CN116859137B - Electrostatic detection early warning system and method based on data analysis - Google Patents
Electrostatic detection early warning system and method based on data analysis Download PDFInfo
- Publication number
- CN116859137B CN116859137B CN202311118041.6A CN202311118041A CN116859137B CN 116859137 B CN116859137 B CN 116859137B CN 202311118041 A CN202311118041 A CN 202311118041A CN 116859137 B CN116859137 B CN 116859137B
- Authority
- CN
- China
- Prior art keywords
- electrostatic
- areas
- detection
- area
- work
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 83
- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000007405 data analysis Methods 0.000 title claims abstract description 15
- 230000003068 static effect Effects 0.000 claims abstract description 41
- 230000000694 effects Effects 0.000 claims abstract description 22
- 230000005611 electricity Effects 0.000 claims description 36
- 230000002159 abnormal effect Effects 0.000 claims description 21
- 238000012937 correction Methods 0.000 claims description 20
- 238000005259 measurement Methods 0.000 claims description 13
- 238000009825 accumulation Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000012163 sequencing technique Methods 0.000 claims description 4
- 239000004744 fabric Substances 0.000 claims description 3
- 241000894007 species Species 0.000 description 16
- 238000012544 monitoring process Methods 0.000 description 6
- 238000004891 communication Methods 0.000 description 5
- 230000006378 damage Effects 0.000 description 5
- 230000005684 electric field Effects 0.000 description 3
- 238000004880 explosion Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 239000000428 dust Substances 0.000 description 2
- 239000007789 gas Substances 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- -1 vapors Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R29/00—Arrangements for measuring or indicating electric quantities not covered by groups G01R19/00 - G01R27/00
- G01R29/24—Arrangements for measuring quantities of charge
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05F—STATIC ELECTRICITY; NATURALLY-OCCURRING ELECTRICITY
- H05F3/00—Carrying-off electrostatic charges
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Mathematical Analysis (AREA)
- Theoretical Computer Science (AREA)
- Pure & Applied Mathematics (AREA)
- Remote Sensing (AREA)
- Computational Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Mathematical Optimization (AREA)
- Probability & Statistics with Applications (AREA)
- Evolutionary Biology (AREA)
- Operations Research (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Algebra (AREA)
- Automation & Control Theory (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Emergency Alarm Devices (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention relates to the technical field of data analysis, in particular to an electrostatic detection early warning system and method based on data analysis, comprising the following steps: acquiring a target detection area, and dividing the target detection area into a plurality of working areas according to the activity ranges of staff of different work types in the target detection area; dividing the working area into a plurality of independent detection areas by using grids, wherein the grids adopt rectangular grids, and the side lengths of the rectangular grids are equal to the detection radius of the electrostatic sensor; arranging electrostatic sensors at positions corresponding to the grid center points and taking charge of electrostatic detection of corresponding detection areas; and receiving data feedback of the electrostatic sensor, predicting the electrostatic state according to different working areas, and sending out an early warning signal when the risk level is higher. The invention can solve the problem of static detection early warning after partial sensors are damaged.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to an electrostatic detection early warning system and method based on data analysis.
Background
Static electricity is a phenomenon in which charges accumulate on the surface of an object, usually due to movement of electrons. It is a charge distribution due to an imbalance between positive and negative charges that can accumulate on the surface of an object and cause attractive or repulsive phenomena. Electrostatic phenomena are typically the contact or separation between objects, or due to friction, pressure, or other physical processes. In the electrostatic phenomenon, when two objects are separated, one of the objects may have excessive positive or negative charges, and the other object may have corresponding opposite charges. Such charge imbalance may lead to the formation of an electric field, resulting in charge exchange, attraction or repulsion phenomena between objects. Common electrostatic phenomena are electrostatic attraction, electrostatic discharge, electrostatic interference, and electrostatic charge transfer.
