Big data intelligent decision analysis method and system

Abstract

The invention provides a big data intelligent decision analysis method and system. The method comprises the following steps: acquiring vibration data and mechanical node stress distribution data in power equipment transportation, and extracting high-frequency harmonic distortion characteristics aligned with a vibration data timestamp; performing cross-domain correlation analysis on stress data fluctuation and harmonic characteristic dynamic change, and identifying a symbiotic mode of mechanical stress abnormality and electrical harmonic distortion by using a machine learning model; based on logistics path planning and equipment constraints, a cooperative control instruction for adjusting the transportation path and the power supply loop parameters is generated through a multi-target cooperative optimization algorithm; and constructing a joint risk assessment matrix, and dynamically adjusting the cooperative relationship between the transportation path and the harmonic suppression parameter according to the quantitative analysis result and the cooperative control instruction to realize the closed-loop decision of the power equipment. According to the technical scheme provided by the invention, the power equipment transportation data processing efficiency is remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a big data intelligent decision-making analysis method and system. Background Art

[0002] In the logistics and transportation process of power equipment, mechanical vibration and electrical harmonic distortion are key factors affecting the safety and reliability of the equipment. Power equipment often faces complex road conditions and dynamic electrical environments during transportation. Mechanical vibration may cause damage to the equipment structure, while electrical harmonic distortion may cause electrical failures inside the equipment.

[0003] At present, there are solutions that use independent monitoring and static optimization methods to address the mechanical vibration and electrical harmonics problems in the transportation of power equipment. This solution monitors mechanical stress and electrical harmonics separately during transportation, and then makes static adjustments based on preset thresholds. For example, when mechanical stress exceeds the set threshold, the transportation path is adjusted to reduce vibration; when electrical harmonics exceed the threshold, the harmonic suppression parameters of the power supply circuit are adjusted.

[0004] However, this existing solution has obvious limitations. First, the monitoring of mechanical stress and electrical harmonics is carried out independently, and there is a lack of comprehensive analysis of the coupling relationship between the two, which makes it impossible to accurately identify the interaction between mechanical vibration and electrical harmonics and their potential impact on the equipment. Secondly, the static adjustment method cannot respond to the dynamically changing road conditions and electrical environment during transportation in real time, and it is difficult to effectively respond to sudden mechanical or electrical abnormal events. Therefore, the existing solution lacks adaptability and reliability under complex transportation conditions and cannot meet the high standards for safe transportation of power equipment. Summary of the invention

[0005] The embodiments of the present application provide a big data intelligent decision-making analysis method and system to solve the problem of poor processing effect of power equipment transportation data in the prior art.

[0006] In a first aspect, the present application provides a big data intelligent decision analysis method, including: Acquire the transport vibration data of the power equipment, synchronously collect the stress distribution data of the key mechanical nodes of the power equipment, and extract the high-frequency harmonic distortion features aligned with the timestamp of the transport vibration data in the power supply circuit; The fluctuation amplitude of stress distribution data and the dynamic changes of high-frequency harmonic distortion characteristics are analyzed across domains, and the symbiotic correlation pattern of mechanical stress anomaly and electrical harmonic distortion under sudden changes in logistics transportation routes is identified through machine learning models. Based on the logistics transportation path planning parameters and the equipment operating environment constraints, a multi-objective collaborative optimization algorithm is used to make joint decisions on the symbiotic association mode, generating collaborative control instructions that simultaneously adjust the vibration avoidance parameters of the transportation path and the harmonic suppression parameters of the power supply circuit; A joint risk assessment matrix of mechanical stress anomaly and electrical harmonic distortion is constructed. Based on the quantitative analysis results of the joint risk assessment matrix and collaborative control instructions, the collaborative relationship between logistics transportation paths and harmonic suppression parameters is dynamically adjusted to achieve closed-loop decision-making for power equipment.

[0007] Optionally, the cross-domain correlation analysis of the fluctuation amplitude of the stress distribution data and the dynamic changes of the high-frequency harmonic distortion characteristics is performed, and the symbiotic correlation pattern of mechanical stress anomaly and electrical harmonic distortion under the condition of sudden change of the logistics transportation path is identified through a machine learning model, including: The fluctuation amplitude of stress distribution data is divided into time windows, and the stress peak sequence within the vibration impact interval is extracted based on the timestamp of the sudden change event of the logistics transportation path. At the same time, the frequency domain energy distribution of the high-frequency harmonic distortion characteristics within the same time window is decomposed to extract the harmonic energy sudden change interval; The time-domain evolution law of the stress peak sequence is spatiotemporally coupled with the frequency-domain diffusion characteristics of the harmonic energy mutation interval, and a cross-domain correlation tensor with the path mutation intensity as the weight is constructed, where the path mutation intensity is calibrated jointly by the curvature change rate of the transport path and the acceleration shock threshold. Embed the mechanical node topology constraints in the cross-domain correlation tensor, capture the cross-interference relationship between the stress propagation path and the harmonic distortion conduction path through dynamic graph convolution operations, and establish a joint representation space of mechanical-electrical coupling features; Based on the migration characteristics of the equipment status before and after the transportation path mutation, the boundary conditions of the hidden variables are iteratively optimized in the joint representation space, so that the mechanical stress abnormal events and the harmonic distortion events form a causal symbiotic cluster in the feature space. A multi-scale path dependency analysis is performed on the symbiotic clusters. The transient correlation component induced by path mutation and the steady-state correlation component of the inherent characteristics of the equipment are separated through an adaptive gating mechanism, and a symbiotic correlation pattern topology map including the mechanical-electrical coupling strength and failure propagation path is output.

[0008] Optionally, based on the logistics transportation path planning parameters and the equipment operating environment constraints, a multi-objective collaborative optimization algorithm is used to make a joint decision on the symbiotic association mode, and a collaborative control instruction is generated to simultaneously adjust the vibration avoidance parameters of the transportation path and the harmonic suppression parameters of the power supply circuit, including: The path curvature change rate and acceleration shock threshold in the logistics transportation path planning parameters are converted into vibration suppression constraints. At the same time, the electromagnetic compatibility limit and temperature rise threshold in the equipment operating environment constraints are mapped into harmonic suppression constraints to construct a joint decision space for mechanical vibration suppression and electrical harmonic suppression. Based on the coupling intensity distribution of mechanical stress anomaly and harmonic distortion in the symbiotic association mode, the vibration suppression priority and harmonic suppression priority are divided in the joint decision space. The priority is dynamically adjusted by the ratio of the mechanical vibration propagation path length to the harmonic distortion conduction path length at the path mutation point. The vibration attenuation characteristic curve of the transport path vibration avoidance parameters and the frequency domain response characteristic curve of the harmonic suppression parameters are introduced into the joint decision space. The conflict area and cooperative area of ​​the mechanical-electrical control parameters are determined through dual-channel feature cross-validation, and a collaborative control instruction set including the vibration avoidance path correction vector and the harmonic suppression frequency adjustment amount is output.

[0009] Optionally, the temporal evolution law of the stress peak sequence is spatially and temporally coupled with the frequency domain diffusion characteristics of the harmonic energy mutation interval to construct a cross-domain correlation tensor with the path mutation intensity as the weight, wherein the path mutation intensity is jointly calibrated by the curvature change rate of the transport path and the acceleration shock threshold, including: The time-domain evolution law of the stress peak sequence is fitted in sections to extract the rising slope, falling slope and duration characteristics of the stress peak in the vibration impact interval. At the same time, the frequency-domain diffusion characteristics of the harmonic energy mutation interval are divided into frequency bands to extract the peak frequency and energy attenuation rate characteristics of the energy distribution in each frequency band. The rising slope, falling slope and duration characteristics of the stress peak sequence are aligned in time and space with the peak frequency and energy attenuation rate characteristics of the harmonic energy mutation interval. Based on the time evolution curve of the stress peak and the frequency domain diffusion curve of the harmonic energy, a cross-domain correlation tensor with the path mutation intensity as the weight is constructed, where the path mutation intensity is dynamically calibrated by the product of the curvature change rate of the transport path and the acceleration shock threshold.

[0010] Optionally, the cross-domain correlation tensor with path mutation intensity as weight is constructed based on the time evolution curve of the stress peak and the frequency domain diffusion curve of the harmonic energy, including: extracting the rising slope, falling slope and duration characteristics of the stress peak in the vibration impact interval based on the time evolution curve of the stress peak, and extracting the peak frequency and energy attenuation rate characteristics of the harmonic energy in each frequency band based on the frequency domain diffusion curve of the harmonic energy; The time evolution characteristics of the stress peak and the frequency domain diffusion characteristics of the harmonic energy are aligned in time and space, and the characteristics are weighted based on the path mutation intensity, where the path mutation intensity is dynamically calibrated by the product of the curvature change rate of the transport path and the acceleration shock threshold; The weighted spatiotemporal alignment features are integrated according to the spatiotemporal distribution of path mutation events to generate a cross-domain correlation tensor with path mutation intensity as weight. The cross-domain correlation tensor cross-validates the features through the geometric distance of the stress propagation path and the electrical distance of the harmonic distortion conduction path.

[0011] Optionally, the joint risk assessment matrix of mechanical stress anomaly and electrical harmonic distortion is constructed, and the collaborative relationship between the logistics transportation path and the harmonic suppression parameters is dynamically adjusted based on the quantitative analysis results of the joint risk assessment matrix and the collaborative control instructions to achieve closed-loop decision-making of power equipment, including: Based on the stress peak sequence of mechanical stress anomaly events and the frequency domain energy distribution of harmonic distortion events, a joint risk assessment matrix of mechanical stress anomaly and electrical harmonic distortion is constructed, where the row vectors of the joint risk assessment matrix represent the intensity distribution of stress anomaly events, the column vectors represent the frequency domain energy distribution of harmonic distortion events, and the matrix elements are calibrated by the coupling strength of stress peak and harmonic energy. According to the quantitative analysis results of the joint risk assessment matrix, the high-risk areas of mechanical stress anomaly and electrical harmonic distortion are extracted, and the curvature change rate of the logistics transportation path and the harmonic suppression parameters of the power supply circuit are dynamically adjusted by combining the vibration avoidance path correction vector and the harmonic suppression frequency adjustment amount in the collaborative control instructions; The adjusted curvature change rate of the logistics transportation path and the harmonic suppression parameters of the power supply circuit are fed back to the joint risk assessment matrix, and the closed-loop decision of the power equipment is achieved by iteratively updating the matrix elements.

[0012] Optionally, the method extracts high-risk areas of mechanical stress anomaly and electrical harmonic distortion according to the quantitative analysis results of the joint risk assessment matrix, combines the vibration avoidance path correction vector and the harmonic suppression frequency adjustment amount in the collaborative control instruction, and dynamically adjusts the curvature change rate of the logistics transportation path and the harmonic suppression parameters of the power supply circuit, including: The significant areas of coupling strength between mechanical stress anomaly and electrical harmonic distortion are screened out from the joint risk assessment matrix. The significant areas are determined by cross-validation of the time domain evolution law of the stress peak sequence and the frequency domain diffusion characteristics of the harmonic energy mutation interval, and the coupling strength of the significant areas is prioritized in combination with the quantitative analysis results. Combined with the vibration avoidance path correction vector in the collaborative control instruction, the curvature change rate of the logistics transportation path is adjusted. At the same time, combined with the harmonic suppression frequency adjustment amount, the harmonic suppression parameters of the power supply circuit are dynamically adjusted. The curvature change rate is dynamically calibrated by the joint weight of the path mutation intensity and the harmonic suppression frequency adjustment amount. The adjusted curvature change rate and harmonic suppression parameters are fed back to the joint risk assessment matrix, and the coupling intensity distribution in significant areas is iteratively updated to achieve dynamic coordinated optimization of logistics transportation paths and harmonic suppression parameters.

