Electromagnetic noise analysis device and method, and risk assessment device and control device equipped with the same

Abstract

This electromagnetic noise analysis device is configured so as to be able to perform noise intensity assessment by frequency band and analysis of the influence of signal encoding, thereby enabling assessment of electromagnetic interference risk while taking into account the influence of encoding such as error correction and interleaved processing during communication. To achieve the foregoing, the electromagnetic noise analysis device is configured by comprising: an electromagnetic noise intensity calculation unit that calculates the intensity of electromagnetic noise produced by a system due to driving on the basis of drive parameters for driving the system which is configured by comprising a plurality of apparatuses; a fragility calculation unit that, on the basis of the drive parameters, calculates the fragility of each apparatus with respect to an electromagnetic noise pattern produced by the system; and a risk calculation unit that, on the basis of the electromagnetic noise intensity calculated by the electromagnetic noise intensity calculation unit and the fragility of each apparatus calculated by the fragility calculation unit, calculates the risk resulting from electromagnetic noise to each apparatus.

Description

[Technical Field]

[0001] The present invention relates to an electromagnetic noise analysis device and method, and a risk determination device and control device equipped with the same. [Background technology]

[0002] Background art related to the present invention includes a technology such as that described in Patent Document 1. Patent Document 1 describes, as a means for solving the problem of providing an electromagnetic noise analysis device, control device, and control method that take into account continuous changes in the running state of a vehicle or railway, a control device that includes a vehicle running control unit that outputs vehicle drive parameters that are the running state of the vehicle based on vehicle operation information, a signal conversion unit that converts the vehicle drive parameters into noise parameters that are electrical parameters, and an electromagnetic noise analysis unit that calculates the amount of electromagnetic noise propagating through the vehicle based on the noise parameters. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2018-18293 Summary of the Invention [Problem to be solved by the invention]

[0004] Patent Document 1 describes a method for analyzing noise at the system level of vehicles and railways, in which an electromagnetic noise model of each component and chassis that make up the system is created and connected to perform noise analysis. However, the method disclosed in Patent Document 1 only determines noise intensity for each frequency band, and is unable to analyze the impact of communication coding in digital communications, meaning that it is unable to determine the risk of electromagnetic interference that takes into account the impact of coding such as error correction and interleaving during communications.

[0005] The present invention solves the problems of the prior art described above, and provides an electromagnetic noise analysis device and method that can determine noise intensity for each frequency band in digital communications and analyze the effects of communication encoding, thereby making it possible to determine the risk of electromagnetic interference taking into account the effects of encoding such as error correction and interleaving processing during communications, as well as a risk determination device and control device equipped with the same. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems, the present invention provides an electromagnetic noise analysis device comprising an electromagnetic noise intensity calculation unit that calculates the intensity of electromagnetic noise generated from a system configured to include a plurality of devices by driving the system from drive parameters that drive the system; a vulnerability calculation unit that calculates the vulnerability of each device to an electromagnetic noise pattern generated by the system from the drive parameters; and a risk calculation unit that calculates the risk for each device caused by electromagnetic noise from the electromagnetic noise intensity calculated by the electromagnetic noise intensity calculation unit and the vulnerability of each device calculated by the vulnerability calculation unit. The victim device vulnerability calculation unit includes a noise waveform calculation unit that calculates the waveform of electromagnetic noise, a transmission unit that generates a transmission signal using bit string data and transmits the generated transmission signal, a noise injection unit that adds the transmission signal transmitted from the transmission unit and the noise waveform calculated by the noise waveform calculation unit, a reception unit that decodes the signal added by the noise injection unit and converts it into decoded bit string data, and an error rate calculation unit that calculates an error rate for each device using the bit string data decoded and generated by the reception unit and the bit string data used by the transmission unit. It is composed of:

[0007] Furthermore, in order to solve the above-mentioned problems, the present invention provides a method for analyzing electromagnetic noise using an electromagnetic noise analysis device equipped with an electromagnetic noise intensity calculation unit, a vulnerability calculation unit, and a risk calculation unit, which includes inputting drive parameters for driving a system comprising a plurality of devices into the electromagnetic noise intensity calculation unit to drive the system and thereby determining the intensity of electromagnetic noise generated from the system, inputting the drive parameters into the vulnerability calculation unit to determine the vulnerability of each of the plurality of devices with respect to an electromagnetic noise pattern generated by the system, and inputting information on the electromagnetic noise intensity determined in the electromagnetic noise intensity calculation unit and information on the vulnerability of each device determined in the vulnerability calculation unit into the risk calculation unit to determine the risk for each device caused by the electromagnetic noise. The victim device vulnerability calculation unit includes a noise waveform calculation unit, a transmission unit, a noise application unit, a reception unit, and a noise waveform error rate calculation unit, in which the noise waveform calculation unit calculates the waveform of electromagnetic noise, the transmission unit creates a transmission signal using bit string data and transmits the created transmission signal, the noise application unit adds the transmission signal sent from the transmission unit to the noise waveform calculated by the noise waveform calculation unit, the reception unit decodes the added data in the noise application unit and converts it into decoded bit string data, and the noise waveform error rate calculation unit calculates the noise waveform error rate for each device using the decoded bit string data converted in the reception unit and the bit string data used in the transmission unit. Do so. [Effects of the Invention]

[0008] According to the present invention, it is possible to determine the risk of electromagnetic interference by taking into account the effects of coding such as error correction and interleaving processing during digital communication.