Static electricity, while a common phenomenon in nature, can cause some hazards and problems in some cases. Static electricity can cause charge accumulation on the surface of the product, which can cause damage to the product if such charge is not timely released or neutralized. Particularly in the manufacturing industry, electronic components, semiconductor devices, and sensitive equipment may be subject to electrostatic discharge, resulting in equipment failure or reduced performance. In flammable gas or dust environments, electrostatic discharge may cause a fire or explosion. In industrial equipment and electronic equipment, static electricity may cause malfunction of electronic components, resulting in failure of the equipment. This can have an impact on the continuous operation of the production line and on the performance of the equipment. Electrostatic discharge may interfere with the proper operation of wireless communication and electromagnetic equipment, thereby affecting communication quality and data transmission.
In the prior art, the effect of monitoring the static electricity distribution in the workshop is achieved by selecting a proper sensor and arranging the sensor according to the layout and flow of the workshop to cover different areas. However, in the practical process, the sensors are difficult to realize the coverage of all areas, and as the service time increases, part of the sensors can be damaged and interrupted in communication, so that the problem of untimely detection and early warning can be caused.
Disclosure of Invention
The invention aims to provide an electrostatic detection early warning system and method based on data analysis, which solve the technical problems.
The aim of the invention can be achieved by the following technical scheme:
the electrostatic detection early warning method based on data analysis comprises the following steps:
acquiring a target detection area, and dividing the target detection area into a plurality of working areas according to the activity ranges of staff of different work types in the target detection area;
dividing the working area into a plurality of independent detection areas by using grids, wherein the grids adopt rectangular grids, and the side lengths of the rectangular grids are equal to the detection radius of the electrostatic sensor;
arranging electrostatic sensors at positions corresponding to the grid center points and taking charge of electrostatic detection of corresponding detection areas;
receiving data feedback of the electrostatic sensor, predicting the electrostatic state according to different working areas, sending an early warning signal when the risk level is high, sending an early warning signal when the risk coefficient is greater than or equal to a risk threshold value, and notifying a background worker to eliminate static electricity;
the specific steps of predicting and early warning the static state are as follows:
acquiring risk coefficients Fi of all working areas, comparing the risk coefficients Fi with a preset risk threshold value Fmax, and defining the working area where Fi is not less than Fmax as a management and control area;
acquiring position information of an abnormal sensor in the management and control area, connecting the abnormal sensor positioned at the outermost periphery, and generating a risk area;
early warning is carried out according to the boundary information of the risk area;
when the electrostatic sensor loses data connection, respectively acquiring measurement data of the adjacent electrostatic sensors, calculating standard deviation of the group of measurement data, and taking an average value of the group of measurement data as output of the electrostatic sensor losing data connection when the standard deviation is smaller than a preset value; when the standard deviation is larger than or equal to a preset value, the electrostatic sensor losing the data connection is defined as an abnormal sensor.
As a further scheme of the invention: the specific dividing steps of the working area are as follows:
acquiring the activity tracks of all staff in the target detection area;
marking the staff according to different working types of the staff;
acquiring the activity tracks of the workers of different work types, and taking the concentrated areas of the activity tracks as the activity areas of the workers of different work types;
acquiring non-overlapping parts among different active areas as working areas of the work species;
and acquiring the overlapped part between different active areas as a working area of the mixed work species.
As a further scheme of the invention: if there are multiple working areas of mixed work species communicating with each other, then the working areas are combined into one working area.
As a further scheme of the invention: the method also comprises the step of setting correction coefficients of different working areas, wherein the specific steps are as follows:
acquiring work types corresponding to different work areas, wherein the work areas of the mixed work types correspond to a plurality of work types;
sequencing the influence of static electricity accumulation according to different work species, wherein the work species with the higher rank are more prone to cause static electricity accumulation;
setting the correction coefficient of the working area as K, wherein the correction coefficient K is equal to the ranking of a single work type in the working area of the work type; in the working area of the mixed work species, the correction coefficient K is equal to the ranking of the work species which are ranked the top among the plurality of work species.
