An electrical equipment installation and debugging system based on digital twinning

Abstract

The application discloses an electrical equipment installation and debugging system based on digital twinning, and relates to the technical field of electrical equipment installation and debugging.The system comprises a data acquisition module, a digital twinning platform module, a layout planning module, a verification and debugging module and a model feedback module.The digital twinning platform module enables the system to accurately simulate the mutual influence among multiple devices in a virtual space, including thermal radiation accumulation, airflow organization change and electromagnetic interference coupling, etc., so that the electrical equipment can foresee and avoid systematic risks caused by improper device cluster layout in the installation design stage; in addition, the system can automatically optimize the non-uniform spacing according to the heat dissipation influence among the devices, and when detecting that the electrical equipment is located at an unacceptable environmental risk point, the system generates a better topological scheme through rule base matching and space collision detection, thereby avoiding problems such as accelerated aging or device failure caused by improper installation of electrical equipment.

Description

Technical Field

[0001] This invention relates to the field of electrical equipment technology, and in particular to an electrical equipment installation and commissioning system based on digital twins. Background Technology

[0002] With the continuous development and integration of electrical technology and intelligent information technology, electrical automation technology has been widely used in my country's industrial sector. By leveraging intelligent information technology, functions such as condition monitoring, assisted installation and commissioning of electrical equipment are realized. This not only improves the installation efficiency and quality of electrical equipment, but also saves manpower and time costs to a certain extent, helping enterprises create greater economic benefits.

[0003] Chinese patent application number 202411096762.6 discloses an artificial intelligence-based electrical installation and debugging system. This invention collects environmental parameters of the electrical equipment installation area and electrical parameters of the electrical equipment, integrates the dataset to establish a three-dimensional simulation model and a neural network model, and uses these two models to simulate and debug the circuit structure and installation location of the electrical installation, as well as the functional problems that may occur after installation.

[0004] However, this solution is mainly applicable to the installation and commissioning of a single electrical device, focusing on the compatibility between the device and the installation environment. In real industrial scenarios, when faced with a system framework composed of multiple electrical devices, the above solution not only incurs huge implementation costs but also easily overlooks the mutual influence generated by adjacent electrical devices working together. Taking a distribution cabinet system as an example, this solution can simulate and debug the installation position of components in a single cabinet, but when multiple distribution cabinets are connected in series or parallel, adjacent distribution cabinets will affect each other. For example, heat radiation, airflow obstruction, and electromagnetic interference are the most obvious interference factors, and existing technologies do not address the characteristics of this type of system framework.

[0005] Furthermore, due to the different adjacent environments, the equipment in different locations also faces different installation conditions and operating conditions. Taking the heat dissipation status of five series-connected distribution cabinets as an example, the distribution cabinets at the first and last ends have only one adjacent side with the adjacent distribution cabinets, and the other side may be exposed to the computer room environment. Therefore, their heat dissipation conditions are relatively good. However, the remaining distribution cabinets have two adjacent sides with the other adjacent distribution cabinets, and are located in the area with the most complex thermal environment and airflow organization. This inherent difference brought about by the system structure requires that their installation strategies be adjusted in a targeted manner, which is something that the existing methods that focus on individual equipment cannot cover.

[0006] To address these issues, this invention proposes an electrical equipment installation and commissioning system based on digital twins. Summary of the Invention

[0007] The purpose of this invention is to provide an electrical equipment installation and commissioning system based on digital twins to solve the technical problems mentioned in the background section.

[0008] To achieve the above objectives, the present invention provides the following technical solution: an electrical equipment installation and commissioning system based on digital twins, comprising: The data acquisition module is used to collect environmental parameters of the target installation area, actual installation data of electrical equipment, and operating parameters. The digital twin platform module has a pre-built library of electrical equipment parametric models, an environmental assessment rule library, and a physical simulation engine, which are used to construct a target digital twin containing electrical equipment models and their layout relationships based on the spatial information and environmental parameters of the target installation area. The layout planning module, which connects the digital twin platform module and the data acquisition module, is used to conduct risk assessment and planning of the equipment layout in the target digital twin based on the environmental parameters, and generate an installation layout scheme. The verification and debugging module is used to compare the physical installation and operating status with the installation layout scheme or digital twin simulation results based on the actual installation data and operating parameters, and generate debugging instructions based on the deviation data. The model feedback module, which connects the verification and debugging module and the digital twin platform module, is used to calibrate the model parameters in the target digital twin based on the deviation data, and feed the calibration information back to the digital twin platform module to optimize its preset model library or rule library.