[0013] In a second aspect, the present application embodiment provides a big data intelligent decision analysis system, including: The acquisition module acquires the transport vibration data of the power equipment, synchronously acquires the stress distribution data of the key mechanical nodes of the power equipment, and extracts the high-frequency harmonic distortion features aligned with the timestamp of the transport vibration data in the power supply circuit; The analysis module conducts cross-domain correlation analysis on the fluctuation amplitude of stress distribution data and the dynamic changes of high-frequency harmonic distortion characteristics, and identifies the symbiotic correlation pattern between mechanical stress anomaly and electrical harmonic distortion under sudden changes in logistics transportation routes through machine learning models; The adjustment module makes joint decisions on the symbiotic association mode based on the logistics transportation path planning parameters and the equipment operating environment constraints through a multi-objective collaborative optimization algorithm, and generates collaborative control instructions for simultaneously adjusting the vibration avoidance parameters of the transportation path and the harmonic suppression parameters of the power supply circuit; The control module constructs a joint risk assessment matrix of mechanical stress anomaly and electrical harmonic distortion. Based on the quantitative analysis results of the joint risk assessment matrix and the collaborative control instructions, the collaborative relationship between the logistics transportation path and the harmonic suppression parameters is dynamically adjusted to achieve closed-loop decision-making of power equipment.

[0014] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a big data intelligent decision-making analysis method as described in the first aspect above.

[0015] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a big data intelligent decision-making analysis method as described in the first aspect.

[0016] In an embodiment of the present application, transport vibration data of electric equipment is acquired, stress distribution data of key mechanical nodes of electric equipment is synchronously collected, and high-frequency harmonic distortion features aligned with the timestamp of transport vibration data are extracted in the power supply circuit; the fluctuation amplitude of stress distribution data and the dynamic change of high-frequency harmonic distortion features are cross-domain correlated and analyzed, and the symbiotic correlation pattern of mechanical stress anomaly and electrical harmonic distortion under the condition of sudden change of logistics transportation path is identified through a machine learning model; based on the logistics transportation path planning parameters and the equipment operating environment constraints, a joint decision is made on the symbiotic correlation pattern through a multi-objective collaborative optimization algorithm, and a collaborative control instruction is generated to simultaneously adjust the vibration avoidance parameters of the transportation path and the harmonic suppression parameters of the power supply circuit; a joint risk assessment matrix of mechanical stress anomaly and electrical harmonic distortion is constructed, and the collaborative relationship between the logistics transportation path and the harmonic suppression parameters is dynamically adjusted based on the quantitative analysis results of the joint risk assessment matrix and the collaborative control instructions, so as to realize closed-loop decision-making of electric equipment.

[0017] The technical solution of this application has the following beneficial effects: This application realizes the spatiotemporal synchronous monitoring of mechanical vibration and electrical characteristics by synchronously collecting the transportation vibration data of power equipment, mechanical node stress distribution data, and high-frequency harmonic distortion characteristics aligned with timestamps, laying a data foundation for cross-domain analysis. By correlating the dynamic changes of stress fluctuations and harmonic distortion characteristics, the machine learning model is used to identify the symbiotic correlation pattern of mechanical stress anomalies and harmonic distortion when the transportation path changes suddenly, revealing the mechanical-electrical coupling mechanism. Based on the path planning parameters and equipment constraints, the coordinated control instructions of the vibration avoidance parameters and harmonic suppression parameters of the transportation path are generated through multi-objective optimization to solve the conflict problem between mechanical and electrical control and realize global optimization of cross-domain parameters. Construct a joint risk assessment matrix of mechanical stress and harmonic distortion, and dynamically adjust the curvature change rate and harmonic suppression frequency of the transportation path in combination with the quantitative analysis results to form a closed-loop control logic and enhance the adaptive ability of power equipment in complex transportation.

[0018] Furthermore, the stress peak sequence and harmonic energy mutation interval are extracted by time window division to achieve refined extraction of mechanical-electrical features. The stress time-domain evolution law and the harmonic frequency-domain diffusion characteristics are spatiotemporally coupled, and a cross-domain correlation tensor with path mutation intensity (calibrated by curvature change rate and acceleration shock threshold) as the weight is constructed to quantify the mechanical-electrical coupling intensity. After embedding the mechanical node topology constraints, the cross-interference between stress propagation and harmonic conduction paths is captured by dynamic graph convolution, and a joint feature space is established to clarify the failure propagation path of mechanical vibration and harmonic distortion. Iterative optimization of hidden variable boundary conditions generates causal directional symbiotic clusters, combines multi-scale analysis to separate transient and steady-state correlation components, and outputs a topological map containing coupling intensity and failure paths. This method realizes closed-loop control of power equipment transportation safety and provides technical support for the coordinated optimization of mechanical-electrical systems.

[0019] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 A flowchart of a big data intelligent decision analysis method provided by the present application is shown; Figure 2 A schematic diagram of the structure of a big data intelligent decision-making analysis system provided by the present application is shown; Figure 3 A schematic diagram of the structure of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0023] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0024] This application establishes a spatiotemporal correlation data foundation of mechanical vibration and electrical characteristics by synchronously collecting vibration data of the power equipment transportation path, stress distribution data of key mechanical nodes, and high-frequency harmonic distortion characteristics with time stamp alignment in the power supply circuit; uses a machine learning model to perform cross-domain correlation analysis on the dynamic changes of mechanical stress fluctuation amplitude and harmonic distortion characteristics, and explores the symbiotic correlation pattern of mechanical stress anomaly and harmonic distortion under the condition of transportation path mutation; combines logistics path planning parameters and equipment operating environment constraints, and makes joint decisions on the symbiotic correlation pattern through a multi-objective collaborative optimization algorithm, and generates collaborative control instructions for synchronously adjusting the vibration avoidance parameters and harmonic suppression parameters of the transportation path; constructs a mechanical-electrical joint risk assessment matrix, and dynamically optimizes the collaborative relationship between the transportation path curvature and the harmonic suppression frequency based on the quantitative analysis results, forming a complete decision-making chain from state perception to closed-loop control, and realizing intelligent management and control of power equipment transportation safety.

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0026] Figure 1 A flowchart of a big data intelligent decision analysis method is provided for the embodiment of the present application, such as Figure 1 As shown, the method includes: 101. Obtain the transportation vibration data of the power equipment, synchronously collect the stress distribution data of the key mechanical nodes of the power equipment, and extract the high-frequency harmonic distortion features aligned with the timestamp of the transportation vibration data in the power supply circuit; Transport vibration data: refers to the collection of information such as the vibration intensity, frequency and duration suffered by power equipment during transportation, which is used to evaluate the mechanical stability of the equipment during transportation.

[0027] Stress distribution data: reflects the magnitude and distribution of internal stress at key mechanical nodes of power equipment when subjected to external forces, and is an important indicator for judging the structural safety of equipment.

[0028] High-frequency harmonic distortion characteristics: describes the degree of distortion of the current or voltage waveform in the power supply circuit, especially the performance in the high-frequency band, which is closely related to the electrical performance of the equipment.

[0029] Timestamp alignment: It is a data synchronization technology that ensures that data from different sensors are consistent in the time dimension for subsequent correlation analysis.

[0030] In the embodiment of the present application, high-precision vibration sensors, strain gauges and other monitoring equipment are first deployed on the surface and internal key nodes of the power equipment to collect transport vibration data such as vibration acceleration and frequency during transportation, as well as stress values ​​(i.e., stress distribution data) at key parts of the equipment in real time. These sensors transmit data to the local data acquisition module for preliminary processing and storage. At the same time, a power quality monitor is installed in the power supply circuit, and its sampling frequency is set to be consistent with the vibration data acquisition equipment to monitor high-frequency harmonic distortion characteristics, and the timestamps of the two data are aligned through a time synchronization algorithm, such as using GPS signals or network time protocol (NTP). During the data collection process, the sensors need to be calibrated regularly to ensure the accuracy and reliability of the data. Finally, a comprehensive data set containing transport vibration data, stress distribution data, and high-frequency harmonic distortion characteristics aligned with them in time is obtained, which provides a basis for subsequent cross-domain correlation analysis.

[0031] In an actual power equipment transportation project, a power supply company needs to transport a large transformer from the manufacturing plant to the substation. Before transportation, technicians installed vibration sensors and strain gauges on key parts such as the transformer casing and internal windings, and connected the power quality monitor to the transformer's power supply circuit. When the transport vehicle starts, these monitoring devices start to work synchronously and collect data in real time. The time synchronization algorithm ensures the one-to-one correspondence between vibration data and power quality data in time. For example, during transportation, when the vehicle passes a bumpy road, the vibration sensor records the peak value of the vibration acceleration at this time, and the power quality monitor also captures the high-frequency harmonic distortion characteristics in the power supply circuit. The two are accurately matched through timestamps, providing an accurate data basis for subsequent analysis.

[0032] 102. Conduct cross-domain correlation analysis on the fluctuation amplitude of stress distribution data and the dynamic changes of high-frequency harmonic distortion characteristics, and identify the symbiotic correlation pattern of mechanical stress anomaly and electrical harmonic distortion under the condition of sudden changes in logistics transportation paths through machine learning models; Fluctuation amplitude of stress distribution data: refers to the range and rate of change of stress value with time, position and other factors during transportation. It reflects the dynamic response of the internal structure of the equipment under the influence of transportation vibration.

[0033] Dynamic changes in high-frequency harmonic distortion characteristics: describes the trend and pattern of the harmonic distortion level in the power supply circuit as the transportation status changes, which may be indirectly affected by changes in the mechanical stress of the equipment.

[0034] Cross-domain correlation analysis: It is an analysis method that integrates multi-source data and aims to explore the potential relationships between data in different fields.

[0035] Machine learning model: An intelligent tool that is trained through algorithms and can learn patterns and make predictions from large amounts of data. It is used here to identify complex symbiotic association patterns.

[0036] In the embodiment of the present application, the stress distribution data and high-frequency harmonic distortion characteristic data obtained in step 101 are first preprocessed, including data cleaning, normalization and other operations to eliminate dimensional differences and noise interference. Then, a machine learning algorithm, such as a long short-term memory network (LSTM) or a convolutional neural network (CNN), is used to train the processed data. During the training process, the fluctuation amplitude of the stress distribution data and the dynamic changes of the high-frequency harmonic distortion characteristics are used as input features, and the sudden change conditions of the logistics transportation path (such as sharp turns, uphill and downhill, etc.) are used as labels to allow the model to learn the association pattern between the two under different transportation conditions. By continuously adjusting the model parameters and optimizing the loss function, the accuracy and generalization ability of the model are improved. Finally, a machine learning model that can identify the symbiotic association pattern of mechanical stress anomalies and electrical harmonic distortion under sudden changes in logistics transportation paths is obtained, providing a basis for subsequent joint decision-making.

[0037] Continuing with the above transformer transportation example, during the transportation process, when the vehicle suddenly encounters a sharp turn, the transformer is subjected to large lateral vibration. At this time, the strain gauge installed on the transformer records obvious fluctuations in the stress distribution data, and the power quality monitor detects that the high-frequency harmonic distortion characteristics in the power supply circuit have also changed. These data are transmitted to the data analysis center in real time, and after preprocessing, they are input into the pre-trained machine learning model. Based on the previously learned patterns, the model quickly identifies the symbiotic association pattern of this stress anomaly and harmonic distortion, and determines that the current transportation state may have an adverse effect on the mechanical and electrical properties of the transformer. For example, the model finds that when the stress fluctuation amplitude exceeds a certain threshold, the dynamic change of the high-frequency harmonic distortion characteristics also presents a specific pattern, which matches the pattern of sudden changes in the transportation path in the previous training data, thereby providing key information for subsequent control decisions.

