CN107450510A - A kind of signal resolution method using diagnostic signal fast positioning CAN signal - Google Patents

Abstract

The present invention proposes a signal analysis method for quickly locating CAN signals using diagnostic signals, comprising the following steps: connecting the diagnostic instrument to the diagnostic interface of the benchmarking vehicle, making a diagnostic request to the benchmarking vehicle, and recording the diagnostic signal; using CAN signal acquisition hardware and Connect the communication CAN line to read the communication CAN data, use the CAN signal acquisition software to display the data on the communication CAN, record the CAN data; convert the recorded diagnostic signal and CAN data format; determine the effective identification area of all CAN signals in the diagnostic signal and CAN data , calculate the similarity of the effective identification area; output the similarity of the effective identification area and the CAN signal in matrix form, verify the CAN signal in the matrix according to the order of the similarity of the effective identification area from high to low, and complete the CAN signal position. The method is easy to operate, high in operation speed and widely applicable.

Description

A signal analysis method for quickly locating CAN signals by using diagnostic signals

technical field

The invention relates to the field of electric vehicle performance testing, in particular to a signal analysis method for quickly locating CAN signals by using diagnostic signals.

Background technique

With the vigorous development of the new energy electric vehicle industry, accurate testing and evaluation of international advanced new energy vehicles has become one of the main tasks of the current automotive industry. Accurate and reliable signal acquisition can provide a basic guarantee for high-quality testing and evaluation of various new energy vehicles. Compared with signals obtained by other means, communication CAN signals have a series of advantages such as high reliability, high frequency, and better real-time performance. And with the continuous introduction of various advanced new energy vehicles, higher requirements are put forward for the timeliness of test evaluation. Therefore, it is of great significance for the testing and evaluation of new energy vehicles to obtain as many key signals as possible from the communication CAN and shorten the signal analysis cycle.

At present, general CAN signal analysis needs to observe the change law of the message data on the Trace interface, find out the data area that conforms to the change law of the signal to be analyzed, verify that the signal in the data area is the desired signal, and then calibrate its coefficient and offset. The above method has high technical requirements for the operator, and the operator should have good data sensitivity. Different operators take different times to decipher the signal. With the complexity of CAN in new energy vehicles, the CAN data capacity on the bus is relatively large, which makes the data area search work cumbersome. And because the Trace interface displays hexadecimal or decimal data, while the actual message signals are arranged in binary, it may be difficult to observe the change characteristics of individual signals. Critical signals that are difficult to parse have to be obtained by means of relatively easy-to-implement diagnostic requests. The frequency of the diagnostic request signal is not only low, but the diagnostic message it sends may interfere with the normal communication of the bus to a certain extent, and even affect the normal operation of the vehicle in some cases. Therefore, it is an urgent need for current signal analysis work to quickly and accurately analyze CAN signals and avoid diagnostic requests as much as possible.

Contents of the invention

In order to overcome the above-mentioned defects in the prior art, the object of the present invention is to provide a signal analysis method for quickly locating CAN signals using diagnostic signals, which is easy to operate, high in computing speed, and wide in scope of application.

In order to achieve the above-mentioned purpose of the present invention, the present invention provides a kind of signal analysis method utilizing diagnosis signal to quickly locate CAN signal, comprising the following steps:

S1, connect the diagnostic instrument to the diagnostic interface of the benchmarking vehicle, make a diagnostic request to the benchmarking vehicle, and record the diagnostic signal;

S2, use the CAN signal acquisition hardware to connect with the communication CAN line to read the communication CAN data, use the CAN signal acquisition software to display the data on the communication CAN, and record the CAN data;

S3, converting the recorded diagnostic signal and CAN data to a format;

S4, determine the effective identification area of the diagnosis signal and all CAN signals in the CAN data, and calculate the similarity of the effective identification area;

S5, output the similarity of the effective identification area and the CAN signal in the form of a matrix, the matrix includes the message ID, signal length, signal start bit, signal proportional coefficient, signal offset, data of all CAN signals within the specified range Format, data type, similarity to diagnostic signal;

S6, verifying the CAN signals in the matrix in descending order of the similarity of the effective identification areas, and completing the positioning of the CAN signals.

