CN117058826A - Acoustic security sensor operation detecting system based on artificial intelligence - Google Patents
Acoustic security sensor operation detecting system based on artificial intelligence Download PDFInfo
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Abstract
The application belongs to the field of sensor detection, relates to a data analysis technology, and aims to solve the problem that the existing acoustic security sensor operation detection system cannot ensure the detection precision of an acoustic security sensor after being used for a period of time, and particularly relates to an acoustic security sensor operation detection system based on artificial intelligence, which comprises an operation detection platform, wherein the operation detection platform is in communication connection with a detection analysis module, a difference analysis module, a stability analysis module and a storage module; the detection and analysis module is used for detecting and analyzing the running state of the acoustic security sensor: the detection objects are uniformly arranged in the simulation test space after being connected with the alarm, and test sound is sent out through a sound simulator arranged in the simulation test space; the application can detect and analyze the running state of the acoustic security sensor, and feeds back the alarm timeliness of the acoustic security sensor by adopting the sound simulator to send out test sound in the simulation test space.
Description
Technical Field
The application belongs to the field of sensor detection, relates to a data analysis technology, and particularly relates to an acoustic security sensor operation detection system based on artificial intelligence.
Background
The intelligent security alarm system is a security system of a family formed by various sensors, function keys, detectors and actuators of the family, is a brain of the family security system, and has the functions of fire prevention, theft prevention, gas leakage alarm, emergency help seeking and the like.
The existing acoustic security sensor operation detection system can only detect the alarm accuracy of the acoustic security sensor, but cannot monitor the operation difference and stability of the acoustic security sensor, so that the detection accuracy of the acoustic security sensor after a period of use cannot be guaranteed.
The application provides a solution to the technical problem.
Disclosure of Invention
The application aims to provide an acoustic security sensor operation detection system based on artificial intelligence, which is used for solving the problem that the existing acoustic security sensor operation detection system cannot ensure the detection precision of an acoustic security sensor after being used for a period of time;
the technical problems to be solved by the application are as follows: how to provide an artificial intelligence-based acoustic security sensor operation detection system capable of monitoring the operation variability and stability of the acoustic security sensor.
The aim of the application can be achieved by the following technical scheme:
the acoustic security sensor operation detection system based on artificial intelligence comprises an operation detection platform, wherein the operation detection platform is in communication connection with a detection analysis module, a difference analysis module, a stability analysis module and a storage module;
the detection and analysis module is used for detecting and analyzing the running state of the acoustic security sensor: randomly selecting L1 acoustic security sensors and marking the acoustic security sensors as detection objects, uniformly arranging the detection objects in a simulation test space after connecting with an alarm, emitting test sound through a sound simulator arranged in the simulation test space, emitting preset alarm test sound after L2 minutes, and marking the detection objects as qualified objects, delay objects or fault objects through the alarm test sound; obtaining the detection coefficient of the acoustic security sensor by carrying out numerical calculation on the number of the delay objects, the fault objects and the detection objects; the detection threshold value is obtained through the storage module, the detection coefficient is compared with the detection threshold value, and whether the running state of the acoustic security sensor meets the requirement or not is judged according to the comparison result;
the difference analysis module is used for detecting and analyzing the operation difference of the acoustic security sensor, obtaining a small flow deviation value, a time flow deviation value and a pop noise deviation value of a qualified object, and carrying out numerical calculation to obtain a difference coefficient of the acoustic security sensor; acquiring a difference threshold value through a storage module, comparing the difference coefficient with the difference threshold value, and judging whether the running difference of the acoustic security sensor meets the requirement or not through a comparison result;
the stability analysis module is used for analyzing the operation stability of the acoustic security sensor.
As a preferred embodiment of the present application, the specific process of marking the detection object as a qualified object, a delay object or a fault object includes: marking the time when the sound simulator gives out alarm test sound as the sound explosion time, marking L3 seconds after the sound explosion time as the detection time, forming a detection period by the sound explosion time and the detection time, and marking a detection object connected with the alarm giving out alarm sound effect in the detection period as a qualified object; marking a detection object connected with an alarm which does not send out alarm sound effect in a detection period as an abnormal object; judging whether an alarm sound effect is emitted by an alarm connected with an abnormal object after the detection moment: if yes, marking the abnormal object as a delay object; if not, marking the abnormal object as a fault object.