[0009] Furthermore, according to the present invention, it is possible to achieve both low cost and low risk for the electromagnetic noise analysis device. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a block diagram showing a configuration of an electromagnetic noise analysis device according to a first embodiment of the present invention. [Figure 2] FIG. 2 is a block diagram showing a detailed configuration of a victim device vulnerability calculation unit of the electromagnetic noise analysis device according to the first embodiment of the present invention. [Figure 3] 1 is a flowchart showing a process flow of an electromagnetic noise analysis method according to a first embodiment of the present invention. [Figure 4A] FIG. 2 is a flowchart showing a process flow of calculating vulnerability of a damaged device in the electromagnetic noise analysis method according to the first embodiment of the present invention. [Figure 4B] FIG. 3 is a diagram showing a detailed processing flow of a noise waveform calculation step in the electromagnetic noise analysis method according to the first embodiment of the present invention. [Figure 4C] FIG. 3 is a flowchart illustrating a detailed processing flow of a transmission signal generation step in the electromagnetic noise analysis method according to the first embodiment of the present invention. [Figure 4D] 3A to 3C are diagrams illustrating the concept of data corresponding to each step of generating a transmission signal in the electromagnetic noise analysis method according to the first embodiment of the present invention. [Figure 5] FIG. 10 is a block diagram showing the configuration of an electromagnetic noise analysis device according to a second embodiment of the present invention. [Figure 6] FIG. 10 is a flowchart showing a process flow of an electromagnetic noise analysis method according to a second embodiment of the present invention. [Figure 7] FIG. 10 is a block diagram showing a detailed configuration of a victim device vulnerability calculation unit of an electromagnetic noise analysis device according to a third embodiment of the present invention. [Figure 8]FIG. 11 is a flowchart showing a process flow of calculating vulnerability of a damaged device in an electromagnetic noise analysis method according to a third embodiment of the present invention. [Figure 9] FIG. 10 is a block diagram showing a detailed configuration of a victim device vulnerability calculation unit of an electromagnetic noise analysis device according to a fourth embodiment of the present invention. [Figure 10] FIG. 10 is a flowchart showing a process flow of an electromagnetic noise analysis method according to a fourth embodiment of the present invention. [Figure 11] FIG. 10 is a block diagram showing a flow of data for machine learning in an electromagnetic noise analysis method according to a fourth embodiment of the present invention. [Figure 12] FIG. 10 is a block diagram showing the configuration of a device equipped with an electromagnetic noise analysis device according to a fifth embodiment of the present invention. [Figure 13] FIG. 10 is a block diagram showing the configuration of a device equipped with an electromagnetic noise analysis device according to a sixth embodiment of the present invention. [Figure 14] FIG. 1 is a diagram illustrating a hardware configuration of an information processing device (computer). DETAILED DESCRIPTION OF THE INVENTION

[0011] A known method for analyzing noise in system-level digital communications for vehicles and railways involves creating and connecting electromagnetic noise models of each component and chassis that make up the system and performing noise analysis. The present invention addresses the problem of this method, which is that it only determines noise intensity for each frequency band and is unable to analyze the effects of communication coding in digital communications. The present invention includes an electromagnetic noise intensity calculation unit that calculates the electromagnetic noise intensity generated in each component of the system (hereinafter referred to as "damaged equipment") that may be affected by electromagnetic noise based on the driving parameters that drive the system; a vulnerability calculation unit that calculates vulnerability to noise patterns (e.g., periodicity) based on the driving parameters that drive the system; and a risk (error rate) calculation unit that calculates the risk due to electromagnetic noise based on the electromagnetic noise intensity and vulnerability. This enables the risk of electromagnetic interference to be determined taking into account the effects of coding, such as error correction and interleaving, during digital communications, thereby achieving both low cost and low risk.

[0012] In the present invention, the electromagnetic noise analysis device is configured to include a drive parameter input unit that inputs the drive state of a noise source, a first signal conversion unit that converts the drive parameters input to the drive parameter input unit into electrical noise parameters, an electromagnetic noise analysis unit that calculates the amount of electromagnetic noise propagating based on the noise parameters converted by the first signal conversion unit, a second signal conversion unit that converts the drive parameters input to the parameter input unit into noise parameters, a vulnerability calculation unit that calculates vulnerability in the noise pattern converted by the second signal conversion unit based on the noise parameters converted by the second signal conversion unit and / or the amount of electromagnetic noise calculated by the electromagnetic noise analysis unit, and a risk determination unit that calculates electromagnetic noise risk from the amount of electromagnetic noise determined by the electromagnetic noise analysis unit and the vulnerability determined by the vulnerability calculation unit.

[0013] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. In all drawings for explaining the embodiment, components having the same functions are assigned the same reference numerals, and repeated explanations thereof will be omitted as a general rule.

[0014] However, the present invention should not be construed as being limited to the description of the following embodiments. Those skilled in the art will readily understand that the specific configurations can be modified without departing from the spirit or intent of the present invention. [Example]

[0015] FIG. 1 shows the configuration of an electromagnetic noise analysis device 100 according to the first embodiment.

[0016] The electromagnetic noise analysis device 100 of this embodiment includes a drive parameter input unit 101, a first signal conversion unit 102, a noise intensity calculation unit 103, a second signal conversion unit 104, a victim device vulnerability calculation unit 105, a risk assessment unit (error rate calculation unit) 106, and a result display unit 107, and performs electromagnetic noise analysis and risk assessment by transferring data between these functional units (functional blocks).

[0017] The electromagnetic noise analysis device 100 is realized by an information processing device (computer) 1400, which includes, as its main components, a processor (CPU) 1401, a memory (RAM) 1402, a storage device 1403, an input device 1404, an output device 1405, a communication device 1406, and a bus 1407, as shown in FIG. 14. The processor 1401 functions as a functional unit (functional block) that provides a predetermined function by executing processing in accordance with a program loaded into the memory 1402. The storage device 1403 stores programs that cause the functional unit to function as well as data used by the functional unit. The storage device 1403 may be a non-volatile storage medium such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive). The input device 1404 may be a keyboard, a pointing device, or the like, and the output device 1405 may be a display, or the like. The input device 1404 and the output device 1405 may be integrated using a touch panel. The communication device 1406 enables communication with other information processing devices via a network. These are communicatively connected to each other via a bus 1407 .

[0018] The electromagnetic noise analysis device 100 does not have to be realized by a single information processing device, but may be realized by multiple information processing devices. Also, some or all of the functions of the electromagnetic noise analysis device 100 may be realized as an application on the cloud.

[0019] The functional units constituting the electromagnetic noise analysis device 100 will be described below. The drive parameter input unit 101 inputs drive parameters, such as the motor rotation speed, voltage, current, and output torque, used when driving a target device (system). The first signal conversion unit 102 converts the signal representing the operating state of the target device input to the drive parameter input unit 101 into noise parameters, which are electrical parameters related to electromagnetic noise (e.g., AC current, voltage, transfer function, etc.). The noise intensity calculation unit 103 uses the noise parameters converted by the first signal conversion unit 102 and the carrier frequency and voltage command value of the input signal calculated by the second signal conversion unit 104 to calculate the noise current, noise voltage, etc. for each victim device as information on the electromagnetic noise intensity generated in the victim device that may be affected by electromagnetic noise among the components constituting the system targeted for electromagnetic noise analysis.

[0020] Meanwhile, signals such as the motor rotation speed and output torque when driving the target device that are input to the drive parameter input unit 101 are also input to the second signal conversion unit 104, which calculates the carrier frequency, voltage command value, etc. from these input signals. These are sent to the victim device vulnerability calculation unit 105, which calculates a vulnerability coefficient. The detailed configuration of the victim device vulnerability calculation unit 105 will be described later.

[0021] The risk determination unit 106 determines the risk of each victim device using the information on the noise intensity calculated by the noise intensity calculation unit 103 and the information on the vulnerability coefficient calculated by the victim device vulnerability calculation unit 105, and sends the determination results to the result display unit 107. The result display unit 107 displays the determination results on the output device 1405.

