CN110858066A - Signal interpretation method, signal interpretation system and computer storage medium - Google Patents

Abstract

The invention discloses a signal interpretation method, comprising: loading a real-time signal; judging the type of the real-time signal; selecting a corresponding criterion for each type of the real-time signal, and sequentially performing interpretation and storage of the real-time signal. Interpret the result to obtain the post-event data of the corresponding type of real-time signal; load the corresponding post-event criterion for the post-event data of each type of real-time signal; interpret the corresponding type of the post-event data according to the post-event criterion. This method realizes the automatic interpretation of the test data in the whole cycle, which not only meets the real-time requirements of the test, but also improves the value of the test data and conducts a comprehensive and in-depth analysis of the test equipment; and the method is accurate in setting the criteria, which can be set differently for different types of parameters. The type of criterion solves the problem of positive and false interpretation of the on-off action of the serial signal in time series, improves the interpretation efficiency of a huge amount of data, saves interpretation time and saves labor costs.

Description

A signal interpretation method, signal interpretation system and computer storage medium

technical field

The invention relates to the technical field of signal data processing, in particular to a signal interpretation method, a signal interpretation system and a computer storage medium.

Background technique

Launch vehicles and missiles need to undergo many tests from the pre-take-off preparation stage to the end of the active stage when they are flying in technical positions and launching positions, or when they are developing models in the laboratory. With millions of parameters, it is a complicated task to carefully distinguish whether so many parameters are normal or not, and it is very easy to make mistakes. For the interpretation of these test data, the judgment requirements are also different in different processing cycles. In the launch preparation process, the time left for interpretation is very short, and it is impossible to complete the detailed interpretation of test parameters manually.

In the prior art, the interpretation of test parameters generally requires more manual intervention. For example, the out-of-tolerance interpretation of the analog signal: first, the operator needs to print out the time curve of the test data and the data comparison parameter table; then according to the criterion list document, compare the normal parameter variation range one by one, and mark the out-of-tolerance data in the figure; finally The interpretation conclusions are formed and recorded manually. The work efficiency is low, and it cannot meet the test and experiment requirements with many acquisition channels and huge data volume, and it is difficult to ensure the reliability of the interpretation results. In addition, there are also some automatic interpretation systems, which can realize the upper and lower limit interpretation of analog signals. Its interpretation function is only for analog signals, the criterion mode is fixed, and it is not easy to expand. It cannot be used for the interpretation of parameters such as time series time series and pulse count.

At present, for the interpretation of data, the criteria in the real-time interpretation processing stage and the post-event interpretation processing stage are the same, and the different requirements of the test for real-time interpretation and post-event interpretation are not considered. In the real-time interpretation stage, in order to ensure the real-time performance of data interpretation, the interpretation requirements are relatively simple. Generally, it is the interpretation of the upper and lower limits of the data at the current moment, and the sub-health status of some equipment cannot be judged; in the post-mortem interpretation, in order to better mine the test data It is necessary to carry out a more comprehensive interpretation of the hidden sub-health state in the real-time interpretation stage, and to carry out a comprehensive and in-depth analysis of the test tasks through the overall interpretation of the data.

SUMMARY OF THE INVENTION

The purpose of the present invention is to solve the problem of in-depth interpretation analysis and inaccurate results because the same criterion is used without considering the different requirements for real-time interpretation and post-mortem interpretation in the current data interpretation.

In order to solve the above problems, a first aspect of the present invention provides a signal interpretation method, comprising: loading a real-time signal; judging the type of the real-time signal; selecting a corresponding criterion for each type of the real-time signal; The criterion interprets the corresponding real-time signal; stores the interpretation result of the real-time signal, and obtains the post-event data of the corresponding type of real-time signal; loads the corresponding post-event criterion for the post-event data of each type of real-time signal ; Interpret the ex post data of the corresponding type according to the ex post criterion.

Further, the real-time signal types include: one or more of real-time analog signals, real-time pulse counting signals and real-time sequential time series signals.

Further, when the real-time signal type includes a real-time analog signal, the use of the criterion to interpret the corresponding real-time signal includes: acquiring the real-time analog signal value; judging whether the real-time analog signal value is within the normal value range; if the real-time analog signal value is within the normal value range, it is judged as normal data; if the real-time analog signal value is not within the normal value range, it is judged as abnormal data; the first interpretation result is stored.

Further, when the real-time signal type includes a real-time pulse count signal, the use of the criterion to interpret the corresponding real-time signal includes: calculating the obtained pulse count increment of the real-time pulse count signal. ; Judging whether the pulse count increment of the real-time pulse count signal is within the normal increment range; if the pulse count increment of the real-time pulse count signal is within the normal increment range, it is judged as normal data; if all If the pulse count increment of the real-time pulse count signal is not within the normal increment range, it is judged as abnormal data; the second judgment result is stored.

