CN115830739B - Monitoring and early warning method based on industrial Internet - Google Patents
Monitoring and early warning method based on industrial Internet Download PDFInfo
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Abstract
The invention relates to the technical field of monitoring and early warning, in particular to a monitoring and early warning method based on the industrial Internet, which comprises a patrol task planning module, a central control module and a control module, wherein the patrol task planning module sets daily patrol times of each patrol area according to the danger level of the patrol area, the central control module calculates the average maintenance times of each device, calculates a daily patrol times adjusting parameter F according to the average maintenance times of each device so as to adjust the daily patrol times, and compensates the daily patrol times of the patrol area according to the average maintenance times of each device in the patrol area; if the patrol of the corresponding patrol area is not completed in the patrol time, the early warning module sends out the non-patrol early warning; if the patrol personnel are not specified, the early warning module sends out unqualified patrol early warning; according to the method and the device for warning the danger, the number of the inspection area is adjusted in a targeted manner according to the danger level of the inspection area and the maintenance condition of equipment in the inspection area, so that the efficiency of warning the danger is improved.
Description
Technical Field
The invention relates to the technical field of monitoring and early warning, in particular to a monitoring and early warning method based on the industrial Internet.
Background
In order to timely know and master the situation of dangerous operation sites, find and eliminate accident hidden trouble, strengthen the safety management work of the operation sites, effectively prevent and reduce various accidents, realize purposeful and key inspection, and thoroughly and completely realize the inspection without omission.
Chinese patent application publication No.: CN113112732B discloses a mill monitoring early warning system based on industry internet, belong to the technical field of monitoring early warning, image information and for temperature information transfer give the singlechip with the intensity of a fire through temperature sensor and camera, singlechip control spray equipment carries out automatic alarm and fire extinguishing function to mill department that catches fire, simultaneously transmit the place of fire to display device and show, make things convenient for the staff to drive to the place of fire fast, avoid the intensity of a fire to be prolonged in addition, spray equipment is in the fire while utilizing the inside dichloro copper acetate of dissolving ball and the dichloro copper acetate aqueous solution that water dissolved and produced can get rid of carbon monoxide and sulfur dioxide in the flue gas, reduce the harm degree of flue gas to workman's health, when the spraying hole on the shower head blocks up, can realize the automatic function of dredging to the spraying hole under the effect of elastic membrane and jam preventing board.
In order to prevent and reduce various accidents, manual inspection of dangerous areas is indispensable, however, in the prior art, inspection times of the inspection areas are not adjusted in a targeted manner according to the dangerous grade of the inspection areas and the maintenance condition of equipment in the inspection areas, so that the efficiency of dangerous early warning is low.
Disclosure of Invention
Therefore, the invention provides an industrial Internet-based monitoring and early warning method, which is used for solving the problem that the efficiency of dangerous early warning is low because the inspection times of the inspection area are not adjusted in a targeted manner according to the dangerous grade of the inspection area and the maintenance condition of equipment in the inspection area in the prior art.
In order to achieve the above purpose, the present invention provides a monitoring and early warning method based on industrial internet, comprising:
step S1, a patrol task planning module sets daily patrol times of each patrol area according to the risk level of the patrol area and designates patrol personnel;
step S2, the central control module obtains the number of the devices in any inspection area and the maintenance times of the devices in the inspection area to calculate the average maintenance times of the devices, and calculates the daily inspection times adjusting parameter F according to the average maintenance times of the devices;
step S3, the central control module adjusts the daily patrol times of the patrol area according to the comparison result of the daily patrol times adjusting parameter F and the preset daily patrol times comparison parameter, and compensates the daily patrol times of the patrol area according to the comparison result of the average maintenance times and the preset average maintenance times of all the devices in the patrol area;
step S4, the patrol execution module calculates patrol interval time according to the compensated daily patrol times to determine patrol time, and if patrol of the corresponding patrol area is not completed in the patrol time, the early warning module sends out non-patrol early warning;
step S5, the patrol execution module acquires face image information of the patrol personnel to judge whether the patrol personnel are specified patrol personnel, and if not, the early warning module sends out unqualified patrol early warning;
and S6, monitoring operation data of each device in real time by a monitoring module, and sending out abnormal operation early warning by the early warning module when the operation data of each device does not belong to a corresponding preset operation data threshold value.
