Sending a working instruction to the smart meter to control the smart meter to enter the working state, the working instruction including: simulated load parameters; According to the preset cooling curve, the temperature inside the high and low temperature chamber is gradually reduced to the preset low temperature, and the humidity inside the high and low temperature chamber is controlled within the preset humidity range; The working data of the smart meter is collected and acquired, including status data and metering data. The working data of the electricity meter model is updated based on the working data, and simulation test results are generated based on the electricity meter model. The step of generating simulation test results based on the electricity meter model includes: using an analysis engine to compare and analyze the working data with the historical health data of the smart meter to determine whether there are any abnormalities in the working data. If there are any abnormalities, an abnormality report is generated and displayed. If there are no abnormalities, the temperature inside the high and low temperature chamber continues to drop until an abnormality is detected. The corresponding abnormal temperature is then recorded and used as a critical temperature value.

2. The method according to claim 1, characterized in that, The method further includes: Obtain the design parameters of the smart meter, including the physical structure parameters, device physical characteristic parameters, and software processing logic of the smart meter; Based on the design parameters, a digital twin-based energy meter model is generated for the smart meter. A virtual connection is established between the electricity meter model and the smart meter to ensure dynamic synchronization.

3. The method according to claim 1, characterized in that, The method further includes: The real-time dynamic information of the smart meter is generated based on the energy meter model and displayed on the user's visualization interface. The real-time dynamic information includes: a three-dimensional visualization model of the energy meter model, a visualization curve corresponding to the working data, and abnormal events. The three-dimensional visualization model is labeled with the real-time temperature of each region; the visualization curves corresponding to the working data include one or more of the following: curves of measurement data changing over time, curves of temperature changing over time for key components, curves of voltage / current changing over time, and curves of power consumption changing over time.

4. The method according to claim 1, characterized in that, After updating the working data of the electricity meter model based on the working data, the method further includes: Based on the working data and the pre-trained risk assessment model, fault warning information of the smart meter is generated. The risk assessment model is trained using a large number of change trends from normal working data to fault working data collected in the history of the smart meter. The method further includes: After generating the anomaly report, the anomaly report is compared and analyzed with the fault warning information to obtain the analysis results.

5. The method according to claim 1, characterized in that, The status data in the working data includes at least one of the following: the temperature of key components of the smart meter, the temperature difference between the inside and outside of the smart meter casing, the internal humidity information of the smart meter, the signal strength and bit error rate of the communication module, or the real-time power consumption of the smart meter.

6. The method according to claim 1, characterized in that, If no abnormality is found, the temperature inside the high and low temperature chamber continues to drop until an abnormality is detected. The corresponding abnormal temperature is then recorded, including: If no abnormality is found, the temperature inside the high and low temperature chamber is continuously reduced multiple times according to different cooling curves, and the abnormal temperature is recorded each time an abnormality is detected. The different cooling curves are used to indicate different cooling rates and different cooling amplitudes within a unit time.

7. A processing device, characterized in that, include: The processor and memory, the memory storing machine-readable instructions executable by the processor, wherein when the processing device is running, the processor executes the machine-readable instructions to perform the steps of the method for testing energy meters in low-temperature environments based on digital twins as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method for testing energy meters in low-temperature environments based on digital twins as described in any one of claims 1 to 6.

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