CN117192269A - Big data monitoring and early warning system and method for electric room environment control box - Google Patents
Big data monitoring and early warning system and method for electric room environment control box Download PDFInfo
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Abstract
The invention relates to the technical field of electric room monitoring, in particular to a big data monitoring and early warning system and method for an electric room environment control box, comprising the steps of monitoring the equipment state of each electric equipment in an electric room by using the electric room environment control box and acquiring each historical fault record of each electric equipment in the electric room; acquiring historical fault data information corresponding to the electrical equipment from a historical fault record; acquiring first influence equipment data of each electrical equipment in an electrical room in the current period; based on first influence equipment data of the electrical equipment, performing fault prediction on each electrical equipment of the electrical room in the current period to obtain marked electrical equipment; acquiring all marking power equipment of the electric room; the electric room environment control box sends out early warning and safety regulation and control to the marked power equipment in the current period, and informs workers to process each marked power equipment in the electric room.
Description
Technical Field
The invention relates to the technical field of electric room safety monitoring, in particular to a big data monitoring and early warning system and method for an electric room environment control box.
Background
The traditional monitoring electric room mode has the following defects that 1, manual inspection is carried out, traditional monitoring to electric room environment needs personnel to enter an electric room regularly to check, manpower and material resources are wasted, real-time monitoring and early warning can not be carried out on electric equipment in the electric room, 2, information is incomplete, traditional monitoring to the electric room only obtains basic information when the electric equipment operates, more comprehensive information can not be obtained, such as temperature, humidity, power factors and the like, 3, reaction time is slow, corresponding measures can be taken after the traditional monitoring method needs to wait for manual inspection or receiving alarm signals, and reaction time is long.
The utility model provides an electricity room environment control box, is the equipment that is used for controlling and monitoring electricity room environment, and it installs in the inside of electricity room generally for maintain and adjust factors such as temperature, humidity, circulation of air and the air quality of electricity room, can real-time supervision data, can discover and handle electrical equipment anomaly fast.
Disclosure of Invention
The invention aims to provide a big data monitoring and early warning system and method for an electrical room environment control box, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a big data monitoring and early warning method for an electrical room environment control box comprises the following steps:
step S100: monitoring the equipment state of each electrical equipment in the electrical room by using an electrical room environment control box, and acquiring each historical fault record of each electrical equipment in the electrical room; acquiring historical fault data information corresponding to the electrical equipment from a historical fault record; screening each piece of historical fault data information of the electrical equipment, removing the historical fault data information related to other electrical equipment, and collecting each piece of retained historical fault data information;
step S200: based on each piece of historical fault data information of the electrical equipment, evaluating the fault influence degree of each piece of equipment data in the historical fault data information on the electrical equipment to obtain first influence equipment data of the electrical equipment;
step S300: acquiring first influence equipment data of each electrical equipment in an electrical room in the current period; based on first influence equipment data of the electrical equipment, performing fault prediction on each electrical equipment of the electrical room in the current period to obtain marked electrical equipment;
step S400: acquiring all marking power equipment of the electric room; the electric room environment control box sends out early warning and safety regulation and control to the marked power equipment in the current period, and informs workers to process each marked power equipment in the electric room.