[0009] Preferably, the logic for the layout planning module to perform risk assessment and layout decision-making includes: In the target digital twin, a linear series arrangement is used as the baseline layout scheme; Based on the environmental parameters, determine whether there are environmental risk points exceeding the safety threshold on the preset path of the baseline layout scheme; If it does not exist, a first-class optimization scheme is generated to optimize the spacing between two adjacent electrical devices in the baseline layout scheme; If it exists, the layout transformation decision process is triggered, and at least one second-type layout transformation scheme is generated under the condition of satisfying the spatial constraints. Subsequently, the generated schemes are evaluated for multi-attribute utility, and the scheme with the highest comprehensive score is selected as the final output installation layout scheme.

[0010] Preferably, the spacing optimization process includes: Retrieve pre-stored thermal performance simulation data associated with different combinations of device power consumption and spacing; With the goal of reducing the overall heat load of the system, an optimization algorithm is used to search for a non-uniform spacing distribution scheme between adjacent devices within the total installation length constraint.

[0011] Preferably, the layout change decision process includes: Based on the type of environmental risk point, a corresponding layout topology countermeasure is matched from a predefined rule base. The countermeasure is used to change the relative positional relationship between multiple electrical devices to form a spatial topology structure different from the linear series arrangement. In the target digital twin, based on the spatial collision detection algorithm, the spatial feasibility of the matching topological countermeasures is verified and the geometric parameters are solved. Generate one or more specific layout schemes that conform to the layout topology countermeasures and satisfy all physical space constraints, as the second type of layout transformation scheme.

[0012] Preferably, the control logic for the multi-attribute utility evaluation is as follows: For the first type of optimization scheme and / or the second type of layout transformation scheme, calculate their scores in terms of risk elimination degree, expected thermal performance index, wiring economy and engineering implementation complexity; Based on the preset weights, the above multidimensional scores are weighted and summed to obtain the comprehensive utility score for each solution; The scheme with the highest overall utility score is selected as the final installation layout scheme output to subsequent modules.

[0013] Preferably, the control method of the verification and debugging module includes two stages executed sequentially: During the installation process verification phase: the actual installation location coordinates of the equipment are obtained and continuously compared with the theoretical coordinates in the installation layout scheme; when the position error exceeds the preset tolerance, a position adjustment command is generated and a prompt is displayed; After installation, during the commissioning phase: After the electrical connection is powered on, key electrical parameters are collected and compared with the simulation prediction values ​​corresponding to the digital twin; when the parameter deviation exceeds the safe range, specific electrical commissioning instructions or protection setting adjustment suggestions are generated.

[0014] Preferably, the calibration control logic of the model feedback module includes: Monitor and record the key measured data and the simulation prediction data of the corresponding points of the target digital twin generated during the verification and debugging process; When the deviation between the measured value and the simulated value at the same measurement point persists for several consecutive sampling periods and exceeds the calibration threshold, the parameter reverse adjustment mechanism is triggered. The mechanism adjusts the internal parameters of the corresponding model in the target digital twin according to the type and direction of the deviation, so that the subsequent simulation predictions converge to the measured values.

[0015] Preferably, the calibration operation of the model feedback module is triggered at least in part within a preset debugging cycle after the equipment is installed and powered on; within this debugging cycle, the simulation model of the target digital twin is calibrated using the collected initial operating parameters and environmental parameters to improve the simulation accuracy of the target digital twin.

[0016] Preferably, the method for constructing the digital twin platform module includes: Import the structural drawings of the target installation area, and extract key structures as spatial constraint boundaries through image recognition; Load the parametric 3D model of the electrical equipment and initialize its electrical and thermodynamic properties based on the design data; The aforementioned spatial constraints are combined with the device model in virtual space to form the target digital twin.