There are many types of key signals required for testing and evaluation of new energy vehicles, and the same signal has different characteristics in different vehicles. Searching according to the characteristics of the signal itself will inevitably be very cumbersome, and the versatility is not strong. This method can quickly locate the message, start bit, signal length, coefficient offset, and data format type information of the CAN signal similar to the diagnostic signal, thereby shortening the signal analysis cycle and obtaining as many key points as possible from the communication CAN. Signals, avoid using diagnostic signals, and reduce communication interference to the vehicle.

Further, the positioned CAN signal has a stable value state, and this state can exist repeatedly. The range of the upper and lower limits of the stable value is much smaller than the range of the upper and lower limits of the signal value.

Further, the diagnostic signal and the CAN data include the whole process of the positioned CAN signal changing from a stable value to an unstable value, and then to a stable value, and both record the same whole process of the CAN signal change, which can More comprehensive positioning of CAN signals.

Further, the diagnostic signal is obtained through the record of the diagnostic instrument under the condition that the diagnostic instrument can record the signal, and obtained through the conversion of the dbc file in the recorded CAN data through the diagnosis request under the condition that the diagnostic instrument cannot record the signal. The method includes the acquisition method of the CAN data under the two conditions that the diagnostic instrument can record the signal and cannot record the signal, which searches the CAN signal more comprehensively and makes the positioning of the CAN signal more rapid and accurate.

Further, the effective identification area is a two-dimensional area, the two-dimensional area starts from the signal value changing from a stable value, returns to the time area between the end of the stable value, and the difference between the signal value and the stable value The value area between is formed, which extracts the effective identification area of all CAN signals, avoiding the problem of missing something in the search.

Further, the similarity of the effective identification area includes the similarity of the time area and the similarity of the value area. The similarity of the time area is calculated by calculating the ratio of the time span difference between the effective identification area of the diagnostic signal and the CAN signal to the time span of the diagnostic signal, and then using 100% is determined by subtracting the resulting ratio; the similarity of the value area is divided into the same number of parts by the time zone of the diagnostic signal and the CAN signal, and then the ratio of the average value of the diagnostic signal to the CAN signal in each time period is calculated, and then all the ratios are calculated The standard deviation of the ratio is divided by the average value, and finally determined by subtracting the obtained value from 100%.

The object of calculating the similarity of the effective identification area is all the CAN signals in the recorded CAN data. The higher the similarity between the CAN signal and the effective identification area of the diagnostic signal, the higher the possibility of being a located CAN signal. The method for calculating the similarity of the effective identification area is simple and accurate, and is helpful for quickly locating the CAN signal.

Further, step S5 includes the following steps:

S5-1. Formulate a similarity threshold, and filter out all CAN signals whose similarity is higher than the threshold;

S5-2, judging the data format and data type of the filtered CAN signal, and calculating the proportional coefficient and offset of the filtered CAN signal;

S5-3, the output result matrix, which specifically includes the message ID, signal length digit, signal start bit, signal proportional coefficient, signal offset, data format, data type, and diagnostic signal of all CAN signals that have been screened out similarity.

The beneficial effects of the present invention are:

1. This method quickly locates the suspected CAN signal data area through the diagnostic signal, and the output matrix contains all the information required for CAN signal analysis and calibration, speeding up the time for observing and locating the signal area and signal calibration, thereby greatly shortening the signal analysis cycle, which is beneficial to new energy The timeliness of the vehicle test evaluation work.

2. The frequency of diagnostic signals is generally lower than that of communication CAN signals, and the acquisition process of diagnostic signals needs to continuously send diagnostic request messages, or interfere with the normal communication of the bus to a certain extent, and even affect the normal operation of the vehicle in some cases. The search range of this method is all signals on the communication CAN, which is beneficial to replace the diagnosis request signal with the communication CAN signal when the analysis work is difficult to advance, so as to increase the signal frequency and reduce the interference to the bus communication. The method has a comprehensive search range and precise positioning, which is beneficial to replace the diagnostic request signal with the communication CAN signal, and has a relatively positive effect on optimizing signal characteristics and reducing communication interference.

3. Since there is no one-to-one correspondence between the diagnostic signal and the communication signal, there may be no corresponding signal sent on the communication CAN when there is a diagnostic signal. In the case that the restriction conditions of the program are set relatively loose and there is still no output result, and all the output results have been verified and proved not to be the desired signal, since this method has fully searched all the CAN signals in the CAN data, it can be preliminarily determined that there is no such signal on the communication CAN. The sending of the signal, or the encrypted processing of the signal causes the search to fail.