As a further scheme of the invention: the specific steps of electrostatic detection by the electrostatic sensor are as follows:
acquiring measurement data Ec of electrostatic sensors in each detection area;
comparing the detected value with a preset detection threshold Emax, and marking the electrostatic sensor as an abnormal sensor when Ec is more than or equal to Emax;
the number of abnormal sensors in each working area is obtained, and a risk coefficient F is calculated, wherein the calculation formula is as follows:；
wherein Fi represents a risk coefficient of the working area i, si represents the total number of electrostatic sensors of the working area i, si' represents the number of abnormal sensors in the working area i, and λ represents a preset proportionality coefficient.
An electrostatic detection early warning system based on data analysis, comprising:
the working area dividing module: acquiring a target detection area, and dividing the target detection area into a plurality of working areas according to the activity ranges of staff of different work types in the target detection area;
and a grid dividing module: dividing the working area into a plurality of independent detection areas by using grids, wherein the grids adopt rectangular grids, and the side lengths of the rectangular grids are equal to the detection radius of the electrostatic sensor;
sensor arrangement module: arranging electrostatic sensors at positions corresponding to the grid center points and taking charge of electrostatic detection of corresponding detection areas;
and the early warning module is used for: receiving data feedback of the electrostatic sensor, predicting the electrostatic state according to different working areas, and sending out an early warning signal when the risk level is higher;
static electricity eliminating module: the conductive fabric is contacted with the position where static electricity is accumulated and is transmitted to the circuit board, a sensing switch on the circuit board turns on a battery to be electrified, the LED lamp is turned on, the LED lamp is automatically extinguished after 3-5 seconds, and the static electricity is reset, so that the static electricity is eliminated in the process.
The invention has the beneficial effects that: common electrostatic sensors include capacitive electrostatic sensors, electric field sensors, potential sensors, charge sensors, digital sensors and the like, but the detection range of any sensor is limited, and damage and communication interruption in the use process are difficult to avoid, so that full-coverage detection and early warning of a target area are difficult to realize;
therefore, in the invention, the whole area to be detected is divided into a plurality of working areas according to the different activity areas of the staff, and different correction coefficients are set according to the different staff compositions or work types in the working areas; after the output data of the electrostatic sensor is actually received, evaluating different working areas according to different correction coefficients, and screening out working areas with higher risks, namely the management and control areas in the invention; the management and control area is used as a monitoring target, the risk area is obtained after the monitoring area is connected with each other according to the position information of the electrostatic sensor at the outermost periphery of the monitoring area, and early warning is carried out;
therefore, in the scheme, the early warning after static electricity accumulation in part of the working area can be solved, and static electricity elimination in the risk area can be realized.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of an electrostatic detection early warning method based on data analysis.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention discloses an electrostatic detection early warning method based on data analysis, which comprises the following steps:
acquiring a target detection area, and dividing the target detection area into a plurality of working areas according to the activity ranges of staff of different work types in the target detection area;
dividing the working area into a plurality of independent detection areas by using grids, wherein the grids adopt rectangular grids, and the side lengths of the rectangular grids are equal to the detection radius of the electrostatic sensor;
arranging electrostatic sensors at positions corresponding to the grid center points and taking charge of electrostatic detection of corresponding detection areas;
receiving data feedback of the electrostatic sensor, predicting the electrostatic state according to different working areas, and sending out an early warning signal when the risk level is higher; when the risk coefficient is greater than or equal to the risk threshold value, an early warning signal is sent out to inform a background worker to eliminate static electricity;
the specific steps of predicting and early warning the static state are as follows:
acquiring risk coefficients Fi of all working areas, comparing the risk coefficients Fi with a preset risk threshold value Fmax, and defining the working area where Fi is not less than Fmax as a management and control area;
acquiring position information of an abnormal sensor in the management and control area, connecting the abnormal sensor positioned at the outermost periphery, and generating a risk area;
early warning is carried out according to the boundary information of the risk area;
when the electrostatic sensor loses data connection, respectively acquiring measurement data of the adjacent electrostatic sensors, calculating standard deviation of the group of measurement data, and taking an average value of the group of measurement data as output of the electrostatic sensor losing data connection when the standard deviation is smaller than a preset value; when the standard deviation is larger than or equal to a preset value, the electrostatic sensor losing the data connection is defined as an abnormal sensor.