Further, in the step S1, the risk levels of the patrol areas include a first risk level, a second risk level, and a third risk level, wherein the risk level of the first risk level > the risk level of the second risk level > the risk level of the third risk level, and the patrol task planning module sets the daily patrol times of each patrol area according to the risk level of the patrol area,
if the dangerous level of the patrol area is the first dangerous level, the central control module sets the daily patrol times as N1, and sets N1=N0×α3;
if the dangerous level of the patrol area is the second dangerous level, the central control module sets the daily patrol times as N1, and sets N1=N0×α2;
if the dangerous level of the inspection area is the third dangerous level, the central control module sets the number of daily inspection to be N1, and sets N1=N0×α1;
wherein N0 is the preset initial daily patrol times, alpha 1 is the first preset daily patrol times adjustment coefficient, alpha 2 is the second preset daily patrol times adjustment coefficient, alpha 3 is the third preset daily patrol times adjustment coefficient, and 0 < alpha 1 < alpha 2 < alpha 3 < 3.
Further, in the step S2, the central control module calculates the average maintenance number Bp of each device in any inspection area according to the following formula, and sets the average maintenance number Bp
Wherein Bi is the maintenance times of the ith equipment, A is the number of equipment in the inspection area;
the central control module calculates the adjustment parameter F of the daily patrol times according to the following formula, and sets
Wherein A0 is the number of preset devices, and Bp0 is the preset average maintenance times.
Further, in the step S3, a first preset daily patrol frequency comparison parameter F1 and a second preset daily patrol frequency comparison parameter F2 are set in the central control module, F1 is smaller than F2, the central control module compares the daily patrol frequency adjustment parameter F with F1 and F2 respectively and adjusts the daily patrol frequency according to the comparison result,
if F is more than or equal to F2, the central control module adjusts the daily patrol times to N2, and N2 = N1 xF/F2 is set;
if F1 is less than or equal to F2, the central control module adjusts the daily patrol times to N2, and N2 = N1 is set;
if F is less than F1, the central control module adjusts the number of daily tours to N2, and sets n2=n1×f/F1.
Further, in the step S3, the central control module compares the average maintenance frequency Bp of each device in the inspection area with a preset average maintenance frequency Bp0 and determines whether to compensate the daily inspection frequency of the inspection area according to the comparison result,
if Bp is less than Bp0, the central control module judges that the average maintenance times Bp of all the devices in the inspection area meet the standard, and the daily inspection times of the inspection area are not required to be compensated;
if Bp is more than or equal to Bp0, the central control module judges that the average maintenance times Bp of all the devices in the inspection area do not meet the standard, and the daily inspection times of the inspection area are required to be compensated.
Further, the central control module calculates the difference delta Bp between Bp and Bp0 and compensates the daily inspection frequency of the inspection area according to the delta Bp, the delta Bp=Bp-Bp 0 is set, a first preset average maintenance frequency difference delta Bp1 and a second preset average maintenance frequency difference delta Bp2 are arranged in the central control module, delta Bp1 is smaller than delta Bp2, the central control module compares the delta Bp with the delta Bp1 and the delta Bp2 respectively and compensates the daily inspection frequency of the inspection area according to the comparison result,
if Δbp is greater than or equal to Δbp2, the central control module compensates the daily patrol times of the patrol area to N3, and sets n3=n2×β3;
if Δbp1 is less than or equal to Δbp < Δbp2, the central control module compensates the daily patrol times of the patrol area to N3, and sets n3=n2×β2;
if Δbp is less than Δbp1, the central control module compensates the daily patrol times of the patrol area to N3, and sets n3=n2×β1;
wherein N2 is the adjusted daily patrol times, beta 1 is a first preset daily patrol times compensation coefficient, beta 2 is a second preset daily patrol times compensation coefficient, beta 3 is a third preset daily patrol times compensation coefficient, and beta 1 is more than 1.3 and beta 2 is more than 1.8 and less than beta 3.
