JP7734832B2 - Electromagnetic noise analysis device and method, and risk assessment device and control device equipped with the same - Google Patents

Description

The present invention relates to an electromagnetic noise analysis device and method, as well as a risk assessment device and control device equipped with the same.

The background art related to the present invention is the technology described in Patent Document 1. Patent Document 1 describes a control device that solves 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 vehicles and railways. The control device includes a vehicle running control unit that outputs vehicle drive parameters, which represent the running state of the vehicle, based on vehicle operating information, a signal conversion unit that converts the vehicle drive parameters into noise parameters, which 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.

JP 2018-18293 A

Patent Document 1 describes a method for analyzing noise at the system level in 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. This means 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.

The present invention solves the problems of the prior art described above by providing an electromagnetic noise analysis device and method that can determine noise intensity for each frequency band in digital communications and analyze the impact of communication encoding, thereby enabling the assessment of electromagnetic interference risk taking into account the impact of encoding such as error correction and interleaving processing during communications, as well as a risk assessment device and control device equipped with the same.

In order to solve the above-mentioned problems, in the present invention, an electromagnetic noise analysis device is provided with an electromagnetic noise intensity calculation unit that calculates the intensity of electromagnetic noise generated from a system configured with a plurality of devices by driving the system from driving 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 driving 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, and the victim device vulnerability calculation unit is provided with a noise waveform calculation unit that calculates the waveform of the electromagnetic noise, a transmission unit that creates a transmission signal using bit string data and transmits the created transmission signal, a noise injection unit that adds the transmission signal transmitted from the transmission unit to the noise waveform calculated by the noise waveform calculation unit, a receiving 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 the error rate for each device using the bit string data decoded and generated by the receiving unit and the bit string data used by the transmission unit .

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 having 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 determine 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 against 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 vulnerability of each device caused by the electromagnetic noise. The victim device vulnerability calculation unit calculates the risk for each device caused by the affected device vulnerability, and is equipped with 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 the 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 and found by the noise waveform calculation unit, the reception unit decodes the data added 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 .

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.

In addition, the present invention makes it possible to achieve both low cost and low risk for electromagnetic noise analysis devices.

1 is a block diagram showing a configuration of an electromagnetic noise analysis device according to a first embodiment of the present invention; 3 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; FIG. FIG. 2 is a flowchart showing a process flow of the electromagnetic noise analysis method according to the first embodiment of the present invention. FIG. 1 is a flowchart showing a process flow for calculating vulnerability of a damaged device in the electromagnetic noise analysis method according to the first embodiment of the present invention. 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. 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. 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. FIG. 10 is a block diagram showing the configuration of an electromagnetic noise analysis device according to a second embodiment of the present invention. FIG. 10 is a flowchart showing a process flow of an electromagnetic noise analysis method according to a second embodiment of the present invention. FIG. 11 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. FIG. 10 is a flowchart showing the process flow of victim device vulnerability calculation in the electromagnetic noise analysis method according to the third embodiment of the present invention. 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. FIG. 10 is a flowchart showing a process flow of an electromagnetic noise analysis method according to a fourth embodiment of the present invention. 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. 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. 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. FIG. 1 is a diagram illustrating a hardware configuration of an information processing device (computer).

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 to perform noise analysis. The present invention addresses the problem with this method, which is that it only determines noise intensity for each frequency band and is unable to analyze the impact 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 (such as periodicity) based on the driving parameters that drive the system; and a risk (error rate) calculation unit that calculates the risk of electromagnetic noise based on the electromagnetic noise intensity and vulnerability. This enables the risk of electromagnetic interference to be determined taking into account the impact of coding, such as error correction and interleaving, during digital communications, thereby achieving both low cost and low risk.

In the present invention, an 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 the vulnerability of 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 assessment unit that calculates the electromagnetic noise risk from the amount of electromagnetic noise calculated by the electromagnetic noise analysis unit and the vulnerability calculated by the vulnerability calculation unit.

The following describes in detail an embodiment of the present invention with reference to the drawings. In all drawings used to explain this embodiment, parts with the same function are designated by the same reference numerals, and repeated explanations will generally be omitted.