Additional aspects and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

Description of drawings

The above and/or additional aspects and advantages of the present invention will become apparent and comprehensible from the description of the embodiments in conjunction with the following drawings, wherein:

Fig. 1 is a schematic diagram of CAN signal and diagnostic signal characteristics;

Fig. 2 is the flowchart of this method;

Fig. 3 is a schematic diagram of the principle of analyzing the engine speed signal by using the diagnostic signal.

detailed description

Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

In the description of the present invention, unless otherwise specified and limited, it should be noted that the terms "installation", "connection" and "connection" should be understood in a broad sense, for example, it can be mechanical connection or electrical connection, or two The internal communication of each element may be directly connected or indirectly connected through an intermediary. Those skilled in the art can understand the specific meanings of the above terms according to specific situations.

The present invention provides a signal analysis method for quickly locating the CAN signal by using the diagnostic signal, combining the shape characteristics of the CAN signal and the corresponding diagnostic signal curve, as shown in Figure 1, that is, the diagnostic instrument signal and the CAN signal curve without coefficient offset Incomplete coincidence, comprehensively consider the operability of the actual recorded data, the feasibility of the screening signal algorithm, use data processing software, program to process the diagnostic signal and all CAN signals, locate the effective identification area of the two, and calculate the effective identification area of the two The similarity of the identification area, output the message ID, signal length, signal start bit, signal proportional coefficient, signal offset, data format, data type, and diagnostic signal similarity of all CAN signals with high similarity. For the next verification to complete the analysis and calibration of the CAN signal. Specifically, as shown in Figure 2, the following steps are included:

S1, using a diagnostic instrument to connect to the diagnostic interface of the benchmarking vehicle, making a diagnostic request to the benchmarking vehicle, and recording the diagnostic signal. The diagnostic signal is obtained through the record of the diagnostic instrument under the condition that the diagnostic instrument can record the signal, and obtained through the conversion of the dbc file in the recorded CAN data through the diagnosis request under the condition that the diagnostic instrument cannot record the signal. Specifically, after the diagnostic instrument sends out a diagnostic request, there is a diagnostic signal on the bus, and the diagnostic signal is compiled into the dbc, and the CAN data is recorded, and the dbc is loaded in the CANOE software and then the signal is exported.

S2, use the CAN signal acquisition hardware to connect with the communication CAN line to read the communication CAN data, use the CAN signal acquisition software to display the data on the communication CAN, and record the CAN data.

S3, converting the recorded diagnostic signal and CAN data into formats. Specifically, it is converted into a format corresponding to the data analysis and processing software and then read into the data analysis and processing software to perform steps S4 and S5. Among many data analysis and processing software, you can choose but not limited to use MATLAB for processing. The diagnostic signal is divided into two types: the signal recorded by the diagnostic instrument and the diagnostic signal contained in the CAN data. Most of the signals recorded by the diagnostic instrument do not need to convert the format. For CAN data, save as ASC format in CANOE software, then copy and paste the required information into EXCEL or txt file to complete the format conversion.

S4. Determine the effective identification area between the diagnostic signal and all CAN signals in the CAN data, and calculate the similarity of the effective identification area.

Wherein, the effective identification area is a two-dimensional area, and the two-dimensional area starts from the signal value changing from the stable value to the time area between the end of the signal value and the stable value, and the value between the signal value and the stable value area formation.

The similarity of the effective identification area includes the similarity of the time area and the similarity of the numerical value area. The similarity of the time area is calculated by calculating the ratio of the time span difference between the effective identification area of the diagnostic signal and the CAN signal to the time span of the diagnostic signal, and then subtracting it by 100%. Determination of the obtained ratio; the similarity of the numerical area is divided into the same number of parts by the time zone of the diagnostic signal and the CAN signal, and then the ratio of the average value of the diagnostic signal to the CAN signal in each time period is calculated, and then the standard deviation of all ratios is calculated Divide by the average value of the ratio, and finally subtract the resulting value from 100% to determine.

S5, output the similarity of the effective identification area and the CAN signal in the form of a matrix, the matrix includes the message ID, signal length, signal start bit, signal proportional coefficient, signal offset, Data format, data type, similarity to diagnostic signals.