Further, when the real-time signal type includes the real-time sequential time-series signal, the use of the criterion to interpret the corresponding real-time signal, the real-time sequential time-series signal interpretation includes: obtaining the time sequence. Time-series time-series data values of the time-series signal within the on-off action time; comparing the time-series time-series data values with corresponding criteria to obtain a determination result; and storing the third determination result.

Further, when the criterion requires that the time-series time-series signal is connected within the on-off action time, determine whether the time-series time-series data values are all 1; if the time-series time-series data values are all 1, The determination result is that the real-time sequential time-series signal is normal data; if one of the sequential time-series data values is 0, the determination result is that the real-time sequential time-series signal is abnormal data.

Further, when the criterion requires that the time-series time-string signal is disconnected within the on-off action time, determine whether the time-series time-series data values are all 0; if the time-series time-series data values are all 0, Then it is judged that the real-time time-series time-series signal is normal data; if one of the time-series time-series data values is 1, it is judged that the real-time time-series time-series signal is abnormal data.

Further, the post-event data includes post-event simulation data, post-event pulse count data and post-event time series time series data.

Further, when the post-event data includes post-event simulation data, the interpreting the corresponding type of the post-event data according to the post-event criterion includes: acquiring the post-event simulation data; judging whether the post-event simulation data has periodic term interference. And the amount of interference exceeds expectations; if it is, it is judged as abnormal data; otherwise, continue to judge whether there is a trend item in the post-event simulation data; if there is a trend item, it is judged as abnormal data; if there is no trend item, it is judged as normal. data; store the fourth interpretation result.

Further, when the post-event data includes the post-event pulse count data, judging the post-event data of the corresponding type according to the post-event criterion includes: acquiring the total number of pulses of the pulse counting data; judging the total number of pulses Whether it is within the normal range; if the total number of pulses is within the normal range, it is judged as normal data; if the total number of pulses is not within the normal range, it is judged as abnormal data; the fifth interpretation result is stored.

Further, when the post-event data includes the post-event time series time series data, the interpreting the corresponding type of the post-event data according to the post-event criterion includes: calculating the total on-off duration of the acquired time-series time series data; Judging whether the total on-off time meets the on-off requirements; if the on-off total time meets the on-off requirements, it is judged that the data is normal; if the on-off total time does not meet the on-off requirements, the data is judged to be abnormal; store The sixth interpretation result.

Another aspect of the present invention provides a signal interpretation system, including: a real-time signal loading module for loading real-time signals; a discrimination module for discriminating the type of the real-time signal; a selection module for each The real-time signal selection corresponding criterion of various types; the real-time signal interpretation module is used to use the criterion to interpret the corresponding real-time signal; the storage module is used to store the interpretation result of the real-time signal and obtain the corresponding real-time signal. The post-event data of the type of real-time signal; the post-event criterion loading module is used to load the corresponding post-event criteria for the post-event data of each type of real-time signal; the post-event data interpretation module is used to interpret the corresponding post-event criteria according to the post-event criteria type of the ex post data.

Another aspect of the present invention provides a computer storage medium, where a computer program is stored on the storage medium, and when the program is executed by a processor, a signal interpretation method according to any one of claims 1-11 is implemented. step.

Yet another aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the claims when executing the program Steps of a signal interpretation method described in any one of 1-11.

The present invention provides a signal interpretation method, comprising: loading a real-time signal; discriminating the type of the real-time signal; selecting a corresponding criterion for each type of the real-time signal; Interpret the signal; store the interpretation result of the real-time signal, and obtain the post-event data of the corresponding type of real-time signal; load the corresponding post-event criterion for the post-event data of each type of real-time signal; interpret the corresponding type according to the post-event criterion The post-event data and other steps described in the above process perform full-cycle data interpretation on multi-type signals.

The above-mentioned technical scheme of the present invention has the following beneficial technical effects:

(1) By designing different interpretation methods of real-time interpretation and post-event interpretation, the full-cycle automatic interpretation of test data can be realized, which not only meets the needs of real-time testing, but also improves the value of test data. Realize online real-time interpretation, and also meet the storage of offline interpretation afterwards, covering the whole cycle of test data;

(2) The criterion setting is accurate, and different types of criterion can be set for different types of parameters, which realizes the full automation of interpretation and recording, solves the problem of positive and false interpretation of the on-off action of serial signals in time series, and improves the huge amount of data. The interpretation efficiency is improved, which saves the interpretation time and saves labor costs;

(3) Through in-depth interpretation of the data, the judgment of the equipment state can be further refined, and some sub-health states of the equipment operation can be judged.

Description of drawings

1 is a flowchart of a signal interpretation method according to a first embodiment of the present invention;

2 is a flowchart of a real-time analog signal interpretation method according to another embodiment of the present invention;

3 is a flowchart of a real-time pulse counting signal interpretation method according to another embodiment of the present invention;

4 is a flowchart of a method for interpreting real-time sequential time series signals according to yet another embodiment of the present invention;

5 is a flowchart of a method for interpreting post-event simulation data according to another embodiment of the present invention;

6 is a flowchart of a method for interpreting post-event pulse count data according to yet another embodiment of the present invention;

7 is a flowchart of a method for interpreting post-event time series data according to yet another embodiment of the present invention;

FIG. 8 is a flowchart of automatic full-cycle interpretation of multi-type signals according to another embodiment of the present invention.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the specific embodiments and the accompanying drawings. It should be understood that these descriptions are exemplary only and are not intended to limit the scope of the invention. Also, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concepts of the present invention.

Obviously, the described embodiments are some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

1 is a flowchart of a signal interpretation method according to a first embodiment of the present invention

According to the first embodiment of the present invention, as shown in FIG. 1 , a signal interpretation method includes:

S1: Load real-time signal;

S2: Determine the type of real-time signal;

S3: Select the corresponding criterion for each type of real-time signal;

S4: use the criterion to interpret the corresponding real-time signal;

S5: store the interpretation result of the real-time signal, and obtain the post-event data of the corresponding type of real-time signal;

S6: Load the corresponding post-event criterion for the post-event data of each type of real-time signal;

S7: Interpret the corresponding type of post-event data according to the post-event criterion.

In this embodiment, the real-time signal is first loaded by the control module, and the type of the real-time signal is determined; then the control module selects corresponding criteria for different real-time signals for interpretation, and stores the real-time signal and the interpretation result in the memory to form The ex post data can be stored in text formats such as txt or word; then the control module loads the corresponding ex post criteria of different types of ex post data; finally, interprets the corresponding type of ex post data according to the ex post criterion to obtain the interpretation result and stores it in the memory , the storage format can be selected as text format such as txt or word. This method realizes the automatic interpretation of the test data in the whole cycle, which not only meets the real-time requirements of the test, but also improves the value of the test data and conducts a comprehensive and in-depth analysis of the test equipment; and the method is accurate in setting the criteria, which can be set differently for different types of parameters. The type of criterion solves the problem of positive and false interpretation of the on-off action of the serial signal in time series, improves the interpretation efficiency of a huge amount of data, saves interpretation time and saves labor costs.

In an optional embodiment, the real-time signal types include: one or more of real-time analog signals, real-time pulse counting signals, and real-time sequential time series signals. The shutdown signal sent by the onboard computer is called the timing signal; it provides the timing reference of each flight segment, and each control signal sent by the program distributor is called the time series signal. Because they are all switch signals, they are collectively referred to as time series signals in launch vehicles/missiles.

FIG. 2 is a flowchart of a real-time analog signal interpretation method according to another embodiment of the present invention.

In an optional embodiment, as shown in FIG. 2 , a real-time analog signal interpretation method is provided, and the specific steps include: S311: obtaining a real-time analog signal value; S312: judging whether the real-time analog signal value is within a normal value range; S313 : If the real-time analog signal value is within the normal value range, it is judged as normal data; S314: If the real-time analog signal value is not within the normal value range, it is judged as abnormal data; S315: Store the first interpretation result.

In this embodiment, the control module interprets the real-time analog signal. During the interpretation process, the real-time analog signal value is obtained first, and then it is judged whether the real-time analog signal value is within the normal value range. If it is within the normal value range If the data is inside, it is judged as normal data, otherwise it is judged as abnormal data, and the control module stores the interpretation result and the received real-time signal in the memory. The normal value range will be determined before the simulation test, not a fixed value. This method accurately and effectively judges the real-time analog signal, saving interpretation time and labor costs.

FIG. 3 is a flowchart of a real-time pulse counting signal interpretation method according to another embodiment of the present invention.

In an optional embodiment, as shown in FIG. 3 , a method for interpreting real-time pulse counting signals is provided, and the specific steps include: S321: calculating the pulse count increment of the acquired real-time pulse counting signals; S322: judging the real-time pulse counting Whether the pulse count increment of the signal is within the normal increment range; S323: If the pulse count increment of the real-time pulse count signal is within the normal increment range, it is judged as normal data; S324: If the pulse count of the real-time pulse count signal is within the normal increment range If the count increment is not within the normal increment range, it is judged as abnormal data; S325: Store the second interpretation result.

In this embodiment, the control module interprets the real-time pulse counting signal. During the interpretation process, first calculates the pulse count increment of the obtained real-time pulse counting signal, and then judges the pulse count of the real-time pulse counting signal. Whether the increment is within the normal increment range, if it is within the normal value range, it is judged as normal data, otherwise it is judged as abnormal data, the control module will interpret the result and the received real-time signal, and store it in the memory. The normal value range will be determined before the simulation test, not a fixed value. This method accurately and effectively judges the real-time pulse count signal, saving interpretation time and labor costs.

FIG. 4 is a flowchart of a method for interpreting real-time sequential time series signals according to yet another embodiment of the present invention.

In an optional embodiment, as shown in FIG. 4 , a method for interpreting real-time time-series time-series signals is provided, and the specific steps include: S331 : acquiring time-series time-series data values of the time-series time-series signals within the on-off action time; S332 : compare the time series data value with the corresponding criterion to obtain the judgment result; S333 : store the third judgment result.

Optionally, when the criterion requires that the time-series time-series signal is ON within the on-off action time, determine whether the time-series time-series data values are all 1; if the time-series time-series data values are all 1, the judgment result is real-time. The time-series time-series signal is normal data; if one of the values of the time-series time-series data is 0, the result is that the real-time time-series time-series signal is abnormal data.

Optionally, when the criterion requires that the time-series time-series signal is disconnected within the on-off action time, determine whether the time-series time-series data values are all 0; if the time-series time-series data values are all 0, it is judged as a real-time sequence The time-series signal is normal data; if one of the time-series time-series data values is 1, it is judged that the real-time sequential time-series signal is abnormal data.

In this embodiment, the control module interprets the real-time time-series time-series signal, and in the process of interpretation, first obtains the time-series time-series data value of the time-series time-series signal within the action time, and determines whether the action time is required to be turned on, and further judge whether the time series time series data value is normal according to the judgment result;

If the time-series time-string signal criterion is required to be ON, then judge whether the time-string data values are all 1. If so, it is judged as normal data; if not, it is judged as abnormal data;

If the time-series time-series signal criterion is required to be disconnected, it is determined whether the time-series data values are all 0, if so, it is determined as normal data, if not, it is determined as abnormal data.

Finally, the control module stores the interpretation result and the received real-time signal in the memory. The method accurately and effectively judges real-time time series time series signals, saves interpretation time, and saves labor costs.

In an optional embodiment, the post-event data includes post-event simulation data, post-event pulse count data, and post-event time series time series data. What the control module receives is the signal, and what is stored is the data.

FIG. 5 is a flowchart of a method for interpreting post-event simulation data according to yet another embodiment of the present invention.

In an optional embodiment, as shown in FIG. 5 , when the post-event data includes post-event simulation data, interpreting the corresponding type of post-event data according to the post-event criterion includes: S511: acquiring post-event simulation data; S512: judging whether the post-event simulation data exists Periodic term interference and the amount of interference exceeds expectations; S513: If there is periodic term interference and the amount of interference exceeds expectations, it is judged as abnormal data; S514: Otherwise, continue to judge whether there is a trend item in the post-event simulation data; S515: If there is a trend item, It is judged as abnormal data; S516 : if there is no trend item, it is judged as normal data; S517 : The fourth interpretation result is stored.

In this embodiment, the control module interprets the post-event simulation data. During the interpretation process, the post-event simulation data is first obtained, and then it is judged whether the post-event simulation data has periodic interference and the amount of interference exceeds expectations. Medium is the amplitude value exceeding 30% of the normal value, if yes, it is judged as abnormal data, otherwise, continue to judge whether there is a trend item in the post-event simulation data, if there is a trend item, judge it as abnormal data, otherwise it is normal data, and finally The control module stores the interpretation results in the memory. The trend item is that although the current data is normal, it is gradually increasing or decreasing, forming an upward or downward trend. Through this trend, it can be judged whether the data is in the normal range. This method can accurately and effectively judge the post-event simulation data. It saves time for interpretation and also saves labor costs.

FIG. 6 is a flowchart of a method for interpreting post-event pulse count data according to another embodiment of the present invention.

In an optional embodiment, as shown in FIG. 6 , when the post-event data includes the post-event pulse count data, judging the corresponding type of post-event data according to the post-event criterion includes: S521: acquiring the total number of pulses of the pulse counting data; S522: It is judged whether the total number of pulses is within the normal range; S523: If the total number of pulses is within the normal range, it is judged as normal data; S524: If the total number of pulses is not within the normal range, it is judged as abnormal data; S525: The fifth interpretation result is stored.

In this embodiment, the control module interprets the pulse count data afterwards. During the interpreting process, the total number of pulses of the pulse count data is obtained first, and then it is judged whether the total number of pulses is within the normal range. If it is, it is judged as normal data, otherwise It is judged as abnormal data, and finally the control module stores the interpretation result in the memory. The method accurately and effectively judges the post-event pulse count data, saves interpretation time, and saves labor costs. The normal range range will be specifically determined before the launch vehicle test, and this range will be given during the test. Specifically, it is determined according to the flight sequence of the launch vehicle or missile. The cumulative number of pulse signals can reflect the speed, acceleration, displacement and other parameters in this time period. By comparing this value with the design standard range, it can reflect whether there is an abnormality

FIG. 7 is a flowchart of a method for interpreting post-event time series data according to yet another embodiment of the present invention.

In an optional embodiment, as shown in FIG. 7 , when the post-event data includes the post-event time series data, the interpreting the corresponding type of the post-event data according to the post-event criterion includes: S531: Calculate The obtained total on-off duration of the time series time series data; S532: determine whether the total on-off duration meets the on-off requirement; S533: if the total on-off duration meets the on-off requirement, it is judged that the data is normal; S534: If the total on-off duration does not meet the on-off requirement, it is judged that the data is abnormal; S535: Store the sixth interpretation result.

In this embodiment, the control module interprets the time-series time series data after the fact. In the process of interpretation, firstly calculates the obtained total on-off duration of the time-series time-string data, and then judges whether the total on-off duration conforms to the on-off time. If it is, it is judged that the data is normal, otherwise it is judged that the data is abnormal, and finally the control module stores the interpretation result in the memory. Among them, the total duration of on-off is determined before each interpretation, and is not a unique range value. According to different needs, the range value is different. The method accurately and effectively judges the post-event time series data, saves interpretation time, and saves labor costs.

FIG. 8 is a flowchart of automatic full-cycle interpretation of multi-type signals according to another embodiment of the present invention.

In an optional embodiment, as shown in FIG. 8 , the specific real-time process of providing a full-cycle automatic interpretation method for multi-type signals is as follows:

(1) Load real-time interpretation criteria including analog signals, pulse count signals and time series time series signals, and read the real-time interpretation criteria into the buffer;

(2) Determine the type of interpretation signal to which the channel signal belongs: if it is an analog signal, execute step (3); if it is a pulse count signal, execute step (6); if it is a time series signal, execute step ( 9) step;

(3) Read the real-time data value of the analog channel at the moment;

(4) compare the data value with the corresponding criterion, if the data value is within the normal value range, then it is judged as normal data; otherwise, it is judged as abnormal data; record the interpretation result to the first word document;

(5) Read the data value at the next moment, go to step (4), until the data interpretation at the final time point is completed; and so on, read the data value of the next channel of the original data, until the channel data is no longer real-time input, and then execute step (12);

(6) Load the first criterion and read the pulse count of the corresponding channel at the time point in the criterion, read the pulse count at the time point, and calculate the pulse count increment, that is, the difference between the pulse count and the pulse count;

(7) Compare the pulse count increment with the corresponding criterion to judge whether the pulse count increment is within the normal increment range. If so, it is judged as the normal data of the pulse increment; if not, it is judged as the pulse increment. amount of abnormal data; record the interpretation results to the second word document;

(8) read next criterion entry and corresponding channel data, calculate pulse count increment, go to step (7), until this channel data is no longer input in real time, then execute step (12);

(9) Read the time series data value between the on-off action time of the first channel that needs to be interpreted from the real-time data according to the action time criterion;

(10) Compare the time series data value with the criterion. If the criterion requires that the period is turned on, then judge whether the time series data value is all 1. If so, it is judged as the normal data of the time series data; If the value of a time series data is 0, it is judged as abnormal data of time series data; if the criterion requires the time period to be disconnected, it is judged whether the value of the time series data is all 0, and if so, it is judged as normal data of time series data. ; As long as there is a time string data value of 1, it is judged as abnormal data of time string data; record the interpretation result to the third word document;

(11) Read the next criterion and corresponding original data of the first channel that needs to be interpreted, go to step (10), until the interpretation of all criterion entries of all the first channels is completed; and so on, interpret the next channel Criterion entry, until the channel data is no longer input in real time, then execute step (12);

(12) If the real-time interpretation of all channels of all types of data is completed, the real-time interpretation phase ends, and the post-mortem interpretation phase is transferred.

(13) Load the criteria used for post-mortem interpretation including all data types of all channels, and perform post-mortem interpretation according to the real-time data stored in the real-time interpretation process.

