Method and system for evaluating electromagnetic exposure of dwpt system considering human activity

Abstract

The present application relates to the technical field of dynamic wireless power transfer (DWPT), and especially relates to a DWPT system electromagnetic exposure evaluation method and system considering human activity, aiming at the problem that the existing static evaluation method is difficult to take into account the time-varying characteristics of dynamic system and the difference of human activity distribution, the present application derives the time domain expression of the receiving coil current through establishing a circuit model, quantifies the influence of speed and load on the dynamic change of current; the electromagnetic region is classified into alert zone and waiting zone, and multiple height monitoring planes are set to correspond to the main organs of the human body; the magnetic field data set is obtained by laying measuring points, the regional average magnetic induction intensity, the maximum magnetic induction intensity and the magnetic field energy are calculated as the evaluation target; based on the rank correlation coefficient and the mutual information value, the redundant measuring points are removed, the core measuring point combination is screened by combining the NSGA-II multi-objective optimization, and the multi-objective high-precision synchronous prediction is realized by using the second-order ridge regression, so that the accurate characterization and low-cost measurement of dynamic electromagnetic exposure are realized.

Description

Technical Field

[0001] This invention relates to the field of dynamic wireless power transfer (DWPT) technology, and more particularly to a method and system for assessing electromagnetic exposure of DWPT systems that takes into account human activity. Background Technology

[0002] As a core infrastructure of urban public transportation, urban rail transit systems account for a significant proportion of energy consumption and carbon emissions, making green technology upgrades a trend. Wireless power transfer technology, due to its reliability, safety, and flexibility, is widely used in electric vehicles, medical equipment, drones, and other fields, forming a relatively mature industrial system. Among these, wireless power transfer technology, represented by inductive coupling, has promising application prospects in the transportation sector.

[0003] Currently, inductively coupled wireless power supply systems in the transportation sector mostly employ static power supply methods when the vehicle is stationary. The electromagnetic environment characteristics of these systems have been systematically studied, and electromagnetic exposure assessment methods for static inductive power supply systems are relatively well-developed. Human electromagnetic safety and electromagnetic compatibility assessments can be conducted by precisely selecting key magnetic exposure locations. However, in the field of dynamic inductive power supply, with trams as a typical application, the transient inrush current generated when the coil passes through segments causes severe fluctuations in the magnetic field. Traditional electromagnetic exposure assessment methods under single-location or static conditions are insufficient to accurately reflect the electromagnetic exposure level experienced by the human body during operation. More importantly, during dynamic movement, the real-time changes in the relative positions of the receiver and transmitter result in a time-varying electromagnetic field. Simultaneously, the characteristics of human activity around the system exhibit significant uncertainty and spatial distribution differences, meaning that the human body may be exposed to electromagnetic environments at different times and spatial locations. Existing electromagnetic exposure assessment methods do not simultaneously consider the time-varying characteristics of the electromagnetic field and the differences in human activity distribution, making it difficult to comprehensively assess the electromagnetic exposure risks to the human body under dynamic inductive power supply conditions. Summary of the Invention

[0004] This invention provides a method and system for assessing electromagnetic exposure of a DWPT system that takes into account human activity. The technical problem it solves is: how to fully consider the time-varying nature of dynamic systems and the differences in the distribution of human activity, accurately delineate the electromagnetic environment areas of a DWPT system, and effectively assess the exposure status of these electromagnetic environment areas.

[0005] To address the above technical problems, this invention provides a method for assessing electromagnetic exposure to a DWPT system that takes into account human activity, comprising the following steps:

[0006] S1. Determine the time-domain expression of the receiving coil current in the DWPT system, and determine the main factors affecting the receiving coil current during the relative motion of the coil based on the time-domain expression;

[0007] S2. Based on different combinations of the main factors, different operating conditions are constructed, and the dynamic change curve of the receiving coil current during the motion process under each operating condition is obtained.

[0008] S3. Divide the electromagnetic evaluation area into multiple levels and set up measurement points in each level. Then, use the current curve of each working condition as the excitation input for magnetic field analysis, obtain the magnetic induction intensity data of each measurement point during the movement of the receiving coil, and obtain the measurement point magnetic field dataset of the electromagnetic evaluation area under each working condition.

[0009] S4. Based on the height of the main human organs, different detection planes are set in each graded area to evaluate the maximum magnetic induction intensity in each plane under different operating conditions and analyze the magnetic field change trend along the sampling line of the coil movement direction in each plane.

[0010] S5. Based on the magnetic field change trend along the sampling line of the coil movement direction in each plane, the average magnetic induction intensity, maximum magnetic induction intensity and magnetic field energy of each graded area under each working condition are calculated using the magnetic field dataset of the measurement point, which serve as the magnetic field target for electromagnetic exposure assessment.

[0011] S6. Sequentially splice the magnetic induction intensity and magnetic field target data sequences of each measuring point in the graded area under different working conditions, calculate the rank correlation coefficient between measuring points in each area and the mutual information value between measuring points and each magnetic field target, identify and eliminate redundant measuring points to form subsets, and take the union of each subset to obtain the candidate measuring point set.

[0012] S7. Validate the candidate measurement point set on the validation set, and use the second-order ridge regression model to obtain the magnetic field target prediction formula.

[0013] Further, step S1 specifically includes the following steps:

[0014] S11. Establish the time-domain circuit model of the DWPT system;

[0015] S12. Transform the time-domain circuit model into a complex frequency-domain circuit model;

[0016] S13. Derive the transfer function of the rectifier input voltage and the receiving coil current to the induced voltage of the receiving coil based on the complex frequency domain circuit model.

[0017] S14. Determine the frequency domain expression of the receiving coil current based on the transfer function;

[0018] S15. Perform an inverse Laplace transform on the frequency domain expression of the receiving coil current to obtain the time domain expression of the receiving coil current.

[0019] S16. Determine the main factors affecting the current of the receiving coil during the relative motion of the coils based on the time-domain expression.

[0020] Furthermore, in step S1, the main factors affecting the current of the receiving coil are the mutual inductance between the coils and the load on the receiving side.

[0021] Furthermore, step S2 specifically includes the following steps:

[0022] S21. Fit the relationship between mutual inductance and time;

[0023] S22. Based on the transfer function of the current in the receiving coil to the induced voltage on the receiving side, perform frequency response analysis on the receiving circuit, and analyze the rate of change of mutual inductance based on the relationship between mutual inductance and time.

[0024] S23. Determine the conversion relationship between the speed of the receiving coil and the input frequency on the horizontal axis of the frequency response analysis based on frequency response analysis and the rate of change analysis of mutual inductance.

[0025] S24. Based on the speed-input frequency conversion relationship, substitute the values ​​of different loads into the transfer function and plot the amplitude-frequency response curve to obtain the dynamic law of the receiving coil current changing with the coil position.

[0026] S25. Select typical load and speed combinations to construct heavy-load and light-load operating conditions of the vehicle at different operating speeds, and obtain the dynamic change curve of the receiving coil current during the motion process under each operating condition.

[0027] Furthermore, step S3 specifically includes the following steps:

[0028] S31. Use finite element simulation software to establish a three-dimensional electromagnetic simulation model, and divide the electromagnetic evaluation area into a warning zone near the guide rail and a waiting zone outside the warning zone.

[0029] S32. Sampling lines are set up in the warning zone and waiting area respectively, and measurement points are set up on the sampling lines to obtain an initial set of measurement points covering the entire electromagnetic evaluation area.