It is understood that static electricity can cause various hazards, particularly during plant production, and that static electricity discharge can cause fires or explosions, particularly in environments containing flammable gases, vapors, or dust. Static electricity can ignite these combustible materials, resulting in a fire or explosion event; when the accumulated electrostatic charge flows through a human body or an animal, an electric shock may be induced. This may lead to minor discomfort or serious bodily injury, even life threatening; electrostatic discharge can cause damage to electronic equipment, computers, semiconductor devices, and other sensitive equipment. The tiny circuits and components in these devices are very sensitive to static electricity, etc., so that static electricity conditions inside a workshop or factory need to be monitored from time to time, and the simplest and quick method for detecting static electricity is to use a static electricity sensor.
Common electrostatic sensors include capacitive electrostatic sensors, electric field sensors, potential sensors, charge sensors, digital sensors and the like, but the detection range of any sensor is limited, and damage and communication interruption in the use process are difficult to avoid, so that full-coverage detection and early warning of a target area are difficult to realize;
therefore, in the invention, the whole area to be detected is divided into a plurality of working areas according to the different activity areas of the staff, and different correction coefficients are set according to the different staff compositions or work types in the working areas; after the output data of the electrostatic sensor is actually received, evaluating different working areas according to different correction coefficients, and screening out working areas with higher risks, namely the management and control areas in the invention; the management and control area is used as a monitoring target, the risk area is obtained after the electrostatic sensors are connected with each other according to the position information of the outermost electrostatic sensor in the monitoring area, and early warning is carried out.
In a preferred embodiment of the present invention, the specific dividing steps of the working area are as follows:
acquiring the activity tracks of all staff in the target detection area;
marking the staff according to different working types of the staff;
acquiring the activity tracks of the workers of different work types, and taking the concentrated areas of the activity tracks as the activity areas of the workers of different work types;
acquiring non-overlapping parts among different active areas as working areas of the work species;
and acquiring the overlapped part between different active areas as a working area of the mixed work species.
In this embodiment, if there are a plurality of working areas of mixed work species communicating with each other, the working areas are combined into one working area.
In a preferred embodiment of the present invention, the method further comprises setting correction coefficients of different working areas, wherein the specific steps are as follows:
acquiring work types corresponding to different work areas, wherein the work areas of the mixed work types correspond to a plurality of work types;
sequencing the influence of static electricity accumulation according to different work species, wherein the work species with the higher rank are more prone to cause static electricity accumulation;
setting the correction coefficient of the working area as K, wherein the correction coefficient K is equal to the ranking of a single work type in the working area of the work type; in the working area of the mixed work species, the correction coefficient K is equal to the ranking of the work species which are ranked the top among the plurality of work species.
In a preferred embodiment of the present invention, the specific steps of electrostatic detection by the electrostatic sensor are as follows:
acquiring measurement data Ec of electrostatic sensors in each detection area;
comparing the detected value with a preset detection threshold Emax, and marking the electrostatic sensor as an abnormal sensor when Ec is more than or equal to Emax;
the number of abnormal sensors in each working area is obtained, and a risk coefficient F is calculated, wherein the calculation formula is as follows:；
wherein Fi represents a risk coefficient of the working area i, si represents the total number of electrostatic sensors of the working area i, si' represents the number of abnormal sensors in the working area i, and λ represents a preset proportionality coefficient.