Further, in the step S4, the patrol execution module calculates a patrol interval time T of each patrol area, sets t=24/N3, uses a zero point as a start time, sets a patrol time point every interval time T, sets a time period from T-0.5 to t+0.5 as a patrol time at any patrol time point T, and if the patrol of the patrol area is not completed within the patrol time, the early warning module sends out an unrequired early warning.
Further, in the step S5, the inspection execution module pre-stores the face image information of the specified inspection person, when the inspection and the card punching are performed, the inspection execution module collects the face image information of the inspection person, calculates the similarity S between the collected face image information and the pre-stored face image information of the specified inspection person, compares the similarity S with the preset similarity S0, determines whether the inspection person is the specified inspection person according to the comparison result,
if S is more than or equal to S0, the patrol execution module judges that the patrol personnel are specified patrol personnel, and meets the requirements;
if S is less than S0, the patrol execution module judges that the patrol personnel does not specify the patrol personnel and does not meet the requirements, and the early warning module sends out unqualified patrol early warning.
Further, in the step S6, the monitoring module monitors the operation data of each device in real time, the monitoring module presets the operation data threshold of each operation device, compares the operation data of each device with the corresponding operation data threshold,
if the operation data of each device belong to the corresponding operation data threshold value, the monitoring module judges that the device operates normally;
and if the operation data of each device does not belong to the corresponding operation data threshold value, the monitoring module judges that the operation of the device is abnormal, and the early warning module sends out early warning of the abnormal operation of the device.
Further, when the early warning module sends out early warning, early warning information is sent to management personnel.
Compared with the prior art, the method has the beneficial effects that the inspection times of the inspection area are adjusted in a targeted manner according to the danger level of the inspection area and the maintenance condition of equipment in the inspection area, so that the efficiency of the method for early warning the danger is improved, and the accident potential is discovered and eliminated in time.
Furthermore, the patrol task planning module selects the corresponding daily patrol times adjustment coefficient for each patrol area according to the risk level of the patrol area, and when the risk level of the patrol area is higher, the daily patrol times of the patrol area are more, and by the technical scheme, the efficiency of the method for early warning the risk is further improved, and accident hidden danger is timely found and eliminated.
Further, according to the method, the daily patrol frequency adjustment parameter F is calculated according to the number of the devices in the patrol area and the average maintenance frequency of the devices, the larger the number of the devices in the patrol area is, the larger the average maintenance frequency of the devices in the patrol area is, the potential safety hazard is possibly larger, the more the daily patrol frequency of the patrol area is, the daily patrol frequency adjustment parameter F is a characteristic parameter of the daily patrol frequency, and the daily patrol frequency is adjusted through the daily patrol frequency adjustment parameter F, so that the efficiency of the method for dangerous early warning is further improved, and the accident potential is timely found and eliminated.
Further, the larger the average maintenance times of all the devices in the inspection area is, the larger potential safety hazards possibly exist, the central control module compares the average maintenance times Bp of all the devices in the inspection area with the preset average maintenance times Bp0 and compensates the daily inspection times of the inspection area according to the comparison result.
Further, the inspection execution module calculates the inspection interval time according to the compensated daily inspection times to determine the inspection time, and if inspection of the corresponding inspection area is not completed in the inspection time, the early warning module sends out non-inspection early warning, so that the efficiency of the method for dangerous early warning is further improved, and accident hidden danger is timely found and eliminated.