[0030] S33. Using the curve of the change of the current of the receiving coil during the motion under various working conditions as the excitation input, record the magnetic induction intensity data of each measuring point when the receiving coil moves to different positions, and form the magnetic field dataset of the measuring points in the warning area and the waiting area under various working conditions.

[0031] Furthermore, step S4 specifically includes the following steps:

[0032] S41. Set up monitoring planes at different heights in the warning zone and waiting area, corresponding to the heights of the main human organs, and use a finite element field calculator to calculate and extract the maximum value of the magnetic induction intensity of the monitoring planes.

[0033] S42. Select the sampling line along the direction of coil movement on the monitoring plane, and create a complete processing flow for the magnetic field data of the sampling line in the results report window. Analyze the trend of magnetic field change along the sampling line along the direction of coil movement in each monitoring plane under the regional hierarchical division.

[0034] Furthermore, step S6 specifically includes the following steps:

[0035] S61. Sequentially splice the magnetic induction intensity data sequence and the corresponding magnetic field target sequence obtained by each measuring point in the warning area and waiting area under different working conditions, calculate the rank correlation coefficient between measuring points in each area and the mutual information value between measuring points and three magnetic field targets, and use the combination of the rank correlation coefficient matrix between measuring points and the mutual information matrix between measuring points and a single magnetic field target as the evaluation unit, and form an evaluation unit corresponding to three magnetic field targets in each area.

[0036] S62. Within each evaluation unit, the measurement point pairs whose rank correlation coefficient reaches the preset extremely strong correlation threshold are identified as redundant measurement point pairs. For each redundant measurement point pair, the mutual information value between the measurement point and the corresponding magnetic field target is compared. The measurement points with small mutual information values ​​are removed from the redundant measurement point pairs, and the measurement points with large mutual information values ​​are retained to obtain the redundancy-free measurement point subset of each evaluation unit.

[0037] S63. After removing redundancy from the three evaluation units of the warning zone and the waiting zone respectively, the three subsets of measurement points are sorted according to the size of the mutual information value. The top m measurement points of each are selected and then the union is taken to form the candidate measurement point set of the warning zone and the candidate measurement point set of the waiting zone.

[0038] S64. Using the NSGA-II multi-objective optimization method, the magnetic field dataset of the measurement points in one of the working conditions of the warning zone and the waiting zone is used as the validation set, and the other working conditions are used as the training set. The core measurement point number combination is used as the decision variable in the candidate measurement point set of the warning zone and the candidate measurement point set of the waiting zone, and the combination length is h. Under the optimization objective, the Pareto front set of the core measurement point combination of each region is obtained, and the core measurement point combination is selected from it.

[0039] Furthermore, in step S64, the optimization objective is to minimize the number of core measuring points within the combination and maximize the average coefficient of determination of the selected core measuring point combination for the three magnetic field targets.

[0040] Furthermore, in step S7, the magnetic field target prediction relationship between the warning zone and the waiting zone is as follows:

[0041] ,

[0042] ,

[0043] in, The number of core measuring points within the selected combination. and These are the average magnetic flux density, maximum magnetic flux density, and predicted magnetic field energy values ​​obtained through fitting analysis based on the combination of core measuring points in the warning zone and waiting area. These represent the magnetic induction intensity at the i-th core measuring point in the warning zone and the waiting zone, respectively. and These are the regression coefficients of the first-order term, square term, and interaction term for the three magnetic field targets corresponding to the warning zone and waiting zone, respectively. and These are the intercept terms for the three magnetic field targets corresponding to the warning zone and the waiting zone, respectively.

[0044] The present invention also provides an electromagnetic exposure assessment system for DWPT systems that takes into account human activity, the key feature of which is that it includes an analysis unit and an assessment unit, wherein the analysis unit and the assessment unit are respectively used to perform steps S1 to S5 and steps S6 to S7 in the electromagnetic exposure assessment method for DWPT systems that takes into account human activity.

[0045] The electromagnetic exposure assessment method and system for DWPT systems considering human activity provided by this invention establishes a circuit time-domain / complex frequency-domain model based on a dual-transmitter single-receiver system, derives the receiver transfer function, obtains the time-domain expression of the receiver coil current, identifies the main factors affecting the receiver coil current during relative coil movement, and quantifies their impact. Based on different combinations of the main factors' values, various operating conditions are constructed, and dynamic change curves of the receiver coil current under each condition are obtained. In conjunction with ground-based restricted areas, the electromagnetic assessment area is divided into warning zones and waiting zones, and multiple detection planes are set up in different areas according to the height of major human organs to assess various... The maximum magnetic induction intensity in the plane is determined, and the trend of magnetic field variation along the direction of coil movement is analyzed. Sampling lines and measuring points covering the entire evaluation area are deployed within a graded region to obtain magnetic field datasets for each operating condition. The average magnetic induction intensity, maximum magnetic induction intensity, and magnetic field energy of the region are calculated as the magnetic field targets for electromagnetic exposure assessment. Redundant measuring points are identified and eliminated through analysis of the rank correlation coefficient between measuring points and the mutual information values ​​between measuring points and magnetic field targets, and a set of candidate measuring points strongly correlated with the magnetic field targets is selected. The NSGA-II multi-objective optimization method is used to obtain the core measuring point combination, and a second-order ridge regression model is used to achieve synchronous and accurate prediction of multiple magnetic field targets. This invention fully considers the time-varying characteristics of dynamic wireless power transmission systems and the differences in the spatial distribution of human activity, making it suitable for electromagnetic exposure assessment under actual train operating conditions. It solves the problem of lack of consistency in regional division and measuring point selection in traditional methods. Attached Figure Description

[0046] Figure 1This is a flowchart of the electromagnetic exposure assessment method for the DWPT system that takes human activity into account, provided in an embodiment of the present invention.

[0047] Figure 2 This is a circuit diagram of a DWPT system (dual transmitter, single receiver) based on a segmented LCC-S compensation topology provided in an embodiment of the present invention.

[0048] Figure 3 This is provided by the embodiments of the present invention. Figure 2 The equivalent circuit diagram;

[0049] Figure 4 This is a circuit time-domain model diagram of the DWPT system (single transmitter, single receiver) provided in an embodiment of the present invention;

[0050] Figure 5 This is provided by the embodiments of the present invention. Figure 4 Equivalent circuit diagram of the receiving end;

[0051] Figure 6 This is a time-domain model diagram of the receiving end provided in an embodiment of the present invention;

[0052] Figure 7 This is a diagram of the receiver complex frequency domain model provided in an embodiment of the present invention;

[0053] Figure 8 This is a schematic diagram of the electromagnetic environment region division provided in an embodiment of the present invention;

[0054] Figure 9 This is a schematic diagram of the hierarchical division of the evaluation area provided in an embodiment of the present invention;

[0055] Figure 10 These are diagrams illustrating the current surge phenomena on the receiving side under different operating conditions provided in embodiments of the present invention.

[0056] Figure 11 This is a line graph showing the maximum values ​​of magnetic induction intensity in six selected planes provided in this embodiment of the invention;

[0057] Figure 12 This is a three-dimensional diagram showing the change of the magnetic field along the direction of coil movement on the leg plane within the warning zone and waiting zone, provided in an embodiment of the present invention. Figure 12 In the middle, (a), (b), and (c) correspond to the leg, heart, and brain planes of the warning zone, respectively. Figure 12 (d), (e), and (f) correspond to the leg, heart, and brain planes of the waiting area, respectively.