As a preferred embodiment of the present application, the specific process of comparing the detection coefficient JC with the detection threshold JCmax includes: if the detection coefficient JC is smaller than the detection threshold JCmax, judging that the running state of the acoustic security sensor meets the requirement, sending a difference analysis signal to a running detection platform by a detection analysis module, and sending the difference analysis signal to the difference analysis module after the running detection platform receives the difference analysis signal; if the detection coefficient JC is greater than or equal to the detection threshold JCmax, the running state of the acoustic security sensor is judged to be not met, the detection analysis module sends a detection abnormal signal to the running detection platform, and the running detection platform sends the detection abnormal signal to a mobile phone terminal of a manager after receiving the detection abnormal signal.
As a preferred embodiment of the present application, the process for obtaining the small deviation value, the time deviation value and the pop sound deviation value of the qualified object includes: marking a qualified object connection circuit as a qualified circuit, marking the minimum current value of the qualified circuit as a small current value, marking the difference between the time when the current value of the qualified circuit is the small current value and the time when the sound simulator emits test sound as small current duration, marking the difference between the time when the alarm sound effect is emitted by the qualified object connection alarm and the time when the sound is emitted as sound explosion duration, performing variance calculation on the small current values of all qualified objects to obtain small current deviation values LP, performing variance calculation on the small current duration of all qualified objects to obtain a time current deviation value LS, and performing variance calculation on the sound explosion duration of all qualified objects to obtain a sound explosion deviation value BP.
As a preferred embodiment of the present application, the specific process of comparing the difference coefficient CY with the difference threshold CYmax includes: if the difference coefficient is smaller than the difference threshold value, judging that the running difference of the acoustic security sensor meets the requirement, and sending a stable analysis signal to a running detection platform by the difference analysis module, wherein the running detection platform receives the stable analysis signal and then sends the stable analysis signal to the stable analysis module; if the difference coefficient is larger than or equal to the difference coefficient, the operation difference of the acoustic security sensor is judged to be not satisfied, the difference analysis module sends a difference abnormal signal to the operation detection platform, and the operation detection platform sends the difference abnormal signal to a mobile phone terminal of a manager after receiving the difference abnormal signal.
As a preferred embodiment of the application, the specific process of analyzing the operation stability of the acoustic security sensor by the stability analysis module comprises the following steps: establishing a rectangular coordinate system by taking time as an X axis and taking a current value of a qualified object connecting circuit as a Y axis, drawing qualified curves in the rectangular coordinate system by taking the current value of the qualified object in a simulation test process, marking the number of intersection points of all the qualified curves as intersection point values, marking the ratio of the intersection point values to the number of the qualified objects as a stability coefficient, acquiring a stability threshold value through a storage module, comparing the stability coefficient with the stability threshold value, and judging whether the running stability of the acoustic security sensor meets the requirement or not through a comparison result.
As a preferred embodiment of the present application, the specific process of comparing the stability factor with the stability threshold value includes: if the stability coefficient is smaller than the stability threshold, judging that the running stability of the acoustic security sensor meets the requirement, and sending a detection qualified signal to a running detection platform by a stability analysis module, wherein the running detection platform sends the detection qualified signal to a mobile phone terminal of a manager after receiving the detection qualified signal; if the stability coefficient is greater than or equal to the stability threshold, judging that the running stability of the acoustic security sensor does not meet the requirement, and sending a stability abnormal signal to the running detection platform by the stability analysis module, and sending the stability abnormal signal to a mobile phone terminal of a manager after the stability abnormal signal is received by the running detection platform.