Further, the patrol execution module acquires face image information of the patrol personnel to judge whether the patrol personnel is the appointed patrol personnel, if not, the early warning module sends out unqualified patrol early warning, through the technical scheme, the condition that the patrol personnel are replaced privately is avoided, management of patrol work is enhanced, and therefore the efficiency of the method for dangerous early warning is further improved, and accident hidden danger is timely found and eliminated.
Further, the monitoring module monitors the operation data of each device in real time, and when the operation data of each device does not belong to a corresponding preset operation data threshold value, the early warning module sends out abnormal operation early warning of the device.
Drawings
FIG. 1 is a block diagram of a monitoring and early warning system based on the industrial Internet according to an embodiment of the invention;
fig. 2 is a flowchart of a monitoring and early warning method based on the industrial internet according to an embodiment of the invention.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1, a structural block diagram of a monitoring and early warning system based on an industrial internet according to an embodiment of the present invention includes:
the patrol task planning module is used for setting the daily patrol times of each patrol area according to the risk level of the patrol area and designating patrol personnel;
the central control module is connected with the inspection task planning module and used for calculating the average maintenance times of all the equipment, calculating the daily inspection times adjustment parameter F according to the average maintenance times of all the equipment, adjusting the daily inspection times of the inspection area according to the daily inspection times adjustment parameter F and compensating the daily inspection times of the inspection area according to the average maintenance times of all the equipment in the inspection area;
the patrol execution module is respectively connected with the patrol task planning module and the central control module and is used for collecting face image information of patrol personnel to judge whether the patrol personnel are specified patrol personnel or not and determining patrol time;
the early warning module is respectively connected with the inspection execution module and the monitoring module, and is used for sending out unqualified inspection early warning when the inspection execution module judges that the inspection of the corresponding inspection area is not completed within the inspection time, sending out unqualified inspection early warning when the inspection execution module judges that the inspection personnel are not specified, and sending out equipment operation abnormality early warning when the monitoring module judges that the equipment operation is abnormal;
and the monitoring module is connected with the early warning module and used for monitoring the operation data of each device in real time.
Referring to fig. 2, a flowchart of a monitoring and early warning method based on an industrial internet according to an embodiment of the present invention is shown, where the monitoring and early warning method based on the industrial internet includes:
step S1, a patrol task planning module sets daily patrol times of each patrol area according to the risk level of the patrol area and designates patrol personnel;
step S2, the central control module obtains the number of the devices in any inspection area and the maintenance times of the devices in the inspection area to calculate the average maintenance times of the devices, and calculates the daily inspection times adjusting parameter F according to the average maintenance times of the devices;
step S3, the central control module adjusts the daily patrol times of the patrol area according to the comparison result of the daily patrol times adjusting parameter F and the preset daily patrol times comparison parameter, and compensates the daily patrol times of the patrol area according to the comparison result of the average maintenance times and the preset average maintenance times of all the devices in the patrol area;
step S4, the patrol execution module calculates patrol interval time according to the compensated daily patrol times to determine patrol time, and if patrol of the corresponding patrol area is not completed in the patrol time, the early warning module sends out non-patrol early warning;
step S5, the patrol execution module acquires face image information of the patrol personnel to judge whether the patrol personnel are specified patrol personnel, and if not, the early warning module sends out unqualified patrol early warning;
and S6, monitoring operation data of each device in real time by a monitoring module, and sending out abnormal operation early warning by the early warning module when the operation data of each device does not belong to a corresponding preset operation data threshold value.
According to the method, the patrol times of the patrol area are adjusted in a targeted manner according to the danger level of the patrol area and the maintenance condition of equipment in the patrol area, so that the efficiency of the method for early warning the danger is improved, and accident potential is found and eliminated in time.
