EMC countermeasure presentation system and EMC countermeasure presentation method

Abstract

To provide an EMC countermeasure presentation system capable of efficiently extracting and presenting an emission countermeasure case in another field and another model with high similarity (countermeasure effectiveness) in consideration of a device configuration of an electronic apparatus.SOLUTION: A similar configuration extraction unit 3 configured to extract, based on device configuration data 11 at the time of noise measurement, device configuration data at a short distance by configuration learning data 9 obtained by learning a graph model representing a device configuration when the noise countermeasure and reduction amount frequency data is acquired; and a countermeasure estimation unit 4 configured to estimate, from data obtained from the reduction countermeasure extraction unit and the similar configuration extraction unit, a recommended countermeasure content having a high degree of similarity of a device connection configuration and expected to have an excessive noise reduction effect.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a configuration of an EMC countermeasure presentation system that proposes electromagnetic noise reduction countermeasures to a user, and a method thereof. [Background technology]

[0002] Electromagnetic waves (emissions) emitted by electronic devices can cause electromagnetic interference that disrupts the functioning of other devices. Therefore, to ensure that electronic devices operate normally without malfunctions or breakdowns, measures to prevent excessive electromagnetic noise, in other words, EMC (Electromagnetic Compatibility) measures, are required. The content of these measures is largely based on the experience and knowledge of operators and engineers.

[0003] Background art in this technical field includes, for example, technology such as that disclosed in Patent Document 1. Patent Document 1 discloses that, in relation to a noise analysis device, when noise measurement data measured from a device to be countered is greater than a standard value, a database is searched to select countermeasure components (countermeasure methods) according to the noise, bringing the noise within the standard value, thereby obtaining optimal noise countermeasures in a short time.

[0004] Furthermore, Patent Document 2 discloses a method for reducing the burden of identifying noise causes by learning the narrowband spectral waveform of a single noise-causing component, and identifying and presenting noise-causing component data from a component data list based on inference and classification using the spectral waveform of excessive noise parts in the equipment noise measurement results as input. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 5-334457 [Patent Document 2] International Publication No. 2022 / 168332 Summary of the Invention [Problem to be solved by the invention]

[0006] Incidentally, if it were possible to extract and utilize past examples of similar emission countermeasures in other fields or on other models as electromagnetic noise reduction measures, it would lead to a reduction in the time and costs required to consider countermeasure content.

[0007] However, with current technology, it is difficult to efficiently extract and utilize examples of emission control measures from other fields or other models, and there is a possibility that time will be wasted in investigating even events that have been experienced in the past.

[0008] In the above-mentioned Patent Document 1, only the reduction effect at a predetermined position or a predetermined circuit configuration candidate can be considered, and the effect of the countermeasure is limited.

[0009] Furthermore, in the above-mentioned Patent Document 2, it is necessary to accumulate data on individual components that cause noise, and although it is possible to link noise-causing components with countermeasures, the linking method is subjective, so the accuracy of the reduction effect is low and it is extremely difficult to incorporate other cases.

[0010] Therefore, an object of the present invention is to provide an EMC countermeasure presentation system and an EMC countermeasure presentation method that can efficiently extract and present examples of emission countermeasures in other fields and other models that have a high degree of similarity (measure effectiveness) taking into account the device configuration of the electronic device. [Means for solving the problem]

[0011] In order to solve the above problems, the present invention is characterized by comprising: a required reduction amount extraction unit that converts measured noise frequency data of a device to be evaluated using a predetermined threshold as a reference and generates required reduction amount frequency data; a reduction measure extraction unit that extracts noise reduction amount frequency data of a nearby noise reduction amount using countermeasure reduction amount learning data obtained by learning noise reduction amount frequency data in a noise reduction measure database based on the required reduction amount frequency data generated by the required reduction amount extraction unit; a similar configuration extraction unit that extracts device configuration data of a nearby device using configuration learning data obtained by learning a graph model representing the device configuration when the noise reduction amount frequency data in the noise reduction measure database was obtained based on device configuration data at the time of noise measurement of the device to be evaluated; and a countermeasure estimation unit that estimates recommended countermeasure contents that have a high degree of similarity in device connection configuration and are expected to be effective in reducing excessive noise, based on the data obtained from the reduction measure extraction unit and the similar configuration extraction unit.