[0017] Preferably, the data acquisition module dynamically plans the sensor deployment and data acquisition strategy based on the installation layout scheme, specifically as follows: The target installation area is divided into key area units related to the layout of electrical equipment and physical field strength. High-density environmental parameters are collected in the critical area units, while sparse or periodic collection strategies are used in non-critical areas.

[0018] The beneficial effects of this invention are: This invention utilizes a pre-built model and rule library within a digital twin platform module, combined with multi-dimensional environmental parameter collection of the target installation area. This enables the system to accurately simulate the interactions between multiple devices in a virtual space, including accumulated heat radiation, changes in airflow organization, and electromagnetic interference coupling. This allows electrical equipment to anticipate and mitigate systemic risks caused by improper equipment cluster layout during the installation design phase. Furthermore, when the environment of the target installation area is suitable and physical space is sufficient, the system can automatically optimize non-uniform spacing based on the heat dissipation effects between devices, scientifically allocating heat dissipation space to balance the system's heat load. When electrical equipment is detected to be located at an unacceptable environmental risk point, the system triggers a layout transformation, generating better topology schemes such as L-shapes and misalignments through rule library matching and spatial collision detection, thus avoiding problems such as accelerated aging or equipment failure caused by improper installation of electrical equipment. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the control flow of an electrical equipment installation and commissioning system based on digital twins according to the present invention.

[0020] Figure 2 This is a schematic diagram of the debugging process after installation of the present invention. Detailed Implementation

[0021] 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. Example 1

[0022] In electrical equipment installation and commissioning systems, existing technologies are mainly applicable to the installation and commissioning of single electrical devices, focusing on the degree of matching between the equipment and the installation site environment. However, in real industrial scenarios, when facing a system framework composed of multiple electrical devices, the above solutions not only have huge implementation costs, but also easily overlook the mutual influence generated by adjacent electrical devices when working together. Taking a distribution cabinet system as an example, this solution can simulate and debug the installation position of components in a single cabinet, but when multiple distribution cabinets are connected in series or parallel, adjacent distribution cabinets will affect each other. For example, heat radiation, airflow obstruction, and electromagnetic interference are the most obvious interference factors, and existing technologies do not address the characteristics of this type of system framework.

[0023] Furthermore, due to the different adjacent environments, the equipment in different locations also faces different installation conditions and operating conditions. Taking the heat dissipation status of five series-connected distribution cabinets as an example, the distribution cabinets at the first and last ends have only one adjacent side with the adjacent distribution cabinets, and the other side may be exposed to the computer room environment. Therefore, their heat dissipation conditions are relatively good. However, the remaining distribution cabinets have two adjacent sides with the other adjacent distribution cabinets, and are located in the area with the most complex thermal environment and airflow organization. This inherent difference brought about by the system structure requires that their installation strategies be adjusted in a targeted manner, which is something that the existing methods that focus on individual equipment cannot cover.

[0024] This embodiment was invented to solve the above problems.

[0025] Please see Figure 1 and Figure 2 As shown, an embodiment of the present invention provides an electrical equipment installation and commissioning system based on digital twins, including a data acquisition module, a digital twin platform module, a layout planning module, a verification and commissioning module, and a model feedback module. This embodiment takes the installation and commissioning of five series-connected power distribution cabinets in a data center computer room as an example. The five power distribution cabinets are, in order: No. 1 incoming cabinet, No. 2 ordinary load cabinet, No. 3 core load cabinet (containing a high-power frequency converter), No. 4 ordinary load cabinet, and No. 5 outgoing cabinet.

[0026] The data acquisition module is used to collect environmental parameters of the target installation area, actual installation data of electrical equipment, and operating parameters.

