CN112842297A - Signal processing method, signal processing device, computer equipment and storage medium - Google Patents
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Abstract
The present application relates to a signal processing method, apparatus, computer device and storage medium. The method comprises the following steps: acquiring a first comprehensive signal corresponding to a first moment, wherein the first comprehensive signal comprises a gyroscope signal, a sound signal, a blood pressure signal, a photoelectric signal and a signal; and inputting the first comprehensive signal into an early warning model to obtain first early warning information, wherein the first early warning information comprises an inference event corresponding to a second moment and an occurrence probability corresponding to the inference event, and the second moment is behind the first moment. Based on the method, the inferred event occurring after the current moment and the probability of the inferred event are pre-judged according to the comprehensive signals collected in real time, so that the condition of a patient is early warned in advance, and corresponding relief measures are taken, and the situation that a user cannot take corresponding measures in time when an emergency occurs is avoided.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a signal processing method and apparatus, a computer device, and a storage medium.
Background
ECG is a cardiac electrical signal, and is often used for examining various arrhythmia, ventricular atrial hypertrophy, myocardial infarction, myocardial ischemia, and the like. Conventional 12 lead ECG is typically used in intensive care units, where there is little motion of the patient and therefore the signal can be acquired smoothly. The Holter type electrocardiogram collecting device which is popular at present does not limit the activity of the patient generally, and can monitor the electrocardiogram of the patient for a long time.
Today, these monitors generally identify from the already recorded signals whether the recorded time period signal is a disease signal as a screening solution. However, some cardiac inference events require more signals than the ECG signal, such as respiration, blood oxygenation, etc. as an aid. However, when symptoms such as arrhythmia and myocardial infarction occur as an abrupt temporary event, the conventional electrocardiogram collecting device can only perform examination according to the occurred symptoms, and cannot perform early warning before the symptoms occur.
Disclosure of Invention
In order to solve the technical problem, the present application provides a signal processing method, an apparatus, a computer device and a storage medium.
In a first aspect, the present application provides a signal processing method, including:
acquiring a first comprehensive signal corresponding to a first moment, wherein the first comprehensive signal comprises a gyroscope signal, a sound signal, a blood pressure signal, a photoelectric signal and a signal;
and inputting the first comprehensive signal into an early warning model to obtain first early warning information, wherein the first early warning information comprises an inference event corresponding to a second moment and an occurrence probability corresponding to the inference event, and the second moment is behind the first moment.
Optionally, after the first comprehensive signal is input into an early warning model and first early warning information is obtained, the method further includes:
and inputting the first comprehensive signal into a detection model to obtain a first detection result, wherein the first detection result comprises an abnormal state and an abnormal type corresponding to the first moment.
Optionally, after the time difference between the second time and the first time is a preset interval, and the first integrated signal is input into a detection model to obtain a detection result corresponding to the first time, the method further includes:
acquiring a second comprehensive signal corresponding to the second moment;
inputting the second comprehensive signal into the detection model to obtain a second detection result;
and correcting the early warning model according to the second detection result.
Optionally, the correcting the early warning model according to the second detection result includes:
and when the second detection result is not matched with the first early warning information, correcting the early warning model according to the second detection result to obtain a corrected early warning model, wherein the corrected early warning model is used for carrying out early warning analysis on the comprehensive signal corresponding to the third moment.
Optionally, after the first comprehensive signal is input into an early warning model and first early warning information is obtained, the method further includes:
and when the occurrence probability corresponding to the current inferred event in the first early warning information is greater than the preset threshold value of the corresponding event, generating an early warning report according to the first comprehensive signal, and sending out an early warning signal.
Optionally, before the obtaining of the first integrated signal corresponding to the first time, the method further includes:
within a preset time length, acquiring a sample comprehensive signal, wherein the sample comprehensive signal comprises a plurality of detection signals;
marking each detection signal according to an anomaly detection algorithm to obtain an anomaly mark corresponding to each detection signal;
segmenting each detection signal according to the corresponding abnormal mark of each detection signal to obtain a corresponding early warning signal slice;
and carrying out deep learning according to the early warning signal slices and the abnormal labels corresponding to the detection signals, and establishing the early warning model.