Specifically, in the step S1, the risk levels of the patrol areas include a first risk level, a second risk level, and a third risk level, wherein the risk level of the first risk level > the risk level of the second risk level > the risk level of the third risk level, and the patrol task planning module sets the daily patrol times of each patrol area according to the risk level of the patrol area,
if the dangerous level of the patrol area is the first dangerous level, the central control module sets the daily patrol times as N1, and sets N1=N0×α3;
if the dangerous level of the patrol area is the second dangerous level, the central control module sets the daily patrol times as N1, and sets N1=N0×α2;
if the dangerous level of the inspection area is the third dangerous level, the central control module sets the number of daily inspection to be N1, and sets N1=N0×α1;
wherein N0 is the preset initial daily patrol times, alpha 1 is the first preset daily patrol times adjustment coefficient, alpha 2 is the second preset daily patrol times adjustment coefficient, alpha 3 is the third preset daily patrol times adjustment coefficient, and 0 < alpha 1 < alpha 2 < alpha 3 < 3.
The invention provides a preferable embodiment scheme, wherein N0 is not less than 1 and not more than 2, N0 is a positive integer, a first preset daily patrol times adjustment coefficient alpha 1 is set to 0.5, a second preset daily patrol times adjustment coefficient alpha 2 is set to 1.5, a third preset daily patrol times adjustment coefficient alpha 3 is set to 2.5, and when the calculated N1 is not the positive integer, the value of N1 is set to be the smallest positive integer larger than N1.
According to the method, the corresponding daily patrol times of each patrol area are selected by the patrol task planning module according to the dangerous level of the patrol area, and the daily patrol times of the patrol area are more when the dangerous level of the patrol area is higher.
Specifically, in the step S2, the central control module calculates the average maintenance number Bp of each device in any inspection area according to the following formula, and sets the average maintenance number Bp
Wherein Bi is the maintenance times of the ith equipment, A is the number of equipment in the inspection area;
the central control module calculates the adjustment parameter F of the daily patrol times according to the following formula, and sets
Wherein A0 is the number of preset devices, and Bp0 is the preset average maintenance times.
In this example, 1 < A0 < 4,2 < Bp0 < 5, preferably A0 is 2 and Bp0 is 3.
Specifically, in the step S3, the central control module is provided with a first preset daily patrol times comparison parameter F1 and a second preset daily patrol times comparison parameter F2, F1 is smaller than F2, the central control module compares the daily patrol times adjustment parameter F with F1 and F2 respectively and adjusts the daily patrol times according to the comparison result,
if F is more than or equal to F2, the central control module adjusts the daily patrol times to N2, and N2 = N1 xF/F2 is set;
if F1 is less than or equal to F2, the central control module adjusts the daily patrol times to N2, and N2 = N1 is set;
if F is less than F1, the central control module adjusts the number of daily tours to N2, and sets n2=n1×f/F1.
In this example, 1 < F1 < 2.5 < F2 < 4, preferably, F1 is set to 1.8, F2 is set to 3.2, and when the calculated N2 is not a positive integer, the value of N is set to the smallest positive integer greater than N2.
According to the method, the daily patrol frequency adjusting parameter F is calculated according to the number of the devices in the patrol area and the average maintenance frequency of the devices, the larger the number of the devices in the patrol area is, the larger the average maintenance frequency of the devices in the patrol area is, the potential safety hazard is possibly larger, the daily patrol frequency of the patrol area is supposed to be larger, the daily patrol frequency adjusting parameter F is a characteristic parameter of the daily patrol frequency, and the daily patrol frequency is adjusted through the daily patrol frequency adjusting parameter F, so that the efficiency of the method for dangerous early warning is further improved, and the accident potential is timely found and eliminated.
Specifically, in the step S3, the central control module compares the average maintenance frequency Bp of each device in the inspection area with a preset average maintenance frequency Bp0 and determines whether to compensate the daily inspection frequency of the inspection area according to the comparison result,
if Bp is less than Bp0, the central control module judges that the average maintenance times Bp of all the devices in the inspection area meet the standard, and the daily inspection times of the inspection area are not required to be compensated;
if Bp is more than or equal to Bp0, the central control module judges that the average maintenance times Bp of all the devices in the inspection area do not meet the standard, and the daily inspection times of the inspection area are required to be compensated.