Further, step S100 includes:
step S101: monitoring the equipment state of each electrical equipment in the electrical room by using an electrical room environment control box, and acquiring the occurrence time point of the electrical equipment fault when the electrical equipment in the electrical room is in fault; setting data acquisition time length; setting failure information acquisition period oc of electrical equipment 1 ＝[γ-β，γ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein, gamma is the occurrence time point of the fault of the electrical equipment; beta is the data acquisition time length; acquiring historical fault data information of the electrical equipment in a fault information acquisition period; the historical fault data information comprises average values of various equipment data of the electrical equipment in each unit time period in the fault information acquisition time period; wherein, each item of equipment data is the environmental temperature, the environmental humidity, the power supply, the current and the like of the electrical equipment;
step S102: acquiring fault occurrence time points of all electrical equipment in the electrical room; when the fault occurrence time point of one piece of electrical equipment is within the fault information acquisition time period of the other piece of electrical equipment, the fault information acquisition time period of the other piece of electrical equipment is recorded as a suspected fault information acquisition time period;
step S103: setting a fault information data influence period ≡ 2 ＝[γ，γ+β]The method comprises the steps of carrying out a first treatment on the surface of the When no other electric equipment fails in the failure information acquisition period of any one of the electric equipment in the electric room, marking any one of the electric equipment as main failure electric equipment; acquiring electric equipment with faults in the influence period of the fault information data of the main fault electric equipment, marking the electric equipment as suspected faulty electric equipment, and marking the main fault electric equipment and the suspected faulty electric equipment as primary fault association;
step S104: acquiring the total times of faults of main fault electrical equipment; acquiring the total times of fault association of the main fault electrical equipment and the suspected faulty electrical equipment; calculating a device fault correlation value V between the main fault power device and the suspected faulty electrical device:
wherein C is a The total number of faults of the main fault electrical equipment; c (C) b Associating the main fault electrical equipment with the suspected fault electrical equipment for the total times of faults;
setting a threshold value of a fault association value of equipment; when the equipment fault correlation value between the main fault power equipment and the suspected faulty electrical equipment is larger than the equipment fault correlation value threshold, the suspected faulty electrical equipment is marked as faulty electrical equipment;
step S105: when the faulty electrical equipment breaks down in the influence period of the fault information data of the main faulty electrical equipment, the suspected faulty information acquisition period of the faulty electrical equipment is recorded as a faulty information acquisition period; removing historical fault data information of the faulty electrical equipment in the faulty information acquisition period, and collecting each piece of historical fault data information reserved by the faulty electrical equipment;
in the above steps, because the fault of one electrical device in the electrical room may be caused by the fault of other electrical devices, but in the process of analyzing the influence of the data of each device of the electrical devices, the historical fault data information of the electrical devices caused by the other fault electrical devices is removed, so that the fault information collection period of the electrical devices is firstly selected, no other electrical devices are in fault, the electrical devices are marked as main fault electrical devices, the electrical devices which are not in fault due to the influence of the faults of the other electrical devices can be found out, the fault correlation of the electrical devices can be analyzed, and the affected electrical devices, namely the faulty electrical devices, can be found out.
Further, step S200 includes:
step S201: obtaining the maximum value and the minimum value of each item of equipment data of the electrical equipment in the historical fault data information; obtaining the maximum value and the minimum value of each piece of equipment data of the electrical equipment in each piece of historical fault data information;
step S202: calculating a first equipment data change proportion value H corresponding to any piece of equipment data in the historical fault data information of the electrical equipment 1 ：
Wherein D is max The maximum value of any item of equipment data in the historical fault data information of the electrical equipment; d (D) min The minimum value of any item of equipment data in the historical fault data information of the electrical equipment is set;
step S203: acquiring a first equipment data change proportion value corresponding to any piece of equipment data in each piece of historical fault data information of the electrical equipment; calculating a device data change proportion value H corresponding to any piece of device data of the electrical device:
j is the total number of historical fault data information of the electrical equipment; h i 1 The method comprises the steps that a first equipment data change proportion value corresponding to any piece of equipment data in the ith historical fault data information of the electrical equipment is obtained;
step S204: acquiring a device data change proportion value corresponding to each piece of device data of the electrical device; and selecting the equipment data corresponding to the minimum value of the equipment data change proportion value as first influencing equipment data of the electrical equipment.
Further, step S300 includes:
step S301: acquiring an average value of all equipment data in all historical fault data information of the electrical equipment; calculating a fault factor S of any piece of equipment data of the electrical equipment:
wherein r is the total number of historical fault data information of any piece of equipment data of the electrical equipment; w (w) n An average value of any item of equipment data of the electrical equipment in the nth historical fault data information;
step S302: acquiring the minimum value of the first influence equipment data of the electrical equipment in each piece of historical fault data information, and taking the corresponding value of the minimum value of the first influence equipment data as the marking value of the first influence equipment data of the electrical equipment;
step S303: monitoring equipment data of all electrical equipment of the electric room in the current period by using an electric room environment control box; when the monitoring value of the first influence equipment data of a certain electric equipment in the electric room is larger than the marking value of the first influence equipment data of the certain electric equipment, marking the certain electric equipment as characteristic electric equipment;
step S304: acquiring corresponding values of various equipment data of the characteristic electrical equipment in the current period; obtaining fault factors of various equipment data of the characteristic electrical equipment; calculating a marking fault value Q of the characteristic electrical equipment in the current period:
wherein S is g A fault factor that is the g-th item of device data of the characteristic electrical device; u (U) g The corresponding numerical value of the g-th equipment data of the characteristic electrical equipment in the current period;
step S305: and setting a marking fault value threshold value of each characteristic electrical device, and marking the characteristic electrical device as the marking electrical device when the marking fault value of the characteristic electrical device in the current period is larger than the corresponding marking fault value threshold value.
