CN113706822A - Safety coefficient data model construction method and early warning device based on AI (Artificial Intelligence) - Google Patents
Safety coefficient data model construction method and early warning device based on AI (Artificial Intelligence) Download PDFInfo
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- CN113706822A CN113706822A CN202110989595.8A CN202110989595A CN113706822A CN 113706822 A CN113706822 A CN 113706822A CN 202110989595 A CN202110989595 A CN 202110989595A CN 113706822 A CN113706822 A CN 113706822A
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- 238000013499 data model Methods 0.000 title claims abstract description 39
- 238000010276 construction Methods 0.000 title claims description 8
- 238000013473 artificial intelligence Methods 0.000 title description 39
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- 238000012545 processing Methods 0.000 claims abstract description 20
- 230000002159 abnormal effect Effects 0.000 claims abstract description 18
- 238000012544 monitoring process Methods 0.000 claims abstract description 8
- 238000006073 displacement reaction Methods 0.000 claims abstract description 4
- 238000011156 evaluation Methods 0.000 claims abstract description 4
- 231100000817 safety factor Toxicity 0.000 claims description 67
- 238000000034 method Methods 0.000 claims description 20
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
Abstract
The invention provides an AI-based early warning device for monitoring the safety state of a building, which comprises: the device comprises a data acquisition unit, an alarm unit and an AI processing platform; the data acquisition unit is used for detecting shaking data, three-dimensional deformation data, noise data and foreign matter invasion data of the building; the data acquisition unit is connected with the AI processing platform and transmits the acquired data to the AI processing platform; the AI processing platform compares and analyzes data with a preset safety factor based on a safety factor data model, performs early warning and fault warning on the unnatural regular shaking, displacement and abnormal sound of the building, provides event warning and severity evaluation for three-dimensional deformation and foreign object intrusion, and simultaneously evaluates the safety state of the building. The AI-based early warning device provided by the invention can monitor the safety state of a building in real time based on the safety coefficient data model, realize disaster prevention and early warning, and further reduce the hazard of accidents.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a safety coefficient data model construction method and an AI-based early warning device.
Background
AI application techniques are well established, and three-dimensional model building based on AI implementation has also begun to be applied.
At present, the disasters of buildings are post-treatment, the work of early warning and front-end prevention is not done in place, and a good mechanism or application is not provided for solving the problem, once the buildings collapse, external foreign matter invasion and the like, the normal use of the buildings is influenced slightly, a large amount of casualties are caused seriously, and a large or even serious safety accident is caused. Therefore, the method has great significance for monitoring the safety state of the building. The existing related devices can not monitor the parameters of the building comprehensively, and the monitoring effect is poor.
Disclosure of Invention
The invention aims to provide a safety coefficient data model construction method and an AI-based early warning device, so as to solve the problems pointed out in the background art.
The embodiment of the invention is realized by the following technical scheme: a method of building a safety factor data model, the method comprising:
the method comprises the following steps of firstly, carrying out data mining on influence factors influencing the safety state of a building to obtain state data of each influence factor;
determining various safety factors describing the data model based on the state data;
and step three, associating the various safety factors, determining a safety factor standard value for triggering early warning, and establishing the safety factor data model according to the association relation of the safety factors and the safety factor standard value.
According to a preferred embodiment, the state data includes shaking data, three-dimensional deformation data, noise data, and foreign object intrusion data.
According to a preferred embodiment, said plurality of safety factors comprises: the shaking safety factor, the three-dimensional deformation safety factor, the abnormal sound safety factor and the foreign matter invasion safety factor.
According to a preferred embodiment, the second step specifically includes:
determining a shaking safety factor: the method comprises the steps of obtaining shaking data, setting a safety angle threshold value R, and triggering early warning when a + b is monitored to be larger than R, wherein the shaking data comprises a first direction angle a and a second direction angle b opposite to the first direction angle a, so that a shaking safety coefficient a + b is smaller than or equal to R;
determining a three-dimensional deformation safety coefficient: acquiring the three-dimensional deformation data, setting a safety deformation threshold value A by the three-dimensional deformation data comprising XYZ length, width and height of the building and a deformation degree T, and triggering early warning to obtain a three-dimensional deformation safety coefficient when the sum of damage degrees of XYZ length, width and height of the building is greater than A
Wherein
Is the degree of damage of X and is,
is the degree of damage of the Y, and,
is the degree of damage of Z;
determining the abnormal sound safety coefficient: acquiring the noise data, and triggering an alarm to obtain an abnormal sound safety coefficient V when the noise data is not a conventional built-in sound source;
determining a foreign matter invasion safety coefficient: acquiring the foreign matter invasion data, wherein the foreign matter invasion data comprise object falling a1Foreign matter threat b1And foreign object deviation c1Setting a safety invasion threshold value E, and triggering early warning to obtain a foreign matter invasion safety coefficient when the sum of the influence degrees of the invasion data of various foreign matters is greater than E
Wherein, T1For objects to fall off a1E is a foreign objectAnd the influence degree, q is the influence degree of the potential threat.
According to a preferred embodiment, the third step specifically includes:
root of herbaceous plant
And associating the shaking safety factor, the three-dimensional deformation safety factor, the abnormal sound safety factor and the foreign object invasion safety factor, determining a safety factor standard value 1 for triggering early warning, and establishing a safety factor data model R + A + V + E < 1.
The invention also provides an AI-based early warning device, which is applied to the safety coefficient data model and used for monitoring the safety state of a building, and is characterized by comprising the following components: the device comprises a data acquisition unit, an alarm unit and an AI processing platform;
the data acquisition unit is arranged on the building and used for detecting shaking data, three-dimensional deformation data, noise data and foreign matter invasion data of the building; the data output end of the data acquisition unit is connected with the data input end of the AI processing platform and is used for transmitting the acquired shaking data, the three-dimensional deformation data, the noise data and the foreign matter invasion data to the AI processing platform;
the AI processing platform is used for comparing and analyzing the shaking data, the three-dimensional deformation data, the noise data and the foreign matter invasion data with the preset safety factor, carrying out early warning and fault warning on the unnatural regularity shaking, displacement and abnormal sound of the building, providing event warning and severity evaluation for the three-dimensional deformation and the foreign matter invasion, and simultaneously evaluating the safety state of the building.
According to a preferred embodiment, the data acquisition unit comprises a pan-tilt camera, a microphone, a loudspeaker and an infrared sensor.
According to a preferred embodiment, the AI processing platform comprises an audio DSP, a main control IC, an AI chip, a storage unit, and a WiFi module;
the data output ends of the microphone and the loudspeaker are connected with the data input end of the audio DSP, the data output ends of the pan-tilt camera, the audio DSP and the infrared sensor are connected with the data input end of the main control IC, the main control IC is in two-way connection with the AI chip, and the data output end of the main control IC is connected to the alarm unit and the background.
According to a preferred embodiment, the AI chip is loaded with the safety factor data model,
and the AI chip is used for controlling the safety coefficient data model to operate so as to analyze the shaking data, the three-dimensional deformation data, the noise data and the foreign matter invasion data and determine the safety state of the building.
The present invention also provides an electronic device comprising:
a memory to store a computer program;
and the processor is used for realizing the safety coefficient data model building method when executing the computer program.
The technical scheme of the embodiment of the invention at least has the following advantages and beneficial effects: the AI-based early warning device provided by the invention can monitor the safety state of a building in real time based on the safety coefficient data model, realize disaster prevention and early warning, and further reduce the hazard of accidents.
Drawings
Fig. 1 is a schematic flow chart of a safety coefficient data model construction method provided in embodiment 1 of the present invention;
fig. 2 is a schematic structural diagram of an AI-based early warning device provided in embodiment 1 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Example 1
Referring to fig. 1, fig. 1 is a schematic flow chart of a safety coefficient data model construction method provided in embodiment 1 of the present invention.
Through research of the applicant, the AI application technology is mature, and the establishment of a three-dimensional model based on AI realization is started.
At present, the disasters of buildings are post-treatment, the work of early warning and front-end prevention is not done in place, and a good mechanism or application is not provided for solving the problem, once the buildings collapse, external foreign matter invasion and the like, the normal use of the buildings is influenced slightly, a large amount of casualties are caused seriously, and a large or even serious safety accident is caused. Therefore, the method has great significance for monitoring the safety state of the building. The existing related devices can not monitor the parameters of the building comprehensively, and the monitoring effect is poor. Therefore, the invention provides a safety coefficient data model construction method and an AI-based early warning device to solve the problems pointed out above. The specific scheme is as follows:
a method of building a safety factor data model, the method comprising:
the method comprises the following steps of firstly, carrying out data mining on influence factors influencing the safety state of the building, and acquiring state data of each influence factor, wherein the state data comprises: the data processing method comprises the following steps of (1) shaking data, three-dimensional deformation data, noise data and foreign body invasion data;
determining various safety factors describing the data model based on the state data, wherein the various safety factors comprise: the shaking safety factor, the three-dimensional deformation safety factor, the abnormal sound safety factor and the foreign matter invasion safety factor.
Further, in an embodiment of the present invention, the second step specifically includes:
determining a shaking safety factor: the method comprises the steps of obtaining shaking data, setting a safety angle threshold value R, and triggering early warning when a + b is monitored to be larger than R, wherein the shaking data comprises a first direction angle a and a second direction angle b opposite to the first direction angle a, so that a shaking safety coefficient a + b is smaller than or equal to R;
determining a three-dimensional deformation safety coefficient: acquiring the three-dimensional deformation data, wherein the three-dimensional deformation data comprises XYZ length, width and height of the building and deformation degree T, and setting safety shapeChanging a threshold value A, and triggering early warning when the sum of the damage degrees of XYZ of the length, width and height of the building is greater than A to obtain a three-dimensional deformation safety coefficient
Wherein
Is the degree of damage of X and is,
is the degree of damage of the Y, and,
is the degree of damage of Z;
determining the abnormal sound safety coefficient: acquiring the noise data, and triggering an alarm to obtain an abnormal sound safety coefficient V when the noise data is not a conventional built-in sound source;
determining a foreign matter invasion safety coefficient: and acquiring the foreign matter intrusion data, wherein the foreign matter intrusion data indicate that an object in an abnormal state enters an abnormal path. Such as stones, buildings, debris from high winds, or hazardous materials; the foreign object intrusion data of the embodiment includes object falling a1Foreign matter threat b1And foreign object deviation c1Setting a safety invasion threshold value E, and triggering early warning to obtain a foreign matter invasion safety coefficient when the sum of the influence degrees of the invasion data of various foreign matters is greater than E
Wherein, T1For objects to fall off a1And e is the influence degree of the foreign matters, and q is the influence degree of the potential threats.
And step three, associating the various safety factors, determining a safety factor standard value for triggering early warning, and establishing the safety factor data model according to the association relation of the safety factors and the safety factor standard value. Further, the third step specifically includes:
and associating the shaking safety factor, the three-dimensional deformation safety factor, the abnormal sound safety factor and the foreign object invasion safety factor, determining a safety factor standard value 1 for triggering early warning, and establishing a safety factor data model R + A + V + E < 1. Namely, when the safety factor standard value is exceeded 1, a processing mechanism is triggered, and real-time data transmission and recording are established through a built-in WiFi module and a background.
For the current situation that the application hardware equipment in the related art is mature, referring to fig. 2, the invention further provides an AI-based early warning device, which is applied to the safety coefficient data model described above, and is used for monitoring the safety state of a building, and the AI-based early warning device includes: the device comprises a data acquisition unit, an alarm unit and an AI processing platform;
the data acquisition unit is arranged on the building and used for detecting shaking data, three-dimensional deformation data, noise data and foreign matter invasion data of the building; the data output end of the data acquisition unit is connected with the data input end of the AI processing platform and is used for transmitting the acquired shaking data, the three-dimensional deformation data, the noise data and the foreign matter invasion data to the AI processing platform;
the AI processing platform is used for comparing and analyzing the shaking data, the three-dimensional deformation data, the noise data and the foreign matter invasion data with the preset safety factor, carrying out early warning and fault warning on the unnatural regularity shaking, displacement and abnormal sound of the building, providing event warning and severity evaluation for the three-dimensional deformation and the foreign matter invasion, and simultaneously evaluating the safety state of the building.
In an embodiment of the present invention, the data acquisition unit includes a pan-tilt camera, a microphone, a speaker, and an infrared sensor. The AI processing platform comprises an audio DSP, a master control IC, an AI chip, a storage unit and a WiFi module, wherein the storage unit is used for storing data; the audio DSP, the master control IC, the AI chip, the storage unit and the WiFi module are all arranged on the same circuit board; the data output ends of the microphone and the loudspeaker are connected with the data input end of the audio DSP through a USB, the data output ends of the pan-tilt camera, the audio DSP and the infrared sensor are connected with the data input end of the main control IC, and a pan-tilt of the pan-tilt camera is controlled through a UVC protocol and can also support remote control; the main control IC is in bidirectional connection with the AI chip, the AI chip is used for information processing and can also be connected with a cloud end for storage and AI separation, the computing power is enlarged, and finally a conclusion is returned to the main control IC; and the data output end of the main control IC is connected to the alarm unit and the background, and transmits the alarm and early warning instructions to the alarm device and the background. In addition, the WiFi module can also play the collection and the transmission of a temporary real-time audio and video data, can reduce personnel's high risk.
The safety factor data model is loaded on the AI chip and used for controlling the AI chip to operate the safety factor data model so as to analyze the shaking data, the three-dimensional deformation data, the noise data and the foreign matter invasion data and determine the safety state of the building.
In addition, an embodiment of the present invention further provides an electronic device, including: a memory to store a computer program; and the processor is used for realizing the safety coefficient data model building method when executing the computer program.
In conclusion, the AI-based early warning device provided by the invention can monitor the safety state of the building in real time based on the safety coefficient data model, thereby realizing the prevention and early warning of disasters and further lightening the damage of accidents.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A safety coefficient data model construction method is characterized by comprising the following steps:
the method comprises the following steps of firstly, carrying out data mining on influence factors influencing the safety state of a building to obtain state data of each influence factor;
determining various safety factors describing the data model based on the state data;
and step three, associating the various safety factors, determining a safety factor standard value for triggering early warning, and establishing the safety factor data model according to the association relation of the safety factors and the safety factor standard value.
2. The method for constructing a safety-factor data model of claim 1, wherein the state data includes shaking data, three-dimensional deformation data, noise data, and foreign object intrusion data.
3. The method for constructing a safety-factor data model of claim 2, wherein the plurality of safety factors comprise: the shaking safety factor, the three-dimensional deformation safety factor, the abnormal sound safety factor and the foreign matter invasion safety factor.
4. The safety coefficient data model building method of claim 3, wherein the second step specifically comprises:
determining a shaking safety factor: the method comprises the steps of obtaining shaking data, setting a safety angle threshold value R, and triggering early warning when a + b is monitored to be larger than R, wherein the shaking data comprises a first direction angle a and a second direction angle b opposite to the first direction angle a, so that a shaking safety coefficient a + b is smaller than R;
determining a three-dimensional deformation safety coefficient: acquiring the three-dimensional deformation data, setting a safety deformation threshold value A by the three-dimensional deformation data comprising XYZ length, width and height of the building and a deformation degree T, and triggering early warning to obtain a three-dimensional deformation safety coefficient when the sum of damage degrees of XYZ length, width and height of the building is greater than A
Wherein
Is the degree of damage of X and is,
is the degree of damage of the Y, and,
is the degree of damage of Z;
determining the abnormal sound safety coefficient: acquiring the noise data, and triggering an alarm to obtain an abnormal sound safety coefficient V when the noise data is not a conventional built-in sound source;
determining a foreign matter invasion safety coefficient: acquiring the foreign matter invasion data, wherein the foreign matter invasion data comprise object falling a1Foreign matter threat b1And foreign object deviation c1Setting a safety invasion threshold value E, and triggering early warning to obtain a foreign matter invasion safety coefficient when the sum of the influence degrees of the invasion data of various foreign matters is greater than E
Wherein, T1For objects to fall off a1E is the foreign body influence degree, and q is the potential threat influence degree.
5. The safety coefficient data model building method of claim 4, wherein the third step specifically comprises:
and associating the shaking safety factor, the three-dimensional deformation safety factor, the abnormal sound safety factor and the foreign object invasion safety factor, determining a safety factor standard value 1 for triggering early warning, and establishing a safety factor data model R + A + V + E < 1.
6. An AI-based early warning device applied to the safety factor data model according to any one of claims 1 to 5 for monitoring the safety state of a building, comprising: the device comprises a data acquisition unit, an alarm unit and an AI processing platform;
the data acquisition unit is arranged on the building and used for detecting shaking data, three-dimensional deformation data, noise data and foreign matter invasion data of the building; the data output end of the data acquisition unit is connected with the data input end of the AI processing platform and is used for transmitting the acquired shaking data, the three-dimensional deformation data, the noise data and the foreign matter invasion data to the AI processing platform;
the AI processing platform is used for comparing and analyzing the shaking data, the three-dimensional deformation data, the noise data and the foreign matter invasion data with the preset safety factor, carrying out early warning and fault warning on the unnatural regularity shaking, displacement and abnormal sound of the building, providing event warning and severity evaluation for the three-dimensional deformation and the foreign matter invasion, and simultaneously evaluating the safety state of the building.
7. The AI-based early warning device of claim 6, wherein the data acquisition unit includes a pan-tilt camera, a microphone, a speaker, and an infrared sensor.
8. The AI-based early warning device of claim 7, wherein the AI processing platform comprises an audio DSP, a master IC, an AI chip, a storage unit, and a WiFi module;
the data output ends of the microphone and the loudspeaker are connected with the data input end of the audio DSP, the data output ends of the pan-tilt camera, the audio DSP and the infrared sensor are connected with the data input end of the main control IC, the main control IC is in two-way connection with the AI chip, and the data output end of the main control IC is connected to the alarm unit and the background.
9. The AI-based early warning device of claim 8, wherein the safety factor data model is loaded on the AI chip,
and the AI chip is used for controlling the safety coefficient data model to operate so as to analyze the shaking data, the three-dimensional deformation data, the noise data and the foreign matter invasion data and determine the safety state of the building.
10. An electronic device, comprising:
a memory to store a computer program;
a processor for implementing the method of constructing a safety coefficient data model according to any one of claims 1 to 5 when executing the computer program.
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- 2021-08-26 CN CN202110989595.8A patent/CN113706822A/en active Pending
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| CN103743441A (en) * | 2014-01-20 | 2014-04-23 | 马鞍山南山开发公司 | Multi-factor coupling on-line monitoring system and multi-factor coupling on-line monitoring system method for slope safety |
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Application publication date: 20211126 |