[0022] In this embodiment, the drive parameter input unit 101, the first signal conversion unit 102, and the noise intensity calculation unit 103 constitute an electromagnetic noise intensity calculation unit that calculates the electromagnetic noise intensity occurring in a victim device that constitutes the system from the drive parameters that drive the system. Also, the drive parameter input unit 101, the second signal conversion unit 104, and the victim device vulnerability calculation unit 105 constitute a vulnerability calculation unit that calculates vulnerability to an electromagnetic noise pattern (periodicity, etc.) from the drive parameters that drive the system. Also, the risk determination unit 106 constitutes a risk calculation unit caused by electromagnetic noise from the electromagnetic noise intensity and vulnerability.

[0023] 2 shows a detailed configuration of the victim device vulnerability calculation unit 105. Each functional unit constituting the victim device vulnerability calculation unit 105 (each sub-functional unit constituting the victim device vulnerability calculation unit 105) will be described below.

[0024] The victim device vulnerability calculation unit 105 includes a standard noise creation unit 201 that creates a standard noise waveform, which is a noise signal such as AWGN (Additive White Gaussian Noise), a transmission unit 202 that creates a transmission signal from random bit string data (communication message), a first noise application unit 203 that applies the standard noise waveform created by the standard noise creation unit 201 to the transmission signal created by the transmission unit 202, a first reception unit 204 that decodes the reception signal to which the standard noise waveform has been applied by the first noise application unit 203, and a standard noise waveform error rate calculation unit 205 that compares the signal decoded by the first reception unit 204 with the random bit string data created by the transmission unit 202 and calculates the error rate when the standard noise waveform is applied.

[0025] In addition, the victim device vulnerability calculation unit 105 includes a noise waveform calculation unit 206 that calculates a noise waveform from the carrier frequency and voltage command value of the input signal calculated by the second signal conversion unit 104, a second noise application unit 207 that adds the noise waveform calculated by the noise waveform calculation unit 206 to the transmission signal created by the transmission unit 202, a second receiving unit 208 that decodes the reception signal to which the noise waveform has been applied by the second noise application unit 207, an error rate calculation unit 209 that calculates the error rate when the noise waveform is applied from the signal decoded by the second receiving unit 208, and a comparison unit 210 that compares the error rate when the standard noise waveform calculated by the standard noise waveform error rate calculation unit 205 is applied with the error rate when the noise waveform calculated by the error rate calculation unit 209 is applied.

[0026] Furthermore, the noise waveform calculation unit 206 includes a noise waveform generation unit 2061 and a noise pattern generation unit 2062, which will be described later.

[0027] Next, the procedure for performing electromagnetic noise analysis on a target device using the configuration shown in FIG. 1 will be described with reference to the flow chart of FIG.

[0028] First, drive parameters such as the motor rotation speed, current, voltage, and output torque when driving the target device are input to the drive parameter input unit 101 (S301). Next, the signals representing the operating state of the target device input to the drive parameter input unit 101 are converted into noise parameters, which are electrical parameters related to electromagnetic noise (for example, AC current, voltage, transfer function, etc.), in the first signal conversion unit 102 (S302).

[0029] Meanwhile, the signal representing the operating state of the target device input to the drive parameter input unit 101 in S301 is also received by the second signal conversion unit 104, and the second signal conversion unit 104 determines the carrier frequency, voltage command value, etc. of the signal representing the operating state of the target device (S303).

[0030] Next, from the noise parameters converted in S302 and the carrier frequency and voltage command value of the input signal calculated by the second signal conversion unit 104 in S303, the noise intensity calculation unit 103 calculates noise voltage, noise current, etc. as the noise intensity of the electromagnetic noise generated in the affected equipment that may be affected by the electromagnetic noise (S304).

[0031] Next, victim device vulnerability calculation unit 105 calculates the vulnerability of the victim device using the carrier frequency, voltage command value, etc. obtained by second signal conversion unit 104 (S305). Detailed steps for calculating the vulnerability of the victim device will be described with reference to Fig. 4A. Note that, in the step of calculating the vulnerability of the victim device in S305, information on the noise intensity calculated in S304 may also be used.

[0032] The risk determination unit 106 determines the risk of the victim device based on the noise intensity information such as noise voltage and noise current calculated by the noise intensity calculation unit 103 in S304 and the vulnerability information of the victim device calculated by the victim device vulnerability calculation unit 105 in S305 (S306), and the result of this determination is displayed by the result display unit 107 on the output device 1405 (S307).

[0033] Next, detailed steps for calculating the vulnerability of a victim device by victim device vulnerability calculation unit 105 in S305 will be described with reference to FIG. 4A.

[0034] First, in S303, a second converted signal such as a carrier frequency or a voltage command value of a signal representing the operating state of the target device (e.g., an AC current that drives a motor, the number of rotations of the motor, etc.) obtained by the second signal conversion unit 104 is input to the noise waveform calculation unit 206 (S401), and the noise waveform is calculated from this second converted signal in the noise waveform calculation unit 206 (S402).

[0035] The procedure for calculating the noise waveform in the noise waveform calculation unit 206 in S402 will be described with reference to FIG. 4B(a).

[0036] First, signals representing the operating state of the target device input from the second signal conversion unit 104, such as AC current and the carrier frequency, fundamental frequency, and voltage command value for the motor rotation speed, are input to a noise pattern generation unit 2062 composed of a PWM (Pulse Width Modulation) signal generator, etc. (S4021), and the ON / OFF timing of the power module is calculated using the PWM signal generator constituting the noise pattern generation unit 2062 (S4022).

[0037] Next, noise waveform generation unit 2061 calculates a noise waveform by analog circuit simulation to generate noise waveform 2063 having an intensity peak (noise intensity) 2065 synchronized with carrier period 2064 obtained in S4021 (S4023).

[0038] FIG. 4B(b) shows an example of a standard noise waveform 2066 that has no time fluctuation and a uniform amplitude probability as a noise waveform due to AWGN.

[0039] The noise waveform 2063 in Figure 4B(a) is synchronized with a carrier period 2064 corresponding to the operating state of the target device, and the influence of the carrier period, etc. on the bit error rate can be calculated. Similarly, the influence of the fundamental frequency, voltage command value, etc. can also be calculated.

[0040] In contrast, in the case of a standard noise waveform 2066 with no time fluctuation and uniform amplitude probability as shown in Figure 4B(b), it is not possible to calculate the influence of the carrier period 2064, etc., corresponding to the operating state of the target device, on the bit error rate. Similarly, it is not possible to calculate the influence of the fundamental frequency, voltage command value, etc.