Further, step S400 includes:
step S4O1: acquiring all marking power equipment of the electric room in the current period; acquiring real-time data of various equipment data of various marked power equipment of an electric room in a current period; acquiring equipment information of marked power equipment in an electric room;
step S402: the electric room environment control box sends the equipment information of each marked electric equipment of the electric room and the real-time data of the equipment data in the current period to the electric room background, gives an early warning to an electric room background worker, and informs the worker to process each marked electric equipment in the electric room.
In order to better realize the method, the invention also provides a big data monitoring and early warning system, wherein the monitoring and early warning system comprises a data information module, a relevance module, a marked power equipment module and a safety regulation module;
the data information module is used for screening each piece of historical fault data information of the electrical equipment, removing the historical fault data information related to other electrical equipment, and collecting each piece of retained historical fault data information;
the relevance module is used for evaluating the fault influence of each item of equipment data in the historical fault data information on the power equipment to obtain first influence equipment data of the power equipment;
the marking power equipment module is used for acquiring first influence equipment data of all electrical equipment in the electrical room in the current period; based on first influence equipment data of the electrical equipment, performing fault prediction on each electrical equipment of the electrical room in the current period to obtain marked electrical equipment;
and the safety regulation and control module is used for sending out early warning and safety regulation and control to the marked power equipment in the current period and informing a worker to process each marked power equipment in the electric room.
Further, the data information module comprises a fault association unit and a data information unit;
the fault association unit is used for acquiring the electrical equipment with faults in the fault information data influence time period of the main fault electrical equipment and recording the electrical equipment as suspected faulty electrical equipment;
and the data information unit is used for eliminating the historical fault data information of the faulty electrical equipment in the faulty information acquisition period of the faulty electrical equipment and collecting the historical fault data information reserved by the faulty electrical equipment.
Further, the relevance module comprises a device data change proportion value unit and a relevance unit;
the equipment data change proportion value unit is used for acquiring a first equipment data change proportion value corresponding to any piece of equipment data in each piece of historical fault data information of the electrical equipment; calculating a device data change proportion value corresponding to any item of device data of the electrical device;
the relevance unit is used for acquiring equipment data change proportion values corresponding to the equipment data of the electrical equipment; and selecting the equipment data corresponding to the minimum equipment data change proportion value corresponding to each piece of equipment data of the electrical equipment as first influence equipment data of the electrical equipment.
Further, the marking power equipment module comprises a fault factor unit and a marking power equipment unit;
the fault factor unit is used for acquiring the average value of each piece of equipment data in each piece of historical fault data information of the electrical equipment; calculating a fault factor of any item of equipment data of the electrical equipment;
and the marking power equipment unit is used for marking the characteristic electrical equipment as the marking electrical equipment when the marking fault value of the characteristic electrical equipment in the current period is larger than the corresponding marking fault value threshold value.
Further, the safety regulation module comprises a safety regulation unit;
the safety regulation and control unit is used for acquiring all the marked power equipment of the electric room in the current period; acquiring real-time data of various equipment data of various marked power equipment of an electric room in a current period; and sending the equipment information of each marked power equipment of the electric room and the real-time data of each piece of equipment data in the current period to the electric room background, and sending an early warning to an electric room background worker to inform the worker to process each marked power equipment in the electric room.
Compared with the prior art, the invention has the following beneficial effects: the invention realizes the intelligent monitoring of the state of the electrical equipment in the electrical room by using the electrical room environment control box, and performs early warning according to the real-time state of the electrical equipment, thereby saving the manpower and time consumption, improving the monitoring efficiency and reliability of the electrical equipment, and being far more than the traditional method for processing the electrical equipment.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a method of the present invention for big data monitoring and early warning system and method for electrical room environmental control box;
FIG. 2 is a schematic block diagram of a big data monitoring and early warning system and method for an electrical room environmental control box according to the present invention.