[0038] 103. Based on the logistics transportation path planning parameters and the equipment operating environment constraints, a multi-objective collaborative optimization algorithm is used to make joint decisions on the symbiotic association mode, and generate collaborative control instructions that simultaneously adjust the vibration avoidance parameters of the transportation path and the harmonic suppression parameters of the power supply circuit; Logistics transportation path planning parameters: covers the selection of transportation routes, the setting of transportation speed, the slope and curve conditions of the road sections, etc. These parameters directly affect the vibration environment of the equipment during transportation.

[0039] Equipment operating environment constraints: including equipment mechanical strength limitations, electrical performance requirements, transportation time limitations, etc., are boundary conditions for formulating a reasonable transportation plan.

[0040] Multi-objective collaborative optimization algorithm: It is an algorithm that can seek the optimal balance between multiple conflicting objectives. It is used here to comprehensively consider the dual objectives of transportation route optimization and power supply circuit harmonic suppression.

[0041] Symbiotic correlation pattern: refers to the correlation relationship pattern between the mechanical stress anomaly and the electrical harmonic distortion identified in step 102, which is an important reference for making joint decisions.

[0042] Collaborative control instructions: a comprehensive control command used to simultaneously adjust the transportation path and power supply circuit parameters to achieve the optimal state of power equipment during transportation.

[0043] In the embodiment of the present application, first, according to the specific type and transportation requirements of the power equipment, the adjustable range of the logistics transportation path planning parameters and the specific indicators of the equipment operating environment constraints are determined. Then, a suitable multi-objective collaborative optimization algorithm, such as a particle swarm optimization (PSO) algorithm or a multi-objective genetic algorithm (MOGA), is selected, and the symbiotic association pattern identified in step 102 is used as one of the constraints to construct an optimization model. In the model, the optimization goal is to minimize the impact of equipment vibration during transportation and the harmonic distortion in the power supply circuit, and the optimal combination of transportation path planning parameters and power supply circuit harmonic suppression parameters is found through iterative search of the algorithm. During the optimization process, it is necessary to continuously simulate and evaluate the candidate solutions to verify whether they meet the equipment operating environment constraints, such as the stress limit of the equipment, the allowable range of harmonic distortion, etc. Finally, a collaborative control instruction is generated, including specific transportation path adjustment suggestions (such as avoiding certain bumpy sections) and adjustment values ​​of the power supply circuit harmonic suppression parameters (such as adjusting the parameters of the filter), to achieve precise control of the transportation process of the power equipment.

[0044] In the case of transformer transportation, based on the previously identified symbiotic association patterns and the current actual transportation situation, technicians used a multi-objective collaborative optimization algorithm to optimize the transportation route and power supply circuit parameters. The algorithm comprehensively considers factors such as transportation time, road conditions, mechanical strength and electrical performance of the transformer. After multiple iterative calculations, an optimal solution was obtained: on the transportation route, avoid a section of road with frequent sharp turns and potholes, and choose an alternative route that is relatively flat and has fewer bends; in the power supply circuit, adjust the parameters of the filter to enhance its ability to suppress specific frequency harmonics. The collaborative control instructions convey these suggestions to the transportation dispatch center and the power supply circuit control system at the same time. The transportation vehicles travel according to the new route, and the power supply circuit operates according to the adjusted parameters, thereby effectively reducing the mechanical and electrical risks of the transformer during transportation.

[0045] 104. Construct a joint risk assessment matrix of mechanical stress anomaly and electrical harmonic distortion, and dynamically adjust the synergistic relationship between logistics transportation path and harmonic suppression parameters based on the quantitative analysis results of the joint risk assessment matrix and collaborative control instructions to achieve closed-loop decision-making of power equipment.

[0046] Mechanical stress abnormality risk assessment: From the perspective of the mechanical structure safety of the equipment, a quantitative assessment is conducted on the risks of excessive stress, structural damage, etc. that may occur during transportation.

[0047] Electrical harmonic distortion risk assessment: focuses on analyzing the impact of harmonic distortion in the power supply circuit on the electrical performance of the equipment, power quality, etc., and predicting the possible failure risks.

[0048] Joint risk assessment matrix: It is a quantitative analysis tool that comprehensively considers the risks of abnormal mechanical stress and electrical harmonic distortion. By setting different weights and evaluation criteria, it achieves a fusion assessment of the risks of both.

[0049] Dynamic adjustment of collaborative control instructions: refers to the real-time updating and optimization of the transport path and power supply circuit parameter adjustment suggestions in the collaborative control instructions based on the results of the joint risk assessment to adapt to dynamic changes in the transportation process.

[0050] Closed-loop decision-making: Emphasizes the self-feedback and self-adjustment capabilities of the entire system, ensuring the safety and reliability of power equipment during transportation through continuous cycles of risk assessment and control instruction adjustment.

[0051] In the embodiment of the present application, firstly, the risk assessment indicators and weight coefficients of mechanical stress anomaly and electrical harmonic distortion are determined according to historical data and professional knowledge, and a joint risk assessment matrix is ​​constructed. Then, the data and results obtained in steps 101 to 103 are input into the matrix for quantitative analysis. During the analysis process, a comprehensive risk value is calculated by comprehensively considering factors such as the severity and duration of stress anomaly, as well as the amplitude and frequency of harmonic distortion. According to this risk value, it is determined whether the current transportation and power supply circuit control strategies need to be adjusted. If the risk value exceeds the preset threshold, the information is passed to the multi-objective collaborative optimization algorithm of step 103 through the feedback mechanism, and the collaborative control instructions are regenerated to dynamically adjust the transportation path and power supply circuit parameters. After the adjustment, data collection and risk assessment are performed again to form a closed-loop decision-making process until the comprehensive risk value is reduced to an acceptable range.

[0052] During the transportation of the transformer, as the transportation environment changes, such as weather conditions and road conditions, technicians use the joint risk assessment matrix to monitor the transportation status of the transformer in real time. When the transport vehicle enters a bumpy section, the monitoring data shows that the stress distribution is abnormal, and the harmonic distortion in the power supply circuit also increases. These data are input into the joint risk assessment matrix, and the calculated comprehensive risk value exceeds the preset safety threshold. The system automatically triggers the feedback mechanism and passes the risk information to the optimization algorithm, which recalculates and generates new collaborative control instructions: it is recommended that the transport vehicle reduce the speed to pass the bumpy section, and further optimize the harmonic suppression parameters of the power supply circuit. After receiving the instructions, the transport vehicle and the power supply circuit control system immediately perform the corresponding adjustment operations. After the adjustment, the data is collected again for risk assessment, and it is found that the comprehensive risk value is significantly reduced, and the transportation process is restored to a safe state. This closed-loop decision-making process continues to ensure the mechanical and electrical safety of the transformer throughout the transportation process.

[0053] In summary, steps 101 to 104 achieve comprehensive monitoring, precise analysis, and dynamic control of the mechanical and electrical status of power equipment during transportation. Multi-source data acquisition and timestamp alignment ensure the accuracy and synchronization of data; with the help of machine learning models and multi-objective collaborative optimization algorithms, the intrinsic connections between data are deeply explored and scientific and reasonable control strategies are formulated; using joint risk assessment matrices and closed-loop decision-making mechanisms, real-time risk monitoring and dynamic adjustment of the transportation process are achieved. This method effectively reduces the risk of mechanical damage and electrical failure of power equipment during transportation, ensures the safe transportation and stable operation of equipment, and improves the overall reliability and economic benefits of the power system.

[0054] In order to achieve safe and efficient control of power equipment during logistics and transportation, this study aims to accurately identify the symbiotic pattern of mechanical stress anomalies and electrical harmonic distortion through cross-domain correlation analysis and collaborative optimization technology. The research and development idea is to synchronously collect the transportation vibration data of power equipment, the stress distribution data of mechanical nodes, and the high-frequency harmonic distortion characteristics of the power supply circuit, and use the machine learning model to identify the abnormal symbiotic pattern under the condition of sudden changes in the logistics and transportation path. Based on path planning and equipment operation constraints, collaborative control instructions are generated through a multi-objective collaborative optimization algorithm to dynamically adjust the transportation vibration avoidance and power supply harmonic suppression parameters. At the same time, a joint risk assessment matrix is ​​constructed to achieve closed-loop decision-making and ensure the dual protection of the mechanical and electrical performance of power equipment during transportation. In some embodiments, the fluctuation amplitude of the stress distribution data and the dynamic changes of the high-frequency harmonic distortion characteristics are cross-domain correlated and analyzed in step 102, and the symbiotic correlation pattern of mechanical stress anomalies and electrical harmonic distortion under the condition of sudden changes in the logistics and transportation path is identified through a machine learning model, including: 201. The fluctuation amplitude of stress distribution data is divided into time windows, and the stress peak sequence in the vibration impact interval is extracted based on the timestamp of the sudden change event of the logistics transportation path. At the same time, the frequency domain energy distribution of the high-frequency harmonic distortion characteristics in the same time window is decomposed to extract the harmonic energy sudden change interval; Stress distribution data: refers to the record of stress changes caused by vibration, impact and other factors during the transportation of equipment; the time window division is to divide the continuous stress data into several representative segments for subsequent analysis; Timestamp of sudden change event of logistics transportation route: refers to the specific time point when the route change, emergency braking and other events occur during transportation; stress peak sequence refers to the set of points where stress reaches the maximum value within the vibration impact interval; High-frequency harmonic distortion characteristics: refers to the high-frequency signal distortion caused by nonlinear factors in the electrical system; Frequency domain energy distribution decomposition: It is to divide the energy of the signal according to the frequency distribution in the frequency domain; Harmonic energy mutation interval: refers to the area where the harmonic energy changes significantly within a specific time range.

[0055] In an embodiment of the present application, first, stress distribution data is collected in real time by means of stress sensors installed on the equipment, and the data is divided into multiple time windows according to preset time intervals. Secondly, in combination with the timestamp of the path mutation event provided by the logistics transportation path monitoring system, the interval where the vibration shock occurs is located, and the stress peak sequence is extracted from it. Then, the fast Fourier transform (FFT) algorithm is used to perform frequency domain analysis on the high-frequency harmonic distortion characteristics, calculate the energy distribution of each frequency component within the same time window, and identify the harmonic energy mutation interval. This step requires precise time synchronization technology and efficient signal processing algorithms to ensure the accuracy of the data and the reliability of the analysis, and finally obtain the stress peak sequence and the harmonic energy mutation interval as the basis for subsequent analysis.

[0056] 202. The time-domain evolution law of the stress peak sequence is coupled with the frequency-domain diffusion characteristics of the harmonic energy mutation interval in time and space, and a cross-domain correlation tensor with path mutation intensity as the weight is constructed, where the path mutation intensity is jointly calibrated by the curvature change rate of the transport path and the acceleration shock threshold; The time domain evolution law of the stress peak sequence refers to the changing trend of the stress peak over time; the frequency domain diffusion characteristics of the harmonic energy mutation interval refer to the distribution change of the harmonic energy on different frequency components; Spatiotemporal coupling: It combines the information in the time domain and the frequency domain for comprehensive analysis; the cross-domain correlation tensor is a multidimensional data structure used to describe the correlation between different fields; Path mutation intensity: used to measure the severity of transport path mutation; The curvature change rate of the transport path reflects the change in the degree of path curvature; the acceleration shock threshold is the critical value for judging whether the shock intensity exceeds the normal range.