However, the present invention should not be construed as being limited to the description of the embodiments shown below. Those skilled in the art will readily understand that the specific configuration can be modified without departing from the spirit or intent of the present invention.

Figure 1 shows the configuration of an electromagnetic noise analysis device 100 according to the first embodiment.

The electromagnetic noise analysis device 100 of this embodiment comprises 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).

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, memory (RAM) 1402, storage device 1403, input device 1404, output device 1405, communication device 1406, and 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 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 SSD (Solid State Drive). The input device 1404 may be a keyboard, pointing device, etc., and the output device 1405 may be a display, etc. The input device 1404 and output device 1405 may be integrated using a touch panel. The communication device 1406 enables communication with other information processing devices via a network, and these are connected to each other via a bus 1407 so that they can communicate with each other.

Note that 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. Furthermore, some or all of the functions of the electromagnetic noise analysis device 100 may be realized as an application on the cloud.

The functional units constituting the electromagnetic noise analysis device 100 will now be described. The drive parameter input unit 101 inputs drive parameters, such as the motor rotation speed, voltage, current, and output torque, used when driving the 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 and noise voltage for each affected device as information on the electromagnetic noise intensity generated in the affected device that may be affected by electromagnetic noise among the components constituting the system being subjected to electromagnetic noise analysis.

Meanwhile, signals such as the motor rotation speed and output torque when driving the target device 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.

The risk assessment unit 106 assesses the risk for each victim device using the noise intensity information calculated by the noise intensity calculation unit 103 and the vulnerability coefficient information calculated by the victim device vulnerability calculation unit 105, and sends the assessment results to the result display unit 107. The result display unit 107 displays the assessment results on the output device 1405.

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 victim equipment 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 equipment vulnerability calculation unit 105 constitute a vulnerability calculation unit that calculates vulnerability to electromagnetic noise patterns (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.

Figure 2 shows the detailed configuration of the victim device vulnerability calculation unit 105. We will now explain each functional unit that makes up the victim device vulnerability calculation unit 105 (each sub-functional unit that makes up the victim device vulnerability calculation unit 105).

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 received 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.

The victim device vulnerability calculation unit 105 also 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 received 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.

Furthermore, the noise waveform calculation unit 206 is equipped with a noise waveform generation unit 2061 and a noise pattern generation unit 2062, which will be described later.

Next, the procedure for performing electromagnetic noise analysis on the target device using the configuration shown in Figure 1 will be explained using the flow chart in Figure 3.

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 signal representing the operating state of the target device input to the drive parameter input unit 101 is converted into noise parameters, which are electrical parameters related to electromagnetic noise (e.g., AC current, voltage, transfer function, etc.), in the first signal conversion unit 102 (S302).

Meanwhile, the signal representing the operating state of the target equipment 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 equipment (S303).

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).

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

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

Next, the detailed steps for calculating the vulnerability of the victim device by the victim device vulnerability calculation unit 105 in S305 will be explained using Figure 4A.

First, in S303, a second converted signal such as the carrier frequency or voltage command value of a signal representing the operating state of the target device (e.g., the AC current driving the motor, the motor rotation speed, 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).

The procedure for calculating the noise waveform in the noise waveform calculation unit 206 in S402 is explained using (a) of Figure 4B.

First, signals representing the operating state of the target device input from the second signal conversion unit 104, such as AC current, carrier frequency, fundamental frequency, and voltage command value for the motor rotation speed, are input to the noise pattern generation unit 2062, which is 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).

Next, the noise waveform generation unit 2061 calculates the noise waveform using analog circuit simulation and generates a noise waveform 2063 having an intensity peak (noise intensity) 2065 synchronized with the carrier period 2064 obtained in S4021 (S4023).

Figure 4B (b) shows an example of a standard noise waveform 2066 caused by AWGN, which has no time fluctuation and uniform amplitude probability.

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

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

Meanwhile, the transmitting unit 202 creates a transmission signal (S420) using the procedure shown in Figure 4C. The procedure for creating a transmission signal will now be described.