Detailed Description
The technical solutions of 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 apparent that the described embodiments are only some embodiments of the present invention, 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. 1-2, the present invention provides the following technical solutions: a big data monitoring and early warning method for an electrical room environment control box comprises the following steps:
step S100: monitoring the equipment state of each electrical equipment in the electrical room by using an electrical room environment control box, and acquiring each historical fault record of each electrical equipment in the electrical room; acquiring historical fault data information corresponding to the electrical equipment from a historical fault record; screening each piece of historical fault data information of the electrical equipment, removing the historical fault data information related to other electrical equipment, and collecting each piece of retained historical fault data information;
wherein, step S100 includes:
step S101: monitoring the equipment state of each electrical equipment in the electrical room by using an electrical room environment control box, and acquiring the occurrence time point of the electrical equipment fault when the electrical equipment in the electrical room is in fault; setting data acquisition time length; setting failure information acquisition period oc of electrical equipment 1 ＝[γ-β，γ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein, gamma is the occurrence time point of the fault of the electrical equipment; beta is the data acquisition time length; acquiring historical fault data information of the electrical equipment in a fault information acquisition period; the historical fault data information comprises average values of various equipment data of the electrical equipment in each unit time period in the fault information acquisition time period;
step S102: acquiring fault occurrence time points of all electrical equipment in the electrical room; when the fault occurrence time point of one piece of electrical equipment is within the fault information acquisition time period of the other piece of electrical equipment, the fault information acquisition time period of the other piece of electrical equipment is recorded as a suspected fault information acquisition time period;
step S103: setting a fault information data influence period ≡ 2 ＝[γ，γ+β]The method comprises the steps of carrying out a first treatment on the surface of the When no other electric equipment fails in the failure information acquisition period of any one of the electric equipment in the electric room, marking any one of the electric equipment as main failure electric equipment; acquiring electric equipment with faults in the influence period of the fault information data of the main fault electric equipment, marking the electric equipment as suspected faulty electric equipment, and marking the main fault electric equipment and the suspected faulty electric equipment as primary fault association;
step S104: acquiring the total times of faults of main fault electrical equipment; acquiring the total times of fault association of the main fault electrical equipment and the suspected faulty electrical equipment; calculating a device fault correlation value V between the main fault power device and the suspected faulty electrical device:
wherein C is a The total number of faults of the main fault electrical equipment; c (C) b Associating the main fault electrical equipment with the suspected fault electrical equipment for the total times of faults;
setting a threshold value of a fault association value of equipment; when the equipment fault correlation value between the main fault power equipment and the suspected faulty electrical equipment is larger than the equipment fault correlation value threshold, the suspected faulty electrical equipment is marked as faulty electrical equipment;
step S105: when the faulty electrical equipment breaks down in the influence period of the fault information data of the main faulty electrical equipment, the suspected faulty information acquisition period of the faulty electrical equipment is recorded as a faulty information acquisition period; removing historical fault data information of the faulty electrical equipment in the faulty information acquisition period, and collecting each piece of historical fault data information reserved by the faulty electrical equipment;
step S200: based on each piece of historical fault data information of the electrical equipment, evaluating the fault influence degree of each piece of equipment data in the historical fault data information on the electrical equipment to obtain first influence equipment data of the electrical equipment;
wherein, step S200 includes:
step S201: obtaining the maximum value and the minimum value of each item of equipment data of the electrical equipment in the historical fault data information; obtaining the maximum value and the minimum value of each piece of equipment data of the electrical equipment in each piece of historical fault data information;
step S202: calculating a first equipment data change proportion value H corresponding to any piece of equipment data in the historical fault data information of the electrical equipment 1 ：
Wherein D is max The maximum value of any item of equipment data in the historical fault data information of the electrical equipment; d (D) min The minimum value of any item of equipment data in the historical fault data information of the electrical equipment is set;
step S203: acquiring a first equipment data change proportion value corresponding to any piece of equipment data in each piece of historical fault data information of the electrical equipment; calculating a device data change proportion value H corresponding to any piece of device data of the electrical device:
j is the total number of historical fault data information of the electrical equipment; h i 1 The method comprises the steps that a first equipment data change proportion value corresponding to any piece of equipment data in the ith historical fault data information of the electrical equipment is obtained;