[0057] In an embodiment of the present application, first, the stress peak sequence obtained in step 201 is analyzed in the time domain to extract the key characteristic parameters of its evolution law, such as the frequency of peak occurrence, growth or attenuation trend, etc. Secondly, the frequency domain diffusion characteristics of the harmonic energy mutation interval are quantitatively analyzed to determine the energy change amplitude and range of different frequency components. Then, combined with the transportation path monitoring data, the path curvature change rate and the acceleration impact threshold are calculated to jointly calibrate the path mutation intensity. Finally, with the path mutation intensity as the weight, the time domain characteristics of the stress peak sequence and the frequency domain characteristics of the harmonic energy mutation interval are coupled to construct a cross-domain correlation tensor. This step requires complex mathematical modeling and tensor operation technology to realize the fusion of multi-dimensional information, and finally obtains a cross-domain correlation tensor that can reflect the coupling relationship between mechanical stress and electrical harmonics.

[0058] 203. Embed the mechanical node topology constraints in the cross-domain correlation tensor, capture the cross-interference relationship between the stress propagation path and the harmonic distortion conduction path through dynamic graph convolution operation, and establish a joint representation space of mechanical-electrical coupling features; Mechanical node topology constraints: refers to limiting the connection relationship between nodes based on the mechanical structure characteristics of the equipment; Dynamic graph convolution operation: a deep learning algorithm based on graph structure, used to process complex relationships between nodes; Stress propagation path: refers to the route by which stress is transmitted in the equipment structure; Harmonic distortion conduction path: refers to the propagation path of harmonic signals in the electrical system; Cross-interference relationship: refers to the mutual influence between the two; Joint representation space: a unified feature space that integrates mechanical and electrical features.

[0059] In an embodiment of the present application, first, a node topology model is established according to the mechanical structure of the device, and it is embedded in the cross-domain association tensor obtained in step 202. Secondly, the data in the tensor is processed using a dynamic graph convolution algorithm, and the cross-interference relationship between the stress propagation path and the harmonic distortion conduction path is captured by learning the connection weights and feature propagation laws between the nodes. Then, these relationships are integrated into a unified feature space to form a joint representation space of mechanical-electrical coupling features. This step requires advanced deep learning technology and optimization algorithms to realize complex graph structure analysis, and finally obtain a joint representation space that can fully reflect the mechanical-electrical coupling characteristics of the equipment.

[0060] 204. Based on the migration characteristics of the equipment status before and after the transportation path mutation, the boundary conditions of the hidden variables are iteratively optimized in the joint representation space, so that the mechanical stress abnormal events and the harmonic distortion events form a symbiotic cluster with causal orientation in the feature space; The migration characteristics of equipment status before and after the transportation route mutation: refers to the change of equipment performance indicators before and after the route mutation; Hidden variable boundary conditions: refers to the conditions used to limit the value range of hidden variables in the model; Symbiotic clusters: refers to the mutually correlated regions formed by abnormal mechanical stress events and harmonic distortion events in the feature space; In an embodiment of the present application, first, the migration characteristic parameters of the equipment state before and after the sudden change of the transportation path, such as changes in stress level and harmonic content, are extracted based on the equipment state monitoring data. Secondly, these parameters are introduced into the joint representation space as prior knowledge, and the hidden variable boundary conditions are adjusted through an iterative optimization algorithm. Then, in the optimized feature space, the symbiotic clusters of abnormal mechanical stress events and harmonic distortion events are identified, and the causal directivity is determined by analyzing the correlation characteristics between them. This step requires efficient optimization algorithms and causal inference techniques to achieve effective division of the feature space and clear representation of causal relationships, and finally obtain symbiotic clusters with clear causal directivity.

[0061] 205. Multi-scale path dependency analysis is performed on the symbiotic clusters. The transient correlation component induced by path mutation and the steady-state correlation component of the inherent characteristics of the equipment are separated through an adaptive gating mechanism, and the symbiotic correlation pattern topology map containing the mechanical-electrical coupling strength and failure propagation path is output.

[0062] Multi-scale path dependency analysis: refers to analyzing the characteristics of symbiotic clusters from different time and spatial scales; Adaptive gating mechanism: a control mechanism that can dynamically adjust the output according to the input signal; Transient correlation component: refers to the short-term correlation change caused by path mutation; steady-state correlation component refers to the long-term correlation characteristics inherent in the device itself; Symbiotic association pattern topology diagram: a graphical tool used to display various association relationships in symbiotic clusters.

[0063] In an embodiment of the present application, first, a multi-scale path dependency analysis is performed on the symbiotic cluster obtained in step 204, and its features are extracted from both macroscopic and microscopic levels. Secondly, an adaptive gating mechanism is used to separate the associated components in the symbiotic cluster, and transient associated components and steady-state associated components are identified. Then, the mechanical-electrical coupling strength is calculated based on the separation results, and the failure propagation path is determined. Finally, this information is output in the form of a symbiotic association pattern topology map to intuitively display the complex coupling relationship between mechanical stress and electrical harmonics in the equipment. This step requires the comprehensive use of signal processing technology and graphical display technology to achieve the effective presentation of complex relationships, and ultimately obtain a symbiotic association pattern topology map that can guide equipment fault diagnosis and maintenance.

[0064] Here is a specific example: In a long-distance transportation mission of a large transformer, the transportation route needs to pass through multiple complex road conditions, including continuous sharp turns in mountainous areas and bumpy construction sections in cities. In order to ensure the safety of the transformer during transportation, the technicians used the above-mentioned methods of steps 201 to 205 for real-time monitoring and control. In step 201, they set a 10-second time window based on the transportation plan and road condition information to divide the stress distribution data of key parts of the transformer. When the transport vehicle enters the sharp turn section in the mountainous area, the stress peak sequence in the vibration impact interval is extracted based on the timestamp of the sharp turn event. At the same time, the high-frequency harmonic distortion characteristics in the power supply circuit are decomposed by frequency domain energy distribution, and it is found that the harmonic energy has a mutation interval in a specific frequency band. In step 202, the time domain evolution law of the stress peak sequence is coupled with the frequency domain diffusion characteristics of the harmonic energy mutation interval in time and space. Through analysis, it is found that the stress peak shows a periodic increase trend after entering the sharp turn section, and the frequency domain diffusion characteristics of the harmonic energy mutation interval show that the high frequency band energy has a significant increase. Combined with the curvature change rate of the transport path (high curvature change rate in mountainous sections) and the acceleration shock threshold (acceleration shock exceeds the preset threshold when the vehicle makes a sharp turn), a cross-domain correlation tensor with path mutation intensity as the weight is constructed. In step 203, the topological constraints of the internal mechanical structure of the transformer are embedded in the cross-domain correlation tensor. Using the dynamic graph convolution operation, the cross-interference relationship between the stress propagation path and the harmonic distortion conduction path is captured. For example, when stress propagates in a certain winding node of the transformer, it will cause the harmonic distortion of the adjacent electrical nodes to increase. By establishing a joint representation space of mechanical-electrical coupling features, these cross-interference relationships are integrated in the form of multi-dimensional features. In step 204, based on the migration characteristics of the transformer state before and after the transport path mutation, it is found that both the stress distribution and the high-frequency harmonic distortion have changed significantly after entering the sharp turn section. In the joint representation space, by iteratively optimizing the boundary conditions of the hidden variables, a symbiotic cluster with causal directionality is finally formed. These symbiotic clusters clearly show that in the case of path mutations such as sharp turns, there is a direct causal relationship between mechanical stress anomalies and harmonic distortion. In step 205, a multi-scale path dependency analysis is performed on the symbiotic clusters, and it is found that the transient correlation components are mainly concentrated in special sections such as sharp turns, while the steady-state correlation components are related to the mechanical and electrical characteristics of the transformer itself. Through the adaptive gating mechanism, these two parts of the correlation components are successfully separated, and a symbiotic correlation mode topology diagram containing the mechanical-electrical coupling strength and the failure propagation path is output. This topology diagram intuitively shows the coupling between the internal stress and harmonic distortion of the transformer under different transportation sections and the possible failure propagation paths. Based on this topology diagram, the technicians adjusted the transportation speed and route in time, and optimized the harmonic suppression parameters of the power supply circuit, which effectively reduced the risk of the transformer during transportation and ensured its safe delivery to the destination.

[0065] In summary, through steps 201 to 205, accurate monitoring, in-depth analysis and coordinated control of mechanical stress and electrical harmonic distortion of power equipment during transportation are achieved. This method can not only capture the changes in the mechanical and electrical states of equipment in a complex transportation environment in real time, but also deeply explore the inherent relationship between the two, providing a scientific basis for transportation path optimization and equipment parameter adjustment. Through multi-scale path dependency analysis and adaptive gating mechanism, the ability to understand and predict equipment behavior is further improved, effectively preventing equipment failures caused by mechanical and electrical problems during transportation, and ensuring the reliability and economy of the power system.

[0066] In order to accurately identify the coupling relationship between mechanical stress and electrical harmonics in logistics transportation, this study proposes a spatiotemporal coupling and multi-scale analysis method. The stress peak sequence and harmonic energy mutation interval are extracted by time window division, and a cross-domain correlation tensor with path mutation intensity as the weight is constructed, and the mechanical node topology constraint is embedded. The cross-interference between stress and harmonic conduction paths is captured by dynamic graph convolution, and a joint representation space is established. The boundary conditions of hidden variables are iteratively optimized to form causal directional symbiotic clusters, separate transient and steady-state correlation components, and output the symbiotic correlation pattern topology map of coupling intensity and failure propagation path to achieve accurate evaluation of equipment status and fault warning. In some embodiments, the symbiotic correlation pattern is jointly decided by a multi-objective collaborative optimization algorithm based on the logistics transportation path planning parameters and the equipment operating environment constraints described in step 103, and a collaborative control instruction is generated to simultaneously adjust the vibration avoidance parameters of the transportation path and the harmonic suppression parameters of the power supply circuit, including: 301. Convert the path curvature change rate and acceleration shock threshold in the logistics transportation path planning parameters into vibration suppression constraints, and map the electromagnetic compatibility limit and temperature rise threshold in the equipment operating environment constraints into harmonic suppression constraints, and construct a joint decision space for mechanical vibration suppression and electrical harmonic suppression; Vibration suppression constraint conditions: refers to the conditions for limiting mechanical vibration based on the path curvature change rate and acceleration shock threshold; Harmonic suppression constraints: refers to the conditions for limiting electrical harmonics based on electromagnetic compatibility limits and temperature rise thresholds; Joint decision space: It is a multi-dimensional parameter space that comprehensively considers mechanical vibration and electrical harmonic suppression, and is used for subsequent optimization decisions.

[0067] In the embodiment of the present application, the path curvature change rate and acceleration shock threshold are first obtained from the logistics path planning system and converted into quantitative constraints for vibration suppression. At the same time, the electromagnetic compatibility limit and temperature rise threshold are obtained from the equipment operation environment monitoring system and mapped into quantitative constraints for harmonic suppression. Then, these constraints are integrated into a unified joint decision space to form a multidimensional parameter space, which provides a basis for subsequent optimization.

[0068] 302. Based on the coupling intensity distribution of mechanical stress anomaly and harmonic distortion in the symbiotic association mode, the vibration suppression priority and harmonic suppression priority are divided in the joint decision space, and the priority is dynamically adjusted by the ratio of the mechanical vibration propagation path length to the harmonic distortion conduction path length at the path mutation point; Symbiotic correlation mode: refers to the coupling relationship between mechanical stress anomaly and harmonic distortion; Coupling intensity distribution: refers to the distribution of the intensity of this coupling relationship in different regions; Priority: refers to the relative importance of vibration suppression and harmonic suppression in the joint decision space.