[0058] Figure 13 This is a heatmap of the rank correlation coefficient between measurement points S1 and S2 provided in an embodiment of the present invention. Figure 13 (a) and (b) in the middle correspond to measurement points in sets S1 and S2, respectively;

[0059] Figure 14 This is a Pareto front map of the core measuring points combination of the warning zone and waiting area provided in the embodiments of the present invention. Figure 14 (a) and (b) correspond to the warning area and the waiting area, respectively.

[0060] Figure 15 This is a scatter plot of the predicted and actual values ​​of the magnetic field target under the core measuring point combination in the warning zone provided in this embodiment of the invention. Figure 15 (a), (b), and (c) correspond to the three magnetic field targets in the warning zone, respectively. , and ;

[0061] Figure 16 This is a scatter plot of the predicted and actual values ​​of the magnetic field target under the combination of core measuring points in the waiting area provided in this embodiment of the invention. Figure 16 (a), (b), and (c) correspond to the three magnetic field targets in the waiting area, respectively. , and . Detailed Implementation

[0062] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. The embodiments are given for illustrative purposes only and should not be construed as limiting the present invention. The accompanying drawings are for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0063] This invention first provides a method for assessing electromagnetic exposure of a DWPT system that takes into account human activity, such as... Figure 1 As shown in the flowchart, the method includes the following steps:

[0064] S1. Determine the time-domain expression of the receiving coil current in the DWPT system, and determine the main factors affecting the receiving coil current during the relative motion of the coil based on the time-domain expression;

[0065] S2. Based on different combinations of the main factors, different operating conditions are constructed, and the dynamic change curve of the receiving coil current during the motion process under each operating condition is obtained.

[0066] S3. Divide the electromagnetic evaluation area into multiple levels and set up measurement points in each level. Then, use the current curve of each working condition as the excitation input for magnetic field analysis, obtain the magnetic induction intensity data of each measurement point during the movement of the receiving coil, and obtain the measurement point magnetic field dataset of the electromagnetic evaluation area under each working condition.

[0067] S4. Based on the height of the main human organs, different detection planes are set in each graded area to evaluate the maximum magnetic induction intensity in each plane under different operating conditions and analyze the magnetic field change trend along the sampling line of the coil movement direction in each plane.

[0068] S5. Based on the magnetic field change trend along the sampling line of the coil movement direction in each plane, the average magnetic induction intensity, maximum magnetic induction intensity and magnetic field energy of each graded area under each working condition are calculated using the magnetic field dataset of the measurement point, which serve as the magnetic field target for electromagnetic exposure assessment.

[0069] S6. Sequentially splice the magnetic induction intensity and magnetic field target data sequences of each measuring point in the graded area under different working conditions, calculate the rank correlation coefficient between measuring points in each area and the mutual information value between measuring points and each magnetic field target, identify and eliminate redundant measuring points to form subsets, and take the union of each subset to obtain the candidate measuring point set.

[0070] S7. Validate the candidate measurement point set on the validation set, and use the second-order ridge regression model to obtain the magnetic field target prediction formula.

[0071] The following section uses a dual-transmit single-receive DWPT system based on a segmented LCC-S compensation topology as an example to provide a more detailed explanation of each step.

[0072] (1) Step S1: Determine the time-domain expression of the receiving coil current and the main factors affecting the receiving coil current.

[0073] This step specifically includes:

[0074] S11. Establish the time-domain circuit model of the DWPT system;

[0075] S12. Transform the time-domain circuit model into a complex frequency-domain circuit model;

[0076] S13. Derive the transfer function of the rectifier input voltage and the receiving coil current to the induced voltage of the receiving coil based on the complex frequency domain circuit model.

[0077] S14. Determine the frequency domain expression of the receiving coil current based on the transfer function;

[0078] S15. Perform an inverse Laplace transform on the frequency domain expression of the receiving coil current to obtain the time domain expression of the receiving coil current.

[0079] S16. Determine the main factors affecting the current of the receiving coil during the relative motion of the coils based on the time-domain expression.

[0080] In step S11, the circuit diagram of the DWPT system is first constructed. The circuit diagram of the dual-transmitter single-receiver DWPT system based on the segmented LCC-S compensated topology is as follows: Figure 2 As shown, its equivalent circuit diagram is as follows: Figure 3 As shown, it includes two transmitters and one receiver. At the two transmitters, and It is a DC input power supply, through a power MOSFET. The two high-frequency inverters are used for energy conversion. and This represents the output voltage of the two high-frequency inverters. , , , These are the self-inductance and internal resistance of transmitting coil 1 and transmitting coil 2, respectively. , , and , , These form two compensation networks, transmitting coil 1 and transmitting coil 2, respectively. , , , These are the currents flowing through the two inverters and transmitting coils 1 and 2, respectively. At the receiving end, , , These are the self-inductance, compensation capacitance, and internal resistance of the receiving coil. It is the equivalent impedance on the receiving side. It is the input voltage of the rectifier. It is a load Voltage at both ends, It is the filter capacitor of the rectifier. , , These are the current in the receiving coil, the rectifier output current, and the load resistance, respectively. The current and the direction of current flow are defined as clockwise. , , These are the induced voltages of transmitting coil 1, transmitting coil 2, and receiving coil, respectively. , These represent the mutual inductance between transmitting coil 1, transmitting coil 2, and receiving coil, respectively.

[0081] A dual-transmitter single-receiver (DWPT) system comprises two transmit coils and one receive coil. The movement of the receive coil can be divided into two phases: a steady-state phase and a segmentation phase. In the steady-state phase, the receive coil is coupled to only one transmit coil, meaning the receive coil is completely above the transmit coil. In the segmentation phase, the receive coil is coupled to both transmit coils simultaneously, meaning the receive coil moves to a position above the adjacent portions of the two transmit coils.

[0082] DWPT system coil design typically employs a rectangular structure with a long transmitting coil and a short receiving coil. Since the transmitting coil structural parameters are identical, time-domain model analysis is performed only on the circuit between one transmitting coil and one receiving coil in the DWPT system, as well as the AC / DC side circuit at the receiving end. The parameter is appended with "(t)" to indicate its time-domain representation, and the parameter subscript is appended with "...". _rms " indicates the valid value of the parameter. Figure 4 This is a time-domain model diagram of the transmitter and receiver circuits of a DWPT system. Figure 5 This is the equivalent circuit diagram of the AC / DC side of the receiving end. (Reference) Figure 5 The time-domain induced voltage of the receiving coil and its effective value can be expressed as:

[0083]

[0084]

[0085] in, It is the system's operating angular frequency. Let be the current in any of the transmitting coils. The mutual inductance between the transmitting and receiving coils is typically... To simplify calculations, the cross-coupling between the transmitting coils can be ignored. When the receiving coil moves above an adjacent transmitting coil, it has a strong coupling relationship with both transmitting coils, which is explained by mutual inductance. This represents the mutual inductance between the receiving coil and the transmitting coil.

[0086] refer to Figure 5 Write and simplify the KVL and KCL time-domain equations for the AC and DC sides of the receiving circuit, respectively, to obtain:

[0087]

[0088]

[0089] For a diode rectifier, the AC side voltage and DC side voltage and current satisfy the following relationship:

[0090]

[0091] Among them, custom parameters , This is the voltage drop across the rectifier diode.

[0092] Simplifying, we get:

[0093]

[0094] in, for The first derivative with respect to time t.

[0095] In step S12, the following can be further performed: Figure 4 The time-domain circuit model of the AC side of the receiving end is transformed into Figure 6 As shown. Further... Figure 6 The receiver AC side time-domain circuit model shown is converted to as follows: Figure 7 The circuit model in the complex frequency domain is shown. The KVL and KCL equations for this complex frequency domain model are as follows:

[0096]

[0097]

[0098] Here, s is a complex variable representing the complex frequency.

