CN116668482A - Intelligent building two-bus monitoring system and method based on artificial intelligence - Google Patents

Intelligent building two-bus monitoring system and method based on artificial intelligence Download PDF

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CN116668482A
CN116668482A CN202310648557.5A CN202310648557A CN116668482A CN 116668482 A CN116668482 A CN 116668482A CN 202310648557 A CN202310648557 A CN 202310648557A CN 116668482 A CN116668482 A CN 116668482A
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event
abnormal
sub
influence
building
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CN116668482B (en
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冯正国
王立新
寇毅
余建娟
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Nanjing Guotie Electric Co ltd
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Nanjing Guotie Electric Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/903—Querying
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C3/00—Registering or indicating the condition or the working of machines or other apparatus, other than vehicles
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y10/00—Economic sectors
    • G16Y10/80—Homes; Buildings
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y20/00—Information sensed or collected by the things
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/10—Detection; Monitoring
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/20—Analytics; Diagnosis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/40—Maintenance of things
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/50—Safety; Security of things, users, data or systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/28—Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
    • H04L12/40—Bus networks
    • H04L12/40006—Architecture of a communication node
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

The invention relates to the technical field of building supervision, in particular to an intelligent building two-bus monitoring system and method based on artificial intelligence, wherein the system comprises a sub-event influence correlation analysis module, and the sub-event influence correlation analysis module combines historical monitoring data in a building monitoring database to extract each correlation sub-event corresponding to each abnormal event; and analyzing influence correlation coefficients among the correlation sub-events in different abnormal events, and generating an abnormal factor correlation sub-event set corresponding to each abnormal event respectively by combining the obtained influence correlation coefficients. When the electromechanics with abnormal states connected with the buses are cooperatively processed, each fault factor causing the corresponding equipment to fail is considered and checked; by analyzing the association influence conditions among the fault factors corresponding to different abnormal devices, the priority of the fault investigation is generated, so that the efficiency of the fault investigation is improved, the fault investigation is ensured to be more thorough, and the effective management and control of the building is realized.

Description

Intelligent building two-bus monitoring system and method based on artificial intelligence
Technical Field
The invention relates to the technical field of building supervision, in particular to an intelligent building two-bus monitoring system and method based on artificial intelligence.
Background
At present, the country is building a smart city, and the smart city senses, analyzes and integrates various key information of a city operation core system in the fields of city planning, design, construction, management, operation and the like by using intelligent computing technologies such as Internet of things, cloud computing, big data, space geographic information integration and the like, so as to respond intelligently to various demands including folk life, environmental protection, public safety and city service; the construction of the intelligent building is a good embodiment for developing the intelligent city, and the safety control of the building can effectively ensure the safety of residents living in the building, thereby realizing the effective management of the residents of the building.
The existing intelligent building two-bus monitoring system is a system for centralized management and monitoring of various electromechanical devices connected through buses in a building, and comprises an air conditioner fresh air unit, an air supply and exhaust fan, a water collecting pit and drainage pump, an elevator, power transformation and distribution, illumination and the like; the existing building two-bus monitoring system only introduces a connection mode of buses and electromechanical equipment in a building and a cooperative processing (fault checking) mode when the electromechanical equipment is in an abnormal state, but the existing processing mode has larger defects, only considers from the perspective of fault equipment, divides the fault equipment into processing priorities by analyzing the influence caused by the fault equipment, and needs to further check each fault factor causing corresponding equipment when processing the fault equipment; the influence of the association between different abnormal devices is not considered (one device may be caused by the fault of the other device, so that the association between the factors of the two devices may exist), and then the fault detection processing is directly performed according to the prior art, so that the following defects exist: on the one hand, the troubleshooting efficiency is lower, and on the other hand, the situation that the troubleshooting is not thorough (the situation that the fault equipment is cured and the root cause is not cured) can also occur, and before the fault factors of the rest fault equipment with the association relation with the factors causing the equipment fault are unprocessed, the fault can still occur again in a short period even if the fault equipment is temporarily repaired.
Disclosure of Invention
The invention aims to provide an intelligent building two-bus monitoring system and method based on artificial intelligence, 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: an intelligent building two-bus monitoring method based on artificial intelligence, comprising the following steps:
s1, monitoring the running states of the numbered devices in a building in real time through a sensor, and extracting a set constructed by abnormal events corresponding to the numbered devices of the building in abnormal states at the same time as an abnormal event set at the corresponding time; acquiring event influence information of corresponding positions of elements in an abnormal event set in historical monitoring data (the historical monitoring data in the invention are all building monitoring data which are stored in a database and have the same building structure as a building to be monitored), and analyzing abnormal influence sweep values corresponding to each element in the abnormal event set; the equipment and the sensors arranged in the building are connected with a building two-bus;
s2, extracting each associated sub-event corresponding to each abnormal event by combining historical monitoring data in a building monitoring database; analyzing influence association coefficients among associated sub-events in different abnormal events, and generating an abnormal factor associated sub-event set corresponding to each abnormal event respectively by combining the obtained influence association coefficients;
s3, acquiring each abnormal event set and corresponding abnormal factor associated sub-event set corresponding to the current time, extracting a union set of each obtained abnormal factor associated sub-event set, analyzing the crowd risk influence characteristic values corresponding to each element in the union set, and generating an abnormal event fault investigation sequence corresponding to the current time;
s4, marking the equipment corresponding to the corresponding abnormal event according to the abnormal event fault investigation sequence corresponding to the current time in sequence, sending the marking result and the abnormal event fault investigation sequence corresponding to the current time to an administrator as early warning information, and controlling the power supply state of the marking equipment connected with the building two buses in the fault investigation process of the administrator.
Further, the associated sub-event of the abnormal event corresponds to a sub-device of the device to which the abnormal event belongs, all the sub-devices corresponding to the associated sub-event of the abnormal event form the device to which the abnormal event belongs, and the associated sub-event of the abnormal event is obtained through database query;
the sub-devices corresponding to the elements in the abnormal factor association sub-event set corresponding to the abnormal event are not all the sub-devices of the device to which the abnormal event belongs, namely the combination of the sub-devices corresponding to all the elements in the abnormal factor association sub-event set corresponding to the abnormal event is more than or equal to the device to which the corresponding abnormal event belongs;
the event influence information at the position corresponding to the abnormal event is recorded as { A1, B1}, wherein A1 represents the ratio of the area of the resident where the abnormal event occurs in the building to the total area of the building resident, B1 represents the interference characteristic wave and coefficient suffered by the resident where the abnormal event occurs,
the householder that the abnormal event has passed through is acquired through sensor identification,
the invention marks the collection of households with abnormal event as H= { H1, H2, & gt, hj }, wherein Hj represents the jth household with corresponding abnormal event; the region of the resident who gets the occurrence of the abnormal event in the building is denoted as hz= { HZ1, HZ2,.. The term HZj }, where HZj represents the region of the resident in the building to which Hj corresponds, and the A1 is equal to the area of the region corresponding to the union HZ 1}, HZ 2. & gtu HZj of all elements in HZ; the sensors employed in the present invention to identify households with different incidents,
when the interference characteristic wave and coefficient of the residents with the abnormal event is obtained, the union of the identified wave and time periods of each resident with the abnormal event is collected, the interval length corresponding to the union is divided by the total duration of the abnormal event, and the obtained quotient is used as the interference characteristic wave and coefficient of the residents with the abnormal event.
The invention records the sweep time period received by the resident corresponding to Hj as HBj, the length of the section corresponding to the obtained union is equal to the sum of the section lengths corresponding to each discontinuous time section in the union HB 1U 2U HBj of the sweep time period corresponding to the resident corresponding to all elements in H.
Further, when the abnormal influence value corresponding to each element in the abnormal event set is analyzed in S1, event influence information at positions corresponding to each element in the abnormal event set is obtained, the abnormal influence value corresponding to each element in the abnormal event set is equal to the product of the first value and the second value in the event influence information corresponding to the corresponding element, and the abnormal influence value corresponding to the i-th element in the abnormal event set is recorded as Di.
Further, when each associated sub-event corresponding to each abnormal event is extracted in the step S2, a ratio of the number of times of the abnormal event caused by each associated sub-event in the history data to the total number of times of occurrence of the corresponding abnormal event is obtained, and the obtained ratio is bound with the associated sub-event of the corresponding abnormal event;
the method for analyzing the influence correlation coefficient between the correlated sub-events in different abnormal events in S2 comprises the following steps:
s201, acquiring each associated sub-event corresponding to each abnormal event in the abnormal event set, and marking the kth associated sub-event in the abnormal event corresponding to the ith element in the abnormal event set as M [ i, k ];
s202, obtaining M [ i1, k1 ]]And M [ i, k ]]The number of influence relations between the two is recorded as Q (M[i1,k1]，M[i,k]) ，
Q (M[i1,k1]，M[i,k]) ＝RL (M[i1,k1]，M[i,k]) ×PC (M[i1,k1]，M[i,k]) ，
Wherein RL is a (M[i1,k1]，M[i,k]) Represents M [ i1, k1 ]]Corresponds to building areas and M [ i, k ]]Corresponding to the position coefficient between building areas, when M [ i1, k1]Corresponds to building areas and M [ i, k ]]When the shortest distance between the corresponding building areas is smaller than a first preset value, determining RL (M[i1,k1]，M[i,k]) =1, otherwise, RL (M[i1,k1]，M[i,k]) ＝0，
PC (M[i1,k1]，M[i,k]) Represents M [ i1, k1 ] in the history data]And M [ i, k ]]The interference frequency coefficient between the two, when there is an abnormal combination event { T whose time interval is smaller than the second preset value M[i1,k1] ，T M[i,k] When } PC (M[i1,k1]，M[i,k]) Equal to T M[i1,k1] ≤T M[i,k] Abnormal combined event { T whose number and time interval are smaller than the second preset value M[i1,k1] ，T M[i,k] Ratio of total number; when no abnormal combination event { T having a time interval smaller than a second preset value exists M[i1,k1] ，T M[i,k] When } PC (M[i1,k1]，M[i,k]) Equal to 0;
when Q is (M[i1,k1]，M[i,k]) When not equal to 0, M [ i1, k1 ] is determined]Is M [ i, k ]]Is a nuisance event of (1); otherwise, determine M [ i1, k1 ]]Is M [ i, k ]]Is a non-interfering event of (a).
Further, the method for generating the abnormal factor associated sub-event set corresponding to each abnormal event in S2 includes the following steps:
s211, acquiring influence association coefficients among associated sub-events in different abnormal events;
s212, summarizing each associated sub-event corresponding to each abnormal event into a blank set, and constructing a sub-event summary set of the corresponding abnormal event;
s213, obtaining all the interference events corresponding to the elements in the sub-event summary set of the abnormal event, adding the obtained interference events into the corresponding sub-event summary set to obtain an abnormal factor associated sub-event set of the corresponding abnormal event, obtaining the binding ratio of each interference event added in the abnormal factor associated sub-event set,
correlating the abnormal factors corresponding to the ith abnormal event with the interference events M [ i2, k2 in the sub-event set]The binding ratio is denoted as W ( i ， M [ i2 , k 2 ]) ，
W (i，M[i2,k2]) ＝∑ g＝1 g1 W M[i2,k2] ×Q (M[i2,k2]，Mg) ×W Mg +max{W Mg |1≤g≤g1}，
Wherein W is M[i2,k2] Representing associated sub-events M [ i2, k2 in the ith 2 nd exception event]Binding ratio, W Mg Represents the g-th quilt M [ i2, k 2] in the ith abnormal event]Ratio, Q of binding of interfering related sub-events (M[i2,k2]，Mg) Represents M [ i2, k 2]]Influence correlation coefficient with Mg, max { W Mg And when the number of the W is equal to or more than 1 and equal to or less than g1, the number of the W is equal to or less than 1 and equal to or less than g1 Mg Is the maximum value of (a).
Further, the method for analyzing and concentrating the crowd risk influence characteristic values corresponding to the elements in the step S3 includes the following steps:
s31, acquiring each abnormal event set and corresponding abnormal factor associated sub-event set corresponding to the current time, extracting a union set of the obtained abnormal factor associated sub-event sets, and marking the union set as U, wherein each element in the U corresponds to an associated sub-event of an abnormal event;
the abnormal factor associated sub-event sets corresponding to different abnormal events may have the same elements, and the binding ratios of the same elements are different, i.e. the binding ratios of the same associated sub-event in the different abnormal factor associated sub-event sets may be different;
s32, obtaining and concentrating the crowd risk influence characteristic value corresponding to the r element, marking as Fr,
Fr＝∑ gr＝1 gr1 W (gr，mr) ×Dgr，
wherein mr represents the associated sub-event corresponding to the r-th element in the obtained and concentrated set,
gr indicates an abnormal event set corresponding to the current time, a sub-event set associated with a corresponding abnormal factor contains the g-th abnormal event of mr,
W (gr，mr) representing the binding ratio of the element mr in the abnormal factor associated sub-event set corresponding to the abnormal event gr,
dgr indicates an abnormal event set corresponding to the current time, and an abnormal influence sweep value corresponding to the abnormal event gr;
and when an abnormal time fault investigation sequence corresponding to the current time is generated in the step S3, arranging elements in a union set of the obtained sub-event sets associated with each abnormal factor according to the sequence from the large value to the small value of the corresponding crowd risk influence characteristic value, wherein the obtained sequence is used as the abnormal event fault investigation sequence corresponding to the current time.
An intelligent building two-bus monitoring system based on artificial intelligence, which comprises the following modules:
the abnormal event influence and analysis module monitors the running states of the numbered devices in the building in real time through a sensor, and extracts a set constructed by the abnormal events corresponding to the numbered devices of the building in abnormal states at the same time as an abnormal event set at the corresponding time; event influence information of positions corresponding to elements in the abnormal event set in the historical monitoring data is obtained, and abnormal influence swept values corresponding to each element in the abnormal event set are analyzed; the equipment and the sensors arranged in the building are connected with a building two-bus;
the sub-event influence correlation analysis module is used for extracting each correlation sub-event corresponding to each abnormal event by combining historical monitoring data in the building monitoring database; analyzing influence association coefficients among associated sub-events in different abnormal events, and generating an abnormal factor associated sub-event set corresponding to each abnormal event respectively by combining the obtained influence association coefficients;
the fault investigation sequence optimization module is used for acquiring each abnormal event set corresponding to the current time and a corresponding abnormal factor association sub-event set, extracting a union set of the obtained abnormal factor association sub-event sets, analyzing the obtained union set to obtain a cluster intelligence risk influence characteristic value corresponding to each element, and generating an abnormal event fault investigation sequence corresponding to the current time;
the main line control and early warning module marks the equipment corresponding to the corresponding abnormal event according to the abnormal event fault investigation sequence corresponding to the current time in sequence, sends the marked result and the abnormal event fault investigation sequence corresponding to the current time to an administrator as early warning information, and controls the power supply state of marking equipment connected with the building two buses in the fault investigation process of the administrator.
Further, the abnormal event influence and analysis module comprises an abnormal event set construction module, an event information acquisition module and an abnormal influence analysis module,
the abnormal event set construction module monitors the running states of the numbered devices in the building in real time through a sensor, and extracts abnormal event constructed sets corresponding to the numbered devices of the building in abnormal states at the same time as abnormal event sets at corresponding time;
the event information acquisition module acquires event influence information at positions corresponding to elements in the abnormal event set in the historical monitoring data,
the abnormal influence analysis module analyzes abnormal influence swept values corresponding to each element in the abnormal event set by combining the data acquisition result in the event information acquisition module.
Further, the sub-event influence association analysis module comprises an association sub-event acquisition module, an influence association coefficient acquisition module and an abnormal factor association sub-event set generation module,
the related sub-event acquisition module is used for extracting each related sub-event corresponding to each abnormal event by combining historical monitoring data in the building monitoring database;
the influence correlation coefficient acquisition module analyzes influence correlation coefficients between associated sub-events in different abnormal events,
and the abnormal factor associated sub-event set generation module is used for generating an abnormal factor associated sub-event set corresponding to each abnormal event respectively by combining the obtained influence associated coefficients.
Compared with the prior art, the invention has the following beneficial effects: when the invention performs cooperative processing (fault investigation) on the electromechanical system with abnormal state connected with the bus, not only the consideration is performed from the perspective of fault equipment, but also each fault factor causing the fault of the corresponding equipment is also considered; by analyzing the association influence conditions among fault factors (associated sub-events of the abnormal events) corresponding to different abnormal devices, the priority of fault investigation is generated, so that the efficiency of fault investigation is improved, the fault investigation is ensured to be more thorough, and the effective management and control of the building is realized.
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 schematic flow chart of an intelligent building two-bus monitoring method based on artificial intelligence;
FIG. 2 is a schematic diagram of an intelligent building two-bus monitoring system based on artificial intelligence.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides the following technical solutions: an intelligent building two-bus monitoring method based on artificial intelligence, comprising the following steps:
s1, monitoring the running states of the numbered devices in a building in real time through a sensor, and extracting a set constructed by abnormal events corresponding to the numbered devices of the building in abnormal states at the same time as an abnormal event set at the corresponding time; event influence information of positions corresponding to elements in the abnormal event set in the historical monitoring data is obtained, and abnormal influence swept values corresponding to each element in the abnormal event set are analyzed; the equipment and the sensors arranged in the building are connected with a building two-bus;
the related sub-events of the abnormal event correspond to the sub-devices of the device to which the abnormal event belongs, all the sub-devices corresponding to the related sub-events of the abnormal event form the device to which the abnormal event belongs, and the related sub-events of the abnormal event are obtained through database query;
the sub-devices corresponding to the elements in the abnormal factor association sub-event set corresponding to the abnormal event are not all sub-devices of the device to which the abnormal event belongs, namely, the combination of the sub-devices corresponding to all the elements in the abnormal factor association sub-event set corresponding to the abnormal event is greater than or equal to the device to which the corresponding abnormal event belongs;
the event influence information at the position corresponding to the abnormal event is recorded as { A1, B1}, wherein A1 represents the ratio of the area of the resident where the abnormal event occurs in the building to the total area of the building resident, B1 represents the interference characteristic wave and coefficient suffered by the resident where the abnormal event occurs,
the households with the abnormal event are obtained through sensor identification, and a set formed by the households with the abnormal event is recorded as H= { H1, H2, & gt, hj }, and Hj represents the jth household with the corresponding abnormal event; the region of the resident who gets the occurrence of the abnormal event in the building is denoted as hz= { HZ1, HZ2,.. The term HZj }, where HZj represents the region of the resident in the building to which Hj corresponds, and the A1 is equal to the area of the region corresponding to the union HZ 1}, HZ 2. & gtu HZj of all elements in HZ; the invention recognizes that the sensors employed by households with different incidents are different,
in the embodiment, if the abnormal event is an elevator fault, the adopted sensor is a camera around the elevator hoistway, and if the abnormal event is a heating fault, the adopted sensor is a flow sensor on each node valve on the heating pipeline;
acquiring a union of the identified sweep time periods respectively received by each sweep resident when the interference characteristic sweep coefficient received by the sweep resident of the abnormal event is acquired, dividing the interval length corresponding to the union by the duration total duration of the abnormal event, and taking the obtained quotient as the interference characteristic sweep coefficient received by the sweep resident of the abnormal event; recording the sweep time period received by the households corresponding to Hj as HBj, wherein the length of the section corresponding to the obtained union is equal to the sum of the lengths of the sections corresponding to each discontinuous time section in the union HB 1U 2U HBj of the sweep time period corresponding to the households corresponding to all elements in H;
in this embodiment, if the union of the corresponding time periods of the abnormal time a and the corresponding time period of the living being is [ e1, e2 ]. U.e. e3, e4], the length of the interval corresponding to the obtained union is equal to (e 2-e 1) + (e 4-e 3);
when the abnormal influence spread value corresponding to each element in the abnormal event set is analyzed in the S1, event influence information at positions corresponding to the elements in the abnormal event set is obtained, the abnormal influence spread value corresponding to each element in the abnormal event set is equal to the product of the first value and the second value in the event influence information corresponding to the corresponding element, and the abnormal influence spread value corresponding to the ith element in the abnormal event set is recorded as Di.
S2, extracting each associated sub-event corresponding to each abnormal event by combining historical monitoring data in a building monitoring database; analyzing influence association coefficients among associated sub-events in different abnormal events, and generating an abnormal factor associated sub-event set corresponding to each abnormal event respectively by combining the obtained influence association coefficients;
when each associated sub-event corresponding to each abnormal event is extracted in the S2, the ratio of the times of the abnormal event caused by each associated sub-event in the historical data to the total times of occurrence of the corresponding abnormal event is obtained, and the obtained ratio is bound with the associated sub-event of the corresponding abnormal event;
the method for analyzing the influence correlation coefficient between the correlated sub-events in different abnormal events in S2 comprises the following steps:
s201, acquiring each associated sub-event corresponding to each abnormal event in the abnormal event set, and marking the kth associated sub-event in the abnormal event corresponding to the ith element in the abnormal event set as M [ i, k ];
s202, obtaining M [ i1, k1 ]]And M [ i, k ]]The number of influence relations between the two is recorded as Q (M[i1,k1]，M[i,k]) ，
Q (M[i1,k1]，M[i,k]) ＝RL (M[i1,k1]，M[i,k]) ×PC (M[i1,k1]，M[i,k]) ，
Wherein RL is a (M[i1,k1]，M[i,k]) Represents M [ i1, k1 ]]Corresponds to building areas and M [ i, k ]]Corresponding to the position coefficient between building areas, when M [ i1, k1]Corresponds to building areas and M [ i, k ]]When the shortest distance between the corresponding building areas is smaller than a first preset value, determining RL (M[i1,k1]，M[i,k]) =1, otherwise, RL (M[i1,k1]，M[i,k]) ＝0，
PC (M[i1,k1]，M[i,k]) Represents M [ i1, k1 ] in the history data]And M [ i, k ]]The interference frequency coefficient between the two, when there is an abnormal combination event { T whose time interval is smaller than the second preset value M[i1,k1] ，T M[i,k] When } PC (M[i1,k1]，M[i,k]) Equal to T M[i1,k1] ≤T M[i,k] Abnormal combined event { T whose number and time interval are smaller than the second preset value M[i1,k1] ，T M[i,k] Ratio of total number; when no abnormal combination event { T having a time interval smaller than a second preset value exists M[i1,k1] ，T M[i,k] When } PC (M[i1,k1]，M[i,k]) Equal to 0;
when Q is (M[i1,k1]，M[i,k]) When not equal to 0, M [ i1, k1 ] is determined]Is M [ i, k ]]Is a nuisance event of (1); otherwise, determine M [ i1, k1 ]]Is M [ i, k ]]Is a non-interfering event of (a).
The method for generating the abnormal factor associated sub-event set corresponding to each abnormal event in the S2 comprises the following steps:
s211, acquiring influence association coefficients among associated sub-events in different abnormal events;
s212, summarizing each associated sub-event corresponding to each abnormal event into a blank set, and constructing a sub-event summary set of the corresponding abnormal event;
s213, obtaining all the interference events corresponding to the elements in the sub-event summary set of the abnormal event, adding the obtained interference events into the corresponding sub-event summary set to obtain an abnormal factor associated sub-event set of the corresponding abnormal event, obtaining the binding ratio of each interference event added in the abnormal factor associated sub-event set,
correlating the abnormal factors corresponding to the ith abnormal event with the interference events M [ i2, k2 in the sub-event set]The binding ratio is denoted as W ( i ， M [ i2 , k 2 ]) ，
W (i，M[i2,k2]) ＝∑ g＝1 g1 W M[i2,k2] ×Q (M[i2,k2]，Mg) ×W Mg +max{W Mg |1≤g≤g1}，
Wherein W is M[i2,k2] Representing associated sub-events M [ i2, k2 in the ith 2 nd exception event]Binding ratio, W Mg Represents the g-th quilt M [ i2, k 2] in the ith abnormal event]Ratio, Q of binding of interfering related sub-events (M[i2,k2]，Mg) Represents M [ i2, k 2]]Influence correlation coefficient with Mg, max { W Mg And when the number of the W is equal to or more than 1 and equal to or less than g1, the number of the W is equal to or less than 1 and equal to or less than g1 Mg Is the maximum value of (a).
S3, acquiring each abnormal event set and corresponding abnormal factor associated sub-event set corresponding to the current time, extracting a union set of each obtained abnormal factor associated sub-event set, analyzing the crowd risk influence characteristic values corresponding to each element in the union set, and generating an abnormal event fault investigation sequence corresponding to the current time;
the method for analyzing the obtained and concentrated crowd risk influence characteristic values corresponding to the elements in the S3 comprises the following steps:
s31, acquiring each abnormal event set and corresponding abnormal factor associated sub-event set corresponding to the current time, extracting a union set of the obtained abnormal factor associated sub-event sets, and marking the union set as U, wherein each element in the U corresponds to an associated sub-event of an abnormal event;
s32, obtaining and concentrating the crowd risk influence characteristic value corresponding to the r element, marking as Fr,
Fr＝∑ gr＝1 gr1 W (gr，mr) ×Dgr，
wherein mr represents the associated sub-event corresponding to the r-th element in the obtained and concentrated set,
gr indicates an abnormal event set corresponding to the current time, a sub-event set associated with a corresponding abnormal factor contains the g-th abnormal event of mr,
W (gr，mr) representing the binding ratio of the element mr in the abnormal factor associated sub-event set corresponding to the abnormal event gr,
dgr indicates an abnormal event set corresponding to the current time, and an abnormal influence sweep value corresponding to the abnormal event gr;
when an abnormal time fault investigation sequence corresponding to the current time is generated in the S3, arranging elements in a union set of the obtained sub-event sets associated with each abnormal factor according to the sequence from the big to the small of the corresponding crowd risk influence characteristic value, wherein the obtained sequence is used as the abnormal event fault investigation sequence corresponding to the current time;
in this embodiment, the sequence of elements in the fault detection sequence of the abnormal event intuitively reflects the execution priority corresponding to the fault detection target (associated sub-event) in the fault detection process, and the execution priority corresponding to the fault detection target which is higher in the obtained sequence.
S4, marking the equipment corresponding to the corresponding abnormal event according to the abnormal event fault investigation sequence corresponding to the current time in sequence, sending the marking result and the abnormal event fault investigation sequence corresponding to the current time to an administrator as early warning information, and controlling the power supply state of the marking equipment connected with the building two buses in the fault investigation process of the administrator.
As shown in fig. 2, an intelligent building two-bus monitoring system based on artificial intelligence, the system comprises the following modules:
the abnormal event influence and analysis module monitors the running states of the numbered devices in the building in real time through a sensor, and extracts a set constructed by the abnormal events corresponding to the numbered devices of the building in abnormal states at the same time as an abnormal event set at the corresponding time; event influence information of positions corresponding to elements in the abnormal event set in the historical monitoring data is obtained, and abnormal influence swept values corresponding to each element in the abnormal event set are analyzed; the equipment and the sensors arranged in the building are connected with a building two-bus;
the sub-event influence correlation analysis module is used for extracting each correlation sub-event corresponding to each abnormal event by combining historical monitoring data in the building monitoring database; analyzing influence association coefficients among associated sub-events in different abnormal events, and generating an abnormal factor associated sub-event set corresponding to each abnormal event respectively by combining the obtained influence association coefficients;
the fault investigation sequence optimization module is used for acquiring each abnormal event set corresponding to the current time and a corresponding abnormal factor association sub-event set, extracting a union set of the obtained abnormal factor association sub-event sets, analyzing the obtained union set to obtain a cluster intelligence risk influence characteristic value corresponding to each element, and generating an abnormal event fault investigation sequence corresponding to the current time;
the main line control and early warning module marks the equipment corresponding to the corresponding abnormal event according to the abnormal event fault investigation sequence corresponding to the current time in sequence, sends the marked result and the abnormal event fault investigation sequence corresponding to the current time to an administrator as early warning information, and controls the power supply state of marking equipment connected with the building two buses in the fault investigation process of the administrator.
The abnormal event influence wave and analysis module comprises an abnormal event set construction module, an event information acquisition module and an abnormal influence analysis module,
the abnormal event set construction module monitors the running states of the numbered devices in the building in real time through a sensor, and extracts abnormal event constructed sets corresponding to the numbered devices of the building in abnormal states at the same time as abnormal event sets at corresponding time;
the event information acquisition module acquires event influence information at positions corresponding to elements in the abnormal event set in the historical monitoring data,
the abnormal influence analysis module analyzes abnormal influence swept values corresponding to each element in the abnormal event set by combining the data acquisition result in the event information acquisition module.
The sub-event influence association analysis module comprises an association sub-event acquisition module, an influence association coefficient acquisition module and an abnormal factor association sub-event set generation module,
the related sub-event acquisition module is used for extracting each related sub-event corresponding to each abnormal event by combining historical monitoring data in the building monitoring database;
the influence correlation coefficient acquisition module analyzes influence correlation coefficients between associated sub-events in different abnormal events,
and the abnormal factor associated sub-event set generation module is used for generating an abnormal factor associated sub-event set corresponding to each abnormal event respectively by combining the obtained influence associated coefficients.
It is noted that 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 (9)

1. An intelligent building two-bus monitoring method based on artificial intelligence is characterized by comprising the following steps:
s1, monitoring the running states of the numbered devices in a building in real time through a sensor, and extracting a set constructed by abnormal events corresponding to the numbered devices of the building in abnormal states at the same time as an abnormal event set at the corresponding time; event influence information of positions corresponding to elements in the abnormal event set in the historical monitoring data is obtained, and abnormal influence swept values corresponding to each element in the abnormal event set are analyzed; the equipment and the sensors arranged in the building are connected with a building two-bus;
s2, extracting each associated sub-event corresponding to each abnormal event by combining historical monitoring data in a building monitoring database; analyzing influence association coefficients among associated sub-events in different abnormal events, and generating an abnormal factor associated sub-event set corresponding to each abnormal event respectively by combining the obtained influence association coefficients;
s3, acquiring each abnormal event set and corresponding abnormal factor associated sub-event set corresponding to the current time, extracting a union set of each obtained abnormal factor associated sub-event set, analyzing the crowd risk influence characteristic values corresponding to each element in the union set, and generating an abnormal event fault investigation sequence corresponding to the current time;
s4, marking the equipment corresponding to the corresponding abnormal event according to the abnormal event fault investigation sequence corresponding to the current time in sequence, sending the marking result and the abnormal event fault investigation sequence corresponding to the current time to an administrator as early warning information, and controlling the power supply state of the marking equipment connected with the building two buses in the fault investigation process of the administrator.
2. The intelligent building two-bus monitoring method based on artificial intelligence as set forth in claim 1, wherein: the related sub-events of the abnormal event correspond to the sub-devices of the device to which the abnormal event belongs, all the sub-devices corresponding to the related sub-events of the abnormal event form the device to which the abnormal event belongs, and the related sub-events of the abnormal event are obtained through database query;
the event influence information at the position corresponding to the abnormal event is recorded as { A1, B1}, wherein A1 represents the ratio of the area of the resident where the abnormal event occurs in the building to the total area of the building resident, B1 represents the interference characteristic wave and coefficient suffered by the resident where the abnormal event occurs,
and when the residents with the abnormal event are obtained through the sensor identification, acquiring the union of the identified sweep time periods of each sweep resident, dividing the interval length corresponding to the obtained union by the duration total duration of the abnormal event, and taking the obtained quotient as the interference characteristic sweep coefficient of the residents with the abnormal event.
3. The intelligent building two-bus monitoring method based on artificial intelligence as set forth in claim 1, wherein: when the abnormal influence spread value corresponding to each element in the abnormal event set is analyzed in the S1, event influence information at positions corresponding to the elements in the abnormal event set is obtained, the abnormal influence spread value corresponding to each element in the abnormal event set is equal to the product of the first value and the second value in the event influence information corresponding to the corresponding element, and the abnormal influence spread value corresponding to the ith element in the abnormal event set is recorded as Di.