Optionally, the segmenting each detection signal according to the abnormal time corresponding to each detection signal to obtain a corresponding early warning signal slice includes:
segmenting each detection signal to obtain a plurality of segment signals, wherein the segment signals carry corresponding starting time and ending time;
and selecting a segment signal corresponding to the end time which is a preset interval away from the abnormal time as the early warning signal slice before the abnormal time corresponding to each detection signal.
In a second aspect, the present application provides a signal processing apparatus comprising:
the device comprises a first signal acquisition module, a second signal acquisition module and a control module, wherein the first signal acquisition module is used for acquiring a first comprehensive signal corresponding to a first moment, and the first comprehensive signal comprises a gyroscope signal, a sound signal, a blood pressure signal, a photoelectric signal and a signal;
and the early warning module is used for inputting the first comprehensive signal into an early warning model to obtain first early warning information, wherein the first early warning information comprises an inference event corresponding to a second moment and an occurrence probability corresponding to the inference event, and the second moment is greater than the first moment.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring a first comprehensive signal corresponding to a first moment, wherein the first comprehensive signal comprises a gyroscope signal, a sound signal, a blood pressure signal, a photoelectric signal and a signal;
and inputting the first comprehensive signal into an early warning model to obtain first early warning information, wherein the first early warning information comprises an inference event corresponding to a second moment and an occurrence probability corresponding to the inference event, and the second moment is behind the first moment.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring a first comprehensive signal corresponding to a first moment, wherein the first comprehensive signal comprises a gyroscope signal, a sound signal, a blood pressure signal, a photoelectric signal and a signal;
and inputting the first comprehensive signal into an early warning model to obtain first early warning information, wherein the first early warning information comprises an inference event corresponding to a second moment and an occurrence probability corresponding to the inference event, and the second moment is behind the first moment.
The signal processing method, the signal processing device, the computer equipment and the storage medium comprise the following steps: acquiring a first comprehensive signal corresponding to a first moment, wherein the first comprehensive signal comprises a gyroscope signal, a sound signal, a blood pressure signal, a photoelectric signal and a signal; and inputting the first comprehensive signal into an early warning model to obtain first early warning information, wherein the first early warning information comprises an inference event corresponding to a second moment and an occurrence probability corresponding to the inference event, and the second moment is behind the first moment. Based on the method, the inferred event occurring after the current moment and the probability of the inferred event are pre-judged according to the comprehensive signals collected in real time, so that the condition of a patient is early warned in advance, and corresponding relief measures are taken, and the situation that a user cannot take corresponding measures in time when an emergency occurs is avoided.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a diagram of an exemplary signal processing method;
FIG. 2 is a flow diagram of a signal processing method according to an embodiment;
FIG. 3 is a schematic diagram of a signal processing principle in one embodiment;
FIG. 4 is a block diagram of a signal processing apparatus according to an embodiment;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but 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 application.
Fig. 1 is a diagram of an application environment of a signal processing method in one embodiment. Referring to fig. 1, the signal processing method is applied to a signal processing system. The signal processing system includes a signal acquisition terminal 110 and a server 120. The signal acquisition terminal 110 and the server 120 are connected via a network. The signal acquisition terminal 110 includes at least one of an ECG sensor, a high-precision gyroscope sensor, a sound sensor, a photoelectric sensor, a blood pressure sensor, or other biosensors. The server 120 may be implemented as a stand-alone server or a server cluster composed of a plurality of servers.
In one embodiment, fig. 2 is a flow chart illustrating a signal processing method in one embodiment, and referring to fig. 2, a signal processing method is provided. The embodiment is mainly exemplified by applying the method to the signal acquisition terminal 110 (or the server 120) in fig. 1, and the signal processing method specifically includes the following steps:
step S210, a first integrated signal corresponding to a first time is obtained.