Specifically, the central control module calculates the difference delta Bp between Bp and Bp0 and compensates the daily inspection frequency of the inspection area according to the delta Bp, delta Bp=Bp-Bp 0 is set, a first preset average maintenance frequency difference delta Bp1 and a second preset average maintenance frequency difference delta Bp2 are arranged in the central control module, delta Bp1 is smaller than delta Bp2, the central control module compares delta Bp with delta Bp1 and delta Bp2 respectively and compensates the daily inspection frequency of the inspection area according to the comparison result,
if Δbp is greater than or equal to Δbp2, the central control module compensates the daily patrol times of the patrol area to N3, and sets n3=n2×β3;
if Δbp1 is less than or equal to Δbp < Δbp2, the central control module compensates the daily patrol times of the patrol area to N3, and sets n3=n2×β2;
if Δbp is less than Δbp1, the central control module compensates the daily patrol times of the patrol area to N3, and sets n3=n2×β1;
wherein N2 is the adjusted daily patrol times, beta 1 is a first preset daily patrol times compensation coefficient, beta 2 is a second preset daily patrol times compensation coefficient, beta 3 is a third preset daily patrol times compensation coefficient, and beta 1 is more than 1.3 and beta 2 is more than 1.8 and less than beta 3.
In this example, Δbp1 < 4 < Δbp2 < 6, Δbp1 is preferably set to 3, Δbp2 is set to 5, the first preset daily patrol frequency compensation coefficient β1 is set to 1.4, the second preset daily patrol frequency compensation coefficient β2 is set to 1.5, the third preset daily patrol frequency compensation coefficient β3 is set to 1.6, and when the calculated N3 is not a positive integer, the value of N3 is set to a minimum positive integer greater than N3.
The greater the average maintenance times of all the devices in the inspection area, the greater the potential safety hazard possibly exists, and the central control module compares the average maintenance times Bp of all the devices in the inspection area with the preset average maintenance times Bp0 and compensates the daily inspection times of the inspection area according to the comparison result.
Specifically, in the step S4, the patrol execution module calculates a patrol interval time T of each patrol area, sets t=24/N3, uses a zero point as a start time, sets a patrol time point every interval time T, sets a time period from T-0.5 to t+0.5 as a patrol time at any patrol time point T, and if the patrol of the patrol area is not completed within the patrol time, the early warning module sends out an early warning of non-patrol.
According to the method, the patrol execution module calculates the patrol interval time according to the compensated daily patrol times to determine the patrol time, and if the patrol of the corresponding patrol area is not completed in the patrol time, the early warning module sends out non-patrol early warning, so that the efficiency of the method for early warning the danger is further improved, and accident hidden danger is timely found and eliminated.
Specifically, in the step S5, the patrol execution module pre-stores the face image information of the specified patrol personnel, when the patrol is performed, the patrol execution module collects the face image information of the patrol personnel, calculates the similarity S between the collected face image information and the pre-stored face image information of the specified patrol personnel, compares the similarity S with the preset similarity S0, determines whether the patrol personnel is the specified patrol personnel according to the comparison result,
if S is more than or equal to S0, the patrol execution module judges that the patrol personnel are specified patrol personnel, and meets the requirements;
if S is less than S0, the patrol execution module judges that the patrol personnel does not specify the patrol personnel and does not meet the requirements, and the early warning module sends out unqualified patrol early warning.
In this example, 95% < S0 < 100%, preferably S0 is 96%.
The inspection execution module acquires face image information of the inspection personnel to judge whether the inspection personnel is the appointed inspection personnel, and if not, the early warning module sends out unqualified inspection early warning.