[0069] In an embodiment of the present application, firstly, according to the joint decision space constructed in step 301, the coupling relationship between mechanical stress anomaly and harmonic distortion is analyzed in combination with the coupling strength distribution in the symbiotic association mode. Then, the ratio of the mechanical vibration propagation path length to the harmonic distortion conduction path length at the path mutation point is calculated, and the priority of vibration suppression and harmonic suppression is dynamically adjusted according to the ratio. For example, when the mechanical vibration propagation path length is much greater than the harmonic distortion conduction path length, the priority of vibration suppression is increased; otherwise, the priority of harmonic suppression is increased. Finally, the adjusted priority division result is applied to the joint decision space to provide a basis for subsequent optimization.

[0070] 303. The vibration attenuation characteristic curve of the transport path vibration avoidance parameters and the frequency domain response characteristic curve of the harmonic suppression parameters are introduced into the joint decision space. The conflict area and cooperative area of ​​the mechanical-electrical control parameters are determined through dual-channel feature cross-validation, and a collaborative control instruction set including the vibration avoidance path correction vector and the harmonic suppression frequency adjustment amount is output.

[0071] Vibration attenuation characteristic curve: refers to the curve showing the vibration suppression effect of vibration avoidance parameters as the path changes; Frequency domain response characteristic curve: refers to the curve of the suppression effect of harmonic suppression parameters on harmonics of different frequencies; Dual-channel feature cross-validation: refers to the feature validation of two channels, vibration and harmonics, to determine the conflicting and collaborative areas of control parameters; Collaborative control instruction set: refers to an instruction set that includes the vibration avoidance path correction vector and the harmonic suppression frequency adjustment amount.

[0072] In an embodiment of the present application, the vibration attenuation characteristic curve of the vibration avoidance parameters of the transport path and the frequency domain response characteristic curve of the harmonic suppression parameters are first introduced into the joint decision space. Then, through dual-channel feature cross-validation, the characteristics of the two channels of vibration and harmonics are analyzed to determine the conflict area and coordination area of ​​the mechanical-electrical control parameters. For example, when the vibration attenuation characteristic curve overlaps with the frequency domain response characteristic curve in certain areas, it indicates that the area is a coordination area; when the two are contradictory, it indicates that the area is a conflict area. Finally, based on the analysis results of the coordination area and the conflict area, a coordinated control instruction set including the vibration avoidance path correction vector and the harmonic suppression frequency adjustment amount is generated to optimize the parameters of the transport path and the power supply circuit.

[0073] Here is a specific example: In a long-distance transportation mission of electric equipment, the transportation path planning system provides the path curvature change rate and acceleration shock threshold, and the equipment operation environment monitoring system provides the electromagnetic compatibility limit and temperature rise threshold. First, according to step 301, these parameters are converted into vibration suppression constraints and harmonic suppression constraints, and a joint decision space is constructed. Then, in step 302, the coupling intensity distribution of mechanical stress anomaly and harmonic distortion is analyzed in combination with the symbiotic association mode, and the priority of vibration suppression and harmonic suppression is dynamically adjusted. For example, near the path mutation point, the mechanical vibration propagation path is longer, so the priority of vibration suppression is increased. Finally, in step 303, the vibration attenuation characteristic curve of the vibration avoidance parameter and the frequency domain response characteristic curve of the harmonic suppression parameter are introduced, and the conflict area and the cooperative area are determined through dual-channel feature cross-validation, and a cooperative control instruction set is generated. According to the instruction set, the vibration avoidance parameters of the transportation path are adjusted, and the harmonic suppression parameters of the power supply circuit are optimized, so as to finally ensure that the mechanical vibration and electrical harmonics of the electric equipment are within the controllable range during transportation.

[0074] In summary, through steps 301 to 303, the coordinated optimization control of mechanical vibration and electrical harmonics of power equipment during logistics transportation is realized. This method not only effectively suppresses the impact of mechanical vibration on equipment during transportation, but also optimizes the suppression effect of electrical harmonics, and reduces the risk of equipment failure caused by mechanical-electrical coupling problems. By dynamically adjusting priorities and generating collaborative control instruction sets, real-time optimization of transportation paths and power supply circuit parameters is achieved, improving the safety and reliability of power equipment transportation.

[0075] In order to achieve the coordinated suppression of mechanical vibration and electrical harmonics of power equipment in logistics transportation, this study proposes an optimization method based on a joint decision space. By converting the path curvature change rate and the acceleration shock threshold into vibration suppression constraints, and mapping the electromagnetic compatibility limit and the temperature rise threshold into harmonic suppression constraints, a joint decision space is constructed. Based on the coupling intensity distribution of mechanical stress anomalies and harmonic distortion, the priority of vibration suppression and harmonic suppression is dynamically adjusted, and the vibration attenuation characteristic curve of the vibration avoidance parameter and the frequency domain response characteristic curve of the harmonic suppression parameter are introduced. Through dual-channel feature cross-validation, the conflict and coordination areas of the mechanical-electrical control parameters are determined, and a coordinated control instruction set containing the vibration avoidance path correction vector and the harmonic suppression frequency adjustment amount is output, thereby optimizing the transportation path and power supply circuit parameters and improving the safety and reliability of power equipment transportation. In some embodiments, step 202 couples the time domain evolution law of the stress peak sequence with the frequency domain diffusion characteristics of the harmonic energy mutation interval in time and space, and constructs a cross-domain correlation tensor with the path mutation intensity as the weight, wherein the path mutation intensity is jointly calibrated by the transportation path curvature change rate and the acceleration shock threshold, including: 401. Perform segmented fitting on the time domain evolution law of the stress peak sequence, extract the rising slope, falling slope and duration characteristics of the stress peak in the vibration impact interval, and divide the frequency domain diffusion characteristics of the harmonic energy mutation interval into frequency bands, and extract the peak frequency and energy attenuation rate characteristics of the energy distribution in each frequency band; Segmented fitting: refers to dividing the variation law of the stress peak sequence in the time domain into several segments for fitting; Rising slope, falling slope and duration characteristics: refer to the key characteristic parameters of the stress peak in the vibration impact range; frequency band division refers to dividing the frequency domain diffusion characteristics of the harmonic energy mutation interval according to the frequency range; In the embodiment of the present application, the collected stress peak sequence is first processed in sections using signal analysis technology, and the three key features of the rising slope, falling slope and duration in the vibration impact interval are extracted through mathematical model fitting. At the same time, the frequency domain analysis method is used to divide the frequency domain diffusion characteristics of the harmonic energy mutation interval into frequency bands, determine the energy distribution in each frequency band, and calculate its peak frequency and energy attenuation rate. For example, during the transportation of wind turbines, this method is used to analyze the stress and high-frequency harmonic data during transportation, accurately identify potential mechanical and electrical risk points, and provide reliable data support for subsequent transportation route optimization and equipment protection measures.

[0076] 402. The rising slope, falling slope and duration characteristics of the stress peak sequence are aligned with the peak frequency and energy decay rate characteristics of the harmonic energy mutation interval in time and space. Based on the time evolution curve of the stress peak and the frequency domain diffusion curve of the harmonic energy, a cross-domain correlation tensor with path mutation intensity as the weight is constructed, where the path mutation intensity is dynamically calibrated by the product of the curvature change rate of the transport path and the acceleration shock threshold.

[0077] Time-space alignment: refers to aligning the time domain characteristics of the stress peak sequence with the frequency domain characteristics of the harmonic energy mutation interval in time and space; Cross-domain correlation tensor: refers to a multidimensional data structure used to describe the correlation relationship between different fields; In an embodiment of the present application, first, the time domain characteristics of the stress peak sequence obtained in step 401 are aligned in time and space with the frequency domain characteristics of the harmonic energy mutation interval. Then, based on the time evolution curve of the stress peak and the frequency domain diffusion curve of the harmonic energy, a cross-domain correlation tensor is constructed. Finally, the product of the curvature change rate of the transport path and the acceleration shock threshold is used as a dynamic calibration of the path mutation intensity, and it is embedded as a weight into the cross-domain correlation tensor. This step requires complex mathematical modeling and tensor operation technology to provide a basis for subsequent optimization decisions. For example, in the long-distance transportation of large transformers, through the construction of time and space alignment and cross-domain correlation tensors, the coupling relationship between mechanical vibration and electrical harmonics at the mutation point of the transport path is accurately captured, which provides a scientific basis for adjusting the transport path and optimizing the power supply circuit parameters.

[0078] Here is a specific example: In a long-distance transportation mission of a high-voltage transformer, the transportation path planning system provides detailed path parameters, including the path curvature change rate (maximum curvature change rate is 0.03 radians / meter) and the acceleration shock threshold (set to 2.0g). At the same time, the equipment operation environment monitoring system provides electromagnetic compatibility limits (total harmonic voltage distortion rate does not exceed 3%) and temperature rise thresholds (equipment operating temperature must not exceed 55°C). During transportation, the equipment is equipped with high-precision vibration sensors and electrical parameter monitoring devices to collect stress and high-frequency harmonic data in real time. During transportation, the vehicle passes through a sharp turning section (path curvature change rate suddenly changes to 0.03 radians / meter), and a short acceleration shock occurs (impact acceleration reaches 1.8g). At this time, the vibration sensor records a significant change in the stress peak sequence. According to step 401, the stress peak sequence is segmented and fitted, and the following features are extracted: the rising slope of the stress peak is 1.5 MPa / s, the falling slope is 0.9 MPa / s, and the duration is 0.2 seconds. At the same time, the frequency domain diffusion characteristics of the harmonic energy mutation interval are divided into frequency bands, and it is found that the main energy is concentrated in the 150 Hz~250 Hz frequency band, with a peak frequency of 220 Hz and an energy decay rate of 12 dB / s. Secondly, in step 402, the time domain characteristics of the stress peak (rising slope, falling slope and duration) are aligned with the frequency domain characteristics of the harmonic energy (peak frequency and energy decay rate) in time and space. Based on the time evolution curve of the stress peak and the frequency domain diffusion curve of the harmonic energy, a cross-domain correlation tensor is constructed. The path mutation intensity is dynamically calibrated by the product of the curvature change rate of the transport path (0.03 radians / meter) and the acceleration shock threshold (2.0g), and the path mutation intensity is 0.06. This intensity is embedded in the cross-domain correlation tensor as a weight. Through the analysis of the cross-domain correlation tensor, it is found that the influence of mechanical vibration on electrical harmonics is particularly significant in the sharp turn area. Specifically, there is an obvious spatiotemporal correlation between the high slope change of mechanical vibration and the high-frequency energy mutation of electrical harmonics. Therefore, the transportation team adjusted the vibration-proof parameters of the transportation route based on the analysis results. For example, a high-damping vibration-reducing pad was added to the bottom of the transformer, which reduced the vibration transmission rate by 40%. At the same time, the harmonic suppression parameters of the power supply circuit were optimized. By adjusting the parameters of the harmonic filter, the total distortion rate of the harmonic voltage in the 220 Hz frequency band was reduced to 2.5%, which is lower than the electromagnetic compatibility limit. After these optimization measures, the transformer did not experience abnormal conditions caused by excessive mechanical vibration or electrical harmonics during subsequent transportation, ensuring the safety and reliability of the equipment during transportation.