Specifically, a first comprehensive signal collected by a plurality of biosensors at a first moment is obtained, and the comprehensive signal comprises physiological parameters such as a gyroscope signal, a sound signal, a blood pressure signal, a photoelectric signal and a signal.
Step S220, inputting the first comprehensive signal into an early warning model to obtain first early warning information, wherein the first early warning information comprises an inference event corresponding to a second moment and an occurrence probability corresponding to the inference event, and the second moment is after the first moment.
Specifically, the early warning model is a neural network model trained in advance, the first early warning information is an inference result obtained by the early warning model according to a first comprehensive signal, the first early warning information comprises at least one inference event and occurrence probability corresponding to each inference event, the probability of occurrence of the event corresponding to each signal is estimated according to the early warning model, so that the inference event which will occur at a second moment after the first moment and the occurrence probability of the inference event are comprehensively predicted, the inference event is an abnormal event which will occur according to the comprehensive signal inference, corresponding rescue measures can be extracted and taken before symptoms of a patient occur according to the inference event, the condition of the patient needs to be determined according to the symptoms when the user occurs the emergency event, and then a large amount of rescue time is wasted by taking corresponding measures according to the condition of the patient.
In one embodiment, the first integrated signal is input into a detection model, so as to obtain a first detection result, where the first detection result includes an abnormal state and an abnormal type corresponding to the first time.
Specifically, the first integrated signal is simultaneously input into the detection model and the early warning model, and the early warning model is used for detecting the inferred event corresponding to the second moment and the occurrence probability of the inferred event, that is, according to the possibility that the early warning model is used for detecting the occurrence of the subsequent abnormal event, early warning is performed according to the inferred event. The detection model is used for detecting an abnormal state and an abnormal type corresponding to the first comprehensive signal at the first moment, the first detection result is a diagnosis result obtained according to the first comprehensive signal at the first moment, namely the detection model is used for detecting whether an abnormal event occurs at the current moment, the abnormal state comprises normal and abnormal, and the abnormal type is a disease type obtained according to the first comprehensive signal.
In one embodiment, a time difference between the second time and the first time is a preset interval, and the first integrated signal is input into a detection model, and after a detection result corresponding to the first time is obtained, a second integrated signal corresponding to the second time is obtained; inputting the second comprehensive signal into the detection model to obtain a second detection result; and correcting the early warning model according to the second detection result.
Specifically, an inference event and the occurrence probability of the inference event are obtained according to a first comprehensive signal, a second comprehensive signal corresponding to a second moment is obtained at the second moment, a second detection result corresponding to the second comprehensive signal, namely a diagnosis result obtained after the first moment, can be detected according to a detection model, the diagnosis result corresponding to the second moment can be used for detecting the accuracy of the inference event and the occurrence probability of the inference event obtained according to an early warning model at the first moment, and if the second detection result corresponding to the second moment is matched with the inference event corresponding to the first moment, the inference result output by the early warning model at the first moment is verified to be accurate.
In an embodiment, when the second detection result is not matched with the first warning information, the warning model is corrected according to the second detection result to obtain a corrected warning model, and the corrected warning model is used for performing warning analysis on the comprehensive signal corresponding to the third moment.
Specifically, if the second detection result corresponding to the second moment is not matched with the inferred event corresponding to the first moment, the early warning model is corrected according to the second detection result corresponding to the second moment to obtain a corrected early warning model, the early warning model performs early judgment on the abnormal event of the subsequently acquired comprehensive signal according to the corrected early warning model, and so on, the inferred result output by the early warning model at the previous moment is corrected according to the detection result output by the detection model at the current moment, and the early warning model is iteratively updated, so that the accuracy of the output result of the early warning model is improved.
In an embodiment, after the first comprehensive signal is input into the early warning model to obtain first early warning information, when an occurrence probability corresponding to a current inferred event in the first early warning information is greater than a preset threshold of a corresponding event, an early warning report is generated according to the first comprehensive signal, and an early warning signal is sent out.