step S204: acquiring a device data change proportion value corresponding to each piece of device data of the electrical device; selecting equipment data corresponding to the minimum value of the equipment data change proportion value as first influencing equipment data of the electrical equipment;
for example, the maximum value and the minimum value of the first item of equipment data of the electrical equipment in the 1 st historical fault data information are divided into 10 and 2, and the maximum value and the minimum value of the second item of equipment data of the electrical equipment in the 1 st historical fault data information are divided into 8 and 2; the maximum value and the minimum value of the first item of equipment data of the electrical equipment in the 2 nd historical fault data information are divided into 6 and 2, and the maximum value and the minimum value of the second item of equipment data of the electrical equipment in the 2 nd historical fault data information are divided into 10 and 2;
calculating a first equipment data change proportion value H corresponding to first equipment data in the 1 st historical fault data information of the electrical equipment 1 ：
Wherein 10 is the maximum value of the first item of equipment data in the 1 st historical fault data information of the electrical equipment; 2 is the minimum value of the first item of equipment data in the 1 st historical fault data information;
the electrical equipment has a first equipment data change proportion value of 80% of the first equipment data in the 1 st historical fault data information; the electrical equipment has a first equipment data change proportion value of 66.66% for the first equipment data in the 2 nd historical fault data information; calculating a device data change proportion value H corresponding to first item of device data of the electrical device:
acquiring equipment data change proportion value corresponding to equipment data of the 2 nd electric equipment to be 77.5%;
because 73.3% <77.5%, the first item of device data is selected as the first influencing device data of the electrical device;
step S300: acquiring first influence equipment data of each electrical equipment in an electrical room in the current period; based on first influence equipment data of the electrical equipment, performing fault prediction on each electrical equipment of the electrical room in the current period to obtain marked electrical equipment;
wherein, step S300 includes:
step S301: acquiring an average value of all equipment data in all historical fault data information of the electrical equipment; calculating a fault factor S of any piece of equipment data of the electrical equipment:
wherein r is the total number of historical fault data information of any piece of equipment data of the electrical equipment; w (w) n An average value of any item of equipment data of the electrical equipment in the nth historical fault data information;
step S302: acquiring the minimum value of the first influence equipment data of the electrical equipment in each piece of historical fault data information, and taking the corresponding value of the minimum value of the first influence equipment data as the marking value of the first influence equipment data of the electrical equipment;
step S303: monitoring equipment data of all electrical equipment of the electric room in the current period by using an electric room environment control box; when the monitoring value of the first influence equipment data of a certain electric equipment in the electric room is larger than the marking value of the first influence equipment data of the certain electric equipment, marking the certain electric equipment as characteristic electric equipment;
step S304: acquiring corresponding values of various equipment data of the characteristic electrical equipment in the current period; obtaining fault factors of various equipment data of the characteristic electrical equipment; calculating a marking fault value Q of the characteristic electrical equipment in the current period:
wherein S is g A fault factor that is the g-th item of device data of the characteristic electrical device; u (U) g The corresponding numerical value of the g-th equipment data of the characteristic electrical equipment in the current period;
wherein the fault factor of the 1 st item of equipment data of the characteristic electrical equipment is 100, the fault factor of the 2 nd item of equipment data is 10, and the fault factor of the 3 rd item of equipment data is 200; the 1 st item of equipment data of the characteristic electrical equipment corresponds to a value 80 in the current period, the 2 nd item of equipment data corresponds to a value 5 in the current period, and the 3 rd item of equipment data corresponds to a value 60 in the current period; calculating a marked fault value of the characteristic electrical equipment in the current period
Step S305: setting a marking fault value threshold of each characteristic electrical device, and marking the characteristic electrical device as the marking electrical device when the marking fault value of the characteristic electrical device in the current period is larger than the corresponding marking fault value threshold;
step S400: acquiring all marking power equipment of the electric room; the electric room environment control box sends out early warning and safety regulation and control to the marked power equipment in the current period and informs workers of processing the marked power equipment in the electric room;
wherein, step S400 includes:
step S4O1: acquiring all marking power equipment of the electric room in the current period; acquiring real-time data of various equipment data of various marked power equipment of an electric room in a current period; acquiring equipment information of marked power equipment in an electric room;
step S402: the electric room environment control box sends the equipment information of each marked electric equipment of the electric room and the real-time data of the equipment data in the current period to the electric room background, gives an early warning to an electric room background worker, and informs the worker to process each marked electric equipment in the electric room;
in order to better realize the method, the invention also provides a big data monitoring and early warning system, wherein the monitoring and early warning system comprises a data information module, a relevance module, a marked power equipment module and a safety regulation module;
the data information module is used for screening each piece of historical fault data information of the electrical equipment, removing the historical fault data information related to other electrical equipment, and collecting each piece of retained historical fault data information;
the relevance module is used for evaluating the fault influence of each item of equipment data in the historical fault data information on the power equipment to obtain first influence equipment data of the power equipment;
the marking power equipment module is used for acquiring first influence equipment data of all electrical equipment in the electrical room in the current period; based on first influence equipment data of the electrical equipment, performing fault prediction on each electrical equipment of the electrical room in the current period to obtain marked electrical equipment;
the safety regulation and control module is used for sending out early warning and safety regulation and control to the marked power equipment in the current period and notifying a worker to process each marked power equipment in the electric room;
the data information module comprises a fault association unit and a data information unit;
the fault association unit is used for acquiring the electrical equipment with faults in the fault information data influence time period of the main fault electrical equipment and recording the electrical equipment as suspected faulty electrical equipment;
the data information unit is used for eliminating historical fault data information of the faulty electrical equipment in the faulty information acquisition period of the faulty electrical equipment and collecting each piece of historical fault data information reserved by the faulty electrical equipment;
the relevance module comprises a device data change proportion value unit and a relevance unit;
the equipment data change proportion value unit is used for acquiring a first equipment data change proportion value corresponding to any piece of equipment data in each piece of historical fault data information of the electrical equipment; calculating a device data change proportion value corresponding to any item of device data of the electrical device;
the relevance unit is used for acquiring equipment data change proportion values corresponding to the equipment data of the electrical equipment; selecting equipment data corresponding to the minimum value of the equipment data change proportion value corresponding to each piece of equipment data of the electrical equipment as first influence equipment data of the electrical equipment;
the marking power equipment module comprises a fault factor unit and a marking power equipment unit;
the fault factor unit is used for acquiring the average value of each piece of equipment data in each piece of historical fault data information of the electrical equipment; calculating a fault factor of any item of equipment data of the electrical equipment;
the marking power equipment unit is used for marking the characteristic electrical equipment as marking electrical equipment when the marking fault value of the characteristic electrical equipment in the current period is larger than the corresponding marking fault value threshold value;
the safety regulation module comprises a safety regulation unit;
the safety regulation and control unit is used for acquiring all the marked power equipment of the electric room in the current period; acquiring real-time data of various equipment data of various marked power equipment of an electric room in a current period; and sending the equipment information of each marked power equipment of the electric room and the real-time data of each piece of equipment data in the current period to the electric room background, and sending an early warning to an electric room background worker to inform the worker to process each marked power equipment in the electric room.
It should be noted that in this document relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. 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 (10)
1. The big data monitoring and early warning method for the electrical room environment control box is characterized by comprising the following steps of:
step S100: monitoring the equipment state of each electrical equipment in the electrical room by using an electrical room environment control box, and acquiring each historical fault record of each electrical equipment in the electrical room; acquiring historical fault data information corresponding to the electrical equipment from the historical fault record; screening each piece of historical fault data information of the electrical equipment, removing the historical fault data information related to other electrical equipment, and collecting each piece of retained historical fault data information;
step S200: based on each piece of historical fault data information of the electrical equipment, evaluating the fault influence degree of each piece of equipment data in the historical fault data information on the electrical equipment to obtain first influence equipment data of the electrical equipment;
step S300: acquiring first influence equipment data of each electrical equipment in an electrical room in the current period; based on first influence equipment data of the electrical equipment, performing fault prediction on each electrical equipment of the electrical room in the current period to obtain marked electrical equipment;
step S400: acquiring all marking power equipment of the electric room; the electric room environment control box sends out early warning and safety regulation and control to the marked power equipment in the current period, and informs workers to process each marked power equipment in the electric room.
2. The big data monitoring and early warning method for an electrical room environment control box according to claim 1, wherein the step S100 includes:
step S101: monitoring the equipment state of each electrical equipment in the electrical room by using an electrical room environment control box, and acquiring the occurrence time point of the electrical equipment fault when the electrical equipment in the electrical room is in fault; setting data acquisition time length; setting failure information acquisition period oc of electrical equipment 1 ＝[γ-β，γ]The method comprises the steps of carrying out a first treatment on the surface of the Wherein, gamma is the occurrence time point of the fault of the electrical equipment; beta is the data acquisition time length; acquiring historical fault data information of the electrical equipment in a fault information acquisition period; the historical fault data information comprises an average value of data of each piece of equipment of the electrical equipment in each unit time period in a fault information acquisition time period;
step S102: acquiring fault occurrence time points of all electrical equipment in the electrical room; when the fault occurrence time point of one piece of electrical equipment is within the fault information acquisition time period of another piece of electrical equipment, the fault information acquisition time period of the other piece of electrical equipment is recorded as a suspected fault information acquisition time period;
step S103: setting a fault information data influence period ≡ 2 ＝[γ，γ+β]The method comprises the steps of carrying out a first treatment on the surface of the When no other electric equipment fails in the failure information acquisition period of any one electric equipment in the electric room, marking the any one electric equipment as main failure electric equipment; acquiring electric equipment which has faults within a fault information data influence period of main fault electric equipment, marking the electric equipment as suspected faulty electric equipment, and marking the main fault electric equipment and the suspected faulty electric equipment as primary fault association;
step S104: acquiring the total times of faults of main fault electrical equipment; acquiring the total times of fault association of the main fault electrical equipment and the suspected faulty electrical equipment; calculating a device fault correlation value V between the main fault power device and the suspected faulty electrical device:
wherein C is a The total number of faults of the main fault electrical equipment; c (C) b Associating the main fault electrical equipment with the suspected fault electrical equipment for the total times of faults;
setting a threshold value of a fault association value of equipment; when the equipment fault correlation value between the main fault power equipment and the suspected faulty electrical equipment is larger than the equipment fault correlation value threshold, the suspected faulty electrical equipment is marked as faulty electrical equipment;
step S105: when the faulty electrical equipment breaks down in the influence period of the fault information data of the main faulty electrical equipment, recording the suspected faulty information acquisition period of the faulty electrical equipment as a faulty information acquisition period; and eliminating the historical fault data information of the faulty electrical equipment in the faulty information acquisition period, and collecting the historical fault data information of the faulty electrical equipment.
3. The method for big data monitoring and early warning of electrical room environmental control box according to claim 2, wherein the step S200 comprises:
step S201: obtaining the maximum value and the minimum value of each item of equipment data of the electrical equipment in the historical fault data information; obtaining the maximum value and the minimum value of each piece of equipment data of the electrical equipment in each piece of historical fault data information;
step S202: calculating a first equipment data change proportion value H corresponding to any piece of equipment data in the historical fault data information of the electrical equipment 1 ：
Wherein D is max The maximum value of any item of equipment data in the historical fault data information of the electrical equipment; d (D) min The minimum value of any item of equipment data in the historical fault data information of the electrical equipment is set;
step S203: acquiring a first equipment data change proportion value corresponding to any piece of equipment data in each piece of historical fault data information of the electrical equipment; calculating a device data change proportion value H corresponding to any piece of device data of the electrical device:
j is the total number of historical fault data information of the electrical equipment;the method comprises the steps that a first equipment data change proportion value corresponding to any piece of equipment data in the ith historical fault data information of the electrical equipment is obtained;
step S204: acquiring a device data change proportion value corresponding to each piece of device data of the electrical device; and selecting the equipment data corresponding to the minimum value of the equipment data change proportion value as the first influencing equipment data of the electrical equipment.
4. The big data monitoring and early warning method for an electrical room environment control box according to claim 3, wherein the step S300 comprises:
step S301: acquiring an average value of all equipment data in all historical fault data information of the electrical equipment; calculating a fault factor S of any piece of equipment data of the electrical equipment:
wherein r is historical fault data of any item of equipment data of the electrical equipmentThe total number of information; w (w) n An average value of any item of equipment data of the electrical equipment in the nth historical fault data information;
step S302: acquiring the minimum value of the first influence equipment data of the electrical equipment in each piece of historical fault data information, and taking the corresponding value of the minimum value of the first influence equipment data as the marking value of the first influence equipment data of the electrical equipment;
step S303: monitoring equipment data of all electrical equipment of the electric room in the current period by using an electric room environment control box; when the monitoring value of the first influence equipment data of a certain electric equipment in the electric room is larger than the marking value of the first influence equipment data of the certain electric equipment, marking the certain electric equipment as characteristic electric equipment;
step S304: acquiring corresponding values of various equipment data of the characteristic electrical equipment in the current period; obtaining fault factors of various equipment data of the characteristic electrical equipment; calculating a marking fault value Q of the characteristic electrical equipment in the current period:
wherein S is g A fault factor that is the g-th item of device data of the characteristic electrical device; u (U) g The corresponding numerical value of the g-th equipment data of the characteristic electrical equipment in the current period;
step S305: and setting a marking fault value threshold value of each characteristic electrical device, and marking the characteristic electrical device as the marking electrical device when the marking fault value of the characteristic electrical device in the current period is larger than the corresponding marking fault value threshold value.