[0099] In step S13, the complex frequency domain model of the rectifier input voltage and the receiving coil current can be obtained and rearranged into a transfer function form:

[0100]

[0101] In step S14, based on the transfer function The expression can be viewed as a second-order transfer function with additional zeros. The expression can be written as:

[0102]

[0103] in, The steady-state gain of the receiving coil current RMS value, It is the natural oscillation frequency. It is the damping ratio. It's midnight.

[0104] In step S15, the damping ratio is calculated based on the system circuit parameters. Based on this, the damping state of the receiving-side circuit is determined, when the system is in an underdamped state ( When ), the system damping frequency By performing an inverse Laplace transform on the frequency domain expression of the receiving coil current, its time domain expression can be obtained:

[0105]

[0106] in, It represents the time constant, which is the time required for the current surge (transient response) to decay to approximately 36.8% of its initial amplitude.

[0107] This step simplifies the solution process of differential equations using the complex frequency domain (higher-order cases are difficult to solve directly and the process is cumbersome). A Laplace transform is used to convert the time-domain differential equations into algebraic equations in the complex frequency domain. This allows for direct solution of the current expression and further yields the system's transfer function. In this way, key parameters such as the system's natural oscillation frequency, damping ratio, and zero points can be intuitively revealed, which is essential for subsequent frequency response analysis, damping state assessment, and current impact characteristic analysis. Without converting to the complex frequency domain, the system structure would be difficult to standardize into a second-order system model. Finally, the complex frequency domain results are converted back to the time domain because the change in the receiving coil current is dynamic, and the time-domain model is more suitable for physical analysis.

[0108] In step S16, based on the time-domain expression of the receiving coil current, it can be known that the receiving coil current is related to... These parameters are related. During the movement of the receiving coil along the transmitting coil, the mutual inductance between the coils and... Determined by their relative spatial position, the operating speed of the receiving coil affects the rate of change of relative position, thus affecting mutual inductance and... The rate of change. Furthermore, in actual vehicle operation, passenger boarding and alighting behaviors cause changes in electricity demand, thus affecting the load on the receiving side. Different values ​​are taken under different operating conditions. Based on the above operating characteristics, the mutual inductance and and load It was identified as the main factor affecting the dynamic changes of the receiving coil current.

[0109] (2) Step S2: Construct different operating conditions and obtain the current variation curve of the receiving coil under each operating condition.

[0110] Step S2 specifically includes the following steps:

[0111] S21, Fitted Mutual Inductance and Over time The changing relationship;

[0112] S22. Transfer function of receiving coil current to induced voltage on the receiving side Frequency response analysis was performed on the receiving circuit, and based on mutual inductance and Over time The changing relationship is used to analyze the rate of change of mutual inductance;

[0113] S23. Determine the speed of the receiving coil based on frequency response analysis and the rate of change analysis of mutual inductance. Frequency response analysis with input frequency on the horizontal axis Conversion relationships;

[0114] S24. Based on the speed-input frequency conversion relationship, substitute the values ​​of different load parameters into the transfer function. The amplitude-frequency response curve was plotted to obtain the dynamic law of the receiving coil current changing with the coil position;

[0115] S25. Select typical load and speed combinations to construct heavy load (high passenger flow or normal passenger flow) and light load (empty load or low passenger flow) operating conditions of the vehicle at different operating speeds, and obtain the dynamic change curve of the receiving coil current during the motion process under each operating condition.

[0116] In step S21, the mutual inductance during the movement of the receiving coil can be obtained through simulation, actual measurement, and other means. With displacement The data on mutual inductance were obtained by fitting the data using the sine sum function in the curve fitting toolbox of Matlab software (achieving a goodness of fit of 0.95 or higher). With displacement Mathematical description of the change:

[0117]

[0118] in, These represent the constant coefficients obtained from the fitting process. In this embodiment, They are respectively , They are respectively , They are respectively , They are respectively , They are respectively , They are respectively , They are respectively , where e is the natural base.

[0119] Let its speed be... ,but Thus, mutual intuition and Over time Mathematical description of the change:

[0120]

[0121] In step S22, the transfer function between the receiving coil current and the induced voltage on the receiving side, derived in step S1, is used. Frequency response analysis was performed on the receiving circuit to quantify the transient impact characteristics of the receiving coil current under different operating conditions. When the receiving coil moves into the coupling range of the transmitting coil, the mutual inductance and rate of change show a trend of first increasing and then decreasing, a trend consistent with that of a sine wave. The upward trend within the range is consistent, and it can be considered that the mutual inductance and the process of rising from zero to the maximum value mainly contain a sine wave component, the period of which is 4 times the time taken for the upward process.

[0122] In step S23, the speed of the receiving coil can be determined based on the above analysis. and Input function frequency in frequency response analysis Conversion relationships between them:

[0123]

[0124] in, The distance the induced voltage of the receiving coil moves from its lowest to its highest point.

[0125] In step S24, the values ​​of different loads are substituted into the transfer function. Amplitude-frequency response curves are then plotted, with each curve corresponding to a specific load. The horizontal axis of the amplitude-frequency response curve represents the input function frequency. The frequency can be equivalently correlated with the operating speed of the receiving coil; the vertical axis represents the amplitude gain, whose changes quantitatively reflect the peak current and fluctuations of the receiving coil under corresponding operating speed conditions, thus describing the transient impact characteristics of the receiving coil. By comparing the curves corresponding to different loads on the same amplitude-frequency diagram, the combined influence of load and speed on the current impact of the receiving coil can be quantified simultaneously. This allows us to obtain the dynamic law of the receiving coil current changing with the coil position.

[0126] In step S25, based on the above analysis, typical load and speed combinations are selected to construct heavy-load (high passenger flow or normal passenger flow) and light-load (empty or low passenger flow) operating conditions of the vehicle at different operating speeds, and the dynamic change curve of the receiving coil current during the motion process under each operating condition is obtained.

[0127] (3) Step S3: Divide the electromagnetic evaluation area into levels and construct the magnetic field dataset of measurement points in each level area.

[0128] Step S3 specifically includes the following steps:

[0129] S31. Use Maxwell finite element simulation software to establish a three-dimensional electromagnetic simulation model, and divide the electromagnetic evaluation area into a warning zone near the guide rail and a waiting zone outside the warning zone.

[0130] S32. Sampling lines are laid out along the X, Y, and Z directions in the warning zone and waiting area, and measurement points are laid out on the sampling lines to obtain an initial set of measurement points covering the entire electromagnetic evaluation area.

[0131] S33. Using the curve of the change of the current of the receiving coil during the motion under various working conditions as the excitation input, record the magnetic induction intensity data of each measuring point when the receiving coil moves to different positions, and form the magnetic field dataset of the measuring points in the warning area and the waiting area under various working conditions.

[0132] In step S31, during vehicle operation, the sections that cross / do not cross may be located in densely populated areas such as intersections and platforms. Simultaneously, the requirements for the layout of ground-level restricted areas during operation must be considered. Therefore, the electromagnetic assessment areas for both sections and non-cross may be uniformly divided using the same hierarchical principle, into a warning zone and a waiting zone. The warning zone is a restricted area near the track where pedestrians are prohibited from staying. The waiting zone is located outside the warning zone as a buffer zone, and its width is greater than the width of the warning zone. In this embodiment, the width ratio of the waiting zone to the warning zone is 4:1, and a three-dimensional electromagnetic simulation model is built using Maxwell finite element simulation software. Figure 8 The diagram showing the division between the waiting area and the warning area is provided. As an example, the length L of the warning area and the waiting area is set to 1000mm, the widths W1 and W2 of the warning area and the waiting area are set to 200mm and 800mm respectively, and the height H of the warning area and the waiting area is set to 2000mm. The length L, the width (W1+W2), and the height H constitute the electromagnetic evaluation area.