Specifically, the current inference event is any inference event in first early warning information corresponding to a first moment, each inference event has a corresponding preset threshold value, the preset threshold value is a probability value, if the occurrence probability corresponding to the current inference event is greater than the preset threshold value corresponding to the event, the probability that the current inference event occurs is high, an early warning report is generated according to the first comprehensive signal, the early warning report is used for uploading a server background or is sent to a supervision medical worker and a mobile terminal held by a guardian of a patient, relevant information of the patient about to occur symptoms is timely informed to relevant personnel, meanwhile, an early warning signal is sent out, the personnel nearby or the guardian holding a remote monitoring terminal is timely informed to take corresponding rescue measures, and the patient is prevented from being unsuited for rescue when the symptoms occur.
In one embodiment, before the obtaining of the first integrated signal corresponding to the first time, a sample integrated signal is obtained within a preset time duration, where the sample integrated signal includes a plurality of detection signals; marking each detection signal according to an anomaly detection algorithm to obtain an anomaly mark corresponding to each detection signal; segmenting each detection signal according to the corresponding abnormal mark of each detection signal to obtain a corresponding early warning signal slice; and carrying out deep learning according to the early warning signal slices and the abnormal labels corresponding to the detection signals, and establishing the early warning model.
Specifically, the sample integrated signal is a signal continuously collected within a preset time length, the sample integrated signal includes a plurality of detection signals, the signal length of each detection signal corresponds to the preset time length, the detection signals may specifically be physiological parameters such as gyroscope signals, sound signals, blood pressure signals, photoelectric signals and signals, whether each detection signal is abnormal or not is detected according to an abnormal detection algorithm, a segment in which each detection signal is abnormal is marked to obtain an abnormal mark corresponding to each detection signal, each detection signal is segmented according to the abnormal mark on each detection signal, each detection signal is segmented into a plurality of segment signals, each segment signal carries corresponding time information, the time length of each segment signal from the abnormal mark is different, any one segment signal is selected from the plurality of segment signals as an early warning signal segment, the early warning models obtained by selecting different fragment signals and training abnormal labels are also different.
Referring to fig. 3, the continuously recorded integrated multi-modal signal is any one of the detection signals in the integrated signal, and the detection signals are segmented, wherein the signal for early warning is segmented into chi0Signal slice x for early warning1Signal slice x for early warning2For three segment signals obtained by dividing the detection signal, if t is selected before the abnormal mark0Slicing x-shaped slice with corresponding segment signal as early warning signal0I.e. all detection signals are selected to be at a distance t from the abnormal mark in the respective detection signal0The segment signal of (2) is used as an early warning signal slice of each detection signal, and an early warning model obtained by training according to the early warning signal slice corresponding to each detection signal and the abnormal label can be used for deducing an abnormal event after the time length of t0 from the first time, namely the time interval between the first time and the second time is t 0. Different fragment signals are selected as early warning signal slices to determine how far the early warning model can predict the abnormal events.
In one embodiment, each detection signal is segmented to obtain a plurality of segment signals, and the segment signals carry corresponding starting time and ending time; and selecting a segment signal corresponding to the end time which is a preset interval away from the abnormal time as the early warning signal slice before the abnormal time corresponding to each detection signal.
Specifically, each segment signal carries corresponding time information, the time information includes a start time and an end time of the segment signal, an abnormal mark in each detection signal also carries a corresponding abnormal time, the abnormal time is a start time of an abnormal occurrence, a preset interval is a time interval which is before the abnormal time and is away from the abnormal time, the larger the preset interval is, the longer the segment signal of the early warning signal slice and the abnormal mark is, but the stronger the signal corresponding to the early warning signal slice which is closer to the abnormal mark is, the higher the accuracy of the early warning of the abnormal event is, and the preset interval can be customized according to an actual situation.