[0079] In summary, through steps 401 to 402, the coupling analysis and optimization decision of mechanical stress and electrical harmonics of power equipment in the logistics transportation process are realized. This method not only effectively identifies the coupling relationship between mechanical stress and electrical harmonics in the transportation process, but also provides a scientific basis for the optimization of transportation path and power supply circuit parameters by constructing cross-domain correlation tensors. By dynamically adjusting the transportation path and power supply circuit parameters, the safety and reliability of power equipment transportation are improved, and the risk of equipment failure caused by mechanical-electrical coupling problems is reduced.

[0080] In order to accurately identify the coupling relationship between mechanical stress and electrical harmonics of power equipment in logistics transportation, this study proposes a method based on spatiotemporal alignment and cross-domain correlation analysis. By segmentally fitting the stress peak sequence, the rising slope, falling slope and duration characteristics in the vibration impact interval are extracted. At the same time, the harmonic energy mutation interval is divided into frequency bands to extract the peak frequency and energy attenuation rate characteristics. Furthermore, these features are aligned in time and space to construct a cross-domain correlation tensor with path mutation intensity as the weight. The path mutation intensity is dynamically calibrated by the product of the curvature change rate of the transportation path and the acceleration shock threshold. This method aims to realize the coupling analysis of mechanical stress and electrical harmonics, provide a scientific basis for transportation path optimization and equipment protection, and improve the safety and reliability of power equipment transportation. In some embodiments, step 402 constructs a cross-domain correlation tensor with path mutation intensity as weight based on the time evolution curve of the stress peak and the frequency domain diffusion curve of the harmonic energy, including: 501. Based on the time evolution curve of the stress peak, extract the rising slope, falling slope and duration characteristics of the stress peak in the vibration impact interval, and based on the frequency domain diffusion curve of the harmonic energy, extract the peak frequency and energy attenuation rate characteristics of the harmonic energy in each frequency band; Stress peak time evolution curve: refers to the change pattern of stress peak with time. The rising slope and falling slope represent the speed of increase and decrease of stress peak respectively. The duration refers to the length of time that the stress peak is maintained at a high level.

[0081] Harmonic energy frequency domain diffusion curve: refers to the distribution of harmonic energy at different frequencies. The peak frequency is the frequency point with the highest energy, and the energy decay rate indicates how quickly the energy decreases over time.

[0082] In the embodiment of the present application, the collected stress peak data is first analyzed by signal processing technology, its time evolution curve is extracted, and the rising slope, falling slope and duration are calculated. At the same time, the harmonic energy data is analyzed in the frequency domain, and different frequency bands are divided and the peak frequency and energy attenuation rate of each frequency band are extracted. For example, in an electric power equipment transportation, the stress peak collected by the sensor showed the characteristics of rapid rise (rising slope 1.8MPa / s) and slow decline (falling slope 0.6MPa / s) in the vibration shock interval, and the duration was 0.4 seconds. Harmonic energy is clearly distributed in the frequency band of 100 Hz~300 Hz, with a peak frequency of 250 Hz and an energy attenuation rate of 10 dB / s.

[0083] 502. The time evolution characteristics of the stress peak and the frequency domain diffusion characteristics of the harmonic energy are aligned in time and space, and the characteristics are weighted based on the path mutation intensity, where the path mutation intensity is dynamically calibrated by the product of the curvature change rate of the transport path and the acceleration shock threshold; Time-space alignment: refers to aligning the time domain characteristics of the stress peak and the frequency domain characteristics of the harmonic energy in time and space in order to analyze the correlation between them.

[0084] Path mutation intensity: It is an indicator that measures the severity of the change in the transportation path. It is used to weight the features to highlight the impact of path mutation on the equipment.

[0085] In the embodiment of the present application, the stress peak characteristics and harmonic energy characteristics extracted in step 501 are first aligned in time and space to ensure that they are synchronized in time. Then, according to the curvature change rate of the transport path (such as 0.04 radians / meter) and the acceleration shock threshold (such as 2.2g), the path mutation intensity (0.088) is calculated and used as a weight to weight the characteristics. For example, at the sharp turn of the transport path, the path mutation intensity is higher, and the impact on the stress peak and harmonic energy characteristics is more significant. Through weighted processing, the impact of path mutation on the equipment can be more accurately reflected.

[0086] 503. The weighted spatiotemporal alignment features are integrated according to the spatiotemporal distribution of path mutation events to generate a cross-domain correlation tensor with path mutation intensity as weight, where the cross-domain correlation tensor cross-validates the features through the geometric distance of the stress propagation path and the electrical distance of the harmonic distortion conduction path.

[0087] Cross-domain correlation tensor: is a multidimensional data structure used to represent the correlation between stress and harmonic energy characteristics.

[0088] The geometric distance of the stress propagation path refers to the propagation distance of stress in the equipment structure, and the electrical distance of the harmonic distortion conduction path refers to the conduction distance of harmonic energy in the electrical system.

[0089] Cross-validation: refers to verifying features through distance information of two different paths to improve the reliability of the results.

[0090] In the embodiment of the present application, the weighted features in step 502 are first integrated according to the spatiotemporal distribution of the path mutation events to generate a cross-domain correlation tensor. Then, the features are cross-validated by calculating the geometric distance of the stress propagation path and the electrical distance of the harmonic distortion conduction path. For example, during the transportation of power equipment, the geometric distance of the stress propagation path is 1.2 meters, and the electrical distance of the harmonic distortion conduction path is 0.8 meters. Through cross-validation, the correlation between stress and harmonic energy features is confirmed, providing a reliable basis for subsequent optimization decisions.

[0091] Here is a specific example: In a long-distance transportation mission of a high-voltage transformer, the transportation path planning system provides the path curvature change rate (maximum 0.04 radians / meter) and the acceleration shock threshold (2.2g), and the equipment operation environment monitoring system provides the electromagnetic compatibility limit (total harmonic voltage distortion rate does not exceed 3%) and the temperature rise threshold (not exceeding 55°C). During the transportation process, the equipment is equipped with high-precision vibration sensors and electrical parameter monitoring devices to collect stress and high-frequency harmonic data in real time. During the transportation process, the vehicle passed a sharp turn section (the path curvature change rate suddenly changed to 0.04 radians / meter), and a short acceleration shock occurred at the same time (the impact acceleration reached 2.0g). At this time, the vibration sensor recorded a significant change in the stress peak sequence. According to step 501, the rising slope of the extracted stress peak is 1.8 MPa / s, the falling slope is 0.6 MPa / s, and the duration is 0.4 seconds. The harmonic energy is obviously distributed in the 100 Hz~300 Hz frequency band, with a peak frequency of 250 Hz and an energy attenuation rate of 10dB / s. Secondly, in step 502, the stress peak feature is aligned with the harmonic energy feature in time and space, and the feature is weighted based on the path mutation intensity (0.088). Finally, in step 503, the weighted features are integrated according to the time and space distribution of the path mutation event to generate a cross-domain correlation tensor. The features are cross-validated by the geometric distance of the stress propagation path (1.2 meters) and the electrical distance of the harmonic distortion conduction path (0.8 meters), confirming the coupling relationship between stress and harmonic energy. Based on the analysis results of the cross-domain correlation tensor, the transportation team adjusted the vibration avoidance parameters of the transportation path and optimized the harmonic suppression parameters of the power supply circuit to ensure that the mechanical stress and electrical harmonics of the equipment are within the controllable range during transportation.

[0092] In summary, through steps 501 to 503, the coupling analysis and optimization decision of mechanical stress and electrical harmonics of power equipment in the logistics transportation process are realized. This method not only effectively identifies the coupling relationship between mechanical stress and electrical harmonics in the transportation process, but also provides a scientific basis for the parameter optimization of the transportation path and power supply circuit by constructing a cross-domain correlation tensor. By dynamically adjusting the parameters of the transportation path and power supply circuit, the safety and reliability of power equipment transportation are improved, and the risk of equipment failure caused by mechanical-electrical coupling problems is reduced.

[0093] In order to accurately identify the coupling relationship between mechanical stress and electrical harmonics of power equipment in logistics transportation, this study proposes a method of spatiotemporal alignment and weighted feature integration. By extracting the time domain characteristics of stress peak (rising slope, falling slope and duration) and the frequency domain characteristics of harmonic energy (peak frequency and energy decay rate), spatiotemporal alignment is performed and the features are weighted based on the path mutation intensity (dynamically calibrated by the product of the curvature change rate of the transportation path and the acceleration shock threshold). Finally, the features are integrated according to the spatiotemporal distribution of the path mutation event to generate a cross-domain correlation tensor, and cross-validated by the geometric distance of the stress propagation path and the electrical distance of the harmonic conduction path, providing a basis for transportation path optimization and equipment protection, and improving transportation safety and reliability. In some embodiments, the joint risk assessment matrix of mechanical stress anomaly and electrical harmonic distortion is constructed in step 104, and the collaborative relationship between the logistics transportation path and the harmonic suppression parameters is dynamically adjusted based on the quantitative analysis results of the joint risk assessment matrix and the collaborative control instructions to achieve closed-loop decision-making of power equipment, including: 601. Based on the stress peak sequence of mechanical stress anomaly events and the frequency domain energy distribution of harmonic distortion events, a joint risk assessment matrix of mechanical stress anomaly and electrical harmonic distortion is constructed; The row vectors of the joint risk assessment matrix represent the intensity distribution of stress anomaly events, the column vectors represent the frequency domain energy distribution of harmonic distortion events, and the matrix elements are calibrated by the coupling intensity of stress peak value and harmonic energy.

[0094] Stress peak sequence: refers to the peak data sequence of stress changes over time during an abnormal mechanical stress event.

[0095] Frequency domain energy distribution: refers to the distribution of harmonic energy in different frequency bands in a harmonic distortion event.

[0096] Coupling strength: A measure of the strength of the interaction between mechanical stress and electrical harmonics.

[0097] In the embodiment of the present application, the stress peak sequence of the mechanical stress abnormality event is first obtained by the stress sensor, and the frequency domain energy distribution of the harmonic distortion event is obtained by the electrical monitoring equipment. Then, a joint risk assessment matrix is ​​constructed, the row vector of the matrix corresponds to the intensity distribution of the stress abnormality event, the column vector corresponds to the energy distribution of the harmonic distortion event, and the matrix elements are obtained by calculating the coupling strength of the stress peak and the harmonic energy. For example, the stress peak is 1.2 MPa, the peak value of the harmonic energy in the 200Hz frequency band is 10 dB, the coupling strength is 0.8, and the corresponding matrix element is 0.8.

[0098] 602. Based on the quantitative analysis results of the joint risk assessment matrix, extract the high-risk areas of mechanical stress anomaly and electrical harmonic distortion, combine the vibration avoidance path correction vector and harmonic suppression frequency adjustment in the collaborative control instructions, and dynamically adjust the curvature change rate of the logistics transportation path and the harmonic suppression parameters of the power supply circuit; High-risk area: refers to the area with higher coupling intensity in the joint risk assessment matrix, which usually indicates the part with higher coupling risk of stress anomaly and harmonic distortion.

[0099] Vibration Path Correction Vector: A parameter vector used to adjust the transport path to reduce the effects of vibration.

[0100] Harmonic suppression frequency adjustment: used to adjust the value of the harmonic suppression parameters in the power supply circuit.

[0101] In the embodiment of the present application, the joint risk assessment matrix is ​​first quantitatively analyzed to identify high-risk areas (such as areas in the matrix where the coupling strength is greater than 0.7). Then, the curvature change rate of the logistics transportation path and the harmonic suppression parameters of the power supply circuit are dynamically adjusted in combination with the vibration avoidance path correction vector (such as reducing the path curvature change rate by 0.02 radians / meter) and the harmonic suppression frequency adjustment amount (such as adjusting the harmonic suppression parameters of the 200 Hz frequency band) in the collaborative control instruction. For example, at the sharp turn of the transportation path, based on the analysis results of the high-risk area, the path curvature change rate is adjusted from 0.04 radians / meter to 0.02 radians / meter, and the harmonic suppression parameters of the 200 Hz frequency band are adjusted from 10% to 15%.