[0027] In the initial stage, the data acquisition module conducts a basic environmental survey of the target installation area (a passage approximately 10 meters long and 3 meters wide) based on the user-imported two-dimensional floor plan of the computer room. Mobile sensing units deployed at the installation site, integrating temperature and humidity sensors, ultrasonic anemometers, and laser rangefinders, perform preliminary surveys along the axis of the passage. These sensors sample at a frequency of 10Hz, and the raw data is aggregated to the edge computing gateway via a wireless sensor network. The gateway's built-in data preprocessing program first uses a moving average window for filtering, and then removes outliers caused by transient interference that deviate from the mean by more than three standard deviations. Finally, a stable and reliable sequence of background environmental parameters is generated, such as recording temperatures of 24℃ and 26℃ at the two ends of the passage, an average background wind speed of approximately 0.2 m / s, and humidity maintained at 55%RH.

[0028] It should be added that the deployment strategy of the above-mentioned mobile sensing units needs to be dynamically adjusted according to the installation layout scheme generated by the subsequent layout planning module. That is, the target installation area is divided into key area units related to the layout of electrical equipment and physical field strength (such as the area around high-heat cabinets and ventilation dead corners), and high-density sensor arrays are deployed in these units for continuous data collection. Non-key areas adopt a periodic sparse data collection strategy to optimize resource utilization. This dynamic adjustment is based on the real-time optimization of thermal gradient or electromagnetic field changes predicted by simulation to ensure the relevance and efficiency of data collection.

[0029] The processed environmental parameters, along with the computer room building drawings, were submitted to the digital twin platform module. The computer room building drawings included the locations of load-bearing columns, ventilation openings, and cable trenches. In addition, the design parameters of the power distribution cabinets were imported into the digital twin platform module. These design parameters included the physical dimensions of each cabinet (2200mm high, 800mm wide, and 600mm deep), design power consumption (2kW for cabinet 1, 4kW for cabinet 2, 8kW for cabinet 3, 4kW for cabinet 4, and 2kW for cabinet 5), the location of incoming and outgoing terminal blocks, and the expected heat source distribution of key heat-generating components inside the cabinet.

[0030] After receiving the above information, the digital twin platform module performs an instantiation process. This module has a pre-built parametric model library for electrical equipment, containing detailed 3D geometric models of various standard cabinet types and their associated electrical and thermodynamic property templates. Based on the input cabinet model, the digital twin platform module retrieves the corresponding parametric model from the library. Simultaneously, the pre-built environmental assessment rule library provides initial risk thresholds. For example, for general power distribution equipment, a sustained local temperature above 40°C requires close monitoring, and an inter-cabinet airflow velocity below 0.3 m / s may affect heat dissipation and requires attention. Furthermore, the physical simulation engine within the platform module provides solver support for subsequent computational fluid dynamics and electromagnetic field simulations.

[0031] Based on the computer room drawings, the system automatically extracts the two side walls, the columns at both ends, and the drainage grid on the ground as inviolable spatial constraint boundaries through image recognition algorithms. Then, the platform module integrates the parametric models of the five cabinets, the preliminary environmental parameters, and the spatial constraints in the virtual space to construct an initial, computable target digital twin. This target digital twin is not a static model, but a simulation matrix with basic physical properties (such as material thermal conductivity and surface emissivity) and boundary conditions (such as background temperature and wind speed).

[0032] Next, the layout planning module, which is connected to the digital twin platform module and the data acquisition module, begins to work. Its core task is to conduct risk assessment and planning of the equipment layout in the target digital twin based on the aforementioned environmental parameters, and then generate an installation layout scheme.

[0033] The logic for risk assessment and layout decision-making in the above layout planning module includes: Within the virtual channel of the target digital twin, five cabinets are arranged in a linear series, one end to the other, with a minimum installation and maintenance gap between the cabinets (this gap value is set according to the equipment's heat dissipation requirements and installation specifications, for example, initially set to 200mm), and this is used as the baseline layout scheme.