This step specifically includes the following steps:

S5-1. Formulate a similarity cut-off point, and screen out all CAN signals whose similarity is higher than the cut-off point.

S5-2. Judging the data format and data type of the filtered CAN signal, and calculating the proportional coefficient and offset of the filtered CAN signal.

The screening method is: all possible data formats and data types are searched for CAN signals obtained through real vehicle verification and screening. If multiple results have a certain CAN signal, the CAN signal with a higher similarity between the time zone and the value zone is the CAN signal data format and data type. If only one data format and data type program outputs the signal, it can be directly determined as the data format and data type of the CAN signal. Formulate a similarity cut-off point to screen out signals with high similarity between the time zone and the numerical value zone. However, there is no uniform standard for the specific formulation of the similarity cut-off point, which depends on the actual situation.

S5-3 Output the result matrix, specifically including the message ID, signal length digit, signal start bit, signal proportional coefficient, signal offset, data format, data type, similar to the diagnostic signal of all CAN signals that have been screened out Spend. All CAN signal information in the result matrix is arranged in descending order of the similarity between the CAN signal and the diagnostic signal.

S6, verifying the CAN signals in the matrix in descending order of the similarity of the effective identification areas, and completing the positioning of the CAN signals.

The verification method is to connect the CANOE and the diagnostic instrument on the real vehicle, compile the position of the CAN signal into the dbc file and load it, and compare the CAN signal value of the real vehicle with the value on the diagnostic instrument in real time after changing the working conditions to see if they are consistent. The positioning of the CAN signal is correct, if it is inconsistent, it is incorrect.

In this method, the positioned CAN signal has a state of stable value, which can exist repeatedly, and the upper and lower limits of the stable value are much smaller than the upper and lower limits of the signal value. The interpretation of repeatable existence is demonstrated by the following example: the engine speed is 0 when it is stopped, which is a stable value, and the state of stopping can be entered repeatedly. If a certain signal has a stable value in a certain situation, but this situation is very difficult to achieve, even due to accidental factors, this signal cannot repeatedly enter the stable value state.

The diagnostic signal and CAN data include the whole change process of the positioned CAN signal from a stable value to an unstable value, and then to a stable value, and both record the same whole change process of the CAN signal.

Wherein, the similarity between the CAN signal in the search program and the diagnostic signal is positively correlated with the possibility that the CAN signal is a located CAN signal.

Specifically, taking the engine speed signal of a new energy vehicle as an example, the engine speed signal has a stable value of 0 in the engine shutdown state and a variation value greater than 0 in the startup state, which meets the requirements of this method for the characteristics of the analyzed signal. In this example, a diagnostic instrument is used to record the signal. Operate the vehicle to make the engine stop to start and then to stop, and at the same time manually record the diagnostic instrument signal and CAN data of the vehicle in the whole process of "engine stop-engine start-engine stop", try to ensure that the two recording data are carried out at the same time, allowing both There is a slight deviation before and after the recording time.

Referring to Figure 3, convert the recorded diagnostic instrument signal and CAN data into an EXCEL format that MATLAB can handle, and then import it into MATLAB. Run the program in MATLAB to process all the CAN signals in the diagnostic instrument signal and CAN data. The value of the positioning signal changes from the stable value to the start, and the signal value returns to the time zone between the end of the stable value. The two-dimensional area formed by the numerical area between the stable values extracts the effective identification area of the two. Then by calculating the ratio of the time span difference between the diagnostic signal and the effective identification area of the CAN signal to the time span of the diagnostic signal, and then subtracting the resulting ratio from 100% to determine the time zone similarity; by dividing the diagnostic signal and the CAN signal time zone into the same Number of copies, calculate the ratio of the diagnostic signal to the average value of the CAN signal in each time period, then calculate the standard deviation of all ratios divided by the average value of the ratio, and finally subtract the resulting value from 100% to obtain the similarity of the numerical area. Formulate the similarity threshold. In this embodiment, the similarity threshold in the time zone is 90%, and the similarity threshold in the numerical value zone is 80%. All CAN signals whose similarity is higher than the threshold are all screened out. Then judge the data format and data type of the filtered CAN signal, and calculate the proportional coefficient and offset of the filtered CAN signal. Finally, the result array is output, which contains the message ID, signal length digit, signal start bit, signal proportional coefficient, signal offset, data format, data type, and diagnostic signal similarity of all the filtered CAN signals. And arrange them in descending order of similarity. In this example, there are multiple suspected signal outputs, and they are also verified one by one in order of similarity from high to low. In this example, the signal with the highest similarity is proved to be the desired signal after verification. After many repeated verifications, all the information required for the analysis and calibration of the engine speed signal was obtained in the result array obtained by the program search, that is, the analysis and calibration of the engine speed signal of a new energy vehicle was completed.