Fig. 2 is a flow chart illustrating a signal processing method according to an embodiment. It should be understood that, although the steps in the flowchart of fig. 2 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 2 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 4, there is provided a signal processing apparatus including:
a first signal obtaining module 310, configured to obtain a first integrated signal corresponding to a first time, where the first integrated signal includes a gyroscope signal, a sound signal, a blood pressure signal, a photoelectric signal, and a signal;
the early warning module 320 is configured to input the first integrated signal into an early warning model to obtain first early warning information, where the first early warning information includes an inference event corresponding to a second time and an occurrence probability corresponding to the inference event, and the second time is greater than the first time.
In one embodiment, the apparatus further comprises:
and the first signal detection module is used for inputting the first comprehensive signal into a detection model to obtain a first detection result, and the first detection result comprises an abnormal state and an abnormal type corresponding to the first moment.
In one embodiment, the apparatus further comprises:
the second signal acquisition module is used for acquiring a second comprehensive signal corresponding to the second moment;
the second signal detection module is used for inputting the second comprehensive signal into the detection model to obtain a second detection result;
and the correcting module is used for correcting the early warning model according to the second detection result.
In one embodiment, the correction module comprises:
and the correcting unit is used for correcting the early warning model according to the second detection result when the second detection result is not matched with the first early warning information to obtain a corrected early warning model, and the corrected early warning model is used for carrying out early warning analysis on the comprehensive signal corresponding to the third moment.
In one embodiment, the apparatus further comprises:
and the early warning report module is used for generating an early warning report according to the first comprehensive signal and sending out an early warning signal when the occurrence probability corresponding to the current inferred event in the first early warning information is greater than a preset threshold value of the corresponding event.
In one embodiment, the apparatus further comprises:
the device comprises a sample acquisition module, a detection module and a processing module, wherein the sample acquisition module is used for acquiring a sample comprehensive signal within a preset time length, and the sample comprehensive signal comprises a plurality of detection signals;
the anomaly marking module is used for marking each detection signal according to an anomaly detection algorithm to obtain an anomaly mark corresponding to each detection signal;
the signal segmentation module is used for segmenting each detection signal according to the abnormal mark corresponding to each detection signal to obtain a corresponding early warning signal slice;
and the model training module is used for carrying out deep learning according to the early warning signal slices and the abnormal labels corresponding to the detection signals and establishing the early warning model.
In one embodiment, the signal slicing module comprises:
the signal segmentation unit is used for segmenting each detection signal to obtain a plurality of segment signals, and the segment signals carry corresponding starting time and ending time;
and the segment selection unit is used for selecting a segment signal corresponding to the end time which is a preset interval away from the abnormal time as the early warning signal segment before the abnormal time corresponding to each detection signal.
FIG. 5 is a diagram illustrating an internal structure of a computer device in one embodiment. The computer device may be specifically the signal acquisition terminal 110 (or the server 120) in fig. 1. As shown in fig. 5, the computer apparatus includes a processor, a memory, a network interface, an input device, and a display screen connected through a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program which, when executed by the processor, causes the processor to implement the signal processing method. The internal memory may also have stored therein a computer program that, when executed by the processor, causes the processor to perform a signal processing method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, the signal processing apparatus provided in the present application may be implemented in the form of a computer program, and the computer program may be run on a computer device as shown in fig. 5. The memory of the computer device may store various program modules constituting the signal processing apparatus, such as the first signal acquiring module 310 and the early warning module 320 shown in fig. 4. The computer program constituted by the respective program modules causes the processor to execute the steps in the signal processing method of the respective embodiments of the present application described in the present specification.
The computer device shown in fig. 5 may perform the acquisition of a first integrated signal corresponding to a first time by the first signal acquisition module 310 in the signal processing apparatus shown in fig. 4, where the first integrated signal includes a gyroscope signal, a sound signal, a blood pressure signal, a photoelectric signal, and a signal. The computer device may input the first integrated signal into the early warning model through the early warning module 320 to obtain first early warning information, where the first early warning information includes an inference event corresponding to a second time and an occurrence probability corresponding to the inference event, and the second time is greater than the first time.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program: acquiring a first comprehensive signal corresponding to a first moment, wherein the first comprehensive signal comprises a gyroscope signal, a sound signal, a blood pressure signal, a photoelectric signal and a signal; and inputting the first comprehensive signal into an early warning model to obtain first early warning information, wherein the first early warning information comprises an inference event corresponding to a second moment and an occurrence probability corresponding to the inference event, and the second moment is behind the first moment.