An electrostatic detection early warning system based on data analysis, comprising:
the working area dividing module: acquiring a target detection area, and dividing the target detection area into a plurality of working areas according to the activity ranges of staff of different work types in the target detection area;
and a grid dividing module: dividing the working area into a plurality of independent detection areas by using grids, wherein the grids adopt rectangular grids, and the side lengths of the rectangular grids are equal to the detection radius of the electrostatic sensor;
sensor arrangement module: arranging electrostatic sensors at positions corresponding to the grid center points and taking charge of electrostatic detection of corresponding detection areas;
and the early warning module is used for: receiving data feedback of the electrostatic sensor, predicting the electrostatic state according to different working areas, and sending out an early warning signal when the risk level is higher;
static electricity eliminating module: the conductive fabric is contacted with the position where static electricity is accumulated and is transmitted to the circuit board, a sensing switch on the circuit board turns on a battery to be electrified, the LED lamp is turned on, the LED lamp is automatically extinguished after 3-5 seconds, and the static electricity is reset, so that the static electricity is eliminated in the process.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (4)
1. The electrostatic detection early warning method based on data analysis is characterized by comprising the following steps of:
acquiring a target detection area, and dividing the target detection area into a plurality of working areas according to the activity ranges of staff of different work types in the target detection area;
dividing the working area into a plurality of independent detection areas by using grids, wherein the grids adopt rectangular grids, and the side lengths of the rectangular grids are equal to the detection radius of the electrostatic sensor;
arranging electrostatic sensors at positions corresponding to the grid center points and taking charge of electrostatic detection of corresponding detection areas;
receiving data feedback of the electrostatic sensor, predicting the electrostatic state according to different working areas, and sending an early warning signal when the risk coefficient is greater than or equal to a risk threshold value to inform a background worker to eliminate static;
the specific steps of predicting and early warning the static state are as follows:
acquiring risk coefficients Fi of all working areas, comparing the risk coefficients Fi with a preset risk threshold value Fmax, and defining the working area where Fi is not less than Fmax as a management and control area;
acquiring position information of an abnormal sensor in the management and control area, connecting the abnormal sensor positioned at the outermost periphery, and generating a risk area;
early warning is carried out according to the boundary information of the risk area;
when the electrostatic sensor loses data connection, respectively acquiring measurement data of the adjacent electrostatic sensors, calculating standard deviation of the measurement data, and taking an average value of the measurement data as output of the electrostatic sensor losing data connection when the standard deviation is smaller than a preset value; when the standard deviation is larger than or equal to a preset value, the electrostatic sensor losing the data connection is defined as an abnormal sensor;
the method also comprises the step of setting correction coefficients of different working areas, wherein the specific steps are as follows:
acquiring work types corresponding to different work areas, wherein the work areas of the mixed work types correspond to a plurality of work types;
sequencing the influence of static electricity accumulation according to different work species, wherein the work species with the higher rank are more prone to cause static electricity accumulation;
setting the correction coefficient of the working area as K, wherein the correction coefficient K is equal to the ranking of a single work type in the working area of the work type; in the working area of the mixed work species, the correction coefficient K is equal to the ranking of the work species which are ranked the most forward in the plurality of work species;
the specific steps of electrostatic detection by the electrostatic sensor are as follows:
acquiring measurement data Ec of electrostatic sensors in each detection area;
comparing the detected value with a preset detection threshold Emax, and marking the electrostatic sensor as an abnormal sensor when Ec is more than or equal to Emax;
the number of abnormal sensors in each working area is obtained, and a risk coefficient F is calculated, wherein the calculation formula is as follows:；
wherein Fi represents a risk coefficient of the working area i, si represents the total number of electrostatic sensors of the working area i, si' represents the number of abnormal sensors in the working area i, and λ represents a preset proportionality coefficient.
2. The method for electrostatic detection and early warning based on data analysis according to claim 1, wherein the specific dividing steps of the working area are as follows:
acquiring the activity tracks of all staff in the target detection area;
marking the staff according to different working types of the staff;
acquiring the activity tracks of the workers of different work types, and taking the concentrated areas of the activity tracks as the activity areas of the workers of different work types;
acquiring non-overlapping parts among different active areas as working areas of the work species;
and acquiring the overlapped part between different active areas as a working area of the mixed work species.