[0034] Then, the layout planning module retrieves more accurate environmental parameters obtained from the data acquisition module to conduct a risk assessment of this virtual cabinet corridor, and analyzes the temperature distribution, wind speed and direction, and possible sources of interference in the preset path (such as an old cable well near the predetermined position of cabinet No. 3 identified from the drawings, which may have a small amount of heat dissipation). Assuming that, after the above analysis, no environmental parameter exceeds the safety threshold at any point along the preset path (e.g., the temperature at all predicted points is below 35℃, and the estimated wind speed at the gap between cabinets is greater than 0.3 m / s), the system will determine the risk to be low and generate a first-class optimization scheme to optimize the spacing between two adjacent distribution cabinets. During this process, the system calls pre-stored thermal performance simulation data from the digital twin platform module. This data, pre-built through numerous offline simulations using lookup tables or response surface models, correlates different equipment power consumption combinations with the system heat distribution results under the spacing between adjacent cabinets. The layout planning module aims to reduce... With the overall system heat load, especially the control of the maximum temperature, as the optimization objective, an optimization algorithm (such as a genetic algorithm or particle swarm optimization algorithm) is adopted. Under the constraint that the total installation length does not exceed the available channel length, the algorithm searches using the four adjacent spacings of the five cabinets as variables. After iterative calculation, it may derive a non-uniform spacing distribution scheme. For example, since cabinet No. 3 generates the most heat, its spacing with cabinets No. 2 and No. 4 is increased from 200mm to 350mm; while the spacing between cabinets No. 1 and No. 2, and between cabinets No. 4 and No. 5 can be maintained at 200mm or slightly adjusted to balance the total length and provide a better heat dissipation channel for the heat-generating core.

[0035] If the data acquisition module detects a persistent abnormal high temperature point (above 40°C) at a certain coordinate point on the baseline layout path during the risk assessment phase, or identifies a significant increase in the concentration of corrosive gases in the area, the system determines that there is a clear environmental risk point. At this time, the layout change decision process is triggered, and at least one second-type layout change scheme is generated under the condition of satisfying the spatial constraints. First, the system attempts the lowest-cost adjustment: shifting the basic linear layout along one side of the aisle width to see if it avoids the hotspot or pollution source. If the shift fails to avoid the risk, or if the risk is regional (e.g., poor ventilation in the latter half of the aisle), the system initiates a deep transformation decision based on a rule-based database. This predefined database includes logic such as "If the risk is a localized, persistent high-temperature source, the countermeasure is to change the equipment topology to isolate the heat source" or "If the risk is a regional ventilation dead zone, the countermeasure is to disperse the equipment clusters in well-ventilated areas." Based on the matched... To counteract this, the system needs to explore new spatial topologies within the target digital twin. For example, if the matched countermeasure is to change the topology to isolate heat sources, the system will use a spatial collision detection algorithm to try changing the five cabinets from a straight line arrangement to an L-shaped arrangement or a staggered arrangement in the virtual machine room. Sensitive or high-heat cabinets will be adjusted to more advantageous positions. Furthermore, the algorithm will verify whether the new layout collides with constraints such as walls and columns, and calculate the specific coordinates and orientation of each cabinet under the new topology. Finally, a scheme that is different from the linear series arrangement will be generated as a second type of layout transformation scheme.

[0036] Subsequently, regardless of whether it is the first type of spacing optimization scheme or the second type of layout transformation scheme, before the final output, the generated scheme needs to be evaluated for multi-attribute utility, and the scheme with the highest comprehensive score is selected as the final output installation layout scheme. The multi-attribute utility evaluation includes: risk elimination degree (such as the extent to which the new scheme reduces the hot spot temperature), expected thermal performance index (the highest system temperature estimated based on simulation data), cabling economy (the total cable length estimated based on the new layout), and engineering implementation complexity (the extent of layout change and whether special brackets are required, etc.).

[0037] The above dimensions are weighted and summed according to preset weights to obtain a comprehensive utility score. Among them, safety risk has the highest weight, followed by economic efficiency. The layout planning module will ultimately select the scheme with the highest comprehensive score and determine it as the final installation layout scheme. This scheme includes the coordinates of each cabinet. Rotation angle and specific requirements for adjacent spacing.