Specifically, in the step S6, the monitoring module monitors the operation data of each device in real time, the monitoring module presets the operation data threshold of each operation device, compares the operation data of each device with the corresponding operation data threshold,
if the operation data of each device belong to the corresponding operation data threshold value, the monitoring module judges that the device operates normally;
and if the operation data of each device does not belong to the corresponding operation data threshold value, the monitoring module judges that the operation of the device is abnormal, and the early warning module sends out early warning of the abnormal operation of the device.
The monitoring module monitors the operation data of each device in real time, and when the operation data of each device does not belong to a corresponding preset operation data threshold value, the early warning module sends out abnormal operation early warning of the device.
Specifically, when the early warning module sends out early warning, the early warning information is sent to a manager, so that the manager can grasp the early warning information in time, the efficiency of the method for early warning the danger is further improved, and accident potential is found and eliminated in time.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. The monitoring and early warning method based on the industrial Internet is characterized by comprising the following steps of:
step S1, a patrol task planning module sets daily patrol times of each patrol area according to the risk level of the patrol area and designates patrol personnel;
step S2, the central control module obtains the number of the devices in any inspection area and the maintenance times of the devices in the inspection area to calculate the average maintenance times of the devices, and calculates the daily inspection times adjusting parameter F according to the average maintenance times of the devices;
step S3, the central control module adjusts the daily patrol times of the patrol area according to the comparison result of the daily patrol times adjusting parameter F and the preset daily patrol times comparison parameter, and compensates the daily patrol times of the patrol area according to the comparison result of the average maintenance times and the preset average maintenance times of all the devices in the patrol area;
step S4, the patrol execution module calculates patrol interval time according to the compensated daily patrol times to determine patrol time, and if patrol of the corresponding patrol area is not completed in the patrol time, the early warning module sends out non-patrol early warning;
step S5, the patrol execution module acquires face image information of the patrol personnel to judge whether the patrol personnel are specified patrol personnel, and if not, the early warning module sends out unqualified patrol early warning;
step S6, the monitoring module monitors the operation data of each device in real time, and when the operation data of each device does not belong to a corresponding preset operation data threshold value, the early warning module sends out abnormal operation early warning of the device;
in the step S2, the central control module calculates the average maintenance frequency Bp of each device in any inspection area according to the following formula, and sets up
the central control module calculates the adjustment parameter F of the daily patrol times according to the following formula, and sets
2. The industrial internet-based monitoring and early warning method according to claim 1, wherein in the step S1, the hazard levels of the inspection areas include a first hazard level, a second hazard level, and a third hazard level, wherein the hazard level of the first hazard level > the hazard level of the second hazard level > the hazard level of the third hazard level, and the inspection task planning module sets the number of daily inspection of each inspection area according to the hazard level of the inspection area,
if the dangerous level of the patrol area is the first dangerous level, the central control module sets the daily patrol times as N1, and sets N1=N0×α3;
if the dangerous level of the patrol area is the second dangerous level, the central control module sets the daily patrol times as N1, and sets N1=N0×α2;
if the dangerous level of the inspection area is the third dangerous level, the central control module sets the number of daily inspection to be N1, and sets N1=N0×α1;
wherein N0 is the preset initial daily patrol times, alpha 1 is the first preset daily patrol times adjustment coefficient, alpha 2 is the second preset daily patrol times adjustment coefficient, alpha 3 is the third preset daily patrol times adjustment coefficient, and 0 < alpha 1 < alpha 2 < alpha 3 < 3.
3. The industrial internet-based monitoring and early warning method according to claim 2, wherein in the step S3, a first preset daily patrol times comparison parameter F1 and a second preset daily patrol times comparison parameter F2, F1 < F2, are provided in the central control module, the central control module compares the daily patrol times adjustment parameter F with F1 and F2 respectively and adjusts the daily patrol times according to the comparison result, wherein,
if F is more than or equal to F2, the central control module adjusts the daily patrol times to N2, and N2 = N1 xF/F2 is set;
if F1 is less than or equal to F2, the central control module adjusts the daily patrol times to N2, and N2 = N1 is set;
if F is less than F1, the central control module adjusts the number of daily tours to N2, and sets n2=n1×f/F1.