3. The method of claim 2, wherein if there are a plurality of working areas of mixed working species, the working areas are combined into one working area.
4. The electrostatic detection early warning system based on data analysis is characterized by comprising:
the working area dividing module: acquiring a target detection area, and dividing the target detection area into a plurality of working areas according to the activity ranges of staff of different work types in the target detection area;
and a grid dividing module: dividing the working area into a plurality of independent detection areas by using grids, wherein the grids adopt rectangular grids, and the side lengths of the rectangular grids are equal to the detection radius of the electrostatic sensor;
sensor arrangement module: arranging electrostatic sensors at positions corresponding to the grid center points and taking charge of electrostatic detection of corresponding detection areas;
and the early warning module is used for: receiving data feedback of the electrostatic sensor, predicting the electrostatic state according to different working areas, and sending out an early warning signal when the risk coefficient is greater than or equal to a risk threshold value;
the method also comprises the step of setting correction coefficients of different working areas, wherein the specific steps are as follows:
acquiring work types corresponding to different work areas, wherein the work areas of the mixed work types correspond to a plurality of work types;
sequencing the influence of static electricity accumulation according to different work species, wherein the work species with the higher rank are more prone to cause static electricity accumulation;
setting the correction coefficient of the working area as K, wherein the correction coefficient K is equal to the ranking of a single work type in the working area of the work type; in the working area of the mixed work species, the correction coefficient K is equal to the ranking of the work species which are ranked the most forward in the plurality of work species;
the specific steps of electrostatic detection by the electrostatic sensor are as follows:
acquiring measurement data Ec of electrostatic sensors in each detection area;
comparing the detected value with a preset detection threshold Emax, and marking the electrostatic sensor as an abnormal sensor when Ec is more than or equal to Emax;
the number of abnormal sensors in each working area is obtained, and a risk coefficient F is calculated, wherein the calculation formula is as follows:；
wherein Fi represents a risk coefficient of the working area i, si represents the total number of electrostatic sensors of the working area i, si' represents the number of abnormal sensors in the working area i, and lambda represents a preset proportionality coefficient;
static electricity eliminating module: the conductive fabric is contacted with the position where static electricity is accumulated and is transmitted to the circuit board, a sensing switch on the circuit board turns on a battery to be electrified, the LED lamp is turned on, the LED lamp is automatically extinguished after 3-5 seconds, and the static electricity is reset, so that the static electricity is eliminated in the process.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311118041.6A CN116859137B (en) | 2023-09-01 | 2023-09-01 | Electrostatic detection early warning system and method based on data analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311118041.6A CN116859137B (en) | 2023-09-01 | 2023-09-01 | Electrostatic detection early warning system and method based on data analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116859137A CN116859137A (en) | 2023-10-10 |
| CN116859137B true CN116859137B (en) | 2023-11-21 |
Family
ID=88221869
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311118041.6A Active CN116859137B (en) | 2023-09-01 | 2023-09-01 | Electrostatic detection early warning system and method based on data analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116859137B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US3614602A (en) * | 1968-12-20 | 1971-10-19 | Smith Corp A O | Measuring apparatus including rotatably mounted apertured plate means for measuring the charge in electrically charged flowing liquid |