[0038] Once the installation layout is determined, the verification and debugging module's work is divided into two sequentially executed phases, specifically including: Installation Process Verification Phase: Installers install the cabinets on-site according to the final installation layout plan. At this time, a visual measurement system (such as a binocular stereo vision camera) deployed in the computer room captures the targets or tags on each cabinet in real time to obtain the actual installation position coordinates of the cabinets. The verification and debugging module continuously compares these measured coordinates with the theoretical coordinates in the installation layout plan. The system has preset installation tolerances, such as horizontal position deviations of less than ±2cm and vertical deviations of less than ±1cm. If the system finds that the installation position error of a cabinet exceeds the preset tolerance, it will immediately prompt the installers. The prompts can be made by changing lights and sounding a buzzer to alert the installers. The installers can make adjustments according to the specific adjustment instructions given in the visualization equipment, such as moving cabinet No. 3 5cm to the north side, until all cabinet positions are adjusted to meet the tolerance range. Only then is the first phase completed.

[0039] Post-installation commissioning phase: After electrical connections are powered on, the data acquisition module collects key electrical parameters of the equipment, such as voltage, current, and power factor at the input and output terminals of each distribution cabinet, temperature of key busbars inside the cabinet, and readings from insulation monitoring devices. Simultaneously, the physical simulation engine in the digital twin platform performs electrical-thermal coupling simulation based on the current installation layout, actual collected environmental parameters, and the equipment's design load to obtain theoretical simulation predictions. The verification and commissioning module compares the actual collected operating parameters with the simulation predictions. The system has preset safety ranges, such as current deviation not exceeding 10% of the rated value. The busbar temperature does not exceed the allowable value of the insulation class. When a parameter deviation exceeds the safe range, the system will generate specific electrical commissioning instructions or protection setting adjustment suggestions. For example, if it is found that the actual current of a certain phase of cabinet No. 3 is 15% higher than the simulation prediction value, and the busbar temperature rises rapidly and approaches the threshold, the instructions generated by the verification and commissioning module include: check the load connection of the XX circuit of cabinet No. 3, verify whether it exceeds the design value, and suggest temporarily adjusting the protection setting of the circuit breaker of this circuit to 105% for observation. After the commissioning personnel check and adjust according to the above instructions, the system will continue to monitor parameter changes until the operating parameters stabilize within the safe range.

[0040] It is important to note that throughout the verification and debugging process, the model feedback module monitors and records the key measured data and the simulation prediction data corresponding to the target digital twin points generated during the verification and debugging process. The system sets dynamic calibration thresholds for different types of parameters. For example, the temperature deviation threshold can be set based on the percentage of the equipment's rated temperature rise. When the system identifies that the measured value and the simulation prediction value of a certain measuring point (e.g., the temperature measuring point of busbar No. 3) have a continuous deviation within 10 consecutive sampling periods, and the absolute value and duration of this deviation both exceed the preset calibration threshold (e.g., the temperature deviation is continuously greater than 5°C), the model feedback module will trigger the parameter reverse adjustment mechanism. This mechanism analyzes the specific reasons based on the type and direction of the deviation. For example, for example, for The persistent positive deviation in temperature rise (measured values ​​higher than predicted values) is identified by the mechanism as an excessively high equivalent heat dissipation coefficient in the cabinet model within the target digital twin, or an underestimation of the internal heat source power. For deviations in electromagnetic interference parameters, the electromagnetic shielding efficiency parameter in the model may be adjusted. Therefore, the system constructs a loss function (such as mean square error) based on the deviation data and uses optimization algorithms (such as gradient descent) for iterative calculations to automatically adjust the internal parameters of the corresponding model in the target digital twin. For example, it may appropriately reduce the convective heat transfer coefficient of the cabinet surface or fine-tune the correction factor for its heating power. The goal of these adjustments is to ensure that subsequent simulation predictions under the same or similar operating conditions converge to the new measured values, thereby improving the simulation accuracy of the digital twin for the current specific physical environment.

[0041] In addition, the calibration operation of the model feedback module is triggered at least in part within a preset debugging cycle after the equipment is installed and powered on. That is, within the preset debugging cycle after the system is powered on (such as within 48 to 72 hours, which can be configured according to project requirements), the equipment will experience various debugging load changes. This is the golden window for obtaining rich operating data. During this debugging cycle, the system uses the collected initial operating parameters and environmental parameters to perform a centralized and rapid calibration of the simulation model of the target digital twin.