[0041] Meanwhile, in transmitting section 202, a transmission signal is generated in accordance with the procedure shown in Fig. 4C (S420). The procedure for generating the transmission signal will be described.

[0042] First, random bit string data 4211 as shown in Fig. 4D is generated (S421), and this generated random bit string data 4211 is converted into a word string 4212 to create communication word string data 4213 (S422). The random bit string data 4211 generated in S421 is used in the error rate calculation steps S405 and S409.

[0043] Next, the created communication word sequence data 4213 is coded (using error correction code, interleaving, encryption, etc.) (S423), and the coded communication word sequence data is modulated (S424) and output (S425) as a transmission signal 4214. The transmission signal 4214 output in S425 is used in noise addition steps S403 and S407.

[0044] Next, the noise waveform calculated by the noise waveform calculation unit 206 in S402 and the transmission signal output from the transmission unit 202 in S420 are input to the second noise application unit 207, and the noise waveform is added to the transmission signal (S403) to create a received signal 4215.

[0045] Next, a decoding process is performed to decode the received signal 4215 created in S403 and convert it into decoded bit string data 4216 (S404), and the random bit string data 4211 generated in S421 is compared with the decoded bit string data 4216 converted in S404 to calculate the error rate (S405).

[0046] Similarly, based on the second converted signal input in S401, the standard noise generator 201 calculates a standard noise waveform such as AWGN (Additive White Gaussian Noise) (S406). Next, the transmission signal generated in the transmitter 202 in S420 and the standard noise waveform calculated in S406 are input to the first noise applying unit 203, which adds the standard noise waveform to the transmission signal (S407) to generate a received signal.

[0047] Next, a decoding process is performed to decode the received signal created in S407 and convert it into decoded bit string data (S408), and the random bit string data 4211 created in S421 is compared with the decoded bit string data converted in S408 to calculate the error rate (S409).

[0048] In this way, by taking into account the effects of encoding, error correction, interleaving, etc. during communication between the transmitting unit 202 and the first and second receiving units 204 and 208, it becomes possible to determine the risk of electromagnetic interference taking these into account.

[0049] Next, the vulnerability of the victim device is calculated (S410) using the error rate calculated in S405 and the error rate data calculated in S409, and the result is sent to the risk assessment step in S306.

[0050] The processing flow explained in FIG. 3 and FIGS. 4A to 4C is performed for each component constituting the system that is the target of electromagnetic noise analysis, and for each damaged device that may be damaged by electromagnetic noise.

[0051] According to this embodiment, it is possible to determine the risk of electromagnetic interference for each component (damaged device) constituting the system, taking into consideration the influence of coding such as error correction and interleaving processing during communication. [Example]

[0052] The configuration of an electromagnetic noise analysis device 500 according to a second embodiment of the present invention is shown in Fig. 5. The electromagnetic noise analysis device 500 is also realized by an information processing device (computer) 1400 as shown in Fig. 14. Among the functional units (functional blocks) constituting the electromagnetic noise analysis device 500 in this embodiment, those that are the same as the functional units constituting the electromagnetic noise analysis device 100 described in the first embodiment are given the same numbers, and detailed descriptions thereof will be omitted.

[0053] The electromagnetic noise analysis device 500 in this embodiment differs from the electromagnetic noise analysis device 100 described in embodiment 1 in that the victim device vulnerability calculation unit 105 is replaced with the configuration described in Figure 2 and is composed of a noise waveform calculation unit 206, a transmission unit 202, a noise application unit 501, a reception unit 502, and an error rate calculation unit 209.

[0054] The electromagnetic noise analysis device 500 includes a drive parameter input unit 101, a first signal conversion unit 102, a noise intensity calculation unit 103, a second signal conversion unit 104, a noise waveform calculation unit 206, a transmission unit 202, a noise application unit 501, a reception unit 502, an error rate calculation unit 209, a risk determination unit 503, and a result display unit 504, and performs electromagnetic noise analysis and risk determination by transferring data between these functional units (functional blocks).

[0055] The noise injection unit 501 and the receiving unit 502 correspond to the second noise injection unit 207 and the second receiving unit 208 in the first embodiment, respectively. That is, in the present embodiment, the victim device vulnerability calculation unit 105 in the first embodiment corresponds to a configuration including the noise waveform calculation unit 206, the transmitting unit 202, the noise injection unit 501, the receiving unit 502, and the error rate calculation unit 209.

[0056] In this embodiment, the drive parameter input unit 101, the first signal conversion unit 102, and the noise intensity calculation unit 103 constitute an electromagnetic noise intensity calculation unit that calculates the electromagnetic noise intensity generated in the affected equipment that constitutes the system from the drive parameters that drive the system.

[0057] Furthermore, the drive parameter input unit 101, second signal conversion unit 104, noise waveform calculation unit 206, transmission unit 202, noise application unit 501, reception unit 502, and error rate calculation unit 209 constitute a vulnerability calculation unit that calculates vulnerability to electromagnetic noise patterns (periodicity, etc.) from the drive parameters that drive the system, as needed. Furthermore, the risk determination unit 503 constitutes a risk calculation unit caused by electromagnetic noise from the electromagnetic noise intensity and vulnerability, as needed.

[0058] Next, the procedure for performing electromagnetic noise analysis on a target device using the configuration shown in Fig. 5 will be described with reference to the flow chart of Fig. 6. The flow chart shown in Fig. 6 corresponds to a combination of the flow charts shown in Fig. 3 and Fig. 4A in the first embodiment with steps S406 to S409 deleted.

[0059] First, drive parameters such as the motor rotation speed, current, voltage, and output torque when driving the target device are input to drive parameter input unit 101 (S601). Next, first signal conversion unit 102 converts the signal representing the operating state of the target device input to drive parameter input unit 101 into noise parameters, which are electrical parameters related to electromagnetic noise (e.g., AC current, voltage, transfer function, etc.) (S602). Next, from the converted noise parameters, noise intensity calculation unit 103 calculates noise voltage, noise current, etc. as the noise intensity of electromagnetic noise generated in a victim device that may be affected by electromagnetic noise (S603). The above processing is the same as steps S301, S302, and S304 in the first embodiment.

[0060] Meanwhile, the signal representing the operating state of the target device (e.g., the AC current driving the motor, the motor rotation speed, etc.) input to the drive parameter input unit 101 in S601 is also received by the second signal conversion unit 104, and the second signal conversion unit 104 determines the carrier frequency, voltage command value, etc. of the signal representing the operating state of the target device (S604). Next, as described in S402 of the first embodiment using (a) of FIG. 4B, the noise waveform calculation unit 206 calculates a noise waveform 2063 from the determined carrier frequency, voltage command value, etc. (S605). Here, when calculating this noise waveform 2063, the noise intensity 2065 may be determined using the information on the noise intensity calculated in S603. While FIG. 4B shows an example in which a peak value is used as the noise intensity 2065, the present invention is not limited to this, and an average value, an effective value, etc. may also be used.