| CN103926479A (en) * | 2014-04-30 | 2014-07-16 | 辽宁工程技术大学 | Charge monitoring device in coal gas migration process and monitoring method thereof |
| CN105092989A (en) * | 2014-04-30 | 2015-11-25 | 北京纳米能源与系统研究所 | Method and system of calculating piezoelectric charge distribution at piezoelectric electronics device interface |
| CN107589316A (en) * | 2017-08-25 | 2018-01-16 | 中国科学院近代物理研究所 | Charge measurement circuit |
| DE102018003088A1 (en) * | 2018-04-16 | 2019-10-17 | Aron Kirschen | Arrangement and method for reducing the interference from the modulator in an electrostatic semiconductor sensor for measuring electrostatic and low-frequency electric fields |
| CN110531246A (en) * | 2019-08-09 | 2019-12-03 | 晶晨半导体（深圳）有限公司 | A method of electrostatic weak link is quickly found out by Gong Gefa |
-
2023
- 2023-09-01 CN CN202311118041.6A patent/CN116859137B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US3614602A (en) * | 1968-12-20 | 1971-10-19 | Smith Corp A O | Measuring apparatus including rotatably mounted apertured plate means for measuring the charge in electrically charged flowing liquid |
| CN103926479A (en) * | 2014-04-30 | 2014-07-16 | 辽宁工程技术大学 | Charge monitoring device in coal gas migration process and monitoring method thereof |
| CN105092989A (en) * | 2014-04-30 | 2015-11-25 | 北京纳米能源与系统研究所 | Method and system of calculating piezoelectric charge distribution at piezoelectric electronics device interface |
| CN107589316A (en) * | 2017-08-25 | 2018-01-16 | 中国科学院近代物理研究所 | Charge measurement circuit |
| DE102018003088A1 (en) * | 2018-04-16 | 2019-10-17 | Aron Kirschen | Arrangement and method for reducing the interference from the modulator in an electrostatic semiconductor sensor for measuring electrostatic and low-frequency electric fields |
| CN110531246A (en) * | 2019-08-09 | 2019-12-03 | 晶晨半导体（深圳）有限公司 | A method of electrostatic weak link is quickly found out by Gong Gefa |
Non-Patent Citations (1)
| Title |
|---|
| 系列导体平行板静电平衡问题的讨论;黄存可 等;《大学物理》;第32卷(第9期);18-22 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116859137A (en) | 2023-10-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US5992436A (en) | Monitoring steam traps using RF signaling | |
| KR102051809B1 (en) | Battery Protection apparatus and Method Using Gas Sensors | |
| US20110118896A1 (en) | Smart Environmental Control System For An Enclosure With Diagnostics | |
| US11400604B2 (en) | Gripping device with monitoring of the operating state thereof | |
| CN116859137B (en) | Electrostatic detection early warning system and method based on data analysis | |
| WO2018012741A1 (en) | System for observing resistance and current of ground line | |
| CN202309804U (en) | Handset having waterproof circuit | |
| JP6198778B2 (en) | Variable disconnection device and variable disconnection control method for battery system | |
| CN108170080A (en) | A kind of data center machine room environmental monitoring system | |
| US11128114B2 (en) | Hazard detection system for portable electrical devices | |
| CN113140088A (en) | Electric power fire alarm monitoring system | |
| CN106226464A (en) | A kind of Electronic Nose gas detecting system with defencive function | |
| CN115493645A (en) | Method and device for monitoring running state of PLC control cabinet | |
| CN115211618A (en) | Anti-static alarm garment | |
| JP2022101676A (en) | Fire alarm facility | |
| CN209656791U (en) | It is a kind of building or structures electrostatic detection system | |
| CN115171325A (en) | Automatic alarm electric fire extinguishing controller | |
| CN207566379U (en) | A kind of belt feeder belt protective device against longitudinal tear, comprehensive protector and system | |
| CN207717230U (en) | A kind of Internet of Things wireless temperature measurement data transmission system | |
| CN217279357U (en) | Natural gas production monitoring system | |
| CN108288356A (en) | A kind of domestic gas deflation warning system | |
| CN108594139B (en) | Method for detecting critical failure of battery | |
| KR20230153766A (en) | ESS Fire Sign Prediction System Using Integrated Off-Gas Sensor | |
| CN219590913U (en) | Temperature monitoring smoke alarm device | |
| CN108738221B (en) | Anti-static bracelet and grounding detection device for improving detection stability |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |