CN110858066A - Signal interpretation method, signal interpretation system and computer storage medium - Google Patents
Signal interpretation method, signal interpretation system and computer storage medium Download PDFInfo
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Abstract
The invention discloses a signal interpretation method, which comprises the following steps: loading a real-time signal; judging the type of the real-time signal; selecting corresponding criteria for each type of real-time signals, sequentially executing interpretation and storing interpretation results of the real-time signals to obtain posterior data of the real-time signals of the corresponding types; loading corresponding post-event criteria aiming at post-event data of each type of real-time signals; and judging and reading the posterior data of the corresponding type according to the posterior criterion. The method realizes the full-period automatic interpretation of the test data, not only meets the requirement of test real-time performance, but also can improve the value of the test data and carry out comprehensive and deep analysis on test equipment; the method has the advantages that the criterion is accurately set, different types of criteria can be set according to different types of parameters, the problem that the serial signal connection and disconnection actions are correctly and wrongly interpreted during time sequence is solved, the interpretation efficiency of huge data volume is improved, the interpretation time is saved, and meanwhile labor cost is saved.
Description
Technical Field
The present invention relates to the field of signal data processing technologies, and in particular, to a signal interpretation method, a signal interpretation system, and a computer storage medium.
Background
When a carrier rocket and a missile carry out the mode flight of a technical place and a launch place or the model development of a laboratory, a lot of tests are needed from a preparation stage before takeoff to the end of flight in an active section, millions of parameters of various types of signals are generated in each test, whether the parameters are normal or not needs to be carefully distinguished, the method is a complicated work and is very easy to make mistakes. The interpretation of these test data is also subject to different interpretation requirements in different processing cycles. The time left for interpretation within the transmission preparation procedure is very short, and manual means are simply not possible to accomplish a detailed interpretation of the test parameters.
In the prior art, interpretation of test parameters generally involves more manual intervention. And (3) if the analog signal is out of tolerance: firstly, an operator needs to print a time curve graph of test data and a data comparison parameter table; then comparing the normal parameter variation ranges one by one according to the criterion list documents, and marking the over-tolerance data in the graph; finally, forming an interpretation conclusion and manually recording. The working efficiency is low, the test and test requirements of a plurality of acquisition channels and huge data volume cannot be met, and the credibility of the interpretation result is difficult to ensure. In addition, there are some automatic interpretation systems that can implement upper and lower limit interpretation of analog signals. The interpretation function only aims at analog signals, the criterion mode is fixed and is not easy to expand, the method cannot be used for interpretation of parameters such as time sequence time series and pulse counting, cannot adapt to interpretation of various information of different test items, and is lack of expandability.
At present, for the interpretation of data, the criterion in the real-time interpretation processing stage and the criterion in the post-interpretation processing stage are the same, and the different requirements of the test on the real-time interpretation and the post-interpretation are not considered. In the real-time interpretation stage, in order to ensure the real-time performance of data interpretation, the interpretation requirement is relatively simple, generally, the upper and lower limits of data at the current moment are interpreted, and the sub-health state of some equipment cannot be judged; in the case of after-the-fact interpretation, in order to better mine the rules in the test data, the hidden sub-health state in the real-time interpretation stage needs to be more comprehensively interpreted, and the overall interpretation of the data is carried out, so that the test task is comprehensively and deeply analyzed.
Disclosure of Invention
The invention aims to solve the problems that the interpretation analysis is not deep and the result is inaccurate because the same criterion is used without considering the different requirements of the test on real-time interpretation and post-event interpretation for the interpretation of the data at present.
To solve the above problem, a first aspect of the present invention provides a signal interpretation method, including: loading a real-time signal; judging the type of the real-time signal; selecting corresponding criteria for each type of said real-time signal; judging the corresponding real-time signals by adopting the criterion; storing the interpretation result of the real-time signal to obtain the post data of the real-time signal of the corresponding type; loading corresponding post-event criteria aiming at post-event data of each type of real-time signals; and judging and reading the posterior data of the corresponding type according to the posterior criterion.
Further, the real-time signal types include: one or more of a real-time analog signal, a real-time pulse count signal, and a real-time sequence time series signal.
Further, when the real-time signal type includes a real-time analog signal, the interpreting the corresponding real-time signal by using the criterion includes: acquiring the real-time analog signal value; judging whether the real-time analog signal value is within a normal value range; if the real-time analog signal value is within the normal value range, judging the real-time analog signal value as normal data; if the real-time analog signal value is not within the normal value range, judging the real-time analog signal value as abnormal data; the first interpretation result is stored.