In the description of this specification, descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific examples", or "some examples" mean that specific features described in connection with the embodiment or example , structure, material or feature is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Although the embodiments of the present invention have been shown and described, those skilled in the art can understand that various changes, modifications, substitutions and modifications can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the invention is defined by the claims and their equivalents.

Claims (7)

1. A signal analysis method utilizing diagnostic signal to locate CAN signal fast, is characterized in that: comprise the following steps: S1, connect the diagnostic instrument to the diagnostic interface of the benchmarking vehicle, make a diagnostic request to the benchmarking vehicle, and record the diagnostic signal; S2, use the CAN signal acquisition hardware to connect with the communication CAN line to read the communication CAN data, use the CAN signal acquisition software to display the data on the communication CAN, and record the CAN data; S3, converting the recorded diagnostic signal and CAN data to a format; S4, determine the effective identification area of the diagnosis signal and all CAN signals in the CAN data, and calculate the similarity of the effective identification area; S5, output the similarity of the effective identification area and the CAN signal in the form of a matrix, the matrix includes the message ID, signal length, signal start bit, signal proportional coefficient, signal offset, data of all CAN signals within the specified range Format, data type, similarity to diagnostic signal; S6. Verify the CAN signals in the matrix in descending order of the similarity of the effective identification areas, and complete the positioning of the CAN signals. 2. The signal analysis method for quickly locating CAN signals by using diagnostic signals according to claim 1, characterized in that: the located CAN signal has a stable value state, and this state can exist repeatedly. 3. The signal analysis method for quickly locating CAN signals utilizing diagnostic signals according to claim 1, characterized in that: said diagnostic signals and CAN data include changes of the located CAN signals from stable values to unstable values, and then to The change process of the stable value, both of them record the same change process of the CAN signal. 4. The signal analysis method for quickly locating CAN signals using diagnostic signals according to claim 1, characterized in that: the diagnostic signal is obtained by diagnostic instrument recording under the condition that the diagnostic instrument can record the signal, and under the condition that the diagnostic instrument cannot record the signal Next, obtained through dbc file conversion in the recorded CAN data by diagnostic request. 5. The signal analysis method for quickly locating CAN signals using diagnostic signals according to claim 1, characterized in that: the similarity of the effective identification area includes the similarity of the time zone and the similarity of the numerical value zone, and the similarity of the time zone is diagnosed by calculation The ratio of the time span difference between the effective identification area of the signal and the CAN signal to the time span of the diagnostic signal is determined by subtracting the resulting ratio from 100%; the similarity of the numerical value area is divided into the same number of copies by the time area of the diagnostic signal and the CAN signal, and then Calculate the ratio of the average value of the diagnostic signal to the CAN signal in each time period, then calculate the standard deviation of all ratios divided by the average value of the ratio, and finally subtract the obtained value from 100% to determine. 6. The signal analysis method for quickly locating CAN signals utilizing diagnostic signals according to claim 1, characterized in that: the effective identification area is a two-dimensional area, and the two-dimensional area changes from a stable value to a signal value Onset, regress to the time zone between the end of the stable value, and the numerical zone between the signal value and the stable value are formed. 7. the signal analysis method utilizing diagnosis signal to quickly locate CAN signal according to claim 1, is characterized in that: step S5 comprises the following several steps: S5-1. Formulate a similarity threshold, and filter out all CAN signals whose similarity is higher than the threshold; S5-2, judging the data format and data type of the filtered CAN signal, and calculating the proportional coefficient and offset of the filtered CAN signal; S5-3, the output result matrix, which specifically includes the message ID, signal length digit, signal start bit, signal proportional coefficient, signal offset, data format, data type, and diagnostic signal of all CAN signals that have been screened out similarity.