In one embodiment, the processor, when executing the computer program, further performs the steps of: and inputting the first comprehensive signal into a detection model to obtain a first detection result, wherein the first detection result comprises an abnormal state and an abnormal type corresponding to the first moment.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring a second comprehensive signal corresponding to the second moment; inputting the second comprehensive signal into the detection model to obtain a second detection result; and correcting the early warning model according to the second detection result.
In one embodiment, the processor, when executing the computer program, further performs the steps of: and when the second detection result is not matched with the first early warning information, correcting the early warning model according to the second detection result to obtain a corrected early warning model, wherein the corrected early warning model is used for carrying out early warning analysis on the comprehensive signal corresponding to the third moment.
In one embodiment, the processor, when executing the computer program, further performs the steps of: and when the occurrence probability corresponding to the current inferred event in the first early warning information is greater than the preset threshold value of the corresponding event, generating an early warning report according to the first comprehensive signal, and sending out an early warning signal.
In one embodiment, the processor, when executing the computer program, further performs the steps of: within a preset time length, acquiring a sample comprehensive signal, wherein the sample comprehensive signal comprises a plurality of detection signals; marking each detection signal according to an anomaly detection algorithm to obtain an anomaly mark corresponding to each detection signal; segmenting each detection signal according to the corresponding abnormal mark of each detection signal to obtain a corresponding early warning signal slice; and carrying out deep learning according to the early warning signal slices and the abnormal labels corresponding to the detection signals, and establishing the early warning model.
In one embodiment, the processor, when executing the computer program, further performs the steps of: segmenting each detection signal to obtain a plurality of segment signals, wherein the segment signals carry corresponding starting time and ending time; and selecting a segment signal corresponding to the end time which is a preset interval away from the abnormal time as the early warning signal slice before the abnormal time corresponding to each detection signal.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring a first comprehensive signal corresponding to a first moment, wherein the first comprehensive signal comprises a gyroscope signal, a sound signal, a blood pressure signal, a photoelectric signal and a signal; and inputting the first comprehensive signal into an early warning model to obtain first early warning information, wherein the first early warning information comprises an inference event corresponding to a second moment and an occurrence probability corresponding to the inference event, and the second moment is behind the first moment.
In one embodiment, the computer program when executed by the processor further performs the steps of: and inputting the first comprehensive signal into a detection model to obtain a first detection result, wherein the first detection result comprises an abnormal state and an abnormal type corresponding to the first moment.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring a second comprehensive signal corresponding to the second moment; inputting the second comprehensive signal into the detection model to obtain a second detection result; and correcting the early warning model according to the second detection result.
In one embodiment, the computer program when executed by the processor further performs the steps of: and when the second detection result is not matched with the first early warning information, correcting the early warning model according to the second detection result to obtain a corrected early warning model, wherein the corrected early warning model is used for carrying out early warning analysis on the comprehensive signal corresponding to the third moment.
In one embodiment, the computer program when executed by the processor further performs the steps of: and when the occurrence probability corresponding to the current inferred event in the first early warning information is greater than the preset threshold value of the corresponding event, generating an early warning report according to the first comprehensive signal, and sending out an early warning signal.
In one embodiment, the computer program when executed by the processor further performs the steps of: within a preset time length, acquiring a sample comprehensive signal, wherein the sample comprehensive signal comprises a plurality of detection signals; marking each detection signal according to an anomaly detection algorithm to obtain an anomaly mark corresponding to each detection signal; segmenting each detection signal according to the corresponding abnormal mark of each detection signal to obtain a corresponding early warning signal slice; and carrying out deep learning according to the early warning signal slices and the abnormal labels corresponding to the detection signals, and establishing the early warning model.