[0012] The present invention is also characterized by including the steps of: (a) converting measured noise frequency data of a device to be evaluated using a predetermined threshold as a reference to generate required noise reduction amount frequency data; (b) extracting noise reduction amount frequency data of a nearby noise reduction amount using countermeasure reduction amount learning data obtained by learning noise reduction amount frequency data in a noise reduction countermeasure database based on the required noise reduction amount frequency data generated in step (a); (c) extracting device configuration data of a nearby noise reduction amount using configuration learning data obtained by learning a graph model representing the device configuration at the time the noise reduction amount frequency data in the noise reduction countermeasure database was obtained based on device configuration data at the time of noise measurement of the device to be evaluated; and (d) estimating recommended countermeasure contents that have a high degree of similarity in device connection configuration and are expected to be effective in reducing excessive noise, based on the data obtained from steps (b) and (c). [Effects of the Invention]

[0013] According to the present invention, it is possible to realize an EMC countermeasure presentation system and an EMC countermeasure presentation method that can efficiently extract and present examples of emission countermeasures in other fields and other models that have a high degree of similarity (measure effectiveness) taking into account the device configuration of the electronic device.

[0014] This contributes to reducing the time and cost required to implement measures to reduce electromagnetic noise.

[0015] Problems, configurations, and effects other than those described above will become apparent from the following description of the embodiments. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a diagram showing a schematic configuration of an EMC countermeasure presentation system according to a first embodiment of the present invention. [Figure 2] 2 is a diagram schematically illustrating the function of a necessary reduction amount extraction unit 5 in FIG. 1. FIG. [Figure 3] 2 is a diagram showing an example of presentation by the presentation unit 6 of FIG. 1. FIG. [Figure 4] 2 is a diagram schematically illustrating the function of a reduction measure extraction unit 2 in FIG. 1. FIG. [Figure 5] FIG. 1 is a diagram schematically illustrating an electromagnetic connection model. [Figure 6] FIG. 10 is a diagram illustrating a model registration process in generating structured learning data. [Figure 7] FIG. 1 is a diagram illustrating a model registration process in similar structure estimation. [Figure 8] FIG. 10 is a diagram illustrating an example of a search result. [Figure 9] FIG. 2 is a diagram schematically illustrating the function of a countermeasure estimation unit 4 in FIG. [Figure 10] 2 is a flowchart showing a data construction process in the noise reduction countermeasure database 7 of FIG. 1. [Figure 11] FIG. 2 is a diagram showing an example of measured noise data 10 in FIG. [Figure 12] 11 is a flowchart showing the processing in the data standardization processing unit 13 of FIG. 10. [Figure 13]FIG. 11 is a diagram schematically illustrating the function of a difference calculation unit 14 in FIG. [Figure 14] 11 is a diagram schematically illustrating the function of a countermeasure difference detection unit 17 in FIG. 10. FIG. [Figure 15] 11 is a diagram schematically illustrating the function of a data table generating unit 15 in FIG. 10. FIG. [Figure 16] 3 is a flowchart showing an EMC countermeasure presentation method according to the first embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0017] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. In the drawings, the same components are designated by the same reference numerals, and detailed description of overlapping parts will be omitted. [Example]

[0018] First Embodiment An EMC countermeasure presentation system and an EMC countermeasure presentation method according to a first embodiment of the present invention will be described with reference to FIGS.

[0019] FIG. 1 is a diagram showing a schematic configuration of an EMC countermeasure presentation system 1 according to this embodiment.

[0020] As shown in FIG. 1, the EMC countermeasure presentation system 1 of this embodiment mainly comprises a reduction countermeasure extraction unit 2, a similar configuration extraction unit 3, a countermeasure estimation unit 4, a required reduction amount extraction unit 5, and a presentation unit 6.

[0021] Note that, although FIG. 1 shows an example in which the EMC countermeasure presentation system 1 is configured to include a noise reduction countermeasure database (DB) 7, the noise reduction countermeasure database (DB) 7 may be installed externally and connected to the EMC countermeasure presentation system 1 via wired or wireless communication.

[0022] The EMC countermeasure presentation system 1 also includes an input device (not shown) for inputting data required for processing, for example, measured noise data 10 such as measured noise frequency data of the device to be evaluated, and configuration data 11 of the device to be evaluated.

[0023] As a specific example of the hardware configuration of the EMC countermeasure presentation system 1, the reduction countermeasure extraction unit 2, similar configuration extraction unit 3, countermeasure estimation unit 4, and required reduction amount extraction unit 5 can be configured using a CPU (Central Processing Unit), the noise reduction countermeasure database (DB) 7 can be configured using a storage device (memory), and the presentation unit 6 and input device (not shown) can be configured using an I / O (input / output device).

[0024] The EMC countermeasure presentation system 1 of this embodiment converts the measured noise data of the device to be evaluated into the required noise reduction amount, compares it with the noise reduction amount in the noise reduction countermeasure database (DB) 7, and presents appropriate countermeasure contents to the user based on the similarity of the device configuration.

[0025] The necessary reduction amount extraction unit 5 converts the input measured noise data 10 such as measured noise frequency data of the device to be evaluated into a difference from a predetermined threshold value (such as a standard value) to generate necessary reduction amount frequency data.

[0026] The reduction countermeasure extraction unit 2 extracts noise countermeasure reduction amount frequency data that is close in distance based on the required reduction amount frequency data generated by the required reduction amount extraction unit 5 and using countermeasure reduction amount learning data 8 that has learned the noise countermeasure reduction amount frequency data in the noise reduction countermeasure database (DB) 7.

[0027] The similar configuration extraction unit 3 extracts device configuration data with close proximity based on the configuration data 11 of the device to be evaluated at the time of input noise measurement, using configuration learning data 9 that has learned a graph model representing the device configuration when the noise reduction amount frequency data in the noise reduction countermeasure database (DB) 7 was obtained.

[0028] The countermeasure estimation unit 4 estimates recommended countermeasures that have a high degree of similarity in device connection configuration and are expected to be effective in reducing excessive noise, based on the data obtained from the reduction countermeasure extraction unit 2 and the similar configuration extraction unit 3.

[0029] The presentation unit 6 presents to the user the recommended countermeasure content estimated by the countermeasure estimation unit 4. Examples of the presentation unit 6 include a display device and an audio output device.

[0030] The noise reduction countermeasure database (DB) 7 stores in advance the device structure of the device to be evaluated, specific countermeasure contents, and noise reduction amount.

[0031] FIG. 2 is a diagram schematically showing the function of the necessary reduction amount extractor 5 in FIG.

[0032] As shown in FIG. 2, the required reduction amount extracting unit 5 converts the measured noise data of the device under evaluation into a difference amount from a user-specified threshold value.

[0033] In this case, processing such as expressing the improvement amount in a predetermined frequency step width or interpolating between improvement amount data points may be added in accordance with the required detection accuracy.

[0034] FIG. 3 is a diagram showing an example of presentation by the presentation unit 6 of FIG.

[0035] As shown in Fig. 3, the presentation unit 6 presents the estimation results of the countermeasure estimation unit 4 to the user as a presentation unit output list. For example, adding a ground wire to component N001 is presented as a proposed countermeasure with a recommendation level of 1. Also, adding a ferrite core to the cable between components N001 and N002 is presented as a proposed countermeasure with a recommendation level of 2. Then, the expected reduction amount, expected noise data after the reflection, etc. are displayed as the expected improvement effects of these proposed countermeasures.

[0036] FIG. 4 is a diagram schematically showing the function of the reduction measure extraction unit 2 in FIG.

[0037] As shown in FIG. 4, the reduction measure extraction unit 2 compares the required reduction amount converted by the required reduction amount extraction unit 5 with the improvement amount data in the noise reduction measure database (DB) 7 by correlation processing, pattern matching, etc., and extracts (proposes) one or more combined measures.

[0038] The similar configuration extraction unit 3 looks at the similarity of the device configuration of the device to be evaluated, and is also able to extract information on other models and other devices where similar configurations (the same device) appear at the top. Because the graph structure that represents the device structure is complex, the similar configuration extraction unit 3 is best suited to using a GCN (Graph Convolution Neural-network).

[0039] FIG. 5 is a diagram schematically illustrating an electromagnetic connection model.

[0040] As shown in Figure 5, the electromagnetic connection model is constructed by imagining that components A to G, such as devices, cables, and housings that make up the device under evaluation, are connected using connection information such as connection probability and frequency dependency. This electromagnetic connection model can be expressed as a combination of an adjacency matrix A and a feature matrix X. The adjacency matrix A is a matrix that represents which nodes are connected, and the feature matrix X is a matrix that represents the feature vector of each node.

[0041] As an example of node features, component attributes may be variables that represent system-level component types, such as boards, signal devices, motors, cables, and housings. Circuit-level components, such as elements and wiring, may also be used as long as their connection relationships can be expressed on the same layer. A nested structure with system-level components may also be used. Detailed component information may include oscillation frequency in the case of noise sources, and cable length in the case of cables.

[0042] Examples of edge features include direct electrical connection being "1," and connection weighting from 0 to 1 based on past examples and the distance between conductors on CAD. Also, parasitic capacitance may be weighted with a connection that varies with frequency, such as "0" for non-connection at low frequencies and "1" for connection at high frequencies.

[0043] FIG. 6 is a diagram illustrating a model registration process in generating structured learning data.

[0044] As shown in Figure 6, in generating structural learning data, a GCN (Graph Convolution Neural-network) is applied to construct a model for graph pattern matching.

[0045] First, the CAD information is converted to generate an electromagnetic connection model, as described above with reference to FIG.

[0046] Next, weighting and averaging are performed on all nodes using n-th order convolution operations, etc., to generate a feature matrix model. At this time, the node degree can be calculated using the formula in Figure 6. The feature amount of each node is expressed by adding / averaging the feature amounts of adjacent nodes. In other words, it expresses information about how each node is connected. It can also be a calculation that includes the feature amount of edges.

[0047] Then, the category to which the graph belongs is classified based on the latent variables of all nodes, and a similarity graph mapping is generated.

[0048] Note that the feature matrix model and similarity graph mapping may be supervised learning in which the explanatory variable is the feature matrix model and the objective variable is the classification ID (field) or the countermeasure reduction amount.

[0049] FIG. 7 is a diagram illustrating a model registration process in similar structure estimation.

[0050] First, the CAD information, etc. that serves as the search query is converted to generate an electromagnetic connection model. The electromagnetic connection model is as described above with reference to Figure 5.

[0051] Next, weighting and averaging are performed on all nodes using n-th order convolution operations, etc., to generate a feature matrix model. The generation of the feature matrix model is as described above with reference to Figure 6.

[0052] Then, graph features are calculated from the latent variables of all nodes to generate a similar graph mapping. The generated similar graph mapping is used to rank the closest models based on the Euclidean distance between the existing data and the model.

[0053] FIG. 8 is a diagram showing an example of search results, showing an example for a medical device.

[0054] As shown in Fig. 8, each reference configuration, such as a similar model, a model when inverter unit test data was acquired, and a model when an automobile was evaluated, is displayed in order of score. In the example of Fig. 8, the models are displayed in order of highest score. This extracts similar structural data from an EMC perspective that cannot be searched using ordinary systems.

[0055] FIG. 9 is a diagram schematically illustrating the function of the countermeasure estimation unit 4 in FIG.

[0056] As shown in FIG. 9, the countermeasure estimation unit 4 refers to a data table, which will be described later with reference to FIG.

[0057] Furthermore, the countermeasure estimation unit 4 estimates the recommended countermeasure content (see FIG. 3) based on the extraction result from the reduction countermeasure extraction unit 2 and the extraction result from the similar configuration extraction unit 3 (see FIG. 8).

[0058] If the scores of both the reduction measure extraction section result (score 1) and the similar configuration extraction section result (score 2) are high, the countermeasure effect is certain.

[0059] Note that, since there is a possibility that the configuration may differ or that the noise frequency characteristics may change, the list may also include test data for other products and components.

[0060] Alternatively, sorting by score 1 and score 2, or setting a new score with a predetermined weighting may be performed.

[0061] FIG. 10 is a flowchart showing the data construction process in the noise reduction countermeasure database 7 of FIG.

[0062] As shown in Figure 10, in the data construction process in the noise reduction countermeasure database 7, noise data for a reference device configuration is compared with the same data after the countermeasure has been applied (after the device configuration has been changed), and the relative difference between the configuration difference and the amount of noise reduction is evaluated.

[0063] First, when noise data 12 under different measurement conditions such as voltage, current, power, electric field, and magnetic field are input, the time series data and spectrogram data of the measured noise frequency data are converted into two-dimensional frequency axis data in the data standardization processing unit 13. This allows both the time series data and spectrogram data of the measured noise frequency data to be processed as two-dimensional frequency axis data.

[0064] Next, the difference calculation unit 14 converts the amount of difference from a user-specified threshold, and stores the comparison data as an improvement amount that is independent of the measurement conditions.

[0065] On the other hand, when the design and countermeasure information 16 of the device to be evaluated is input, the countermeasure difference detection unit 17 eliminates ambiguity in the contents of the changes.

[0066] A data table is generated in a data table generation unit 15 based on the data generated in the difference calculation unit 14 and the data generated in the countermeasure difference detection unit 17, and is stored in the noise reduction countermeasure database 7. The data table will be described later with reference to FIG.

[0067] FIG. 11 is a diagram showing an example of the measured noise data 10 of FIG.

[0068] Even when it comes to "noise data," there are various formats depending on the measurement conditions (observation location / data, settings). For example, noise data specified by standards (measured with an antenna 3m away from the test object) often has few test conditions, and the amount of data is often insufficient to serve as a database for measuring reduction effects. As a result, much of the noise reduction effect data is measured at the laboratory level / on-site trial and error studies, and there is no uniqueness in the measurement locations or formats, making it difficult to compare result graphs with each other.

[0069] As the noise data, for example, two-dimensional noise data as shown in FIG. 11, as well as three-dimensional noise data such as a spectrogram, are conceivable.

[0070] The EMC countermeasure presentation system 1 of this embodiment compares noise data of a reference device configuration with the same data after the countermeasure has been applied (after the device configuration has been changed), and evaluates the configuration difference and the relative difference in the amount of noise reduction.

[0071] FIG. 12 is a flowchart showing the processing in the data standardization processing unit 13 of FIG.

[0072] As shown in FIG. 12, the data standardization processing unit 13 performs processing to convert the format of input data into frequency axis data.

[0073] First, when the noise data 12 is input, the decision unit 18 decides whether the input noise data 12 is time-series data 19 or a spectrogram 24 .

[0074] Data determined to be time series data 19 is Fourier transformed in frequency spectrum transformation 20 .

[0075] On the other hand, the data determined to be the spectrogram 24 is converted into two dimensions by a dimension reduction unit 25 using peak hold, quasi-peak, average processing, or the like.

[0076] Frequency axis data 21 is generated based on the data Fourier transformed by the frequency spectrum transform 20 and the data two-dimensionalized by the dimension reduction unit 25 .

[0077] The frequency axis data 21 is subjected to a logarithmic / antilogarithmic unification process 22 to unify the vertical and horizontal axes to a predetermined scale, and is generated as processed frequency axis data 23. The processed frequency axis data 23 includes a mixture of power [W], voltage [V], current [I], electric field [V / m], and magnetic field [A / m].

[0078] FIG. 13 is a diagram schematically showing the function of the difference calculation unit 14 in FIG.

[0079] 13, the difference calculation unit 14 calculates the difference from the measurement result in the reference configuration (must be specified) and converts it into the amount of improvement of the countermeasure. Then, the comparison data is saved as the amount of improvement that does not depend on the measurement conditions.

[0080] In this case, processing such as expressing the improvement amount in a predetermined frequency step width or interpolating between improvement amount data points may be added in accordance with the required detection accuracy.

[0081] The reference configuration may be specified by the user, or may be determined automatically from the configuration data, such as by using a simpler configuration as the reference.

[0082] FIG. 14 is a diagram schematically illustrating the function of the countermeasure difference detection unit 17 in FIG.

[0083] 14, the countermeasure difference detection unit 17 performs processing to detect differences in the structure before and after the countermeasure from data (such as a graph model) indicating the electrical connection configuration of the device to be evaluated, and then eliminates ambiguity about the changes.

[0084] The registration information may be written at a precision that matches the level of the graph model, and may also be written at the board level.

[0085] FIG. 15 is a diagram schematically showing the function of the data table generating unit 15 in FIG.

[0086] As shown in FIG. 15, the data table generating unit 15 generates and registers a data table by comparing the content of the countermeasure with the amount of noise reduction achieved by the countermeasure.

[0087] The base configuration is registered with reference to a reference structural graph model. The countermeasure difference data is registered with information that enables the base configuration and the countermeasure configuration to be reconstructed from the base configuration and the data. The countermeasure improvement amount data is registered with data or an image of the improvement amount.

[0088] FIG. 16 is a flowchart showing an EMC countermeasure presentation method according to this embodiment.

[0089] When the process starts in the EMC countermeasure presentation system 1, first, in step S1, model conversion of the target configuration data is performed.

[0090] Next, in step S2, pattern matching of the configuration learning data is performed.

[0091] Next, in step S3, the extraction results are stored.

[0092] In parallel with the processes of steps S1 to S3, the measured noise data is converted into a required reduction amount in step S8, and then, in step S9, matching of the countermeasure reduction amount data is performed.

[0093] Next, in step S4, a recommendation level is calculated based on the pattern matching result of the configuration learning data in step S2 and the matching result of the countermeasure reduction amount data in step S9.

[0094] Next, in step S5, the content of recommended measures based on the recommendation level calculated in step S4 is presented to the user.

[0095] Next, in step S6, the remeasurement results after the recommended measures are reflected are judged. If the result is pass, the process ends. On the other hand, if the result is fail, the process returns to step S8, and the processes from step S8 onwards are repeated, and in step S7, it is judged whether the changes are significant.

[0096] In step S7, if it is determined that the change is significant (Yes), the process returns to step S1 and repeats the process from step S1 onwards. If it is determined that the change is not significant (No), the process returns to step S3 and repeats the process from step S3 onwards. If the change is not significant enough to change the configuration, the previous content can be reused to shorten the processing time.

[0097] As described above, the EMC countermeasure presentation system 1 of this embodiment includes: a required reduction amount extraction unit 5 that converts measured noise frequency data of a device to be evaluated using a predetermined threshold as a reference and generates required reduction amount frequency data; a noise reduction measure extraction unit 2 that extracts noise reduction amount frequency data of a nearby device using countermeasure reduction amount learning data obtained by learning noise reduction amount frequency data in a noise reduction measure database (DB) 7 based on the required reduction amount frequency data generated by the required reduction amount extraction unit 5; a similar configuration extraction unit 3 that extracts device configuration data of a nearby device using configuration learning data obtained by learning a graph model representing the device configuration at the time the noise reduction amount frequency data in the noise reduction measure database (DB) 7 was obtained based on device configuration data at the time of noise measurement of the device to be evaluated; and a countermeasure estimation unit 4 that estimates recommended countermeasure contents that have a high degree of similarity in device connection configuration and are expected to be effective in reducing excessive noise, based on the data obtained from the reduction measure extraction unit 2 and the similar configuration extraction unit 3.

[0098] This makes it possible to utilize countermeasures for other devices and models, reducing the time and cost required to consider emissions countermeasures.

[0099] Furthermore, by utilizing electromagnetic connection graph data that shows electromagnetic connection relationships, it is possible to eliminate the personal nature of the description of countermeasure content, and also to quantitatively detect the degree of impact of changes to countermeasures within the design system.

[0100] The present invention is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations. [Explanation of symbols]

[0101] 1...EMC countermeasure presentation system 2...Reduction measures extraction section 3…Similar configuration extraction part 4. Countermeasures Estimation Department 5...Required reduction amount extraction section 6…Presentation part 7...Noise reduction countermeasure database (DB) 8…Countermeasure reduction amount learning data 9...Configuration learning data 10...Measurement noise data 11...Configuration data 12...Noise data 13...Data commonization processing section 14...Difference calculation section 15...Data table generation section 16. Design and countermeasure information 17...Countermeasure difference detection unit 18…Judgment section 19...Time series data 20...Frequency spectrum transformation 21...Frequency axis data 22...Logarithmic and antilogarithmic unification 23...Processed frequency axis data 24...Spectrogram 25… Dimensional Reduction Department.

Claims

1. a required noise reduction amount extracting unit that converts the measured noise frequency data of the evaluation target device using a predetermined threshold as a reference and generates required noise reduction amount frequency data; a noise reduction countermeasure extraction unit that extracts noise reduction countermeasure frequency data in a short distance using countermeasure reduction amount learning data obtained by learning noise reduction countermeasure frequency data in a noise reduction countermeasure database based on the necessary reduction amount frequency data generated by the necessary reduction amount extraction unit; a similar configuration extraction unit that extracts device configuration data that is close in distance using configuration learning data that has learned a graph model representing the device configuration when the noise reduction amount frequency data in the noise reduction countermeasure database was acquired based on device configuration data at the time of noise measurement of the evaluation target device; a countermeasure estimation unit that estimates recommended countermeasures that have a high degree of similarity in device connection configuration and are expected to have an excessive noise reduction effect, based on the data obtained from the reduction countermeasure extraction unit and the similar configuration extraction unit; An EMC countermeasure presentation system comprising:

2. 2. The EMC countermeasure presentation system according to claim 1, The EMC countermeasure presentation system further comprises a data standardization processing unit that converts the time series data and spectrogram data of the measured noise frequency data into two-dimensional frequency axis data in the data registration process of the noise reduction countermeasure database.

3. 2. The EMC countermeasure presentation system according to claim 1, The EMC countermeasure presentation system is characterized in that the countermeasure estimation unit performs calculations using a GCN (Graph Convolution Neural Network) that convolves and integrates features on a connection model to express them as features of each component.

4. The EMC countermeasures presentation method includes the following steps: (a) converting measured noise frequency data of the device to be evaluated using a predetermined threshold as a reference to generate required reduction amount frequency data; (b) extracting noise countermeasure reduction amount frequency data in a short distance using countermeasure reduction amount learning data obtained by learning the noise countermeasure reduction amount frequency data in the noise reduction countermeasure database based on the necessary reduction amount frequency data generated in the (a) step; (c) extracting device configuration data with close proximity using configuration learning data obtained by learning a graph model representing the device configuration when the noise countermeasure reduction amount frequency data in the noise reduction countermeasure database was acquired based on the device configuration data at the time of noise measurement of the evaluation target device; (d) A step of estimating, from the data obtained from the steps (b) and (c), recommended measures that have a high degree of similarity in the device connection configuration and are expected to be effective in reducing excessive noise.

5. 5. The EMC countermeasure presentation method according to claim 4, (e) converting the time series data and spectrogram data of the measured noise frequency data into two-dimensional frequency axis data in the data registration process of the noise countermeasure reduction amount frequency data; The EMC countermeasure presentation method further comprises:

6. 5. The EMC countermeasure presentation method according to claim 4, The method for presenting EMC countermeasures is characterized in that in the step (d), calculations are performed using a GCN (Graph Convolution Neural-network) that convolves and integrates the features on the connection model to express them as features of each component.

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