Further, when the real-time signal type includes a real-time pulse count signal, the interpreting the corresponding real-time signal by using the criterion includes: calculating the pulse counting increment of the acquired real-time pulse counting signal; judging whether the pulse counting increment of the real-time pulse counting signal is within a normal increment range; if the pulse counting increment of the real-time pulse counting signal is within a normal increment range, judging the real-time pulse counting signal to be normal data; if the pulse counting increment of the real-time pulse counting signal is not within the normal increment range, judging the real-time pulse counting signal to be abnormal data; and storing the second interpretation result.
Further, when the real-time signal type includes the real-time sequence time series signal, the criterion is adopted to interpret the corresponding real-time signal, and the real-time sequence time series signal interpretation includes: acquiring a time sequence time string data value of the time sequence time string signal within the on-off action time; comparing the time series data value with corresponding criterion to obtain a judgment result; and storing the third interpretation result.
Further, when the criterion requires that the string signal is switched on in the time sequence within the on-off action time, judging whether all string data values in the time sequence are 1; if all the time sequence time string data values are 1, the judgment result is that the real-time sequence time string signals are normal data; and if one digit value in the time sequence time string data values is 0, the judgment result shows that the real-time sequence time string signal is abnormal data.
Further, when the criterion requires that the string signal is disconnected in the time sequence within the on-off action time, judging whether all string data values in the time sequence are 0; if all the time sequence time string data values are 0, judging that the real-time sequence time string signals are normal data; and if one digit value in the time sequence time string data values is 1, judging that the real-time sequence time string signal is abnormal data.
Further, the posterior data includes: post-incident analog data, post-incident pulse count data, and post-incident time series data.
Further, when the posterior data includes posterior simulation data, interpreting the corresponding type of the posterior data according to the posterior criterion includes: acquiring the post simulation data; judging whether the post-event simulation data has periodic item interference and the interference amount exceeds the expectation; if yes, judging the data to be abnormal data; otherwise, continuously judging whether the after-the-fact simulation data has a trend item or not; if the trend item exists, judging the trend item to be abnormal data; if no trend item exists, judging the data to be normal data; and storing the fourth interpretation result.
Further, when the posterior data includes the posterior pulse count data, the interpreting the corresponding type of the posterior data according to the posterior criterion includes: acquiring the total number of pulses of the pulse counting data; judging whether the total number of the pulses is within a normal range; if the total number of the pulses is within a normal range, judging the data to be normal data; if the total number of the pulses is not in a normal range, judging the pulse is abnormal data; and storing the fifth interpretation result.
Further, when the posterior data includes the posterior time series data, interpreting the posterior data of the corresponding type according to the posterior criterion includes: calculating the total on-off duration of the acquired time series data; judging whether the total on-off duration meets the on-off requirement or not; if the total on-off duration meets the on-off requirement, judging that the data is normal; if the total on-off duration does not meet the on-off requirement, judging that the data is abnormal; the sixth interpretation result is stored.
Another aspect of the present invention provides a signal interpretation system comprising: the real-time signal loading module is used for loading real-time signals; the judging module is used for judging the type of the real-time signal; a selection module for selecting a corresponding criterion for each type of the real-time signals; the real-time signal interpretation module is used for interpreting the corresponding real-time signal by adopting the criterion; the storage module is used for storing the interpretation result of the real-time signal and obtaining the post data of the corresponding type of the real-time signal; the posterior criterion loading module is used for loading corresponding posterior criteria aiming at posterior data of each type of real-time signals; and the posterior data interpretation module is used for interpreting the posterior data of the corresponding type according to the posterior criterion.
A further aspect of the invention provides a computer storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of a signal interpretation method according to any one of claims 1 to 11.
A further aspect of the invention provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of a signal interpretation method according to any of claims 1 to 11 when executing the program.
The invention provides a signal interpretation method, which comprises the following steps: loading a real-time signal; judging the type of the real-time signal; selecting corresponding criteria for each type of said real-time signal; judging the corresponding real-time signals by adopting the criterion; storing the interpretation result of the real-time signal to obtain the post data of the real-time signal of the corresponding type; loading corresponding post-event criteria aiming at post-event data of each type of real-time signals; and judging the posterior data of the corresponding type according to the posterior criterion, and performing full-period data judgment on the multi-type signals.
The technical scheme of the invention has the following beneficial technical effects:
(1) by designing different interpretation methods of real-time interpretation and afterwards interpretation, the full-period automatic interpretation of the test data is realized, the requirement on the real-time performance of the test is met, the value of the test data can be improved, the test equipment is comprehensively and deeply analyzed, the online real-time interpretation can be realized, the storage of the afterwards offline interpretation is also met, and the full period of the test data is covered;
(2) the judgment method has the advantages that the judgment is accurately set, different types of judgment can be set according to different types of parameters, full automation of interpretation and recording is realized, the problem that serial signal connection and disconnection actions are correctly and wrongly interpreted during time sequence is solved, the interpretation efficiency of huge data volume is improved, the interpretation time is saved, and meanwhile labor cost is saved;
(3) the judgment of the equipment state can be further refined through deep interpretation of the data, and some sub-health states of the equipment operation can be judged.
Drawings
Fig. 1 is a flowchart of a signal interpretation method according to a first embodiment of the present invention;
FIG. 2 is a flow chart of a real-time analog signal interpretation method according to another embodiment of the present invention;
FIG. 3 is a flow chart of a real-time pulse count signal interpretation method according to yet another embodiment of the present invention;
FIG. 4 is a flow chart of a real-time series signal interpretation method according to yet another embodiment of the present invention;
FIG. 5 is a flow diagram of a post hoc analog data interpretation method according to yet another embodiment of the present invention;
FIG. 6 is a flow chart of a method for post-incident pulse count data interpretation according to yet another embodiment of the present invention;
FIG. 7 is a flowchart of a post-sequence time-series data interpretation method according to yet another embodiment of the invention;
fig. 8 is a flow chart of multi-type signal full-period automatic interpretation according to still another embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
FIG. 1 is a flow chart of a signal interpretation method according to a first embodiment of the present invention
According to a first embodiment of the present invention, as shown in fig. 1, a signal interpretation method includes:
s1: loading a real-time signal;
s2: judging the type of the real-time signal;
s3: selecting corresponding criteria for each type of real-time signal;
s4: judging the corresponding real-time signals by adopting a criterion;
s5: storing the interpretation result of the real-time signal to obtain the post data of the corresponding type of the real-time signal;
s6: loading corresponding post-event criteria aiming at post-event data of each type of real-time signals;
s7: and judging and reading the posterior data of the corresponding type according to the posterior criterion.
In this embodiment, a control module loads a real-time signal and determines the type of the real-time signal; then the control module selects corresponding criteria for interpretation aiming at different real-time signals, and stores the real-time signals and interpretation results in a storage to form post data, and the storage form can be selected as text formats such as txt or word; next, the control module loads corresponding post-event criteria of different types of post-event data; finally, the corresponding type of the posterior data is interpreted according to the posterior criterion to obtain an interpretation result, and the interpretation result is stored in a storage device, wherein the storage form can be selected from text formats such as txt or word. The method realizes the full-period automatic interpretation of the test data, not only meets the requirement of test real-time performance, but also can improve the value of the test data and carry out comprehensive and deep analysis on test equipment; the method has the advantages that the criterion is accurately set, different types of criteria can be set according to different types of parameters, the problem that the serial signal connection and disconnection actions are correctly and wrongly interpreted during time sequence is solved, the interpretation efficiency of huge data volume is improved, the interpretation time is saved, and meanwhile labor cost is saved.
In an alternative embodiment, the real-time signal types include: one or more of a real-time analog signal, a real-time pulse count signal, and a real-time sequence time series signal. The shutdown signal sent by the rocket-borne computer is called a time sequence signal; the timing reference of each flight segment is provided, and each control signal sent by the program distributor is called a time series signal. Because all are switch-type signals, time-series signals are generally called as time-series signals in a carrier rocket/missile.
Fig. 2 is a flow chart of a real-time analog signal interpretation method according to another embodiment of the invention.
In an alternative embodiment, as shown in fig. 2, a real-time analog signal interpretation method is provided, which includes the following specific steps: s311: acquiring 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, judging the real-time analog signal value as normal data; s314: if the real-time analog signal value is not within the normal value range, judging the real-time analog signal value as abnormal data; s315: the first interpretation result is stored.
In this embodiment, the control module performs interpretation on the real-time analog signal, and in the interpretation process, firstly obtains the real-time analog signal value, secondly determines whether the real-time analog signal value is within a normal value range, if the real-time analog signal value is within the normal value range, the real-time analog signal value is determined as normal data, otherwise, the real-time analog signal value is determined as abnormal data, and the control module stores the interpretation result and the received real-time signal in the storage. The normal value range can be determined before the simulation test, and is not a fixed value, so that the method can accurately and effectively judge the real-time simulation signal, saves the interpretation time and also saves the labor cost.
Fig. 3 is a flow chart of a real-time pulse count signal interpretation method according to yet another embodiment of the invention.
In an alternative embodiment, as shown in fig. 3, a real-time pulse counting signal interpretation method is provided, which includes the following specific steps: s321: calculating the pulse counting increment of the acquired real-time pulse counting signal; s322: judging whether the pulse counting increment of the real-time pulse counting signal is within a normal increment range or not; s323: if the pulse counting increment of the real-time pulse counting signal is within the normal increment range, judging the real-time pulse counting signal to be normal data; s324: if the pulse counting increment of the real-time pulse counting signal is not within the normal increment range, judging the real-time pulse counting signal to be abnormal data; s325: and storing the second interpretation result.
In this embodiment, the control module performs interpretation on the real-time pulse counting signal, and in the interpretation process, firstly, the pulse counting increment of the acquired real-time pulse counting signal is calculated, secondly, whether the pulse counting increment of the real-time pulse counting signal is within a normal increment range is judged, if the pulse counting increment is within the normal increment range, the pulse counting increment is judged to be normal data, otherwise, the pulse counting increment is judged to be abnormal data, and the interpretation result and the received real-time signal are stored in the storage device by the control module. The normal value range can be determined before the simulation test, and is not a fixed value, and the method can accurately and effectively judge the real-time pulse counting signal, thereby saving the interpretation time and labor cost.
Fig. 4 is a flow chart of a real-time series signal interpretation method according to another embodiment of the invention.
In an alternative embodiment, as shown in fig. 4, a real-time-series signal interpretation method is provided, which includes the following specific steps: s331: acquiring a time sequence time string data value of the time sequence time string signal in the on-off action time; s332: comparing the time series data value with corresponding criterion to obtain a judgment result; s333: and storing the third interpretation result.
Optionally, when the criterion requires that the time sequence string signal is on within the on-off action time, judging whether all the time sequence string data values are 1; if the time sequence time string data values are all 1, judging that the real-time sequence time string signals are normal data according to the judgment result; if one digit value in the time sequence data value is 0, the judgment result is that the real-time sequence signal is abnormal data.
Optionally, when the criterion requires that the time sequence string signal is disconnected within the on-off action time, judging whether all the time sequence string data values are 0; if all the time sequence time string data values are 0, judging that the real-time sequence time string signals are normal data; if one digit value in the time sequence data value is 1, the real-time sequence signal is judged to be abnormal data.
In this embodiment, the control module performs interpretation on the real-time sequence string signal, and in the interpretation process, first obtains a time sequence string data value of the time sequence string signal within an action time, determines whether to require to be switched on within the action time, and further determines whether the time sequence string data value is normal according to a determination result;
if the serial signal criterion requirement in time sequence is connection, judging whether the serial data values are all 1, if so, judging as normal data, and if not, judging as abnormal data;
and if the time sequence time series signal criterion is required to be disconnected, judging whether the time series data values are all 0, if so, judging as normal data, and if not, judging as abnormal data.
And finally, the control module stores the interpretation result and the received real-time signal in a storage. The method can accurately and effectively judge the time sequence time series signal in real time, saves interpretation time and labor cost.
In an optional embodiment, the post-hoc data comprises: post-incident analog data, post-incident pulse count data, and post-incident time series data. The control module receives the signals and stores the signals as data.
Fig. 5 is a flowchart of a post-hoc analog data interpretation method according to still another embodiment of the present invention.
In an alternative embodiment, as shown in fig. 5, when the posterior data includes posterior simulated data, interpreting the posterior data of the corresponding type according to the posterior criterion includes: s511: acquiring post simulation data; s512: judging whether the post simulation data has periodic item interference and the interference amount exceeds the expectation; s513: if the periodic item interference exists and the interference amount exceeds the expectation, judging the data to be abnormal data; s514: otherwise, continuously judging whether the after-the-fact simulation data has a trend item or not; s515: if the trend item exists, judging the trend item to be abnormal data; s516: if no trend item exists, judging the data to be normal data; s517: and storing the fourth interpretation result.
In the embodiment, the control module interprets the post-event simulation data, in the interpretation process, firstly, the post-event simulation data is obtained, secondly, whether the post-event simulation data has periodic item interference and the interference amount exceeds the expectation is judged, in the invention, the amplitude exceeds 30% of a normal value, if yes, the data is interpreted as abnormal data, otherwise, whether the post-event simulation data has a trend item is continuously judged, if the trend item exists, the data is judged to be abnormal data, otherwise, the data is normal data, and finally, the control module stores the interpretation result in the storage. The trend item is that although the current data is normal, the trend item gradually increases or decreases to form an ascending or descending trend, whether the data is in a normal range can be judged through the trend, and the method accurately and effectively judges the post simulation data, saves interpretation time and labor cost.
Fig. 6 is a flowchart of a method for interpreting post-event pulse count data according to yet another embodiment of the present invention.
In an alternative embodiment, as shown in fig. 6, when the post data comprises the post pulse count data, interpreting the post data of the corresponding type according to the post criterion comprises: s521: acquiring the total number of pulses of the pulse counting data; s522: judging whether the total number of pulses is within a normal range; s523: if the total number of the pulses is within the normal range, judging the data to be normal data; s524: if the total number of the pulses is not in the normal range, judging the pulse is abnormal data; s525: and storing the fifth interpretation result.
In this embodiment, the control module interprets the pulse count data after the fact, in the interpretation process, firstly, the total number of pulses of the pulse count data is obtained, secondly, whether the total number of pulses is within a normal range is judged, if the total number of pulses is within the normal range, the total number of pulses is judged to be normal data, otherwise, the total number of pulses is judged to be abnormal data, and finally, the interpretation result is stored in the storage device by the control module. The method can accurately and effectively judge the pulse count data after the fact, saves interpretation time and labor cost. The normal range interval is specifically determined before the launch vehicle test, and is given during the test. In particular according to the flight sequence of the launch vehicle or missile. The pulse signal accumulated number can reflect parameters such as speed, acceleration, displacement and the like in the time period, and whether an abnormity occurs or not can be reflected by comparing the value with a design standard range
FIG. 7 is a flowchart of a post-sequence time-series data interpretation method according to another embodiment of the invention.
In an alternative embodiment, as shown in fig. 7, when the posterior data includes the posterior time series data, the interpreting the posterior data of the corresponding type according to the posterior criterion includes: s531: calculating the total on-off duration of the acquired time series data; s532: judging whether the total on-off duration meets the on-off requirement or not; s533: if the total on-off duration meets the on-off requirement, judging that the data is normal; s534: if the total on-off duration does not meet the on-off requirement, judging that the data is abnormal; s535: the sixth interpretation result is stored.
In the embodiment, the control module interprets the time sequence time string data after the fact, in the interpretation process, firstly, the acquired total on-off duration of the time sequence time string data is calculated, secondly, whether the total on-off duration meets the on-off requirement is judged, if yes, the data is judged to be normal, otherwise, the data is judged to be abnormal, and finally, the control module stores the interpretation result in the storage. The total on-off duration is determined before each interpretation, is not a unique range value, and has different range values according to different requirements. The method can accurately and effectively judge the time series data of the time sequence afterwards, saves the interpretation time and labor cost.
Fig. 8 is a flow chart of multi-type signal full-period automatic interpretation according to still another embodiment of the present invention.
In an alternative embodiment, as shown in fig. 8, a specific real-time process of providing a full-period automatic interpretation method for multi-type signals is as follows:
(1) loading a real-time interpretation criterion comprising an analog signal, a pulse counting signal and a time sequence time series signal, and reading the real-time interpretation criterion into a buffer;
(2) judging the type of the interpretation signal to which the channel signal belongs: if the signal is an analog signal, executing the step (3); if the pulse counting signal is the pulse counting signal, executing the step (6); if the time sequence signal is a time sequence signal, executing the step (9);
(3) reading a real-time data value of the simulation channel at a moment;
(4) comparing the data value with a corresponding criterion, and judging the data value as normal data if the data value is within a normal value range; otherwise, judging the data as abnormal data; recording the interpretation result to a first word document;
(5) reading the data value at the next moment, and turning to the step (4) until the data interpretation at the final time point is completed; in the same way, reading the data value of the next channel of the original data until the data of the channel is not input in real time, and then executing the step (12);
(6) loading the 1 st criterion, reading the pulse count of the corresponding channel at the time point in the criterion, reading the pulse count at the time point, and calculating the pulse count increment, namely the difference between the pulse count and the pulse count;
(7) comparing the pulse counting increment with a corresponding criterion, judging whether the pulse counting increment is in a normal increment range, and if so, judging the pulse counting increment to be normal data of the pulse increment; if not, judging the data to be abnormal data of pulse increment; recording the interpretation result to a second word document;
(8) reading the next criterion item and the corresponding channel data, calculating the pulse counting increment, turning to the step (7) until the channel data is not input in real time any more, and then executing the step (12);
(9) reading a time string data value of a first channel needing to be interpreted between on-off action time from real-time data according to an action time criterion;
(10) comparing the time series data value with a criterion, if the criterion requires that the time interval is switched on, judging whether the time series data value is all 1, if so, judging the time series data value to be normal data of the time series data; judging the abnormal data of the time string data as long as one time string data value is 0; if the time interval is required to be disconnected by the criterion, judging whether the time series data values are all 0, if so, judging the time series data values to be normal data of the time series data; judging the abnormal data of the time string data as long as one time string data value is 1; recording the interpretation result to a third word document;
(11) reading the next criterion of the first channel to be interpreted and corresponding original data, and turning to the step (10) until all the criterion item interpretations of all the first channels are finished; in the same way, the next channel criterion item is read until the channel data is not input in real time any more, and then the step (12) is executed;
(12) and if all the channels of all the types of data are completely interpreted in real time, ending the real-time interpretation stage and turning to a post-interpretation stage.
(13) And loading the criterion for the post-interpretation of all data types of all channels, and performing the post-interpretation according to the real-time data stored in the real-time interpretation process.
(14) Judging the type of the interpretation signal to which the channel signal belongs: if the signal is an analog signal, executing the step (15); if the pulse counting signal is the pulse counting signal, executing the step (18); if the signal is a time sequence time series signal, executing the step (20);
(15) for the analog signal channel, reading all data of the ith channel, judging whether the data has periodic item interference, if the periodic item interference exists and the signal interference amount exceeds the expectation, judging that the signal is abnormal, and storing and recording an interpretation result to a first word document;
(16) judging whether the channel signal has a trend item, if so, judging that the signal is abnormal and storing and recording an interpretation result to a first word document;
(17) reading the data value of the next channel, turning to the step (15) until the data interpretation of the analog channel is completed, and then executing the step (22);
(18) for the pulse signal, reading the total number of pulses of the ith channel, judging whether the total number of pulses is between intervals, and if so, judging that the pulse number of the channel is normal; otherwise, judging that the total number of the channel pulses is abnormal; recording the interpretation result to a second word document;
(19) reading the criterion item of the next pulse channel and the total number of the corresponding channel pulses, turning to the step (18) until all the pulse channel data are interpreted, and then executing the step (22);
(20) reading ith channel data for time series data, and calculating the total on-off duration of the channel data; if the total duration meets the on-off requirement, judging that the channel data is normal, otherwise, judging that the data is abnormal; recording the interpretation result to a third word document;
(21) reading the criterion items of the serial channel and the total on-off duration of the corresponding channel in the next time sequence, turning to the step (20), and executing the step (22) until the data of the serial channel in all the time sequences are completely interpreted;
(22) and (6) ending.
According to the embodiment, the full-cycle automatic interpretation of the multi-type signals is realized, a full-cycle interpretation method for the multi-type signals is designed according to the interpretation requirements of different stages, and the full-cycle interpretation or the staged interpretation can be performed only on a certain type of signals according to the requirements. By designing different interpretation methods of real-time interpretation and postero-interpretation, the full-period automatic interpretation of the test data is realized, the requirement on test instantaneity is met, the value of the test data can be improved, and the test equipment is comprehensively and deeply analyzed.
Another aspect of the present invention provides a signal interpretation system comprising: the real-time signal loading module is used for loading real-time signals; the judging module is used for judging the type of the real-time signal; a selection module for selecting a corresponding criterion for each type of the real-time signals; the real-time signal interpretation module is used for interpreting the corresponding real-time signal by adopting the criterion; the storage module is used for storing the interpretation result of the real-time signal and obtaining the post data of the corresponding type of the real-time signal; the posterior criterion loading module is used for loading corresponding posterior criteria aiming at posterior data of each type of real-time signals; and the posterior data interpretation module is used for interpreting the posterior data of the corresponding type according to the posterior criterion.
Optionally, the real-time signal loading module loads a real-time signal, where the real-time signal includes: one or more of a real-time analog signal, a real-time pulse count signal, and a real-time sequence time series signal.
Optionally, the real-time signal interpretation module is configured to interpret the corresponding real-time signal by using the criterion, and when the real-time signal type includes a real-time analog signal, the interpreting the corresponding real-time signal by using the criterion includes: acquiring the real-time analog signal value; judging whether the real-time analog signal value is within a normal value range; if the real-time analog signal value is within the normal value range, judging the real-time analog signal value as normal data; if the real-time analog signal value is not within the normal value range, judging the real-time analog signal value as abnormal data; the storage module stores the first interpretation result.
Optionally, the real-time signal interpretation module is configured to interpret the corresponding real-time signal by using the criterion, and when the real-time signal type includes a real-time pulse counting signal, the interpreting the corresponding real-time signal by using the criterion includes: calculating the pulse counting increment of the acquired real-time pulse counting signal; judging whether the pulse counting increment of the real-time pulse counting signal is within a normal increment range; if the pulse counting increment of the real-time pulse counting signal is within a normal increment range, judging the real-time pulse counting signal to be normal data; if the pulse counting increment of the real-time pulse counting signal is not within the normal increment range, judging the real-time pulse counting signal to be abnormal data; and the storage module stores the second interpretation result.
Optionally, the real-time signal interpretation module is configured to interpret the corresponding real-time signal by using the criterion, and when the real-time signal type includes the real-time sequence time series signal, the real-time signal interpretation module is configured to interpret the corresponding real-time signal by using the criterion, where the real-time sequence time series signal interpretation includes: acquiring a time sequence time string data value of the time sequence time string signal within the on-off action time; comparing the time series data value with corresponding criterion to obtain a judgment result; and the storage module stores the third interpretation result.
Optionally, the real-time signal interpretation module is configured to interpret the corresponding real-time signal by using the criterion, and when the criterion requires that the string signal is turned on during the time sequence of the on-off action time, determine whether all string data values during the time sequence are 1; if all the time sequence time string data values are 1, the judgment result is that the real-time sequence time string signals are normal data; and if one digit value in the time sequence time string data values is 0, the judgment result shows that the real-time sequence time string signal is abnormal data.
Optionally, the real-time signal interpretation module is configured to interpret the corresponding real-time signal by using the criterion, and when the criterion requires that the string signal is turned off during the time sequence of the on-off action time, determine whether all string data values during the time sequence are 0; if all the time sequence time string data values are 0, judging that the real-time sequence time string signals are normal data; and if one digit value in the time sequence time string data values is 1, judging that the real-time sequence time string signal is abnormal data.
Optionally, the posterior criterion loading module is configured to load a corresponding posterior criterion for posterior data of each type of real-time signal, where the posterior criterion includes: post-incident analog data, post-incident pulse count data, and post-incident time series data.
Optionally, the posterior data interpretation module is configured to interpret the posterior data of the corresponding type according to the posterior criterion, and when the posterior data includes posterior analog data, the interpreting the posterior data of the corresponding type according to the posterior criterion includes: acquiring the post simulation data; judging whether the post-event simulation data has periodic item interference and the interference amount exceeds the expectation; if the periodic item interference exists and the interference amount exceeds the expectation, judging the data to be abnormal data; otherwise, continuously judging whether the after-the-fact simulation data has a trend item or not; if the trend item exists, judging the trend item to be abnormal data; if no trend item exists, judging the data to be normal data; and the storage module stores the fourth interpretation result.
Optionally, the posterior data interpretation module is configured to interpret the posterior data of the corresponding type according to the posterior criterion, and when the posterior data includes the posterior pulse count data, the interpreting the posterior data of the corresponding type according to the posterior criterion includes: acquiring the total number of pulses of the pulse counting data; judging whether the total number of the pulses is within a normal range; if the total number of the pulses is within a normal range, judging the data to be normal data; if the total number of the pulses is not in a normal range, judging the pulse is abnormal data; and the storage module stores the fifth interpretation result.
Optionally, the posterior data interpretation module is configured to interpret the posterior data of the corresponding type according to the posterior criterion, and when the posterior data includes the posterior time-series data, the interpreting the posterior data of the corresponding type according to the posterior criterion includes: calculating the total on-off duration of the acquired time series data; judging whether the total on-off duration meets the on-off requirement or not; if the total on-off duration meets the on-off requirement, judging that the data is normal; if the total on-off duration does not meet the on-off requirement, judging that the data is abnormal; and the storage module stores the sixth interpretation result.
A further aspect of the present invention provides a computer storage medium, on which a computer program is stored, which when executed by a processor implements the steps of a signal interpretation method according to any of the above aspects.
A further aspect of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the signal interpretation method according to the above technical solution.
The invention aims to protect a signal interpretation method, which comprises the following steps: loading a real-time signal; judging the type of the real-time signal; selecting corresponding criteria for each type of said real-time signal; judging the corresponding real-time signals by adopting the criterion; storing the interpretation result of the real-time signal to obtain the post data of the real-time signal of the corresponding type; loading corresponding post-event criteria aiming at post-event data of each type of real-time signals; and judging the posterior data of the corresponding type according to the posterior criterion, and performing full-period data judgment on the multi-type signals. The method realizes the full-period automatic interpretation of the test data, not only meets the requirement of test real-time performance, but also can improve the value of the test data and carry out comprehensive and deep analysis on test equipment; the method has the advantages that the criterion is accurately set, different types of criteria can be set according to different types of parameters, the problem that the serial signal connection and disconnection actions are correctly and wrongly interpreted during time sequence is solved, the interpretation efficiency of huge data volume is improved, the interpretation time is saved, and meanwhile labor cost is saved.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (14)
1. A method of signal interpretation, comprising:
loading a real-time signal;
judging the type of the real-time signal;
selecting corresponding criteria for each type of said real-time signal;
judging the corresponding real-time signals by adopting the criterion;
storing the interpretation result of the real-time signal to obtain the post data of the real-time signal of the corresponding type;
loading corresponding post-event criteria aiming at post-event data of each type of real-time signals;
and judging and reading the posterior data of the corresponding type according to the posterior criterion.
2. The signal interpretation method of claim 1, wherein the real-time signal types comprise: one or more of a real-time analog signal, a real-time pulse count signal, and a real-time sequence time series signal.
3. The signal interpretation method according to claim 2, wherein when the real-time signal type includes a real-time analog signal, said interpreting the corresponding real-time signal using the criterion comprises:
acquiring the real-time analog signal value;
judging whether the real-time analog signal value is within a normal value range;
if the real-time analog signal value is within the normal value range, judging the real-time analog signal value as normal data;
if the real-time analog signal value is not within the normal value range, judging the real-time analog signal value as abnormal data;
the first interpretation result is stored.
4. The signal interpretation method according to claim 2, wherein when the real-time signal type includes a real-time pulse count signal, said interpreting the corresponding real-time signal using the criterion comprises:
calculating the pulse counting increment of the acquired real-time pulse counting signal;
judging whether the pulse counting increment of the real-time pulse counting signal is within a normal increment range;
if the pulse counting increment of the real-time pulse counting signal is within a normal increment range, judging the real-time pulse counting signal to be normal data;
if the pulse counting increment of the real-time pulse counting signal is not within the normal increment range, judging the real-time pulse counting signal to be abnormal data;
and storing the second interpretation result.
5. The signal interpretation method according to claim 2, wherein when the real-time signal type includes the real-time-series signal, the interpretation of the corresponding real-time signal using the criterion includes:
acquiring a time sequence time string data value of the time sequence time string signal within the on-off action time;
comparing the time series data value with corresponding criterion to obtain a judgment result;
and storing the third interpretation result.
6. The signal interpretation method according to claim 5,
when the criterion requires that the time sequence string signal is switched on within the on-off action time, judging whether the time sequence string data values are all 1;
if all the time sequence time string data values are 1, the judgment result is that the real-time sequence time string signals are normal data;
and if one digit value in the time sequence time string data values is 0, the judgment result shows that the real-time sequence time string signal is abnormal data.
7. The signal interpretation method according to claim 5,
when the criterion requires that the time sequence string signal is disconnected within the on-off action time, judging whether the time sequence string data values are all 0 or not;
if all the time sequence time string data values are 0, judging that the real-time sequence time string signals are normal data;
and if one digit value in the time sequence time string data values is 1, judging that the real-time sequence time string signal is abnormal data.
8. The signal interpretation method according to claims 1 to 7, wherein the posterior data comprises: post-incident analog data, post-incident pulse count data, and post-incident time series data.
9. The signal interpretation method according to claim 8, wherein when the posterior data comprises posterior analog data, the interpreting the corresponding type of the posterior data according to the posterior criterion comprises:
acquiring the post simulation data;
judging whether the post-event simulation data has periodic item interference and the interference amount exceeds the expectation;
if the periodic item interference exists and the interference amount exceeds the expectation, judging the data to be abnormal data;
otherwise, continuously judging whether the after-the-fact simulation data has a trend item or not;
if the trend item exists, judging the trend item to be abnormal data;
if no trend item exists, judging the data to be normal data;
and storing the fourth interpretation result.
10. The signal interpretation method according to claim 8, wherein when the posterior data comprises the posterior pulse count data, the interpreting the corresponding type of the posterior data according to the posterior criterion comprises:
acquiring the total number of pulses of the pulse counting data;
judging whether the total number of the pulses is within a normal range;
if the total number of the pulses is within a normal range, judging the data to be normal data;
if the total number of the pulses is not in a normal range, judging the pulse is abnormal data;
and storing the fifth interpretation result.
11. The signal interpretation method according to claim 8, wherein when the posterior data includes the posterior time-series data, the interpreting the corresponding type of the posterior data according to the posterior criterion includes:
calculating the total on-off duration of the acquired time series data;
judging whether the total on-off duration meets the on-off requirement or not;
if the total on-off duration meets the on-off requirement, judging that the data is normal;
if the total on-off duration does not meet the on-off requirement, judging that the data is abnormal;
the sixth interpretation result is stored.
12. A signal interpretation system, comprising:
the real-time signal loading module is used for loading real-time signals;
the judging module is used for judging the type of the real-time signal;
a selection module for selecting a corresponding criterion for each type of the real-time signals;
the real-time signal interpretation module is used for interpreting the corresponding real-time signal by adopting the criterion;
the storage module is used for storing the interpretation result of the real-time signal and obtaining the post data of the corresponding type of the real-time signal;
the posterior criterion loading module is used for loading corresponding posterior criteria aiming at posterior data of each type of real-time signals;
and the posterior data interpretation module is used for interpreting the posterior data of the corresponding type according to the posterior criterion.
13. A computer storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of a signal interpretation method according to any one of claims 1 to 11.
14. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of a signal interpretation method according to any of claims 1 to 11.
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