In one embodiment, the computer program when executed by the processor further performs the steps of: segmenting each detection signal to obtain a plurality of segment signals, wherein the segment signals carry corresponding starting time and ending time; and selecting a segment signal corresponding to the end time which is a preset interval away from the abnormal time as the early warning signal slice before the abnormal time corresponding to each detection signal.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by instructing the relevant hardware through a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double-rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), synchronous Link (Synchlink) DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and bus dynamic RAM (RDRAM).
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of signal processing, the method comprising:
acquiring a first comprehensive signal corresponding to a first moment, wherein the first comprehensive signal comprises a gyroscope signal, a sound signal, a blood pressure signal, a photoelectric signal and a signal;
and inputting the first comprehensive signal into an early warning model to obtain first early warning information, wherein the first early warning information comprises an inference event corresponding to a second moment and an occurrence probability corresponding to the inference event, and the second moment is behind the first moment.
2. The method of claim 1, wherein after inputting the first integrated signal into an early warning model and obtaining first early warning information, the method further comprises:
and inputting the first comprehensive signal into a detection model to obtain a first detection result, wherein the first detection result comprises an abnormal state and an abnormal type corresponding to the first moment.
3. The method according to claim 2, wherein a time difference between the second time and the first time is a preset interval, and after the first integrated signal is input to a detection model and a detection result corresponding to the first time is obtained, the method further comprises:
acquiring a second comprehensive signal corresponding to the second moment;
inputting the second comprehensive signal into the detection model to obtain a second detection result;
and correcting the early warning model according to the second detection result.
4. The method of claim 3, wherein the calibrating the early warning model according to the second detection result comprises:
and when the second detection result is not matched with the first early warning information, correcting the early warning model according to the second detection result to obtain a corrected early warning model, wherein the corrected early warning model is used for carrying out early warning analysis on the comprehensive signal corresponding to the third moment.
5. The method of claim 1, wherein after inputting the first integrated signal into an early warning model and obtaining first early warning information, the method further comprises:
and when the occurrence probability corresponding to the current inferred event in the first early warning information is greater than the preset threshold value of the corresponding event, generating an early warning report according to the first comprehensive signal, and sending out an early warning signal.
6. The method of claim 1, wherein before the obtaining the first integrated signal corresponding to the first time, the method further comprises:
within a preset time length, acquiring a sample comprehensive signal, wherein the sample comprehensive signal comprises a plurality of detection signals;
marking each detection signal according to an anomaly detection algorithm to obtain an anomaly mark corresponding to each detection signal;
segmenting each detection signal according to the corresponding abnormal mark of each detection signal to obtain a corresponding early warning signal slice;
and carrying out deep learning according to the early warning signal slices and the abnormal labels corresponding to the detection signals, and establishing the early warning model.
7. The method of claim 6, wherein the segmenting each of the detection signals according to the abnormal time corresponding to each of the detection signals to obtain a corresponding early warning signal slice comprises:
segmenting each detection signal to obtain a plurality of segment signals, wherein the segment signals carry corresponding starting time and ending time;
and selecting a segment signal corresponding to the end time which is a preset interval away from the abnormal time as the early warning signal slice before the abnormal time corresponding to each detection signal.
8. A signal processing apparatus, characterized in that the apparatus comprises:
the device comprises a first signal acquisition module, a second signal acquisition module and a control module, wherein the first signal acquisition module is used for acquiring a first comprehensive signal corresponding to a first moment, and the first comprehensive signal comprises a gyroscope signal, a sound signal, a blood pressure signal, a photoelectric signal and a signal;
and the early warning module is used for inputting the first comprehensive signal into an early warning model to obtain first early warning information, wherein the first early warning information comprises an inference event corresponding to a second moment and an occurrence probability corresponding to the inference event, and the second moment is greater than the first moment.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 7 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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