[0102] 603. The adjusted curvature change rate of the logistics transportation path and the harmonic suppression parameters of the power supply circuit are fed back to the joint risk assessment matrix, and the closed-loop decision of the power equipment is achieved by iteratively updating the matrix elements.

[0103] Closed-loop decision-making: refers to the process of feeding back the adjusted parameters into the risk assessment model and achieving dynamic optimization through iterative updates.

[0104] In an embodiment of the present application, the adjusted path curvature change rate and the harmonic suppression parameters of the power supply circuit in step 602 are first fed back to the joint risk assessment matrix. Then, the coupling strength of the stress peak and the harmonic energy is recalculated according to the new parameters, and the matrix elements are updated. For example, the adjusted path curvature change rate is 0.02 radians / meter, the harmonic suppression parameter is 15%, the recalculated coupling strength is 0.5, and the matrix element is updated to 0.5. Through iterative updates, closed-loop decision-making in the transportation process of power equipment is achieved to ensure that the equipment is always in a safe state during transportation.

[0105] Here is a specific example: In a long-distance transportation mission of a large transformer, the transportation route planning system provided the path curvature change rate (maximum 0.04 radians / meter) and acceleration shock threshold (2.2g), and the equipment operation environment monitoring system provided the electromagnetic compatibility limit (total harmonic voltage distortion rate not exceeding 3%) and temperature rise threshold (not exceeding 55°C). During transportation, the equipment was equipped with high-precision vibration sensors and electrical parameter monitoring devices to collect stress and high-frequency harmonic data in real time.

[0106] During the transportation process, the vehicle passed a sharp turn (the rate of change of the path curvature suddenly changed to 0.04 radians / meter), and a short acceleration shock occurred (the impact acceleration reached 2.0g). At this time, the vibration sensor recorded a significant change in the stress peak sequence. According to step 601, a joint risk assessment matrix is ​​constructed, and the stress peak is extracted to be 1.2 MPa, the peak value of the harmonic energy in the 200 Hz frequency band is 10 dB, and the coupling strength is 0.8. Then, in step 602, according to the analysis results of the high-risk area, the rate of change of the path curvature is adjusted from 0.04 radians / meter to 0.02 radians / meter, and the harmonic suppression parameter of the 200 Hz frequency band is adjusted from 10% to 15%. Finally, in step 603, the adjusted parameters are fed back to the joint risk assessment matrix, the coupling strength is recalculated to 0.5, and the matrix elements are updated. Through closed-loop decision-making, it is ensured that the transformer does not have abnormal conditions caused by excessive mechanical vibration or electrical harmonics during subsequent transportation.

[0107] In summary, through steps 601 to 603, the joint risk assessment and closed-loop decision-making of mechanical stress and electrical harmonics of power equipment during logistics transportation are realized. This method not only effectively identifies high-risk areas of mechanical stress and electrical harmonics during transportation, but also ensures the safety and reliability of equipment during transportation by dynamically adjusting the transportation path and power supply circuit parameters. Through the closed-loop decision-making mechanism, the transportation path and power supply circuit parameters are further optimized, the risk of equipment failure caused by mechanical-electrical coupling problems is reduced, and the transportation efficiency and stability of equipment operation are improved.

[0108] In order to accurately control the coupling risk of mechanical stress and electrical harmonics of power equipment in logistics transportation, this study proposes a closed-loop decision-making method based on a joint risk assessment matrix. By analyzing the stress peak sequence of mechanical stress anomaly events and the frequency domain energy distribution of harmonic distortion events, a joint risk assessment matrix is ​​constructed, in which the row vector represents the stress anomaly intensity distribution, the column vector represents the harmonic distortion energy distribution, and the matrix elements are calibrated by the coupling intensity. Based on the matrix quantitative analysis, the high-risk area is extracted, and the curvature change rate of the transportation path and the harmonic suppression parameters of the power supply circuit are dynamically optimized in combination with the vibration avoidance path correction vector and the harmonic suppression frequency adjustment amount. By feeding back the adjusted parameters to the matrix and iteratively updating them, a closed-loop decision-making process of the power equipment transportation process is realized, aiming to reduce the coupling risk and ensure the safety and stability of equipment transportation. In some embodiments, according to the quantitative analysis results of the joint risk assessment matrix, step 602 extracts the high-risk areas of mechanical stress anomaly and electrical harmonic distortion, combines the vibration avoidance path correction vector and the harmonic suppression frequency adjustment amount in the collaborative control instruction, and dynamically adjusts the curvature change rate of the logistics transportation path and the harmonic suppression parameters of the power supply circuit, including: 701. Screen out the significant areas of coupling strength between mechanical stress anomaly and electrical harmonic distortion from the joint risk assessment matrix. The significant areas are determined by cross-validation of the time domain evolution law of the stress peak sequence and the frequency domain diffusion characteristics of the harmonic energy mutation interval, and the coupling strength of the significant areas is prioritized in combination with the quantitative analysis results; Joint risk assessment matrix: A matrix used to quantify the risk of coupling between mechanical stress anomalies and electrical harmonic distortion, whose elements represent the coupling strength.

[0109] Significant area: an area with high coupling intensity that needs to be treated first.

[0110] Prioritization: Sort the significant regions according to coupling strength for dynamic optimization.

[0111] In an embodiment of the present application, first, the area with significant coupling strength is screened out from the joint risk assessment matrix. By analyzing the time domain evolution law of the stress peak sequence and the frequency domain diffusion characteristics of the harmonic energy mutation interval, cross-validation is performed to confirm the significant area. For example, the time domain analysis shows that the stress peak rising slope is 1.5 MPa / s, and the frequency domain analysis shows that the peak frequency of the harmonic energy in the 200 Hz band is 10 dB. After cross-validation, the area with significant coupling strength is confirmed. Combined with the quantitative analysis results, the coupling strength of the significant area is prioritized to provide a basis for subsequent optimization.

[0112] 702. Combined with the vibration avoidance path correction vector in the collaborative control instruction, the curvature change rate of the logistics transportation path is adjusted. At the same time, combined with the harmonic suppression frequency adjustment amount, the harmonic suppression parameters of the power supply circuit are dynamically adjusted.

[0113] The curvature change rate is dynamically calibrated by the joint weight of the path mutation intensity and the harmonic suppression frequency adjustment amount.

[0114] Vibration Path Correction Vector: A parameter vector used to adjust the transport path to reduce the effects of vibration.

[0115] Harmonic suppression frequency adjustment: used to adjust the value of the harmonic suppression parameters in the power supply circuit.

[0116] Joint weight: The weight of the comprehensive path mutation strength and frequency adjustment amount, used to dynamically calibrate the curvature change rate.

[0117] In an embodiment of the present application, first, according to the vibration avoidance path correction vector in the collaborative control instruction, the curvature change rate of the logistics transportation path is adjusted. For example, the path curvature change rate is adjusted from 0.04 radians / meter to 0.02 radians / meter. At the same time, combined with the harmonic suppression frequency adjustment amount, the harmonic suppression parameters of the power supply circuit are dynamically adjusted. For example, the harmonic suppression parameters of the 200 Hz frequency band are adjusted from 10% to 15%. The curvature change rate is dynamically calibrated by the joint weight of the path mutation intensity (0.04 radians / meter) and the harmonic suppression frequency adjustment amount (15%) to ensure that the adjusted parameters can effectively reduce the coupling risk.

[0118] 703. The adjusted curvature change rate and harmonic suppression parameters are fed back to the joint risk assessment matrix, and the coupling intensity distribution of significant areas is iteratively updated to achieve dynamic collaborative optimization of logistics transportation paths and harmonic suppression parameters.

[0119] Feedback mechanism: The adjusted parameters are re-input into the risk assessment model to update the coupling strength distribution.

[0120] Dynamic collaborative optimization: Dynamic optimization of transportation routes and power supply loop parameters is achieved through iterative updates.

[0121] In the embodiment of the present application, the curvature change rate (0.02 radians / meter) and the harmonic suppression parameter (15%) adjusted in step 702 are first fed back to the joint risk assessment matrix. The coupling strength distribution of the significant area is iteratively updated and the coupling strength is recalculated. For example, the updated coupling strength drops from 0.8 to 0.5, indicating that the adjustment is effective. Through closed-loop feedback and iterative updates, dynamic coordinated optimization of the logistics transportation path and the harmonic suppression parameters is achieved to ensure the safety and reliability of the equipment during transportation.

[0122] Here is a specific example: In a long-distance transportation mission of a large transformer, the transportation path planning system provides the path curvature change rate (maximum 0.04 radians / meter) and the acceleration shock threshold (2.2g), and the equipment operation environment monitoring system provides the electromagnetic compatibility limit (total harmonic voltage distortion rate does not exceed 3%) and the temperature rise threshold (not exceeding 55°C). During the transportation process, the equipment is equipped with high-precision vibration sensors and electrical parameter monitoring devices to collect stress and high-frequency harmonic data in real time. During the transportation process, the vehicle passed a sharp turn section (the path curvature change rate suddenly changed to 0.04 radians / meter), and a short acceleration shock occurred at the same time (the impact acceleration reached 2.0g). At this time, the vibration sensor recorded a significant change in the stress peak sequence. According to step 701, the coupling strength significant area is screened out from the joint risk assessment matrix, and the significant area is confirmed by cross-validation in the time domain and frequency domain, and the priority is sorted. Next, in step 702, the path curvature change rate is adjusted from 0.04 radians / meter to 0.02 radians / meter in combination with the vibration avoidance path correction vector, and the harmonic suppression parameter of the 200 Hz band is adjusted from 10% to 15%. Finally, in step 703, the adjusted parameters are fed back to the joint risk assessment matrix, and the coupling strength is recalculated. The coupling strength in the significant area is reduced from 0.8 to 0.5. Through closed-loop feedback and iterative updates, it is ensured that the transformer does not have abnormal conditions caused by excessive mechanical vibration or electrical harmonics during subsequent transportation.

[0123] In summary, through steps 701 to 703, the dynamic optimization of the mechanical stress and electrical harmonic coupling risk of power equipment during logistics transportation is achieved. This method not only effectively identifies the areas with significant coupling risks, but also reduces the coupling risk and improves the safety and reliability of equipment transportation by dynamically adjusting the transportation path and power supply circuit parameters. Through closed-loop feedback and iterative update mechanisms, the transportation path and power supply circuit parameters are further optimized, reducing the risk of equipment failure caused by mechanical-electrical coupling problems, and improving transportation efficiency and equipment operation stability.

[0124] Figure 2 A schematic diagram of a big data intelligent decision analysis system is provided for the present application embodiment. Figure 2 As shown, the device comprises: The acquisition module 21 acquires the transport vibration data of the power equipment, synchronously acquires the stress distribution data of the key mechanical nodes of the power equipment, and extracts the high-frequency harmonic distortion features aligned with the timestamp of the transport vibration data in the power supply circuit; Analysis module 22, which performs cross-domain correlation analysis on the fluctuation amplitude of stress distribution data and the dynamic changes of high-frequency harmonic distortion characteristics, and identifies the symbiotic correlation pattern between mechanical stress anomaly and electrical harmonic distortion under the condition of sudden changes in logistics transportation paths through a machine learning model; The adjustment module 23 makes a joint decision on the symbiotic association mode through a multi-objective collaborative optimization algorithm based on the logistics transportation path planning parameters and the equipment operating environment constraints, and generates a collaborative control instruction for simultaneously adjusting the vibration avoidance parameters of the transportation path and the harmonic suppression parameters of the power supply circuit; The control module 24 constructs a joint risk assessment matrix of mechanical stress anomalies and electrical harmonic distortion, and dynamically adjusts the collaborative relationship between the logistics transportation path and the harmonic suppression parameters based on the quantitative analysis results of the joint risk assessment matrix and the collaborative control instructions to achieve closed-loop decision-making of power equipment.