First, random bit string data 4211 as shown in Figure 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.

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

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 the received signal 4215.

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).

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, in S420, the transmission signal generated by the transmitter 202 and the standard noise waveform calculated in S406 are input to the first noise application unit 203, which adds the standard noise waveform to the transmission signal (S407) to generate a received signal.

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 generated in S421 is compared with the decoded bit string data converted in S408 to calculate the error rate (S409).

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

Next, the vulnerability of the affected device is calculated using the error rate calculated in S405 and the error rate data calculated in S409 (S410), and the result is sent to the risk assessment process in S306.

The processing flow described in Figure 3 and Figures 4A to 4C is performed for each component that makes up the system being analyzed for electromagnetic noise, and for each affected device that may be affected by electromagnetic noise.

According to this embodiment, the electromagnetic interference risk can be determined for each component (affected device) that makes up the system, taking into account the effects of coding such as error correction and interleaving processing during communication.

The configuration of an electromagnetic noise analysis device 500 according to a second embodiment of the present invention is shown in Figure 5. The electromagnetic noise analysis device 500 is also realized by an information processing device (computer) 1400 as shown in Figure 14. Of 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 Example 1 are given the same numbers, and detailed descriptions thereof will be omitted.

The electromagnetic noise analysis device 500 in this embodiment differs from the electromagnetic noise analysis device 100 described in Example 1 in that the victim equipment vulnerability calculation unit 105 is replaced with the configuration described in Figure 2 and is configured with 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.

The electromagnetic noise analysis device 500 comprises 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 assessment unit 503, and a result display unit 504, and performs electromagnetic noise analysis and risk assessment by transferring data between these functional units (functional blocks).

The noise application unit 501 and the receiving unit 502 correspond to the second noise application unit 207 and the second receiving unit 208, respectively, in Example 1. That is, in this example, the victim device vulnerability calculation unit 105 in Example 1 corresponds to a configuration consisting of the noise waveform calculation unit 206, the transmitting unit 202, the noise application unit 501, the receiving unit 502, and the error rate calculation unit 209.

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.

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 also 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 also constitutes a risk calculation unit caused by electromagnetic noise from the electromagnetic noise intensity and vulnerability, as needed.

Next, the procedure for performing electromagnetic noise analysis on a target device using the configuration shown in Figure 5 will be explained using the flow diagram in Figure 6. The flow diagram in Figure 6 corresponds to a combination of the flow diagrams shown in Figures 3 and 4A in Example 1, with steps S406 to S409 removed.

First, drive parameters, such as the motor rotation speed, current, voltage, and output torque, used to drive the target device are input to the drive parameter input unit 101 (S601). Next, the first signal conversion unit 102 converts the signals 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.) (S602). Next, the noise intensity calculation unit 103 calculates noise voltage, noise current, etc., as the noise intensity of electromagnetic noise generated in the affected device that may be affected by the electromagnetic noise from the converted noise parameters (S603). The above processing is the same as steps S301, S302, and S304 in Example 1.

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, which then determines the carrier frequency, voltage command value, etc. of the signal representing the operating state of the target device (S604). Next, from the determined carrier frequency, voltage command value, etc., the noise waveform calculation unit 206 calculates the noise waveform 2063 (S605), as described in S402 in Example 1 using (a) of Figure 4B. Here, when calculating this noise waveform 2063, the noise intensity 2065 may be determined using the noise intensity information calculated in S603. While Figure 4B shows an example in which a peak value is used as the noise intensity 2065, this is not limited to this, and an average value, an effective value, etc. may also be used.

Meanwhile, in the transmitting unit 202, a transmission signal is created using a procedure similar to S420 described using Figure 4C in Example 1 (S606).

Next, the noise waveform calculated by the noise waveform calculation unit 206 and the transmission signal created 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 create a received signal (corresponding to 4215 in Figure 4D). Next, the reception unit 502 performs a decoding process to decode this received signal (S608) and converts it into decoded bit string data (corresponding to 4216 in Figure 4D). The error rate calculation unit 209 calculates the error rate in the decoded bit string data using the random bit string data (corresponding to 4211 in Figure 4D) (S609). If necessary, the error rate calculation unit 209 calculates vulnerability (S610) and/or the risk assessment unit 503 performs risk assessment (S611). The vulnerability calculation (S610) and/or risk assessment (S611) are not necessarily required steps and may be omitted in some cases.

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

In this embodiment, as in embodiment 1, the electromagnetic interference risk can be determined for each component (affected device) that makes up the system, taking into account the effects of coding such as error correction and interleaving processing during communication.

A third embodiment of the present invention will be described using Figures 7 and 8. In this embodiment, a noise waveform is compared with a vulnerability 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 Example 1 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 Example 1.

In addition, this embodiment also corresponds to the electromagnetic noise analysis device 500 shown in Figure 5 in Example 2, in which the noise waveform calculation unit 206, transmission unit 202, noise application unit 501, reception unit 502, and error rate calculation unit 209 are replaced with the victim device vulnerability calculation unit 105-1 shown in Figure 7.

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

The processing flow for this embodiment differs from the processing flow described in Example 1 using Figures 3 and 4A only in the step of calculating the vulnerability of the victim device S305 described in Figure 4A; the other steps are the same as those described in Figure 3.

Using Figure 8, the processing flow of step S305-1 of vulnerability calculation of the victim device, which corresponds to S305 in Example 1, is explained.

First, in S303 of Figure 3, a second converted signal such as the carrier frequency or voltage command value of a signal representing the operating state of the target equipment obtained by the second signal conversion unit 104 is input (S801), and the 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 equipment calculated in S304 (S802).

Next, this calculated noise waveform is compared with the vulnerability noise patterns stored in the vulnerability noise pattern model storage unit 702, and vulnerability noise patterns that closely match the calculated noise waveform are 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 this information is used to execute the risk determination process S306 described in Figure 3.

In addition, by replacing the processes of S801 to S804 in Figure 8 with the steps of S605 to S611 in Figure 6 described in Example 2, it can also be applied to Example 2.

According to this embodiment, in addition to the effects described in embodiments 1 and 2, when assessing the risk of electromagnetic interference, the process of adding a noise waveform to a transmitted 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, making it possible to assess the risk of electromagnetic interference of the victim device quickly and in real time.

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 through machine learning will be described using Figures 9 to 11.

Figure 9 shows the configuration of the vulnerability noise pattern model storage unit 702-1 in 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).

The drive parameter input unit 901, like the drive parameter input unit 101 described in Figure 1 of Example 1, inputs drive parameters such as the motor rotation speed, voltage, current, and output torque when driving the target device (system). The 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, voltage command value, etc.

The noise waveform calculation unit 903 calculates the 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 the input layer 1102 of the neural network 1100 in the machine learning model generation unit 904 as shown in Figure 11.

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.

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

The machine learning model storage unit 907 stores the machine learning model generated by the machine learning model generation unit 904 in the storage device 1403. The machine learning model storage unit 907 also compares the noise waveform calculated by the noise waveform calculation unit 701 in Figure 7 with the machine learning model stored in the storage device 1403, extracts vulnerability noise patterns from the machine learning model that have a high degree of match with the calculated noise waveform, and sends information on these extracted vulnerability noise patterns to the vulnerability calculation unit 703 to calculate the vulnerability of the victim device.

In the configuration of the vulnerability noise pattern model storage unit 702-1 shown in Figure 9, the noise signal detected by the noise sensor may be directly input to the noise waveform calculation unit 903. Also, the drive parameter input unit 901 and signal conversion unit 902 may be eliminated, and the noise signal detected by the noise sensor may be directly input to the noise waveform calculation unit 903.

Figure 10 shows the process flow for generating a machine learning model in this embodiment.

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 901 (S1001). Next, the signal conversion unit 902 performs signal conversion processing to calculate the carrier frequency, voltage command value, etc. from the signal representing the operating state of the target device input to the drive parameter input unit 901 (S1002). Next, the noise waveform calculation unit 903 calculates a noise waveform from the converted signal converted in S1002 (S1003). The noise waveform data obtained in S1003 is input to the input layer of the machine learning model generation unit 904 (S1004).

Meanwhile, the transmitting unit 202 creates a transmission signal as described in Example 1 (S1005), and the noise waveform calculation unit 903 adds this to the noise waveform data obtained in S1003 (S1006) to create a received signal (corresponding to 4215 in Figure 4D).

Next, the second receiving unit 208 or the receiving unit 502 performs a decoding process to decode the received signal created in S1006 and converts it into decoded bit string data (corresponding to 4216 in Figure 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 Figure 4C described in Example 1 with the decoded bit string data converted in S1007 to calculate the error rate (S1008).

The error rate information obtained in S1008 is sent to the vulnerability labeling unit 906, which determines the vulnerabilities of the affected device (S1009), and labels the vulnerability data accordingly (S1010). The labeled vulnerability data is input to the output layer of the machine learning model generation unit 904 (S1011).

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).

According to this embodiment, in addition to the effects described in Examples 1 and 2, 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 Examples 1 and 2, and enabling the electromagnetic interference risk of each victim device to be determined quickly and in real time.

As a fifth embodiment of the present invention, the configuration of a risk assessment device 1200 equipped with the electromagnetic noise analysis device 100 or 500 described in embodiments 1 to 4 will be described using Figure 12. The risk assessment device 1200 is also realized by an information processing device (computer) 1400 as shown in Figure 14.

The risk assessment device 1200 according to this embodiment includes functional units (functional blocks) such as a receiving unit 1201 that receives drive parameters from the target device 1210, an electromagnetic noise analysis unit 1202 that performs electromagnetic noise analysis based on the signal received by the receiving unit 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.

Here, the electromagnetic noise analysis unit 1202 corresponds to the electromagnetic noise analysis device 100 or 500 described in Examples 1 to 4, and the display unit 1203 may be shared with the result display unit 107 in Figure 1 or the result display unit 504 in Figure 5.

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

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 as 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.

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

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 embodiment 3, or the electromagnetic noise analysis device 100 or 500 equipped with the vulnerability noise pattern model storage unit 702-1 described in embodiment 4, will be described using Figure 13. The control device 1300 is also realized by an information processing device (computer) 1400 as shown in Figure 14, but has the function of controlling the target device 1310 in addition to the functions related to this embodiment.

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 in the affected device.

The control device 1300 in this embodiment has functional units (functional blocks) such as a receiving unit 1301 that receives driving parameters from the 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.

If the target device 1310 is, for example, an automobile, the driving parameters of the target device 1310 received by the receiving unit 1301 may be caused by electromagnetic noise generated from the power unit that drives the motor.

If the target device 1310 is, for example, an automobile, there are multiple pieces of equipment 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.

In this embodiment, the electromagnetic noise analysis unit 1302 is formed by incorporating 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 Example 3, or by incorporating a configuration in which a vulnerability noise pattern model storage unit 702-1 having a machine learning model storage unit 907 as described in Example 4 is applied to this victim equipment vulnerability calculation unit 105-1, thereby making it possible to evaluate the risk due to electromagnetic noise in real time and control the power unit.

By configuring in this manner, 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.

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

100, 500...Electromagnetic noise analysis device, 101, 901...Drive 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 assessment 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 (10)

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 ,
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: 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;
an electromagnetic noise analysis device comprising 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 the vulnerability of each of the devices based on the bit error rate. 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. 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;
An electromagnetic noise analysis device comprising a machine learning data storage unit that stores the machine learning data created by the machine learning data creation unit. 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: 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 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;
An electromagnetic noise analysis method characterized in that the victim equipment 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 stored in the vulnerability noise pattern model storage unit to calculate the vulnerability of each of the devices based on the bit error rate. 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. 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;
An electromagnetic noise analysis method characterized by inputting the calculated noise waveform into the input side of a neural network, inputting data labeled with vulnerabilities for each device into the output side of the neural network, and creating machine learning data, thereby creating the vulnerability noise pattern model for each device through machine learning. 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 by the electromagnetic noise analysis unit;
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. A control device that drives a system including 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,
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.