4. The industrial Internet-based monitoring and early warning method according to claim 3, wherein in the step S3, the central control module compares the average maintenance frequency Bp of each device in the inspection area with a preset average maintenance frequency Bp0 and determines whether to compensate the daily inspection frequency of the inspection area according to the comparison result,
if Bp is less than Bp0, the central control module judges that the average maintenance times Bp of all the devices in the inspection area meet the standard, and the daily inspection times of the inspection area are not required to be compensated;
if Bp is more than or equal to Bp0, the central control module judges that the average maintenance times Bp of all the devices in the inspection area do not meet the standard, and the daily inspection times of the inspection area are required to be compensated.
5. The industrial internet-based monitoring and early warning method according to claim 4, wherein the central control module calculates a difference Δbp between Bp and Bp0 and compensates the daily inspection frequency of the inspection area according to Δbp, a first preset average maintenance frequency difference Δbp1 and a second preset average maintenance frequency difference Δbp2 are set in the central control module, Δbp1 < Δbp2, the central control module compares Δbp with Δbp1 and Δbp2 respectively and compensates the daily inspection frequency of the inspection area according to the comparison result,
if Δbp is greater than or equal to Δbp2, the central control module compensates the daily patrol times of the patrol area to N3, and sets n3=n2×β3;
if Δbp1 is less than or equal to Δbp < Δbp2, the central control module compensates the daily patrol times of the patrol area to N3, and sets n3=n2×β2;
if Δbp is less than Δbp1, the central control module compensates the daily patrol times of the patrol area to N3, and sets n3=n2×β1;
wherein N2 is the adjusted daily patrol times, beta 1 is a first preset daily patrol times compensation coefficient, beta 2 is a second preset daily patrol times compensation coefficient, beta 3 is a third preset daily patrol times compensation coefficient, and beta 1 is more than 1.3 and beta 2 is more than 1.8 and less than beta 3.
6. The industrial internet-based monitoring and early warning method according to claim 5, wherein in the step S4, the inspection execution module calculates an inspection interval time T of each inspection area, sets t=24/N3, takes a zero point as a start time, sets an inspection time point every interval time T, sets a time period from T-0.5 to t+0.5 as an inspection time at any one of the inspection time points T, and if the inspection of the inspection area is not completed within the inspection time, the early warning module issues an inspection-free early warning.
7. The industrial internet-based monitoring and early warning method according to claim 6, wherein in the step S5, the patrol execution module pre-stores face image information of a specified patrol man, and when the patrol is performed for the card, the patrol execution module collects face image information of the patrol man and calculates similarity S between the collected face image information and the pre-stored face image information of the specified patrol man, the patrol execution module compares the similarity S with a preset similarity S0 and determines whether the patrol man is the specified patrol man according to the comparison result,
if S is more than or equal to S0, the patrol execution module judges that the patrol personnel are specified patrol personnel, and meets the requirements;
if S is less than S0, the patrol execution module judges that the patrol personnel does not specify the patrol personnel and does not meet the requirements, and the early warning module sends out unqualified patrol early warning.
8. The industrial internet-based monitoring and early warning method according to claim 7, wherein in the step S6, the monitoring module monitors the operation data of each device in real time, the monitoring module is pre-provided with the operation data threshold of each operation device, the monitoring module compares the operation data of each device with the corresponding operation data threshold,
if the operation data of each device belong to the corresponding operation data threshold value, the monitoring module judges that the device operates normally;
and if the operation data of each device does not belong to the corresponding operation data threshold value, the monitoring module judges that the operation of the device is abnormal, and the early warning module sends out early warning of the abnormal operation of the device.
9. The industrial internet-based monitoring and early warning method according to claim 1, wherein the early warning module sends early warning information to a manager when sending early warning.
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