[0125] Figure 2 The big data intelligent decision analysis system can execute Figure 1 The implementation principle and technical effect of the big data intelligent decision analysis method described in the embodiment are not repeated here. The specific way in which each module and unit performs operations in the big data intelligent decision analysis system in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0126] In one possible design, Figure 2 A big data intelligent decision analysis system of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0127] The processing component 32 is used for the above Figure 1 A big data intelligent decision-making analysis method according to the embodiment.

[0128] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0129] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0130] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0131] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0132] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0133] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0134] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 A big data intelligent decision analysis method of the illustrated embodiment.

[0135] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0136] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0137] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A big data intelligent decision analysis method, characterized in that: include: Acquire the transportation vibration data of the power equipment, synchronously collect the stress distribution data of the key mechanical nodes of the power equipment, and extract the high-frequency harmonic distortion features aligned with the timestamp of the transportation vibration data in the power supply circuit; The fluctuation amplitude of stress distribution data and the dynamic changes of high-frequency harmonic distortion characteristics are analyzed across domains, and the symbiotic correlation pattern of mechanical stress anomaly and electrical harmonic distortion under sudden changes in logistics transportation paths is identified through machine learning models. Based on the logistics transportation path planning parameters and the equipment operating environment constraints, a multi-objective collaborative optimization algorithm is used to make joint decisions on the symbiotic association mode, generating collaborative control instructions that simultaneously adjust the vibration avoidance parameters of the transportation path and the harmonic suppression parameters of the power supply circuit; A joint risk assessment matrix of mechanical stress anomaly and electrical harmonic distortion is constructed. Based on the quantitative analysis results of the joint risk assessment matrix and collaborative control instructions, the collaborative relationship between logistics transportation paths and harmonic suppression parameters is dynamically adjusted to achieve closed-loop decision-making for power equipment.

2. The method according to claim 1, characterized in that The fluctuation amplitude of stress distribution data and the dynamic changes of high-frequency harmonic distortion characteristics are analyzed across domains, and the symbiotic correlation pattern of mechanical stress anomaly and electrical harmonic distortion under sudden changes in logistics transportation paths is identified through machine learning models, including: The fluctuation amplitude of stress distribution data is divided into time windows, and the stress peak sequence within the vibration impact interval is extracted based on the timestamp of the sudden change event of the logistics transportation path. At the same time, the frequency domain energy distribution of the high-frequency harmonic distortion characteristics within the same time window is decomposed to extract the harmonic energy sudden change interval; The time-domain evolution law of the stress peak sequence is spatiotemporally coupled with the frequency-domain diffusion characteristics of the harmonic energy mutation interval, and a cross-domain correlation tensor with the path mutation intensity as the weight is constructed, where the path mutation intensity is calibrated jointly by the curvature change rate of the transport path and the acceleration shock threshold. Embed the mechanical node topology constraints in the cross-domain correlation tensor, capture the cross-interference relationship between the stress propagation path and the harmonic distortion conduction path through dynamic graph convolution operations, and establish a joint representation space of mechanical-electrical coupling features; Based on the migration characteristics of the equipment status before and after the transportation path mutation, the boundary conditions of the hidden variables are iteratively optimized in the joint representation space, so that the mechanical stress abnormal events and the harmonic distortion events form a causal symbiotic cluster in the feature space. A multi-scale path dependency analysis is performed on the symbiotic clusters. The transient correlation component induced by path mutation and the steady-state correlation component of the inherent characteristics of the equipment are separated through an adaptive gating mechanism, and a symbiotic correlation pattern topology map including the mechanical-electrical coupling strength and failure propagation path is output.

3. The method according to claim 1, characterized in that Based on the logistics transportation path planning parameters and the equipment operating environment constraints, a multi-objective collaborative optimization algorithm is used to make joint decisions on the symbiotic association mode, and generate collaborative control instructions that simultaneously adjust the vibration avoidance parameters of the transportation path and the harmonic suppression parameters of the power supply circuit, including: The path curvature change rate and acceleration shock threshold in the logistics transportation path planning parameters are converted into vibration suppression constraints. At the same time, the electromagnetic compatibility limit and temperature rise threshold in the equipment operating environment constraints are mapped into harmonic suppression constraints to construct a joint decision space for mechanical vibration suppression and electrical harmonic suppression. Based on the coupling intensity distribution of mechanical stress anomaly and harmonic distortion in the symbiotic association mode, the vibration suppression priority and harmonic suppression priority are divided in the joint decision space. The priority is dynamically adjusted by the ratio of the mechanical vibration propagation path length to the harmonic distortion conduction path length at the path mutation point. The vibration attenuation characteristic curve of the transport path vibration avoidance parameters and the frequency domain response characteristic curve of the harmonic suppression parameters are introduced into the joint decision space. The conflict area and cooperative area of ​​the mechanical-electrical control parameters are determined through dual-channel feature cross-validation, and a collaborative control instruction set including the vibration avoidance path correction vector and the harmonic suppression frequency adjustment amount is output.

4. The method according to claim 2, characterized in that: The time-domain evolution law of the stress peak sequence is coupled with the frequency-domain diffusion characteristics of the harmonic energy mutation interval in time and space, and a cross-domain correlation tensor with path mutation intensity as weight is constructed, including: The time-domain evolution law of the stress peak sequence is fitted in sections to extract the rising slope, falling slope and duration characteristics of the stress peak in the vibration impact interval. At the same time, the frequency-domain diffusion characteristics of the harmonic energy mutation interval are divided into frequency bands to extract the peak frequency and energy attenuation rate characteristics of the energy distribution in each frequency band. The rising slope, falling slope and duration characteristics of the stress peak sequence are aligned in time and space with the peak frequency and energy attenuation rate characteristics of the harmonic energy mutation interval. Based on the time evolution curve of the stress peak and the frequency domain diffusion curve of the harmonic energy, a cross-domain correlation tensor with the path mutation intensity as the weight is constructed, where the path mutation intensity is dynamically calibrated by the product of the curvature change rate of the transport path and the acceleration shock threshold.

5. The method according to claim 4, characterized in that Based on the time evolution curve of the stress peak and the frequency domain diffusion curve of the harmonic energy, a cross-domain correlation tensor with the path mutation intensity as the weight is constructed, including: Based on the time evolution curve of the stress peak, the rising slope, falling slope and duration characteristics of the stress peak in the vibration impact range are extracted. At the same time, based on the frequency domain diffusion curve of the harmonic energy, the peak frequency and energy attenuation rate characteristics of the harmonic energy in each frequency band are extracted. The time evolution characteristics of the stress peak and the frequency domain diffusion characteristics of the harmonic energy are aligned in time and space, and the characteristics are weighted based on the path mutation intensity, where the path mutation intensity is dynamically calibrated by the product of the curvature change rate of the transport path and the acceleration shock threshold; The weighted spatiotemporal alignment features are integrated according to the spatiotemporal distribution of path mutation events to generate a cross-domain correlation tensor with path mutation intensity as weight. The cross-domain correlation tensor cross-validates the features through the geometric distance of the stress propagation path and the electrical distance of the harmonic distortion conduction path.

6. The method according to claim 1, characterized in that Construct a joint risk assessment matrix of mechanical stress anomaly and electrical harmonic distortion. Based on the quantitative analysis results of the joint risk assessment matrix and the coordinated control instructions, dynamically adjust the coordinated relationship between the logistics transportation path and the harmonic suppression parameters to achieve closed-loop decision-making for power equipment, including: Based on the stress peak sequence of mechanical stress anomaly events and the frequency domain energy distribution of harmonic distortion events, a joint risk assessment matrix of mechanical stress anomaly and electrical harmonic distortion is constructed, where the row vectors of the joint risk assessment matrix represent the intensity distribution of stress anomaly events, the column vectors represent the frequency domain energy distribution of harmonic distortion events, and the matrix elements are calibrated by the coupling strength of stress peak and harmonic energy. According to the quantitative analysis results of the joint risk assessment matrix, the high-risk areas of mechanical stress anomaly and electrical harmonic distortion are extracted, and the curvature change rate of the logistics transportation path and the harmonic suppression parameters of the power supply circuit are dynamically adjusted by combining the vibration avoidance path correction vector and the harmonic suppression frequency adjustment amount in the collaborative control instructions; The adjusted curvature change rate of the logistics transportation path and the harmonic suppression parameters of the power supply circuit are fed back to the joint risk assessment matrix, and the closed-loop decision of the power equipment is achieved by iteratively updating the matrix elements.

7. The method according to claim 6, characterized in that According to the quantitative analysis results of the joint risk assessment matrix, the high-risk areas of mechanical stress anomaly and electrical harmonic distortion are extracted. Combined with the vibration avoidance path correction vector and harmonic suppression frequency adjustment in the collaborative control instructions, the curvature change rate of the logistics transportation path and the harmonic suppression parameters of the power supply circuit are dynamically adjusted, including: The significant areas of coupling strength between mechanical stress anomaly and electrical harmonic distortion are screened out from the joint risk assessment matrix. The significant areas are determined by cross-validation of the time domain evolution law of the stress peak sequence and the frequency domain diffusion characteristics of the harmonic energy mutation interval, and the coupling strength of the significant areas is prioritized in combination with the quantitative analysis results. Combined with the vibration avoidance path correction vector in the collaborative control instruction, the curvature change rate of the logistics transportation path is adjusted. At the same time, combined with the harmonic suppression frequency adjustment amount, the harmonic suppression parameters of the power supply circuit are dynamically adjusted. The curvature change rate is dynamically calibrated by the joint weight of the path mutation intensity and the harmonic suppression frequency adjustment amount. The adjusted curvature change rate and harmonic suppression parameters are fed back to the joint risk assessment matrix, and the coupling intensity distribution in significant areas is iteratively updated to achieve dynamic coordinated optimization of logistics transportation paths and harmonic suppression parameters.

8. A big data intelligent decision analysis system, characterized in that: include: The acquisition module acquires the transport vibration data of the power equipment, synchronously acquires the stress distribution data of the key mechanical nodes of the power equipment, and extracts the high-frequency harmonic distortion features aligned with the timestamp of the transport vibration data in the power supply circuit; The analysis module conducts cross-domain correlation analysis on the fluctuation amplitude of stress distribution data and the dynamic changes of high-frequency harmonic distortion characteristics, and identifies the symbiotic correlation pattern between mechanical stress anomaly and electrical harmonic distortion under sudden changes in logistics transportation routes through machine learning models; The adjustment module makes joint decisions on the symbiotic association mode based on the logistics transportation path planning parameters and the equipment operating environment constraints through a multi-objective collaborative optimization algorithm, and generates collaborative control instructions for simultaneously adjusting the vibration avoidance parameters of the transportation path and the harmonic suppression parameters of the power supply circuit; The control module constructs a joint risk assessment matrix of mechanical stress anomaly and electrical harmonic distortion. Based on the quantitative analysis results of the joint risk assessment matrix and the collaborative control instructions, the collaborative relationship between the logistics transportation path and the harmonic suppression parameters is dynamically adjusted to achieve closed-loop decision-making of power equipment.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a big data intelligent decision analysis method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a big data intelligent decision analysis method as described in any one of claims 1 to 7 is implemented.

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