[0061] Meanwhile, in the transmitting unit 202, a transmission signal is generated in the same procedure as in S420 described with reference to FIG. 4C in the first embodiment (S606).

[0062] Next, the noise waveform calculated by the noise waveform calculation unit 206 and the transmission signal generated by the transmission unit 202 in S606 are input to the noise injection unit 501, which adds the noise waveform to the transmission signal (S607) to generate a received signal (corresponding to 4215 in FIG. 4D). Next, the reception unit 502 performs a decoding process to decode the received signal (S608) and converts it into decoded bit string data (corresponding to 4216 in FIG. 4D). The error rate calculation unit 209 calculates an error rate in the decoded bit string data using random bit string data (corresponding to 4211 in FIG. 4D) (S609). If necessary, the error rate calculation unit 209 calculates vulnerability (S610) and / or the risk determination unit 503 performs risk determination (S611). The vulnerability calculation (S610) and / or risk determination (S611) are not necessarily required steps and may be omitted in some cases.

[0063] Thereafter, the result display unit 107 displays the error rate and / or vulnerability and / or risk assessment results on the output device 1405 (S612).

[0064] In this embodiment, as in the first embodiment, the electromagnetic interference risk can be determined for each component (damaged device) that makes up the system, taking into account the effects of coding such as error correction and interleaving processing during communication. [Example]

[0065] A third embodiment of the present invention will be described with reference to Figures 7 and 8. In this embodiment, a noise waveform is compared with a vulnerable noise pattern stored in a storage unit to determine vulnerability, and the configuration of the victim device vulnerability calculation unit 105 of the electromagnetic noise analysis device 100 described in Figure 2 in the first embodiment is replaced with a victim device vulnerability calculation unit 105-1 as shown in Figure 7. The rest of the configuration is the same as the configuration of the electromagnetic noise analysis device 100 described in Figure 1 in the first embodiment.

[0066] In addition, this embodiment also corresponds to the electromagnetic noise analysis device 500 shown in FIG. 5 in the second embodiment, in which the noise waveform calculation unit 206, the transmission unit 202, the noise application unit 501, the reception unit 502, and the error rate calculation unit 209 are replaced with the victim device vulnerability calculation unit 105-1 shown in FIG. 7.

[0067] That is, in this embodiment, to calculate the error rate and / or vulnerability of the victim device, a relationship between a noise waveform pattern and a bit error rate that has been obtained in advance is stored in vulnerability noise pattern model storage unit 702. Noise waveform calculation unit 701 calculates a noise waveform from the carrier frequency and voltage command value of the input signal obtained by second signal conversion unit 104 shown in Fig. 1, and compares the calculated noise waveform with the vulnerability noise patterns stored in vulnerability noise pattern model storage unit 702 to extract a vulnerability noise pattern that closely matches the calculated noise waveform. Vulnerability calculation unit 703 calculates the error rate and / or vulnerability of the victim device from information on the extracted vulnerability noise pattern.

[0068] The processing flow of this embodiment is the same as that described in FIG. 3 except for the step of calculating the vulnerability of the victim device S305 described in FIG. 4A in the processing flow described in FIG. 3 and FIG. 4A in the first embodiment.

[0069] The process flow of step S305-1 of vulnerability calculation of a victim device, which corresponds to S305 in the first embodiment, will be described with reference to FIG.

[0070] First, a second converted signal such as the carrier frequency or voltage command value of the signal representing the operating state of the target device obtained by the second signal conversion unit 104 in S303 of FIG. 3 is input (S801), and a noise waveform is calculated in the noise waveform calculation unit 206 from this second converted signal and, if necessary, the noise intensity of the target device calculated in S304 (S802).

[0071] Next, this calculated noise waveform is compared with the vulnerability noise patterns stored in the vulnerability noise pattern model storage unit 702, and a vulnerability noise pattern that closely matches the calculated noise waveform is extracted (S803).The vulnerability calculation unit 703 extracts information about the error rate and / or vulnerability of the victim device from the error rate information stored in the vulnerability noise pattern model storage unit 702 in association with the extracted vulnerability noise pattern (S804), and the risk determination step S306 described in Figure 3 is executed using this information.

[0072] Moreover, by replacing the processes from S801 to S804 in FIG. 8 with the steps from S605 to S611 in FIG. 6 described in the second embodiment, the present invention can also be applied to the second embodiment.

[0073] According to this embodiment, in addition to the effects described in the first and second embodiments, when determining the risk of electromagnetic interference, the process of adding a noise waveform to a transmission signal or decoding bit string data from the added data is no longer necessary, and information regarding the vulnerability of the victim device can be extracted from the noise waveform, so that the risk of electromagnetic interference of the victim device can be determined quickly and in real time. [Example]

[0074] As a fourth embodiment of the present invention, a configuration for creating data to be stored in the vulnerability noise pattern model storage unit 702 described in the third embodiment by machine learning will be described with reference to FIGS. 9 to 11. FIG.

[0075] 9 shows the configuration of the vulnerability noise pattern model storage unit 702-1 according to this embodiment. The vulnerability noise pattern model storage unit 702-1 includes a drive parameter input unit 901, a signal conversion unit 902, a noise waveform calculation unit 903, a machine learning model generation unit 904, a victim device vulnerability calculation unit 905, a vulnerability labeling unit 906, and a machine learning model storage unit 907, and performs the processing described below by transferring data between these functional units (functional blocks).

[0076] 1 in the first embodiment, a drive parameter input unit 901 inputs drive parameters such as the motor rotation speed, voltage, current, and output torque when driving a target device (system). A signal conversion unit 902 converts the signal representing the operating state of the target device input to this drive parameter input unit 101 into a carrier frequency, a voltage command value, and the like.

[0077] The noise waveform calculation unit 903 calculates a noise waveform from the carrier frequency and voltage command value converted by the signal conversion unit 902, and the calculated noise waveform data 1101 is input to an input layer 1102 of a neural network 1100 in the machine learning model generation unit 904 as shown in FIG. 11.

[0078] The victim device vulnerability calculation unit 905 is composed of the victim device vulnerability calculation unit 105 described in Example 1, or the noise waveform calculation unit 206, transmitting unit 202, noise injection unit 501, receiving unit 502 and error rate calculation unit 209 described in Example 2.

[0079] The vulnerability labeling unit 906 labels each error rate of the noise waveform calculated by the victim device vulnerability calculation unit 905, associates it with the noise waveform data input to the input layer, and inputs it as data 1103 to the output layer 1104 side of the neural network 1100 of the machine learning model generation unit 904 as shown in Figure 11.