As a preferred embodiment of the application, the working method of the artificial intelligence-based acoustic security sensor operation detection system comprises the following steps:
step one: detecting and analyzing the running state of the acoustic security sensor: randomly selecting L1 acoustic security sensors and marking the acoustic security sensors as detection objects, uniformly arranging the detection objects in a simulation test space after connecting the detection objects with an alarm, testing the running state of the detection objects in the simulation test space to obtain detection coefficients JC, and judging whether the running state of the detection objects meets the requirements or not through the detection coefficients JC;
step two: detecting and analyzing the operation difference of the acoustic security sensor to obtain a small flow deviation value LP, a time flow deviation value LS and a sound burst deviation value BP of a qualified object, carrying out numerical calculation on the small flow deviation value LP, the time flow deviation value LS and the sound burst deviation value BP to obtain a difference coefficient CY, and judging whether the operation difference of the acoustic security sensor meets the requirement or not through the difference coefficient CY;
step three: analyzing the operation stability of the acoustic security sensor: establishing a rectangular coordinate system by taking time as an X axis and taking a current value of a qualified object connecting circuit as a Y axis, drawing qualified curves in the rectangular coordinate system by taking the current value of the qualified object in a simulation test process, marking the number of intersection points of all the qualified curves as intersection point values, marking the ratio of the intersection point values to the number of the qualified objects as a stability coefficient, and judging whether the running stability of the acoustic security sensor meets the requirement or not through the stability coefficient.
The application has the following beneficial effects:
1. the running state of the acoustic security sensor can be detected and analyzed through the detection and analysis module, the alarm timeliness of the acoustic security sensor is fed back through adopting a sound simulator to send out test sound in a simulation test space, meanwhile, a detection coefficient is obtained through calculation according to the number of delay objects and fault objects, the overall running state of the acoustic security sensor is fed back through the numerical value of the detection coefficient, and early warning is timely carried out when the overall running state is abnormal;
2. the operation difference of the acoustic security sensors can be detected and analyzed through the difference analysis module, the current difference of the acoustic security sensors in the same simulation test environment is detected and analyzed to obtain a difference coefficient, and the operation state difference of the acoustic security sensors in the same batch is monitored through the difference coefficient, so that early warning is carried out when the operation difference does not meet the requirement;
3. the stability analysis module can detect and analyze the operation stability of the acoustic security sensor, a plurality of qualified curves are drawn in a rectangular coordinate system, then the stability coefficient is obtained by analysis according to the intersecting condition of the qualified curves, the operation stability of the acoustic security sensor is fed back through the stability coefficient, and various performances of the acoustic security sensor passing through detection can meet requirements when the acoustic security sensor is used.
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In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of a first embodiment of the present application;
fig. 2 is a flowchart of a method according to a second embodiment of the application.
Detailed Description
The technical solutions of the present application will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
As shown in FIG. 1, the acoustic security sensor operation detection system based on artificial intelligence comprises an operation detection platform, wherein the operation detection platform is in communication connection with a detection analysis module, a difference analysis module, a stability analysis module and a storage module.
The detection analysis module is used for detecting and analyzing the running state of the acoustic security sensor: randomly selecting L1 acoustic security sensors and marking the acoustic security sensors as detection objects, uniformly arranging the detection objects in a simulation test space after connecting with an alarm, emitting test sound through a sound simulator arranged in the simulation test space, emitting preset alarm test sound after L2 minutes, marking the moment of emitting the alarm test sound by the sound simulator as sound explosion moment, marking L3 seconds after the sound explosion moment as detection moment, wherein L1, L2 and L3 are constant values, and setting the values of L1, L2 and L3 by a manager; forming a detection period by the sound explosion time and the detection time, and marking a detection object connected with an alarm which gives out alarm sound effect in the detection period as a qualified object; marking a detection object connected with an alarm which does not send out alarm sound effect in a detection period as an abnormal object; judging whether an alarm sound effect is emitted by an alarm connected with an abnormal object after the detection moment: if yes, marking the abnormal object as a delay object; if not, marking the abnormal object as a fault object; the number of delay objects and fault objects are marked as YC and GZ respectively, and the detection coefficient JC of the acoustic security sensor is obtained through the formula JC= (alpha 1X YC+alpha 2X GZ)/L1, wherein alpha 1 and alpha 2 are both proportional coefficients, and alpha 2 is more than alpha 1 and more than 1; the detection threshold JCmax is obtained through the storage module, and the detection coefficient JC is compared with the detection threshold JCmax: if the