[0133] In step S32, the coordinate axes established in this embodiment are as follows: Figure 9 As shown, the center of the track area corresponding to the warning zone is taken as the origin, the length and width of the guide rail are taken as the X-axis and Y-axis respectively, and the direction perpendicular to the X-axis and Y-axis is taken as the Z-axis.

[0134] An initial set of measurement points covering the entire evaluation area is formed by setting a sampling line every first preset distance (200mm) in the X direction, a sampling line every second preset distance (250mm) in the Y direction, a sampling line every third preset distance (250mm) in the Z direction, and a sampling point every fourth preset distance (20mm) on each sampling line.

[0135] In step S33, the current curve of the receiving coil under each working condition is used as the excitation input for magnetic field analysis. The magnetic induction intensity data of each measuring point when the receiving coil moves to different positions are recorded, and the magnetic field datasets of the measuring points in the warning area and waiting area under each working condition are formed respectively.

[0136] (4) Step S4: Set up the monitoring plane and analyze the trend of magnetic field changes.

[0137] Step S4 specifically includes the following steps:

[0138] S41. Set up monitoring planes at different heights in the warning zone and waiting area, corresponding to the heights of the main human organs, and use the Maxwell field calculator to calculate and extract the maximum value of the magnetic induction intensity of the monitoring planes.

[0139] S42. Select the sampling line along the direction of coil movement on the monitoring plane, and create a complete processing flow for the magnetic field data of the sampling line in the results report window. Analyze the trend of magnetic field change along the sampling line along the direction of coil movement in each monitoring plane under the regional hierarchical division.

[0140] Figure 9 This is a schematic diagram showing the division of the magnetic induction plane in the evaluation area. (See diagram below.) Figure 9 As shown, in this embodiment, a plane is set at the height of the human leg, heart, and brain in the warning zone and waiting zone (500mm / 1000mm / 1500mm), respectively, for a total of six planes. These six planes are called the warning zone leg plane P1, the waiting zone leg plane P2, the warning zone heart plane P3, the waiting zone heart plane P4, the warning zone brain plane P5, and the waiting zone brain plane P6.

[0141] Using the field calculator in Maxwell, the maximum magnetic induction intensity on each detection plane during operation is extracted, quantifying the electromagnetic exposure level at key heights under different operating conditions. Simultaneously, sampling lines along the coil's movement direction are selected, and a complete processing flow for the sampling line magnetic field data is created in the results report window. By comparing and analyzing the trends of magnetic field variations along the coil's movement direction in different regions and at the same height plane, the law governing magnetic field changes with spatial location can be revealed.

[0142] (5) Step S5: Calculate the magnetic field target for electromagnetic exposure assessment

[0143] In this step, the Maxwell field calculator is used to calculate the regional average magnetic flux density, regional maximum magnetic flux density, and regional magnetic field energy within the selected warning and waiting areas during the movement of the receiving coil under various operating conditions. These calculations serve as the magnetic field targets for electromagnetic exposure assessment. Regional average magnetic flux density... Maximum magnetic induction intensity in the region and regional magnetic field energy Calculated using the following formulas respectively:

[0144]

[0145]

[0146]

[0147] in, Indicates the volume of the warning area or waiting area. and Represents spatial points within the region The magnetic induction intensity vector and magnetic field intensity vector at the location.

[0148] (6) Step S6: Filter the candidate measurement point set based on the rank correlation coefficient and mutual information value.

[0149] Step S6 specifically includes the following steps:

[0150] S61. Sequentially splice the magnetic induction intensity data sequence and the corresponding magnetic field target sequence obtained by each measuring point in the warning area and waiting area under different working conditions, calculate the rank correlation coefficient between measuring points in each area and the mutual information value between measuring points and three magnetic field targets, and use the combination of the rank correlation coefficient matrix between measuring points and the mutual information matrix between measuring points and a single magnetic field target as the evaluation unit, and form an evaluation unit corresponding to three magnetic field targets in each area.

[0151] S62. Within each evaluation unit, the measurement point pairs whose rank correlation coefficient reaches the preset extremely strong correlation threshold are identified as redundant measurement point pairs. For each redundant measurement point pair, the mutual information value between the measurement point and the corresponding magnetic field target is compared. The measurement points with small mutual information values ​​in the redundant measurement point pairs are eliminated, and the measurement points with large mutual information values ​​are retained to obtain the redundancy-free measurement point subset of each evaluation unit.

[0152] S63. After removing redundancy from the three evaluation units of the warning zone and the waiting zone respectively, the three subsets of measurement points are sorted according to the size of the mutual information value. The top m measurement points of each are selected and then the union is taken to form the candidate measurement point set S1 of the warning zone and the candidate measurement point set S2 of the waiting zone.

[0153] S64. Using the NSGA-II multi-objective optimization method, the magnetic field dataset of one of the working conditions in the warning zone and the waiting zone is used as the validation set, and the other working conditions are used as the training set. In the candidate measurement point sets S1 and S2, the combination of core measurement point numbers is used as the decision variable, and the combination length is constrained to be h (2≤h≤5). The optimization objectives are: minimizing the number of core measurement points in the combination and maximizing the average determination coefficient R² of the selected core measurement point combination for the three magnetic field targets, thereby obtaining the Pareto front set of core measurement point combinations in each region, and selecting the core measurement point combination from it.

[0154] For each operating condition, the rank correlation coefficient between each measuring point within the warning zone and waiting zone is calculated separately:

[0155]

[0156] in, For measuring points With measuring points Rank correlation coefficient between magnetic flux density sequences This represents the number of sampling points of the receiving coil during its complete motion. In the first sampling points, measuring points With measuring points The magnetic induction intensity ranking difference. Five threshold classifications were set for the rank correlation coefficient between measurement points: extremely weak correlation, weak correlation, moderate correlation, strong correlation, and extremely strong correlation.

[0157] Simultaneously, the mutual information values ​​between each measuring point and the three magnetic field targets were calculated in both the warning zone and the waiting zone:

[0158]

[0159] in, For measuring points A data sequence showing the change of magnetic flux density over time. These are three magnetic field target data sequences that vary over time. The corresponding regions have average magnetic induction intensity Maximum magnetic induction intensity in the region and regional magnetic field energy , For measuring points and The joint probability distribution, and They are measuring points and Marginal probability distribution, and They are respectively and The value space of .

[0160] The magnetic induction intensity data sequences and corresponding magnetic field target sequences obtained by each measuring point in the warning zone and waiting zone under different operating conditions are sequentially spliced ​​together. The rank correlation coefficient between measuring points in each region and the mutual information value between measuring points and three magnetic field targets are calculated respectively. The combination of the rank correlation coefficient matrix between measuring points and the mutual information matrix between measuring points and a single magnetic field target is used as the evaluation unit, and evaluation units corresponding to three magnetic field targets are formed in each region.

[0161] Within each evaluation unit, measurement point pairs whose rank correlation coefficient reaches a preset extremely strong correlation threshold are identified as redundant measurement point pairs. In this embodiment, the preset extremely strong correlation threshold is set to 0.8. For each redundant measurement point pair, the mutual information value between the measurement point and the corresponding magnetic field target is compared, and measurement points with lower mutual information values ​​are eliminated to obtain a subset of measurement points after redundancy removal within each evaluation unit.

[0162] After removing redundancy from the three evaluation units of the warning zone and the waiting zone respectively, the three subsets of measurement points are sorted according to the size of the mutual information value. The top m measurement points of each are selected. In this embodiment, m=10. The union of these subsets forms the candidate measurement point set S1 for the warning zone and the candidate measurement point set S2 for the waiting zone.

[0163] Then, the NSGA-II multi-objective optimization method is adopted. The magnetic field dataset of measurement points in one of the working conditions of the warning zone and the waiting zone is used as the validation set, and the remaining working conditions are used as the training set. Core measurement point combination optimization is performed on the candidate measurement point sets S1 and S2. Under the constraint that the combination length h is at least 2 measurement points and at most 5 measurement points, the optimization objective is:

[0164] ① Minimize the number of core measuring points within the combination;

[0165] ② The average coefficient of determination R of the selected core measuring point combination for the three magnetic field targets 2 maximize.

[0166] In this embodiment, the specific algorithm parameters are: population size of 50, number of iterations of 50, crossover rate of 0.9, and mutation rate of 0.1. Pareto front sets for the warning zone and waiting zone are obtained separately, and core measurement point combinations are selected from them.

[0167] (7) Step S7: The magnetic field target prediction formula is obtained after verification.

[0168] The specific steps are as follows: The selected core measurement points are combined and validated on a validation set; a second-order ridge regression model is used to obtain the prediction formula for the magnetic field target; and performance indicators such as NRMSE are used to evaluate the prediction accuracy.

[0169]

[0170]

[0171] in, The number of core measuring points within the selected combination. and These are the predicted values ​​of the magnetic field target obtained by fitting analysis based on the combination of core measuring points in the warning zone and waiting area; These represent the magnetic induction intensity at the i-th core measuring point in the warning zone and the waiting zone, respectively. and These are the regression coefficients of the first-order term, square term, and interaction term for the three magnetic field targets corresponding to the warning zone and waiting zone, respectively. and These are the intercept terms for the three magnetic field targets corresponding to the warning zone and the waiting zone, respectively.

[0172] Finally, the optimal combination of core measuring points is determined based on the prediction accuracy of all core measuring point combinations.

[0173] Based on the above method, this embodiment of the invention also provides an electromagnetic exposure assessment system for DWPT systems that takes into account human activity. The key feature is that it includes an analysis unit and an assessment unit, wherein the analysis unit and the assessment unit are respectively used to perform steps S1 to S5 and steps S6 to S7 in the electromagnetic exposure assessment method for DWPT systems that takes into account human activity.

[0174] It should be noted that the various processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved. This embodiment does not impose any limitations on these steps.

[0175] The embodiments described in this invention can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with the implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0176] Computer programs for implementing the methods and systems of the present invention may be written in any combination of one or more programming languages ​​and stored in a computer-readable storage medium. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0177] Computer-readable storage media can be tangible media that may contain or store computer programs for use by or in conjunction with an instruction execution system, apparatus, or device. Computer-readable storage media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, optical disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0178] The effectiveness of the present invention and system will be verified below.

[0179] The electromagnetic simulation model constructed in the experiment includes two transmitting coils, one receiving coil, and an evaluation area established at the section where the coils intersect. The initial position of the receiving coil is located above the adjacent center of the transmitting coil. With the initial position of the receiving coil as the center, the evaluation area extends to both sides. In this embodiment, the evaluation area is defined as 1000mm in length and width, and 2000mm in height. It also includes a graded warning zone, a waiting zone (the width ratio of the waiting zone to the warning zone is 4:1), and the planes containing different human organs (legs, heart, and brain).

[0180] In this invention, two load values, 5Ω and 20Ω, are selected to correspond to heavy-load (high passenger flow / normal passenger flow) and light-load (empty / low passenger flow) train operating conditions, respectively. Simultaneously, the receiving coil moves at a constant linear speed above the transmitting coil at speeds of 50 km / h and 70 km / h. This is used to investigate the current surge characteristics of the receiving coil under different speed and load combinations. The corresponding peak current of the receiving coil is shown below. Figure 10 As shown. From Figure 10As can be seen, the receiving coil current exhibits a significant current surge at the segmentation point. At the same speed, the smaller the load, the higher the current surge at the segmentation point; and at the same load, the higher the speed, the higher the current surge at the segmentation point.

[0181] This invention defines four operating conditions: Condition 1 (speed 50 km / h, load 5Ω), Condition 2 (speed 50 km / h, load 20Ω), Condition 3 (speed 70 km / h, load 5Ω), and Condition 4 (speed 70 km / h, load 20Ω). The maximum magnetic induction intensity of the corresponding planes of the legs, heart, and brain in the warning zone and waiting area under different operating conditions is extracted, and the results are as follows. Figure 11 As shown. From Figure 11 It can be seen that in operating conditions 1-4, the maximum magnetic induction intensity of the leg plane in both the warning zone and the waiting zone is significantly higher than that of the heart and brain planes; the maximum magnetic induction intensity of the leg plane in the warning zone is approximately twice that of the same plane in the waiting zone. As the height of the plane increases, the distance between it and the strongly coupled area of ​​the system gradually increases, eventually causing the maximum magnetic induction intensity of the planes in the warning zone and the waiting zone to decrease to almost the same level.

[0182] Taking condition 3 as an example, extract and plot the three-dimensional change of the sampling line magnetic field along the x-axis (i.e., the direction of coil movement) in the leg / heart / brain plane within the warning zone and waiting area, as shown in the example. Figure 12 As shown. Figure 12 (a), (b), and (c) represent the planes of the legs, heart, and brain in the warning zone, respectively. Figure 12 In the diagram, (d), (e), and (f) represent the leg, heart, and brain planes of the waiting area, respectively. From... Figure 12 In terms of overall distribution, the magnetic fields on each sampling line exhibit a clear single-peak trend. The magnetic field strength gradually increases as the receiving coil passes through the segmented position and gradually weakens after passing that position. Under the superposition of magnetic fields, interference regions with local enhancement or cancellation may form at different locations in space, but this does not change the overall single-peak distribution characteristic along the direction of motion. Figure 12 A comparison of (a) and (d) shows that within the leg plane, the overall magnetic field level of the sampling line in the warning zone is significantly higher than that in the waiting zone, and the spatial fluctuation amplitude is greater. In the waiting zone, due to its relatively greater distance from the system, the magnetic field attenuates more significantly during propagation, resulting in a lower overall amplitude and relatively weaker spatial differences. Further comparison... Figure 12 As can be seen in (b) and (e), (c) and (f), as the height of the sampling line increases (from the leg to the heart and then to the brain plane), the overall level of the magnetic field shows a gradual downward trend, and the spatial fluctuation amplitude decreases synchronously, which is consistent with the basic law that the electromagnetic field strength decreases with spatial distance.

[0183] Figure 13 The heatmap of the rank correlation coefficient between measurement points S1 and S2 is shown. Figure 13 In the diagram, (a) and (b) correspond to measurement points in sets S1 and S2, respectively. From... Figure 13 It can be seen that sets S1 and S2 contain 20 and 16 measurement points respectively, and the rank correlation coefficients between most measurement points are lower than the extremely high correlation threshold, showing good independence.

[0184] Figure 14 This is a Pareto front map of the core measuring point combination of the warning zone and waiting area provided in an embodiment of the present invention. Figure 14 In the middle section, (a) and (b) correspond to the restricted area and the waiting area, respectively. From... Figure 14 It can be seen that the warning zone and the waiting zone each have an optimal Pareto front solution under different numbers of core measuring points, and the corresponding combination of core measuring points is determined. The selected combination is validated on the validation set, and a magnetic field target prediction model is established using second-order ridge regression. Its generalization ability is evaluated, and the results are shown in Tables 1 and 2.

[0185] Table 1. Verification results of the restricted area

[0186]

[0187] Table 2 Verification results of the waiting area

[0188]

[0189] The analysis results in Tables 1 and 2 show that, under all Pareto front solution sets corresponding to 2-5 core measuring points within the combination of the warning zone and the waiting zone, the NRMSE index of the magnetic field target is below 5%, indicating that the model has controlled the global prediction error to an extremely low level. Comparing the various evaluation indicators, with two core measuring points selected for both the warning zone and the waiting zone, the final core measuring point combination is formed, and its standardized regression equation expressions are as follows:

[0190]

[0191]

[0192] Figure 15 , Figure 16 The images are scatter plots showing the predicted and actual values ​​of the magnetic field target under the combination of core measuring points in the warning zone and waiting area, respectively. Figure 15 (a), (b), and (c) correspond to the three magnetic field target values ​​of the warning zone. Figure 16 (a), (b), and (c) correspond to the three target magnetic field values ​​in the waiting area. From... Figure 15 and Figure 16 As can be seen, the actual values ​​are distributed on both sides of the perfect prediction line, indicating that the model fits well.

[0193] As can be seen from the simulation results above, the electromagnetic exposure assessment method and system of the DWPT system that takes human activity into account provided by the embodiments of the present invention have good effects on the selection and division of the assessment area and the prediction of magnetic field targets, and can provide a reference for subsequent dynamic system measurement methods.

[0194] In summary, the present invention provides a method and system for electromagnetic exposure assessment of a DWPT system that takes human activity into account. Based on a time-domain / complex frequency-domain model of a dual-transmitter single-receiver system analysis circuit, it derives the transfer function of the receiving side and obtains the time-domain expression of the receiving coil current. It identifies and quantifies the main factors affecting the receiving coil current during relative coil movement, obtaining the dynamic law of current change with the relative position of the coil. Operating conditions are constructed based on different combinations of the main factors. The electromagnetic environment regions at the segmented / non-segmented locations are classified and sampling lines and measurement points are laid out. The current curves of each operating condition are used as excitation input to acquire magnetic induction intensity data at each measurement point during coil movement and form a measurement point magnetic field dataset. Based on the height of the major human organs, different detection planes are set in each graded area to evaluate the maximum magnetic induction intensity in each plane and analyze the magnetic field change trend along the sampling line of the coil movement direction in each plane. The average magnetic induction intensity, maximum magnetic induction intensity, and magnetic field energy are calculated for each graded area as magnetic field targets for electromagnetic exposure assessment. The magnetic induction intensity and magnetic field target data sequences of each measuring point in the graded area under different working conditions are sequentially spliced ​​to calculate the rank correlation coefficient between measuring points in each area and the mutual information value between measuring points and each magnetic field target. Based on this, redundant measuring points are identified and eliminated to form subsets. The union of each subset is used to obtain a set of candidate measuring points. A few core measuring points are used to achieve synchronous prediction of each magnetic field target.

[0195] The present invention has significant advantages over the prior art, which are reflected in:

[0196] 1. Breaking through the limitations of static evaluation, it is the first to achieve coupled analysis of dynamic time-varying characteristics and human activity distribution.

[0197] Background technologies are only applicable to static power supply systems and cannot cope with transient current surges and severe magnetic field fluctuations caused by the relative motion of coils in dynamic wireless power supply (DWPT) systems. This invention, by establishing a time-domain expression for the receiving coil current and quantifying the influence of factors such as speed and load on the dynamic changes of the current, is the first to incorporate the time-varying characteristics of the electromagnetic field and the spatial distribution differences of human activity into a unified evaluation framework, solving the problem that traditional methods cannot accurately reflect the actual level of human exposure during operation.

[0198] 2. An innovative assessment system of "tiered zones + multi-height planes" is introduced to accurately depict differences in spatial exposure.

[0199] Considering the varying heights of major human organs (legs, heart, and brain), this invention, for the first time, proposes a tiered system of warning zones and waiting areas, building upon the traditional single-assessment zone, and establishing multi-height monitoring planes within each area. Experimental results show that the maximum magnetic field strength at the leg level of the warning zone is approximately twice that of the same plane in the waiting area, while the magnetic field strength gradually decreases with increasing height (legs → heart → brain). This tiered assessment system not only aligns with actual ground-level no-entry requirements but also provides differentiated electromagnetic exposure risk assessment criteria for people in different activity areas.

[0200] 3. Based on the rank correlation coefficient and mutual information value, redundant measurement points are eliminated and core measurement points are selected.

[0201] This invention is the first to combine rank correlation coefficient and mutual information value in the field of electromagnetic exposure assessment. By analyzing the correlation between measurement points and the correlation strength between measurement points and magnetic field targets (average magnetic flux density, maximum magnetic flux density, and magnetic field energy), redundant measurement points are eliminated to form a candidate measurement point set. Combined with the NSGA-II multi-objective optimization algorithm, only two core measurement points are selected in the warning zone and waiting zone respectively, which can achieve synchronous and accurate prediction of three magnetic field targets. Experimental verification shows that the prediction error (NRMSE) of the core measurement point combination is less than 5%, and the coefficient of determination is close to 1, which is significantly better than the traditional full measurement point deployment method, and greatly reduces the subsequent measurement cost and data processing complexity.

[0202] 4. Construct a second-order ridge regression prediction model to achieve high-precision synchronous prediction of multiple objectives.

[0203] Based on the core measurement points, this invention employs a second-order ridge regression model to establish the predictive relationship for magnetic field targets. This overcomes the limitation of traditional linear models in capturing nonlinear relationships and demonstrates excellent generalization ability on the validation set. Taking the warning zone as an example, the average coefficient of determination reaches 0.9955 for the two core measurement points and 0.9957 for the waiting zone. The scatter plots of predicted and actual values ​​are closely distributed on both sides of the perfect prediction line, proving that the model has good fitting accuracy and robustness.

[0204] 5. Provide a systematic solution for electromagnetic exposure assessment of dynamic wireless power supply systems.

[0205] This invention forms a complete technical chain from circuit modeling to operating condition construction, region division, data acquisition, feature selection, multi-objective optimization, and regression prediction, filling the methodological gap in the field of electromagnetic exposure assessment for DWPT systems. Its assessment results can provide a scientific basis for the formulation of train operation safety standards, electromagnetic protection design, and regional control strategies, possessing significant engineering application value and social benefits.

[0206] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for assessing electromagnetic exposure to a DWPT system that takes into account human activity, characterized in that, Including the following steps: S1. Determine the time-domain expression of the receiving coil current in the DWPT system, and determine the main factors affecting the receiving coil current during the relative motion of the coil based on the time-domain expression; S2. Based on different combinations of the main factors, different operating conditions are constructed, and the dynamic change curve of the receiving coil current during the motion process under each operating condition is obtained. S3. Divide the electromagnetic evaluation area into multiple levels and set up measurement points in each level. Then, use the current curve of each working condition as the excitation input for magnetic field analysis, obtain the magnetic induction intensity data of each measurement point during the movement of the receiving coil, and obtain the measurement point magnetic field dataset of the electromagnetic evaluation area under each working condition. S4. Based on the height of the main human organs, different detection planes are set in each graded area to evaluate the maximum magnetic induction intensity in each plane under different operating conditions and analyze the magnetic field change trend along the sampling line of the coil movement direction in each plane. S5. Based on the magnetic field change trend along the sampling line of the coil movement direction in each plane, the average magnetic induction intensity, maximum magnetic induction intensity and magnetic field energy of each graded area under each working condition are calculated using the magnetic field dataset of the measurement point, which serve as the magnetic field target for electromagnetic exposure assessment. S6. Sequentially splice the magnetic induction intensity and magnetic field target data sequences of each measuring point in the graded area under different working conditions, calculate the rank correlation coefficient between measuring points in each area and the mutual information value between measuring points and each magnetic field target, identify and eliminate redundant measuring points to form subsets, and take the union of each subset to obtain the candidate measuring point set. S7. Validate the candidate measurement point set on the validation set, and use the second-order ridge regression model to obtain the magnetic field target prediction formula.

2. The electromagnetic exposure assessment method for a DWPT system considering human activity according to claim 1, characterized in that, Step S1 specifically includes the following steps: S11. Establish the time-domain circuit model of the DWPT system; S12. Transform the time-domain circuit model into a complex frequency-domain circuit model; S13. Derive the transfer function of the rectifier input voltage and the receiving coil current to the induced voltage of the receiving coil based on the complex frequency domain circuit model. S14. Determine the frequency domain expression of the receiving coil current based on the transfer function; S15. Perform an inverse Laplace transform on the frequency domain expression of the receiving coil current to obtain the time domain expression of the receiving coil current. S16. Determine the main factors affecting the current of the receiving coil during the relative motion of the coils based on the time-domain expression.

3. The electromagnetic exposure assessment method for the DWPT system taking into account human activity as described in claim 2, characterized in that: In step S1, the main factors affecting the current of the receiving coil are the mutual inductance between the coils and the load on the receiving side.

4. The electromagnetic exposure assessment method for a DWPT system considering human activity according to claim 3, characterized in that, Step S2 specifically includes the following steps: S21. Fit the relationship between mutual inductance and time; S22. Based on the transfer function of the current in the receiving coil to the induced voltage on the receiving side, perform frequency response analysis on the receiving circuit, and analyze the rate of change of mutual inductance based on the relationship between mutual inductance and time. S23. Determine the conversion relationship between the moving speed of the receiving coil and the input frequency on the horizontal axis of the frequency response analysis based on frequency response analysis and the rate of change analysis of mutual inductance. S24. Based on the speed-input frequency conversion relationship, substitute the values ​​of different loads into the transfer function and plot the amplitude-frequency response curve to obtain the dynamic law of the receiving coil current changing with the coil position. S25. Select typical load and speed combinations to construct heavy-load and light-load operating conditions of the vehicle at different operating speeds, and obtain the dynamic change curve of the receiving coil current during the motion process under each operating condition.

5. The electromagnetic exposure assessment method for a DWPT system considering human activity according to claim 1, characterized in that, Step S3 specifically includes the following steps: S31. Use finite element simulation software to establish a three-dimensional electromagnetic simulation model, and divide the electromagnetic evaluation area into a warning zone close to the guide rail and a waiting zone set outside the warning zone. S32. Sampling lines are set up in the warning zone and waiting area respectively, and measurement points are set up on the sampling lines to obtain an initial set of measurement points covering the entire electromagnetic evaluation area. S33. Using the curve of the change of the current of the receiving coil during the motion under various working conditions as the excitation input, record the magnetic induction intensity data of each measuring point when the receiving coil moves to different positions, and form the magnetic field dataset of the measuring points in the warning area and the waiting area under various working conditions.

6. The electromagnetic exposure assessment method for a DWPT system considering human activity according to claim 1, characterized in that, Step S4 specifically includes the following steps: S41. Set up monitoring planes at different heights in the warning zone and waiting area, corresponding to the heights of the main human organs, and use a finite element field calculator to calculate and extract the maximum value of the magnetic induction intensity of the monitoring planes. S42. Select the sampling line along the direction of coil movement on the monitoring plane, and create a complete processing flow for the magnetic field data of the sampling line in the results report window. Analyze the trend of magnetic field change along the sampling line along the direction of coil movement in each monitoring plane under the regional hierarchical division.

7. The electromagnetic exposure assessment method for a DWPT system considering human activity according to claim 1, characterized in that, Step S6 specifically includes the following steps: S61. Sequentially splice the magnetic induction intensity data sequence and the corresponding magnetic field target sequence obtained by each measuring point in the warning area and waiting area under different working conditions, calculate the rank correlation coefficient between measuring points in each area and the mutual information value between measuring points and three magnetic field targets, and use the combination of the rank correlation coefficient matrix between measuring points and the mutual information matrix between measuring points and a single magnetic field target as the evaluation unit, and form an evaluation unit corresponding to three magnetic field targets in each area. S62. Within each evaluation unit, the measurement point pairs whose rank correlation coefficient reaches the preset extremely strong correlation threshold are identified as redundant measurement point pairs. For each redundant measurement point pair, the mutual information value between the measurement point and the corresponding magnetic field target is compared. The measurement points with small mutual information values ​​are removed from the redundant measurement point pairs, and the measurement points with large mutual information values ​​are retained to obtain the redundancy-free measurement point subset of each evaluation unit. S63. After removing redundancy from the three evaluation units of the warning zone and the waiting zone respectively, the three subsets of measurement points are sorted according to the size of the mutual information value. The top m measurement points of each are selected and then the union is taken to form the candidate measurement point set of the warning zone and the candidate measurement point set of the waiting zone. S64. Using the NSGA-II multi-objective optimization method, the magnetic field dataset of the measurement points in one of the working conditions of the warning zone and the waiting zone is used as the validation set, and the other working conditions are used as the training set. The core measurement point number combination is used as the decision variable in the candidate measurement point set of the warning zone and the candidate measurement point set of the waiting zone, and the combination length is h. Under the optimization objective, the Pareto front set of the core measurement point combination of each region is obtained, and the core measurement point combination is selected from it.

8. The electromagnetic exposure assessment method for a DWPT system considering human activity according to claim 6, characterized in that, In step S64, the optimization objective is to minimize the number of core measuring points within the combination and maximize the average coefficient of determination of the selected core measuring point combination for the three magnetic field targets.

9. The electromagnetic exposure assessment method for a DWPT system considering human activity according to claim 5, characterized in that, In step S7, the magnetic field target prediction relationship between the warning zone and the waiting zone is: ， ， in, The number of core measuring points within the selected combination. and These are the average magnetic flux density, maximum magnetic flux density, and predicted magnetic field energy values ​​obtained through fitting analysis based on the combination of core measuring points in the warning zone and waiting area. These represent the magnetic induction intensity at the i-th core measuring point in the warning zone and the waiting zone, respectively. and These are the regression coefficients of the first-order term, square term, and interaction term for the three magnetic field targets corresponding to the warning zone and waiting zone, respectively. and These are the intercept terms for the three magnetic field targets corresponding to the warning zone and the waiting zone, respectively.

10. A DWPT system for electromagnetic exposure assessment that takes into account human activity, characterized in that: The system includes an analysis unit and an evaluation unit, which are respectively used to perform steps S1 to S5 and steps S6 to S7 of the DWPT system electromagnetic exposure assessment method that takes human activity into account, as described in any one of claims 1 to 9.

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