[0042] The model feedback module feeds back the information obtained during the calibration process (e.g., under the ventilation conditions of workshop A, the actual heat dissipation correction factor for model B distribution cabinet is 0.85) to the digital twin platform module. After receiving this feedback from the front line of practice, the platform module can use it to optimize its pre-set model library or rule library. It can associate the correction factor of 0.85 as an additional piece of information with the model B inverter cabinet model in the model library, and give priority to using this value in projects with similar environments in the future; or, it can abstract the layout change decision case of successfully handling the risk of local high humidity into a new, more refined rule and store it in the rule library. In the above way, every successful installation and commissioning project becomes the data fuel for training and optimizing the intelligence of the entire system, making the digital twin platform module more and more accurate with use.

[0043] Finally, the threshold ranges such as the safety range, calibration threshold, and preset debugging cycle set above need to be adaptively adjusted according to different electrical equipment. This embodiment only uses a power distribution cabinet as a simple example for ease of understanding. Furthermore, due to the large number of electrical parameters, only the threshold limits of some parameters are shown in the above example. The threshold limits of the remaining parameters that are not shown can be limited according to national standards, design standards, etc., and will not be elaborated on further in this embodiment.

[0044] In summary, this invention, through the pre-built model and rule library of the digital twin platform module and the collection of multi-dimensional environmental parameters of the target installation area, enables the system to accurately simulate the mutual influence between multiple devices in virtual space, including heat radiation accumulation, airflow organization changes and electromagnetic interference coupling, so that electrical equipment can anticipate and avoid systemic risks caused by improper equipment cluster layout during the installation design stage.

[0045] Furthermore, when the target installation area has a suitable environment and sufficient physical space, the system can automatically optimize the non-uniform spacing based on the heat dissipation impact between each device, and scientifically allocate heat dissipation space to balance the system's heat load. When electrical equipment is detected to be located at an unacceptable environmental risk point, the system triggers a layout transformation, and generates better topology schemes such as L-shaped or staggered topologies through rule base matching and space collision detection.

[0046] In addition, the verification and debugging module ensures the construction accuracy during installation by comparing the physical location with the theoretical coordinates in real time and making guided adjustments. After the equipment is powered on, the system can quickly locate anomalies and generate specific debugging instructions by comparing the measured electrical parameters with the simulation prediction values ​​of the digital twin. This greatly shortens the time for troubleshooting and parameter setting, improves the first-time success rate of debugging, and reduces the risk of the equipment operating with defects due to improper installation.

[0047] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An electrical equipment installation and commissioning system based on digital twins, characterized in that, include: The data acquisition module is used to collect environmental parameters of the target installation area, actual installation data of electrical equipment, and operating parameters. The digital twin platform module has a pre-built library of electrical equipment parametric models, an environmental assessment rule library, and a physical simulation engine, which are used to construct a target digital twin containing electrical equipment models and their layout relationships based on the spatial information and environmental parameters of the target installation area. The layout planning module, which connects the digital twin platform module and the data acquisition module, is used to conduct risk assessment and planning of the equipment layout in the target digital twin based on the environmental parameters, and generate an installation layout scheme. The verification and debugging module is used to compare the physical installation and operating status with the installation layout scheme or digital twin simulation results based on the actual installation data and operating parameters, and generate debugging instructions based on the deviation data. The model feedback module, which connects the verification and debugging module and the digital twin platform module, is used to calibrate the model parameters in the target digital twin based on the deviation data, and feed the calibration information back to the digital twin platform module to optimize its preset model library or rule library.

2. The electrical equipment installation and commissioning system based on digital twins according to claim 1, characterized in that, The logic for risk assessment and layout decision-making in the layout planning module includes: In the target digital twin, a linear series arrangement is used as the baseline layout scheme; Based on the environmental parameters, determine whether there are environmental risk points exceeding the safety threshold on the preset path of the baseline layout scheme; If it does not exist, a first-class optimization scheme is generated to optimize the spacing between two adjacent electrical devices in the baseline layout scheme; If it exists, the layout transformation decision process is triggered, and at least one second-type layout transformation scheme is generated under the condition of satisfying the spatial constraints. Subsequently, the generated schemes are evaluated for multi-attribute utility, and the scheme with the highest comprehensive score is selected as the final output installation layout scheme.