detection coefficient JC is smaller than the detection threshold JCmax, judging that the running state of the acoustic security sensor meets the requirement, sending a difference analysis signal to a running detection platform by a detection analysis module, and sending the difference analysis signal to the difference analysis module after the running detection platform receives the difference analysis signal; if the detection coefficient JC is greater than or equal to the detection threshold JCmax, judging that the running state of the acoustic security sensor does not meet the requirement, sending a detection abnormal signal to a running detection platform by a detection analysis module, and sending the detection abnormal signal to a mobile phone terminal of a manager after the running detection platform receives the detection abnormal signal; the method comprises the steps of detecting and analyzing the running state of an acoustic security sensor, feeding back the alarm timeliness of the acoustic security sensor by adopting a sound simulator to send test sound in a simulation test space, calculating according to the number of delay objects and fault objects to obtain a detection coefficient, feeding back the overall running state of the acoustic security sensor through the numerical value of the detection coefficient, and timely giving an early warning when the overall running state is abnormal.
The difference analysis module is used for detecting and analyzing the operation difference of the acoustic security sensor: marking a qualified object connection circuit as a qualified circuit, marking the minimum current value of the qualified circuit as a small current value, marking the difference between the moment when the current value of the qualified circuit is the small current value and the moment when the sound simulator emits test sound as small current duration, marking the difference between the moment when the alarm is emitted by the qualified object connection alarm and the moment when the sound is exploded as sound duration, performing variance calculation on the small current values of all qualified objects to obtain small current deviation values LP, performing variance calculation on the small current durations of all qualified objects to obtain a time current deviation value LS, performing variance calculation on the sound explosion durations of all qualified objects to obtain a sound deviation value BP, and obtaining a difference coefficient CY of the acoustic security sensor through a formula CY=β1LPβ2LS+β3BP, wherein β1, β2 and β3 are all proportional coefficients, and β3 > β2 > β1 > 1; obtaining a difference threshold value CYmax through a storage module, and comparing a difference coefficient CY with the difference threshold value CYmax: if the difference coefficient CY is smaller than the difference threshold CYmax, judging that the running difference of the acoustic security sensor meets the requirement, and sending a stable analysis signal to the running detection platform by the difference analysis module, wherein the running detection platform receives the stable analysis signal and then sends the stable analysis signal to the stable analysis module; if the difference coefficient CY is larger than or equal to the difference coefficient CYmax, judging that the running difference of the acoustic security sensor does not meet the requirement, sending a difference abnormal signal to a running detection platform by a difference analysis module, and sending the difference abnormal signal to a mobile phone terminal of a manager after the running detection platform receives the difference abnormal signal; detecting and analyzing the running difference of the acoustic security sensors, detecting and analyzing the current difference of the acoustic security sensors under the same simulation test environment to obtain a difference coefficient, and monitoring the running state difference of the acoustic security sensors in the same batch through the difference coefficient, so that early warning is carried out when the running difference does not meet the requirement.
The stability analysis module is used for analyzing the operation stability of the acoustic security sensor: establishing a rectangular coordinate system by taking time as an X axis and taking a current value of a qualified object connecting circuit as a Y axis, drawing qualified curves in the rectangular coordinate system by taking the current value of the qualified object in a simulation test process, marking the number of intersection points of all the qualified curves as intersection point values, marking the ratio of the intersection point values to the number of the qualified objects as a stability coefficient, acquiring a stability threshold value through a storage module, and comparing the stability coefficient with the stability threshold value: if the stability coefficient is smaller than the stability threshold, judging that the running stability of the acoustic security sensor meets the requirement, and sending a detection qualified signal to a running detection platform by a stability analysis module, wherein the running detection platform sends the detection qualified signal to a mobile phone terminal of a manager after receiving the detection qualified signal; if the stability coefficient is greater than or equal to the stability threshold, judging that the running stability of the acoustic security sensor does not meet the requirement, and sending a stability abnormal signal to the running detection platform by the stability analysis module, and sending the stability abnormal signal to a mobile phone terminal of a manager after the running detection platform receives the stability abnormal signal; detecting and analyzing the operation stability of the acoustic security sensor, drawing a plurality of qualified curves in a rectangular coordinate system, analyzing according to the intersecting condition of the qualified curves to obtain a stability coefficient, feeding back the operation stability of the acoustic security sensor through the stability coefficient, and ensuring that all performances of the acoustic security sensor passing through detection can meet requirements when the acoustic security sensor is used.