[0080] The machine learning model saving unit 907 saves the machine learning model generated by the machine learning model generating unit 904 in the storage device 1403. The machine learning model saving unit 907 also compares the noise waveform calculated by the noise waveform calculating unit 701 in Fig. 7 with the machine learning models stored in the storage device 1403, extracts vulnerability noise patterns that highly match the calculated noise waveform from the machine learning models, and sends information on these extracted vulnerability noise patterns to the vulnerability calculation unit 703 to calculate the vulnerability of the victim device.

[0081] 9, the noise signal detected by the noise sensor may be directly input to the noise waveform calculation unit 903. Alternatively, the drive parameter input unit 901 and the signal conversion unit 902 may be deleted, and the noise signal detected by the noise sensor may be directly input to the noise waveform calculation unit 903.

[0082] FIG. 10 shows the flow of a process for generating a machine learning model according to this embodiment.

[0083] First, drive parameters such as the motor rotation speed, current, voltage, and output torque when driving the target device are input to a drive parameter input unit 901 (S1001). Next, a signal conversion unit 902 performs signal conversion processing to calculate a carrier frequency, a voltage command value, and the like from the signal representing the operating state of the target device input to the drive parameter input unit 901 (S1002). Next, a noise waveform calculation unit 903 calculates a noise waveform from the converted signal that has been signal converted in S1002 (S1003). The noise waveform data obtained in S1003 is input to the input layer of a machine learning model generation unit 904 (S1004).

[0084] Meanwhile, the transmitting unit 202 generates a transmission signal as described in Example 1 (S1005), which is then added to the noise waveform data obtained in S1003 in the noise waveform calculation unit 903 (S1006) to generate a received signal (corresponding to 4215 in Figure 4D).

[0085] Next, the second receiving unit 208 or the receiving unit 502 performs a decoding process to decode the received signal generated in S1006 and converts it into decoded bit string data (corresponding to 4216 in FIG. 4D) (S1007), and the error rate calculation unit 209 compares the random bit string data corresponding to the random bit string data generated in S421 in FIG. 4C described in the first embodiment with the decoded bit string data converted in S1007 to calculate an error rate (S1008).

[0086] The information on the error rate calculated in S1008 is sent to the vulnerability labeling unit 906, which calculates the vulnerability of the victim device (S1009), and performs labeling corresponding to the vulnerability data (S1010). The labeled vulnerability data is input to the output layer of the machine learning model generation unit 904 (S1011).

[0087] The machine learning model generation unit 904 associates the noise waveform data input to the input layer in S1004 with the labeled vulnerability data input to the output layer in S1011 to generate a machine learning model (S1012), and the machine learning model storage unit 907 stores the model in the storage device 1403 (S1013).

[0088] According to this embodiment, in addition to the effects described in the first and second embodiments, the vulnerability of each victim device can be determined using a machine learning model, eliminating the need for processes such as adding a noise waveform to a transmission signal and decoding bit string data from the added data as described in the first and second embodiments, and enabling the electromagnetic interference risk of each victim device to be determined quickly and in real time. [Example]

[0089] As a fifth embodiment of the present invention, the configuration of a risk determination device 1200 equipped with the electromagnetic noise analysis device 100 or 500 described in any of the first to fourth embodiments will be described with reference to Fig. 12. The risk determination device 1200 is also realized by an information processing device (computer) 1400 as shown in Fig. 14.

[0090] The risk assessment device 1200 according to this embodiment includes functional units (functional blocks) such as a receiver 1201 that receives drive parameters from a target device 1210, an electromagnetic noise analysis unit 1202 that performs electromagnetic noise analysis based on the signal received by the receiver 1201, and a display unit 1203 that outputs and displays the results of the analysis by the electromagnetic noise analysis unit 1202 on a screen. The results of the analysis by the electromagnetic noise analysis unit 1202 are sent to a control unit 1211 that controls the target device 1210.

[0091] Here, the electromagnetic noise analysis unit 1202 corresponds to the electromagnetic noise analysis device 100 or 500 described in the first to fourth embodiments, and the display unit 1203 may be shared with the result display unit 107 in FIG. 1 or the result display unit 504 in FIG.

[0092] In this embodiment, the electromagnetic noise analysis unit 1202 is configured using the electromagnetic noise analysis device 100 described in embodiment 1 or the electromagnetic noise analysis device 500 described in embodiment 2, so that the risk caused by electromagnetic noise can be determined for victim equipment that may be damaged by electromagnetic noise generated in the target equipment 1210 while the target equipment 1210 is in operation, and the result can be sent to the control unit 1211, which can then control the target equipment 1210, thereby suppressing the generation of electromagnetic noise and preventing damage caused by electromagnetic noise in the victim equipment.

[0093] Furthermore, by adopting a configuration in which the electromagnetic noise analysis unit 1202 is the electromagnetic noise analysis device 100 described in Example 1 or the electromagnetic noise analysis device 500 described in Example 2, and the victim equipment vulnerability calculation unit 105-1 as shown in Figure 7 described in Example 3, or the vulnerability noise pattern model storage unit 702-1 described in Example 4 is applied to this victim equipment vulnerability calculation unit 105-1, it is possible to determine the risk caused by electromagnetic noise in the victim equipment in real time while operating the target equipment 1210.

[0094] This allows information on the risk caused by electromagnetic noise in the affected device to be sent to the control unit 1211, and by having the control unit 1211 control the target device 1210, it is possible to suppress the generation of electromagnetic noise in real time and prevent damage caused by electromagnetic noise in the affected device. [Example]

[0095] As a sixth embodiment of the present invention, the configuration of a control device 1300 equipped with an electromagnetic noise analysis unit 1302 corresponding to the electromagnetic noise analysis device 100 or 500 equipped with the victim device vulnerability calculation unit 105-1 described in the third embodiment, or the electromagnetic noise analysis device 100 or 500 equipped with the vulnerability noise pattern model storage unit 702-1 described in the fourth embodiment will be described with reference to Fig. 13. The control device 1300 is also realized by an information processing device (computer) 1400 as shown in Fig. 14, and is equipped with not only the functions according to this embodiment but also the function of controlling a target device 1310.

[0096] The control device 1300 of this embodiment is configured to suppress the generation of electromagnetic noise from the target device 1210 (e.g., an automobile) in real time while driving the target device 1210, thereby preventing damage caused by electromagnetic noise from occurring in the affected device.

[0097] The control device 1300 according to this embodiment includes functional units (functional blocks) such as a receiving unit 1301 that receives drive parameters from a target device 1310, an electromagnetic noise analysis unit 1302 that performs electromagnetic noise analysis based on the signal received by the receiving unit 1301, and a control unit 1303 that controls the target device 1310 based on the results of the analysis by the electromagnetic noise analysis unit 1302.