3. The electrical equipment installation and commissioning system based on digital twins according to claim 2, characterized in that, The spacing optimization process includes: Retrieve pre-stored thermal performance simulation data associated with different combinations of device power consumption and spacing; With the goal of reducing the overall heat load of the system, an optimization algorithm is used to search for a non-uniform spacing distribution scheme between adjacent devices within the total installation length constraint.

4. The electrical equipment installation and commissioning system based on digital twins according to claim 2, characterized in that, The layout change decision process includes: Based on the type of environmental risk point, a corresponding layout topology countermeasure is matched from a predefined rule base. The countermeasure is used to change the relative positional relationship between multiple electrical devices to form a spatial topology structure different from the linear series arrangement. In the target digital twin, based on the spatial collision detection algorithm, the spatial feasibility of the matching topological countermeasures is verified and the geometric parameters are solved. Generate one or more specific layout schemes that conform to the layout topology countermeasures and satisfy all physical space constraints, as the second type of layout transformation scheme.

5. The electrical equipment installation and commissioning system based on digital twins according to claim 2, characterized in that, The control logic for the multi-attribute utility evaluation is as follows: For the first type of optimization scheme and / or the second type of layout transformation scheme, calculate their scores in terms of risk elimination degree, expected thermal performance index, wiring economy and engineering implementation complexity; Based on the preset weights, the above multidimensional scores are weighted and summed to obtain the comprehensive utility score for each solution; The scheme with the highest overall utility score is selected as the final installation layout scheme output to subsequent modules.

6. The electrical equipment installation and commissioning system based on digital twins according to claim 1, characterized in that, The control method for the verification and debugging module includes two phases executed sequentially: During the installation process verification phase: the actual installation location coordinates of the equipment are obtained and continuously compared with the theoretical coordinates in the installation layout scheme; when the position error exceeds the preset tolerance, a position adjustment command is generated and a prompt is displayed; After installation, during the commissioning phase: After the electrical connection is powered on, key electrical parameters are collected and compared with the simulation prediction values ​​corresponding to the digital twin; when the parameter deviation exceeds the safe range, specific electrical commissioning instructions or protection setting adjustment suggestions are generated.

7. The electrical equipment installation and commissioning system based on digital twins according to claim 1, characterized in that, The calibration control logic of the model feedback module includes: Monitor and record the key measured data and the simulation prediction data of the corresponding points of the target digital twin generated during the verification and debugging process; When the deviation between the measured value and the simulated value at the same measurement point persists for several consecutive sampling periods and exceeds the calibration threshold, the parameter reverse adjustment mechanism is triggered. The mechanism adjusts the internal parameters of the corresponding model in the target digital twin according to the type and direction of the deviation, so that the subsequent simulation predictions converge to the measured values.

8. The electrical equipment installation and commissioning system based on digital twins according to claim 1, characterized in that, The calibration operation of the model feedback module is triggered at least in part within a preset debugging cycle after the equipment is installed and powered on; within this debugging cycle, the simulation model of the target digital twin is calibrated using the collected initial operating parameters and environmental parameters to improve the simulation accuracy of the target digital twin.

9. The electrical equipment installation and commissioning system based on digital twins according to claim 1, characterized in that, The method for constructing the digital twin platform module includes: Import the structural drawings of the target installation area, and extract key structures as spatial constraint boundaries through image recognition; Load the parametric 3D model of the electrical equipment and initialize its electrical and thermodynamic properties based on the design data; The aforementioned spatial constraints are combined with the device model in virtual space to form the target digital twin.

10. The electrical equipment installation and commissioning system based on digital twins according to claim 1, characterized in that, The data acquisition module dynamically plans sensor deployment and data acquisition strategies based on the installation layout scheme, specifically as follows: The target installation area is divided into key area units related to the layout of electrical equipment and physical field strength. High-density environmental parameters are collected in the critical area units, while sparse or periodic collection strategies are used in non-critical areas.

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