(14) Determine the type of interpretation signal to which the channel signal belongs: if it is an analog signal, execute step (15); if it is a pulse count signal, execute step (18); if it is a time series signal, execute step ( 20) step;

(15) For the analog signal channel, read all the data of the i-th channel, and judge whether the data has periodic term interference. If there is periodic term interference and the amount of signal interference exceeds expectations, judge that the signal is abnormal and store and record the interpretation result to the first word. documentation;

(16) judge whether there is a trend item in this channel signal, if there is, then judge that the signal is abnormal and store and record the interpretation result to the first word document;

(17) read the data value of the next channel, go to step (15), until the data interpretation of the analog channel is completed, then execute step (22);

(18) For the pulse signal, read the total number of pulses of the i-th channel, and judge whether it is between the intervals. If it is, then judge that the number of pulses of this channel is normal; otherwise, it is judged that the total number of pulses of this channel is abnormal; record the interpretation results to the first two word documents;

(19) read the next pulse channel criterion entry and the total number of corresponding channel pulses, go to step (18), until all pulse channel data are read, and then execute step (22);

(20) For the time series time series data, read the data of the i-th channel, and calculate the total on-off duration of the channel data; if the total duration meets the on-off requirements, the channel data is judged to be normal, otherwise it is judged that the data is abnormal; record the interpretation results to the first three word documents;

(21) Read the next sequence time series channel criterion entry and the corresponding channel on-off total duration, go to step (20), until all time sequence time series channel data is read, and then execute step (22);

(22) END.

According to this embodiment, the full-cycle automatic interpretation of multi-type signals is realized. According to the needs of different stages of interpretation, a full-cycle interpretation method for multi-type signals is designed, and only a certain type of signals can be used for full-cycle interpretation according to requirements. Interpretation or Phased Interpretation. By designing different interpretation methods of real-time interpretation and post-event interpretation, the full-cycle automatic interpretation of test data is realized, which not only meets the needs of real-time testing, but also improves the value of test data, and conducts comprehensive and in-depth analysis of test equipment.

Another aspect of the present invention provides a signal interpretation system, including: a real-time signal loading module for loading real-time signals; a discrimination module for discriminating the type of the real-time signal; a selection module for each The real-time signal selection corresponding criterion of various types; the real-time signal interpretation module is used to use the criterion to interpret the corresponding real-time signal; the storage module is used to store the interpretation result of the real-time signal and obtain the corresponding real-time signal. The post-event data of the type of real-time signal; the post-event criterion loading module is used to load the corresponding post-event criteria for the post-event data of each type of real-time signal; the post-event data interpretation module is used to interpret the corresponding post-event criteria according to the post-event criteria type of the ex post data.

Optionally, the real-time signal loading module loads real-time signals, wherein the real-time signals include: one or more of real-time analog signals, real-time pulse counting signals and real-time sequential time series signals.

Optionally, the real-time signal interpretation module is used to use the criterion to interpret the corresponding real-time signal, and when the real-time signal type includes a real-time analog signal, the use of the criterion is used to interpret the corresponding real-time signal. Interpreting the real-time signal includes: obtaining the real-time analog signal value; judging whether the real-time analog signal value is within the normal value range; if the real-time analog signal value is within the normal value range, it is judged as normal data; If the real-time analog signal value is not within the normal value range, it is judged as abnormal data; the storage module stores the first interpretation result.

Optionally, the real-time signal interpretation module is used to use the criterion to interpret the corresponding real-time signal. When the real-time signal type includes a real-time pulse count signal, the use of the criterion is used to interpret the corresponding real-time signal. The real-time signal is interpreted, including: calculating the obtained pulse count increment of the real-time pulse count signal; judging whether the pulse count increment of the real-time pulse count signal is within the normal increment range; If the pulse count increment of the counting signal is within the normal increment range, it is judged as normal data; if the pulse count increment of the real-time pulse count signal is not within the normal increment range, it is judged as abnormal data; the storage module, The second interpretation result is stored.

Optionally, a real-time signal interpretation module, configured to use the criterion to interpret the corresponding real-time signal, when the real-time signal type includes the real-time sequential time series signal, the use of the criterion is used to interpret the corresponding real-time signal. Correspondingly, the real-time signal is interpreted, and the interpretation of the real-time time-series time-series signal includes: acquiring the time-series time-series data value of the time-series time-series signal within the on-off action time; comparing the time-series time-series data value with the corresponding The criteria are compared to obtain the judgment result; the storage module is used to store the third judgment result.

Optionally, the real-time signal interpreting module is used to interpret the corresponding real-time signal by using the criterion, and when the criterion requires that the serial signal is turned on within the on-off action time, judge the signal. Whether the time series data values of the time series are all 1; if the data values of the time series time series are all 1, the judgment result is that the real-time time series signal is normal data; if there is one of the time series data values in the time series If the bit value is 0, the judgment result is that the real-time time series time series signal is abnormal data.