[0098] When the target device 1310 is, for example, an automobile, the drive parameters of the target device 1310 received by the receiver 1301 may be caused by electromagnetic noise generated from a power unit that drives a motor.

[0099] For example, if the target device 1310 is an automobile, there are multiple devices that could be affected by electromagnetic noise generated in the power unit. In order to prevent damage caused by this electromagnetic noise while driving the automobile, the risk of electromagnetic noise must be evaluated in real time and the power unit must be controlled.

[0100] In this embodiment, the electromagnetic noise analysis unit 1302 is formed by installing a victim equipment vulnerability calculation unit 105-1 equipped with a vulnerability noise pattern model storage unit 702 in the electromagnetic noise analysis device 100 or 500 described in embodiment 3, or by providing a configuration in which the victim equipment vulnerability calculation unit 105-1 is equipped with a vulnerability noise pattern model storage unit 702-1 having a machine learning model storage unit 907 as described in embodiment 4, thereby making it possible to evaluate the risk due to electromagnetic noise in real time and control the power unit.

[0101] By using this configuration, information on the risk caused by electromagnetic noise in the affected device is sent to the control unit 1303, and the control unit 1303 controls the target device 1310, thereby suppressing the generation of electromagnetic noise in real time and preventing damage caused by electromagnetic noise in the affected device.

[0102] The invention made by the inventor has been specifically described above based on the embodiments, but it goes without saying that the present invention is not limited to the above embodiments and can be modified in various ways without departing from the spirit of the invention. For example, the above embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those having all of the described configurations. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations. [Explanation of symbols]

[0103] 100, 500...electromagnetic noise analysis device, 101, 901...driving parameter input unit, 102...first signal conversion unit, 103...noise intensity calculation unit, 104...second signal conversion unit, 105, 105-1, 905...victim device vulnerability calculation unit, 106, 503...risk determination unit, 107, 504...result display unit, 201...standard noise creation unit, 202...transmission unit, 203...first noise application unit, 204...first reception unit, 205...standard noise waveform error rate calculation unit, 206, 701...noise waveform calculation unit, 207...second noise application unit, 208...second reception unit, 2 09...Error rate calculation unit, 210...Comparator, 501...Noise application unit, 502, 1201, 1301...Receiver, 702, 702-1...Vulnerability noise pattern model storage unit, 703...Vulnerability calculation unit, 902...Signal conversion unit, 903...Noise waveform calculation unit, 904...Machine learning model generation unit, 906...Vulnerability labeling unit, 907...Machine learning model storage unit, 1200...Risk assessment device, 1202, 1302...Electromagnetic noise analysis unit, 1203...Display unit, 1210, 1310...Target equipment, 1211, 1303...Control unit, 1300...Control device

Claims

1. an electromagnetic noise intensity calculation unit that calculates the intensity of electromagnetic noise generated from a system configured to include a plurality of devices by driving the system based on driving parameters for the system; a victim device vulnerability calculation unit that calculates vulnerability of each of the plurality of devices to an electromagnetic noise pattern generated by the system based on the driving parameters; a risk calculation unit that calculates a risk for each device caused by the electromagnetic noise from the electromagnetic noise intensity calculated by the electromagnetic noise intensity calculation unit and the vulnerability of each device calculated by the affected device vulnerability calculation unit; Equipped with The victim device vulnerability calculation unit a noise waveform calculation unit that calculates the waveform of the electromagnetic noise; a transmitter that generates a transmission signal using the bit string data and transmits the generated transmission signal; a noise applying unit that adds the transmission signal transmitted from the transmitting unit to the noise waveform calculated by the noise waveform calculating unit; a receiving unit that decodes the signal added by the noise applying unit and converts it into decoded bit string data; an error rate calculation unit that calculates an error rate for each device using the bit string data generated by decoding in the receiving unit and the bit string data used in the transmitting unit; An electromagnetic noise analysis device comprising:

2. An electromagnetic noise intensity calculation unit that calculates the intensity of electromagnetic noise generated from a system configured to include a plurality of devices by driving the system based on driving parameters for the system; a victim device vulnerability calculation unit that calculates vulnerability of each of the plurality of devices to an electromagnetic noise pattern generated by the system based on the driving parameters; a risk calculation unit that calculates a risk for each device caused by the electromagnetic noise from the electromagnetic noise intensity calculated by the electromagnetic noise intensity calculation unit and the vulnerability of each device calculated by the affected device vulnerability calculation unit; Equipped with The victim device vulnerability calculation unit a noise waveform calculation unit that calculates the waveform of the electromagnetic noise; a vulnerability noise pattern model storage unit that stores a vulnerability noise pattern model for each device in association with a bit error rate; a vulnerability calculation unit that compares the waveform of the electromagnetic noise calculated by the noise waveform calculation unit with the vulnerability noise pattern model for each of the devices stored in the vulnerability noise pattern model storage unit and calculates vulnerability for each of the devices based on the bit error rate; An electromagnetic noise analysis device comprising:

3. 3. The electromagnetic noise analysis device according to claim 2, The electromagnetic noise analysis device is characterized in that the vulnerability noise pattern model storage unit creates and stores the vulnerability noise pattern model for each device through machine learning.

4. 4. The electromagnetic noise analysis device according to claim 3, The vulnerability noise pattern model storage unit includes: a drive parameter input unit for inputting the drive parameters for driving the system configured to include the plurality of devices; a signal conversion unit that converts the driving parameters input to the driving parameter input unit; a second noise waveform calculation unit that calculates a noise waveform from the signal converted by the signal conversion unit; a second victim device vulnerability calculation unit that calculates vulnerability of each of the plurality of devices using the noise waveform calculated by the second noise waveform calculation unit; a vulnerability labeling unit that labels the vulnerability of each device calculated by the second victim device vulnerability calculation unit; a machine learning data creation unit that inputs the noise waveform calculated by the second noise waveform calculation unit to an input side, and inputs data labeled with vulnerabilities for each device by the vulnerability labeling unit to an output side, and creates machine learning data; a machine learning data storage unit that stores the machine learning data created by the machine learning data creation unit; An electromagnetic noise analysis device comprising:

5. A method for analyzing electromagnetic noise using an electromagnetic noise analysis device including an electromagnetic noise intensity calculation unit, a victim device vulnerability calculation unit, and a risk calculation unit, inputting drive parameters for driving a system configured to include a plurality of devices into the electromagnetic noise intensity calculation unit, and driving the system to determine the intensity of electromagnetic noise generated from the system; inputting the driving parameters into the affected device vulnerability calculation unit to calculate vulnerability of each of the plurality of devices to an electromagnetic noise pattern generated by the system; inputting information on the strength of the electromagnetic noise calculated by the electromagnetic noise strength calculation unit and information on vulnerability of each device calculated by the affected device vulnerability calculation unit into the risk calculation unit to calculate a risk for each device caused by the electromagnetic noise; the victim device vulnerability calculation unit includes a noise waveform calculation unit, a transmission unit, a noise application unit, a reception unit, and a noise waveform error rate calculation unit; The noise waveform calculation unit calculates the waveform of the electromagnetic noise; The transmitting unit generates a transmission signal using the bit string data, and transmits the generated transmission signal; the noise applying unit adds the transmission signal transmitted from the transmitting unit to the noise waveform calculated by the noise waveform calculation unit; the receiving unit decodes the added data in the noise applying unit and converts it into decoded bit string data; The noise waveform error rate calculation unit calculates a noise waveform error rate for each device using the decoded bit string data converted in the receiving unit and the bit string data used in the transmitting unit. Electromagnetic noise analysis method characterized by:

6. A method for analyzing electromagnetic noise using an electromagnetic noise analysis device having an electromagnetic noise intensity calculation unit, a damaged device vulnerability calculation unit, and a risk calculation unit, comprising: inputting drive parameters for driving a system configured to include a plurality of devices into the electromagnetic noise intensity calculation unit, and driving the system to determine the intensity of electromagnetic noise generated from the system; inputting the driving parameters into the affected device vulnerability calculation unit to calculate vulnerability of each of the plurality of devices to an electromagnetic noise pattern generated by the system; inputting information on the strength of the electromagnetic noise calculated by the electromagnetic noise strength calculation unit and information on vulnerability of each device calculated by the affected device vulnerability calculation unit into the risk calculation unit to calculate a risk for each device caused by the electromagnetic noise; the victim device vulnerability calculation unit includes a noise waveform calculation unit, a vulnerability noise pattern model storage unit, and a vulnerability calculation unit; The noise waveform calculation unit calculates the waveform of the electromagnetic noise; storing a vulnerability noise pattern model for each device in the vulnerability noise pattern model storage unit in association with a bit error rate; The victim device vulnerability calculation unit compares the waveform of the electromagnetic noise calculated in the noise waveform calculation unit with the vulnerability noise pattern model for each device stored in the vulnerability noise pattern model storage unit, and calculates the vulnerability of each device based on the bit error rate. Electromagnetic noise analysis method characterized by:

7. 7. The electromagnetic noise analysis method according to claim 6, The vulnerability noise pattern model storage unit stores the vulnerability noise pattern model for each of the devices created by machine learning, and the victim device vulnerability calculation unit compares the electromagnetic noise waveform calculated in the noise waveform calculation unit with the vulnerability noise pattern model for each of the devices created by machine learning and stored in the vulnerability noise pattern model storage unit to calculate the vulnerability of each of the devices based on the bit error rate.

8. 8. The electromagnetic noise analysis method according to claim 7, The vulnerability noise pattern model storage unit includes: converting the driving parameters for driving the system configured to include the plurality of devices into signals; Calculating a noise waveform from the converted signal; calculating vulnerability of each of the plurality of devices constituting the system using the calculated noise waveform; Labeling the calculated vulnerabilities for each device; The calculated noise waveform is input to the input side of a neural network, and data labeled with vulnerabilities for each device is input to the output side of the neural network to create machine learning data, thereby creating the vulnerability noise pattern model for each device by machine learning. Electromagnetic noise analysis method characterized by:

9. A risk determination device that determines a risk of each of a plurality of devices due to electromagnetic noise generated from a system configured to include a plurality of devices and a control unit that controls the plurality of devices, by driving the system, an input unit for inputting drive parameters for driving the system; an electromagnetic noise analysis unit that performs an electromagnetic noise analysis using the drive parameters input to the input unit; a display unit that displays the results of the analysis performed by the electromagnetic noise analysis unit; Equipped with The electromagnetic noise analysis unit an electromagnetic noise intensity calculation unit that calculates the intensity of electromagnetic noise generated from the system by the driving based on the driving parameters; a victim device vulnerability calculation unit that calculates vulnerability of each of the devices to an electromagnetic noise pattern generated by the system based on the driving parameters; a risk calculation unit that calculates a risk for each device caused by the electromagnetic noise from the electromagnetic noise intensity calculated by the electromagnetic noise intensity calculation unit and the vulnerability of each device calculated by the affected device vulnerability calculation unit; Equipped with The victim device vulnerability calculation unit a noise waveform calculation unit that calculates the waveform of the electromagnetic noise; a transmitter that generates a transmission signal using the bit string data and transmits the generated transmission signal; a noise applying unit that adds the transmission signal transmitted from the transmitting unit to the noise waveform calculated by the noise waveform calculating unit; a receiving unit that decodes the signal added by the noise applying unit and converts it into decoded bit string data; an error rate calculation unit that calculates an error rate for each device using the bit string data generated by decoding in the receiving unit and the bit string data used in the transmitting unit; Equipped with A risk assessment device characterized in that the results of analysis by the electromagnetic noise analysis unit are fed back to the control unit of the system.

10. A control device that drives a system configured to include a plurality of devices, determines a risk of each of the plurality of devices due to electromagnetic noise generated from the system, and controls the system, an input unit for inputting drive parameters for driving the system; an electromagnetic noise analysis unit that performs an electromagnetic noise analysis using the drive parameters input to the input unit; a control unit that controls the plurality of devices of the system; Equipped with The electromagnetic noise analysis unit an electromagnetic noise intensity calculation unit that calculates the intensity of electromagnetic noise generated from the system by the driving based on the driving parameters; a victim device vulnerability calculation unit that calculates vulnerability of each of the plurality of devices to an electromagnetic noise pattern generated by the system based on the driving parameters; a risk calculation unit that calculates a risk for each device caused by the electromagnetic noise from the electromagnetic noise intensity calculated by the electromagnetic noise intensity calculation unit and the vulnerability of each device calculated by the affected device vulnerability calculation unit; Equipped with The victim device vulnerability calculation unit a noise waveform calculation unit that calculates the waveform of the electromagnetic noise; a transmitter that generates a transmission signal using the bit string data and transmits the generated transmission signal; a noise applying unit that adds the transmission signal transmitted from the transmitting unit to the noise waveform calculated by the noise waveform calculating unit; a receiving unit that decodes the signal added by the noise applying unit and converts it into decoded bit string data; an error rate calculation unit that calculates an error rate for each device using the bit string data generated by decoding in the receiving unit and the bit string data used in the transmitting unit; Equipped with The control device is characterized in that the control unit controls the plurality of devices based on the results of analysis by the electromagnetic noise analysis unit.

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