5. The big data monitoring and early warning method for an electrical room environment control box according to claim 4, wherein the step S400 includes:
step S4O1: acquiring all marking power equipment of the electric room in the current period; acquiring real-time data of various equipment data of various marked power equipment of an electric room in a current period; acquiring equipment information of marked power equipment in an electric room;
step S402: the electric room environment control box sends the equipment information of each marked electric equipment of the electric room and the real-time data of the equipment data in the current period to the electric room background, gives an early warning to an electric room background worker, and informs the worker to process each marked electric equipment in the electric room.
6. The big data monitoring and early warning system applied to the big data monitoring and early warning system and method for the electrical room environment control box according to any of claims 1-5 is characterized in that the monitoring and early warning system comprises a data information module, a relevance module, a marked power equipment module and a safety regulation module;
the data information module is used for screening each piece of historical fault data information of the electrical equipment, removing the historical fault data information related to other electrical equipment, and collecting each piece of retained historical fault data information;
the relevance module is used for evaluating the fault influence of each item of equipment data in the historical fault data information on the power equipment to obtain first influence equipment data of the power equipment;
the marking power equipment module is used for acquiring first influence equipment data of all electrical equipment in the electrical room in the current period; based on first influence equipment data of the electrical equipment, performing fault prediction on each electrical equipment of the electrical room in the current period to obtain marked electrical equipment;
the safety regulation module is used for sending out early warning and safety regulation to the marked power equipment in the current period and informing a worker to process each marked power equipment in the electric room.
7. The big data monitoring and early warning system according to claim 6, wherein the data information module comprises a fault association unit and a data information unit;
the fault association unit is used for acquiring the electrical equipment with faults in the period of influence of the fault information data of the main fault electrical equipment and recording the electrical equipment as suspected faulty electrical equipment;
the data information unit is used for eliminating the historical fault data information of the faulty electrical equipment in the faulty information acquisition period of the faulty electrical equipment and collecting the historical fault data information reserved by the faulty electrical equipment.
8. The big data monitoring and early warning system according to claim 6, wherein the relevance module comprises a device data change proportion value unit and a relevance unit;
the equipment data change proportion value unit is used for acquiring a first equipment data change proportion value corresponding to any piece of equipment data in each piece of historical fault data information of the electrical equipment; calculating a device data change proportion value corresponding to any item of device data of the electrical device;
the relevance unit is used for acquiring equipment data change proportion values corresponding to the equipment data of the electrical equipment; and selecting the equipment data corresponding to the minimum equipment data change proportion value corresponding to each piece of equipment data of the electrical equipment as the first influencing equipment data of the electrical equipment.
9. The big data monitoring and early warning system according to claim 6, wherein the marking power equipment module comprises a fault factor unit, a marking power equipment unit;
the fault factor unit is used for acquiring the average value of each piece of equipment data in each piece of historical fault data information of the electrical equipment; calculating a fault factor of any item of equipment data of the electrical equipment;
and the marking power equipment unit is used for marking the characteristic electrical equipment as marking electrical equipment when the marking fault value of the characteristic electrical equipment in the current period is larger than the corresponding marking fault value threshold value.
10. The big data monitoring and early warning system of claim 6, wherein the safety regulation module comprises a safety regulation unit;
the safety regulation and control unit is used for acquiring all the marked power equipment of the electric room in the current period; acquiring real-time data of various equipment data of various marked power equipment of an electric room in a current period; and sending the equipment information of each marked power equipment of the electric room and the real-time data of each piece of equipment data in the current period to the electric room background, and sending an early warning to an electric room background worker to inform the worker to process each marked power equipment in the electric room.
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