Example two
As shown in fig. 2, an acoustic security sensor operation detection method based on artificial intelligence includes the following steps:
step one: detecting and analyzing the running state of the acoustic security sensor: randomly selecting L1 acoustic security sensors and marking the acoustic security sensors as detection objects, uniformly arranging the detection objects in a simulation test space after connecting the detection objects with an alarm, testing the running state of the detection objects in the simulation test space to obtain detection coefficients JC, and judging whether the running state of the detection objects meets the requirements or not through the detection coefficients JC;
step two: detecting and analyzing the operation difference of the acoustic security sensor to obtain a small flow deviation value LP, a time flow deviation value LS and a sound burst deviation value BP of a qualified object, carrying out numerical calculation on the small flow deviation value LP, the time flow deviation value LS and the sound burst deviation value BP to obtain a difference coefficient CY, and judging whether the operation difference of the acoustic security sensor meets the requirement or not through the difference coefficient CY;
step three: analyzing the operation stability of the acoustic security sensor: establishing a rectangular coordinate system by taking time as an X axis and taking a current value of a qualified object connecting circuit as a Y axis, drawing qualified curves in the rectangular coordinate system by taking the current value of the qualified object in a simulation test process, marking the number of intersection points of all the qualified curves as intersection point values, marking the ratio of the intersection point values to the number of the qualified objects as a stability coefficient, and judging whether the running stability of the acoustic security sensor meets the requirement or not through the stability coefficient.
The operation detection system of the acoustic security sensor based on artificial intelligence is characterized in that when in operation, L1 acoustic security sensors are randomly selected and marked as detection objects, the detection objects are uniformly arranged in a simulation test space after being connected with an alarm, the operation state of the detection objects is tested in the simulation test space, a detection coefficient JC is obtained, and whether the operation state of the detection objects meets the requirement is judged through the detection coefficient JC; detecting and analyzing the operation difference of the acoustic security sensor to obtain a small flow deviation value LP, a time flow deviation value LS and a sound burst deviation value BP of a qualified object, carrying out numerical calculation on the small flow deviation value LP, the time flow deviation value LS and the sound burst deviation value BP to obtain a difference coefficient CY, and judging whether the operation difference of the acoustic security sensor meets the requirement or not through the difference coefficient CY; establishing a rectangular coordinate system by taking time as an X axis and taking a current value of a qualified object connecting circuit as a Y axis, drawing qualified curves in the rectangular coordinate system by taking the current value of the qualified object in a simulation test process, marking the number of intersection points of all the qualified curves as intersection point values, marking the ratio of the intersection point values to the number of the qualified objects as a stability coefficient, and judging whether the running stability of the acoustic security sensor meets the requirement or not through the stability coefficient.
The foregoing is merely illustrative of the structures of this application and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the application or from the scope of the application as defined in the accompanying claims.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions; such as: formula cy=β1×lp+β2×ls+β3×bp; collecting a plurality of groups of sample data by a person skilled in the art and setting a corresponding difference coefficient for each group of sample data; substituting the set difference coefficient and the acquired sample data into a formula, forming a ternary one-time equation set by any three formulas, screening the calculated coefficient, and taking an average value to obtain values of alpha 1, alpha 2 and alpha 3 of 3.68, 2.83 and 2.35 respectively;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the corresponding difference coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected, for example, the difference coefficient is proportional to the value of the small deviation value.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the application disclosed above are intended only to assist in the explanation of the application. The preferred embodiments are not intended to be exhaustive or to limit the application to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the application and the practical application, to thereby enable others skilled in the art to best understand and utilize the application. The application is limited only by the claims and the full scope and equivalents thereof.
Claims (8)
1. The acoustic security sensor operation detection system based on artificial intelligence is characterized by comprising an operation detection platform, wherein the operation detection platform is in communication connection with a detection analysis module, a difference analysis module, a stability analysis module and a storage module;
the detection and analysis module is used for detecting and analyzing the running state of the acoustic security sensor: randomly selecting L1 acoustic security sensors and marking the acoustic security sensors as detection objects, uniformly arranging the detection objects in a simulation test space after connecting with an alarm, emitting test sound through a sound simulator arranged in the simulation test space, emitting preset alarm test sound after L2 minutes, and marking the detection objects as qualified objects, delay objects or fault objects through the alarm test sound; obtaining the detection coefficient of the acoustic security sensor by carrying out numerical calculation on the number of the delay objects, the fault objects and the detection objects; the detection threshold value is obtained through the storage module, the detection coefficient is compared with the detection threshold value, and whether the running state of the acoustic security sensor meets the requirement or not is judged according to the comparison result;
the difference analysis module is used for detecting and analyzing the operation difference of the acoustic security sensor, obtaining a small flow deviation value, a time flow deviation value and a pop noise deviation value of a qualified object, and carrying out numerical calculation to obtain a difference coefficient of the acoustic security sensor; acquiring a difference threshold value through a storage module, comparing the difference coefficient with the difference threshold value, and judging whether the running difference of the acoustic security sensor meets the requirement or not through a comparison result;
the stability analysis module is used for analyzing the operation stability of the acoustic security sensor.
2. The system for detecting the operation of an acoustic security sensor based on artificial intelligence according to claim 1, wherein the specific process of marking the detection object as a qualified object, a delay object or a fault object comprises the following steps: marking the time when the sound simulator gives out alarm test sound as the sound explosion time, marking L3 seconds after the sound explosion time as the detection time, forming a detection period by the sound explosion time and the detection time, and marking a detection object connected with the alarm giving out alarm sound effect in the detection period as a qualified object; marking a detection object connected with an alarm which does not send out alarm sound effect in a detection period as an abnormal object; judging whether an alarm sound effect is emitted by an alarm connected with an abnormal object after the detection moment: if yes, marking the abnormal object as a delay object; if not, marking the abnormal object as a fault object.
3. An artificial intelligence based acoustic security sensor operation detection system according to claim 2 wherein the specific process of comparing the detection coefficient to the detection threshold comprises: if the detection coefficient is smaller than the detection threshold, judging that the running state of the acoustic security sensor meets the requirement, and sending a difference analysis signal to the running detection platform by the detection analysis module, wherein the running detection platform receives the difference analysis signal and then sends the difference analysis signal to the difference analysis module; if the detection coefficient is greater than or equal to the detection threshold, the operation state of the acoustic security sensor is judged to be not satisfied, the detection analysis module sends a detection abnormal signal to the operation detection platform, and the operation detection platform sends the detection abnormal signal to a mobile phone terminal of a manager after receiving the detection abnormal signal.
4. The system for detecting operation of an acoustic security sensor based on artificial intelligence according to claim 3, wherein the process for obtaining the small deviation value, the time-of-flow deviation value and the pop noise deviation value of the qualified object comprises: marking a qualified object connection circuit as a qualified circuit, marking the minimum current value of the qualified circuit as a small current value, marking the difference between the time when the current value of the qualified circuit is the small current value and the time when the sound simulator emits test sound as small current duration, marking the difference between the time when the alarm sound effect is emitted by the qualified object connection alarm and the time when the sound is emitted as sound explosion duration, performing variance calculation on the small current values of all qualified objects to obtain small current deviation values LP, performing variance calculation on the small current duration of all qualified objects to obtain a time current deviation value LS, and performing variance calculation on the sound explosion duration of all qualified objects to obtain a sound explosion deviation value BP.
5. The artificial intelligence based acoustic security sensor operation detection system of claim 4, wherein the specific process of comparing the difference coefficient with the difference threshold comprises: if the difference coefficient is smaller than the difference threshold value, judging that the running difference of the acoustic security sensor meets the requirement, and sending a stable analysis signal to a running detection platform by the difference analysis module, wherein the running detection platform receives the stable analysis signal and then sends the stable analysis signal to the stable analysis module; if the difference coefficient is larger than or equal to the difference coefficient, the operation difference of the acoustic security sensor is judged to be not satisfied, the difference analysis module sends a difference abnormal signal to the operation detection platform, and the operation detection platform sends the difference abnormal signal to a mobile phone terminal of a manager after receiving the difference abnormal signal.
6. The artificial intelligence based acoustic security sensor operation detection system of claim 5, wherein the specific process of the stability analysis module for analyzing the operation stability of the acoustic security sensor comprises: establishing a rectangular coordinate system by taking time as an X axis and taking a current value of a qualified object connecting circuit as a Y axis, drawing qualified curves in the rectangular coordinate system by taking the current value of the qualified object in a simulation test process, marking the number of intersection points of all the qualified curves as intersection point values, marking the ratio of the intersection point values to the number of the qualified objects as a stability coefficient, acquiring a stability threshold value through a storage module, comparing the stability coefficient with the stability threshold value, and judging whether the running stability of the acoustic security sensor meets the requirement or not through a comparison result.
7. The artificial intelligence based acoustic security sensor operation detection system of claim 6, wherein the specific process of comparing the stability factor with the stability threshold comprises: if the stability coefficient is smaller than the stability threshold, judging that the running stability of the acoustic security sensor meets the requirement, and sending a detection qualified signal to a running detection platform by a stability analysis module, wherein the running detection platform sends the detection qualified signal to a mobile phone terminal of a manager after receiving the detection qualified signal; if the stability coefficient is greater than or equal to the stability threshold, judging that the running stability of the acoustic security sensor does not meet the requirement, and sending a stability abnormal signal to the running detection platform by the stability analysis module, and sending the stability abnormal signal to a mobile phone terminal of a manager after the stability abnormal signal is received by the running detection platform.
8. An artificial intelligence based acoustic security sensor operational detection system according to any of claims 1-7 and wherein the method of operation of the artificial intelligence based acoustic security sensor operational detection system comprises the steps of:
step one: detecting and analyzing the running state of the acoustic security sensor: randomly selecting L1 acoustic security sensors and marking the acoustic security sensors as detection objects, uniformly arranging the detection objects in a simulation test space after connecting the detection objects with an alarm, testing the running state of the detection objects in the simulation test space to obtain detection coefficients, and judging whether the running state of the detection objects meets the requirements or not through the detection coefficients;
step two: detecting and analyzing the operation difference of the acoustic security sensor to obtain a small flow deviation value, a time flow deviation value and a sound burst deviation value of a qualified object, carrying out numerical calculation on the small flow deviation value, the time flow deviation value and the sound burst deviation value to obtain a difference coefficient, and judging whether the operation difference of the acoustic security sensor meets the requirement or not through the difference coefficient;
step three: analyzing the operation stability of the acoustic security sensor: establishing a rectangular coordinate system by taking time as an X axis and taking a current value of a qualified object connecting circuit as a Y axis, drawing qualified curves in the rectangular coordinate system by taking the current value of the qualified object in a simulation test process, marking the number of intersection points of all the qualified curves as intersection point values, marking the ratio of the intersection point values to the number of the qualified objects as a stability coefficient, and judging whether the running stability of the acoustic security sensor meets the requirement or not through the stability coefficient.
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| CN117571200A (en) * | 2024-01-16 | 2024-02-20 | 无锡芯感智半导体有限公司 | Pressure sensor water pressure fatigue test system based on artificial intelligence |
| CN117571200B (en) * | 2024-01-16 | 2024-03-22 | 无锡芯感智半导体有限公司 | Pressure sensor water pressure fatigue test system based on artificial intelligence |
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