Optionally, the real-time signal interpretation module is used to use the criterion to interpret the corresponding real-time signal, and when the criterion requires that the time series signal is disconnected within the on-off action time, determine the Whether the time series data values of the time series are all 0; if the time series data values of the time series are all 0, it is judged that the real-time time series signal is normal data; if the time series data value of the time series has a value of 1, it is judged that the real-time sequential time series signal is abnormal data.

Optionally, the post-event criterion loading module is used to load corresponding post-event criteria for the post-event data of each type of real-time signal, wherein the post-event criteria include: post-event simulation data, post-event pulse count data, and post-event time sequence time. string data.

Optionally, a post-event data interpretation module is used to interpret the post-event data of the corresponding type according to the post-event criterion, and when the post-event data includes the post-event simulation data, the post-event data interpretation module is configured to interpret the corresponding type of the post-event data according to the post-event criteria. The post-event data includes: obtaining the post-event simulation data; judging whether the post-event simulation data has periodic term interference and the amount of interference exceeds expectations; if there is periodic term interference and the amount of interference exceeds expectations, it is judged as abnormal data; Whether there is a trend item in the post-event simulation data; if there is a trend item, it is judged as abnormal data; if there is no trend item, it is judged as normal data; the storage module stores the fourth interpretation result.

Optionally, a post-event data interpretation module, configured to interpret the post-event data of a corresponding type according to the post-event criterion, when the post-event data includes the post-event pulse count data, the post-event data interpretation module according to the post-event criterion interprets the corresponding data. The type of the post-event data includes: obtaining the total number of pulses of the pulse count data; judging whether the total number of pulses is within the normal range; if the total number of pulses is within the normal range, then judging as normal data; if the total number of pulses If it is not within the normal range, it is judged as abnormal data; the storage module stores the fifth interpretation result.

Optionally, a post-event data interpretation module, configured to interpret the post-event data of a corresponding type according to the post-event criterion, when the post-event data includes the post-event time series data, the post-event data interpretation The post-event data of the corresponding type includes: calculating the total on-off duration of the time series time series data obtained; judging whether the on-off total duration meets the on-off requirement; if the on-off total duration meets the on-off requirement, then judge The data is normal; if the total on-off time does not meet the on-off requirement, it is judged that the data is abnormal; the storage module stores the sixth interpretation result.

Another aspect of the present invention provides a computer storage medium, where a computer program is stored on the storage medium, and when the program is executed by a processor, the steps of a signal interpretation method described in any of the above solutions are implemented.

Another aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements one of the above technical solutions when the processor executes the program The steps of the signal interpretation method.

The present invention aims to protect a signal interpretation method, including: loading real-time signals; discriminating the types of the real-time signals; selecting corresponding criteria for each type of the real-time signals; Interpretation of the real-time signal; store the interpretation result of the real-time signal, and obtain the post-event data of the corresponding type of real-time signal; load the corresponding post-event criterion for the post-event data of each type of real-time signal; interpret according to the post-event criterion Steps such as the post-event data of the corresponding type perform full-cycle data interpretation on the multi-type signals. This method realizes the automatic interpretation of the test data in the whole cycle, which not only meets the real-time requirements of the test, but also improves the value of the test data and conducts a comprehensive and in-depth analysis of the test equipment; and the method is accurate in setting the criteria, which can be set differently for different types of parameters. The type of criterion solves the problem of positive and false interpretation of the on-off action of the serial signal in time series, improves the interpretation efficiency of a huge amount of data, saves interpretation time and saves labor costs.

It should be understood that the above-mentioned specific embodiments of the present invention are only used to illustrate or explain the principle of the present invention, but not to limit the present invention. Therefore, any modifications, equivalent replacements, improvements, etc. made without departing from the spirit and scope of the present invention should be included within the protection scope of the present invention. Furthermore, the appended claims of this invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalents of such scope and boundaries.

Claims (14)

1. a signal interpretation method, is characterized in that, comprises: Load real-time signals; identifying the type of the real-time signal; Selecting a corresponding criterion for each type of the real-time signal; Use the criterion to interpret the corresponding real-time signal; Store the interpretation result of the real-time signal, and obtain the post-event data of the corresponding type of real-time signal; For the post-event data of each type of real-time signal, load the corresponding post-event criterion; The post-event data of the corresponding type is interpreted according to the post-event criterion. 2 . The signal interpretation method according to claim 1 , wherein the real-time signal types include: one or more of real-time analog signals, real-time pulse counting signals and real-time sequential time series signals. 3 . 3. according to the described signal interpretation method of claim 2, it is characterized in that, when described real-time signal type comprises real-time analog signal, described adopting described criterion to interpret corresponding described real-time signal, comprising: obtaining the real-time analog signal value; Judging whether the real-time analog signal value is within the normal value range; If the real-time analog signal value is within the normal value range, it is judged as normal data; If the real-time analog signal value is not within the normal value range, it is judged as abnormal data; The first interpretation result is stored. 4. according to the described signal interpretation method of claim 2, it is characterized in that, when described real-time signal type comprises real-time pulse count signal, described adopting described criterion to interpret corresponding described real-time signal, comprising: Calculate the pulse count increment of the obtained real-time pulse count signal; Determine whether the pulse count increment of the real-time pulse count signal is within the normal increment range; If the pulse count increment of the real-time pulse count signal is within the normal increment range, it is judged as normal data; If the pulse count increment of the real-time pulse count signal is not within the normal increment range, it is judged as abnormal data; The second interpretation result is stored. 5. The signal interpretation method according to claim 2, wherein when the real-time signal type includes the real-time sequential time series signal, the use of the criterion to interpret the corresponding real-time signal, the The above-mentioned real-time sequence time series signal interpretation includes: acquiring the time-series time-series data value of the time-series time-series signal within the on-off action time; Comparing the time series data value with the corresponding criterion to obtain a discrimination result; The third interpretation result is stored. 6. The signal interpretation method according to claim 5, characterized in that, When the criterion requires that the time-series time-series signal is turned on within the on-off action time, determine whether the time-series time-series data values of the time sequence are all 1; If the time-series time-series data values are all 1, the judgment result is that the real-time time-series time-series signal is normal data; If one of the time-series time-series data values is 0, the determination result is that the real-time time-series time-series signal is abnormal data. 7. The signal interpretation method according to claim 5, characterized in that, When the criterion requires that the time series signal is disconnected within the on-off action time, determine whether the time series data values of the time series are all 0; If the time series data values of the time series are all 0, it is judged that the real-time time series time series signal is normal data; If one of the time-series time-series data values has a value of 1, it is determined that the real-time time-series time-series signal is abnormal data. 8 . The signal interpretation method according to claim 1 , wherein the post-event data comprises: post-event analog data, post-event pulse count data and post-event time series time series data. 9 . 9. according to the described signal interpretation method of claim 8, it is characterized in that, when described post-event data comprises post-event simulation data, described according to described post-event criterion interpreting the described post-event data of corresponding type comprises: obtaining the post-event simulation data; Judging whether the post-event simulation data has periodic term interference and the amount of interference exceeds expectations; If there is periodic term interference and the amount of interference exceeds expectations, it is judged as abnormal data; Otherwise, continue to judge whether there is a trend item in the post-event simulation data; If there is a trend item, it is judged as abnormal data; If there is no trend item, it is judged as normal data; The fourth interpretation result is stored. 10. The signal interpretation method according to claim 8, wherein when the post-event data includes the post-event pulse count data, the post-event data of the corresponding type is interpreted according to the post-event criterion including: Obtain the total number of pulses of the pulse count data; Determine whether the total number of pulses is within the normal range; If the total number of pulses is within the normal range, it is judged as normal data; If the total number of pulses is not within the normal range, it is judged as abnormal data; The fifth interpretation result is stored. 11. The signal interpretation method according to claim 8, wherein when the post-event data includes the post-event time series data, the post-event data of the corresponding type is interpreted according to the post-event criterion, comprising: Calculate the total on-off duration of the obtained time series time series data; Judging whether the total on-off duration meets the on-off requirements; If the total on-off time meets the on-off requirement, it is judged that the data is normal; If the total on-off time does not meet the on-off requirement, judge that the data is abnormal; The sixth interpretation result is stored. 12. A signal interpretation system, comprising: Real-time signal loading module for loading real-time signals; A discriminating module for discriminating the type of the real-time signal; A selection module for selecting corresponding criteria for each type of the real-time signal; a real-time signal interpretation module, used for using the criterion to interpret the corresponding real-time signal; a storage module, used for storing the interpretation result of the real-time signal, and obtaining the post-event data of the corresponding type of real-time signal; The post-event criterion loading module is used to load the corresponding post-event criteria for the post-event data of each type of real-time signal; The post-event data interpretation module is used for interpreting the corresponding type of the post-event data according to the post-event criterion. 13. A computer storage medium, characterized in that, a computer program is stored on the storage medium, and when the program is executed by a processor, the steps of a signal interpretation method according to any one of claims 1-11 are implemented. 14. An electronic device, characterized in that it comprises a memory, a processor and a computer program stored on the memory and running on the processor, and the processor implements claims 1- when the processor executes the program Steps of a signal interpretation method described in any one of 11.

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