4. The intelligent building two-bus monitoring method based on artificial intelligence as set forth in claim 1, wherein: when each associated sub-event corresponding to each abnormal event is extracted in the S2, the ratio of the times of the abnormal event caused by each associated sub-event in the historical data to the total times of occurrence of the corresponding abnormal event is obtained, and the obtained ratio is bound with the associated sub-event of the corresponding abnormal event;
the method for analyzing the influence correlation coefficient between the correlated sub-events in different abnormal events in S2 comprises the following steps:
s201, acquiring each associated sub-event corresponding to each abnormal event in the abnormal event set, and marking the kth associated sub-event in the abnormal event corresponding to the ith element in the abnormal event set as M [ i, k ];
s202, obtaining M [ i1, k1 ]]And M [ i, k ]]The number of influence relations between the two is recorded as Q (M[i1,k1]，M[i,k]) ，
Q (M[i1,k1]，M[i,k]) ＝RL (M[i1,k1]，M[i,k]) ×PC (M[i1,k1]，M[i,k]) ，
Wherein RL is a (M[i1,k1]，M[i,k]) Represents M [ i1, k1 ]]Corresponds to building areas and M [ i, k ]]Corresponding to the position coefficient between building areas, when M [ i1, k1]Corresponds to building areas and M [ i, k ]]When the shortest distance between the corresponding building areas is smaller than a first preset value, determining RL (M[i1,k1]，M[i,k]) =1, otherwise, RL (M[i1,k1]，M[i,k]) ＝0，
PC (M[i1,k1]，M[i,k]) Represents M [ i1, k1 ] in the history data]And M [ i, k ]]The interference frequency coefficient between the two, when there is an abnormal combination event { T whose time interval is smaller than the second preset value M[i1,k1] ，T M[i,k] When } PC (M[i1,k1]，M[i,k]) Equal to T M[i1,k1] ≤T M[i,k] Abnormal combined event { T whose number and time interval are smaller than the second preset value M[i1,k1] ，T M[i,k] Ratio of total number; when no abnormal combination event { T having a time interval smaller than a second preset value exists M[i1,k1] ，T M[i,k] When } PC (M[i1,k1]，M[i,k]) Equal to 0;
when Q is (M[i1,k1]，M[i,k]) When not equal to 0, M [ i1, k1 ] is determined]Is M [ i, k ]]Is a nuisance event of (1); otherwise, determine M [ i1, k1 ]]Is M [ i, k ]]Is a non-interfering event of (a).
5. The intelligent building two-bus monitoring method based on artificial intelligence as set forth in claim 4, wherein: the method for generating the abnormal factor associated sub-event set corresponding to each abnormal event in the S2 comprises the following steps:
s211, acquiring influence association coefficients among associated sub-events in different abnormal events;
s212, summarizing each associated sub-event corresponding to each abnormal event into a blank set, and constructing a sub-event summary set of the corresponding abnormal event;
s213, obtaining all the interference events corresponding to the elements in the sub-event summary set of the abnormal event, adding the obtained interference events into the corresponding sub-event summary set to obtain an abnormal factor associated sub-event set of the corresponding abnormal event, obtaining the binding ratio of each interference event added in the abnormal factor associated sub-event set,
correlating the abnormal factors corresponding to the ith abnormal event with the interference events M [ i2, k2 in the sub-event set]The binding ratio is denoted as W (i，M[i2,k2]) ，
W (i，M[i2,k2]) ＝∑ g＝1 g1 W M[i2,k2] ×Q (M[i2,k2]，Mg) ×W Mg +max{W Mg |1≤g≤g1}，
Wherein W is M[i2,k2] Representing associated sub-events M [ i2, k2 in the ith 2 nd exception event]Binding ratio, W Mg Represents the g-th quilt M [ i2, k 2] in the ith abnormal event]Ratio, Q of binding of interfering related sub-events (M[i2,k2]，Mg) Represents M [ i2, k 2]]Influence correlation coefficient with Mg, max { W Mg And when the number of the W is equal to or more than 1 and equal to or less than g1, the number of the W is equal to or less than 1 and equal to or less than g1 Mg Is the maximum value of (a).
6. The intelligent building two-bus monitoring method based on artificial intelligence as set forth in claim 5, wherein: the method for analyzing the obtained and concentrated crowd risk influence characteristic values corresponding to the elements in the S3 comprises the following steps:
s31, acquiring each abnormal event set and corresponding abnormal factor associated sub-event set corresponding to the current time, extracting a union set of the obtained abnormal factor associated sub-event sets, and marking the union set as U, wherein each element in the U corresponds to an associated sub-event of an abnormal event;
s32, obtaining and concentrating the crowd risk influence characteristic value corresponding to the r element, marking as Fr,
Fr＝∑ gr＝1 gr1 W (gr，mr) ×Dgr，
wherein mr represents the associated sub-event corresponding to the r-th element in the obtained and concentrated set,
gr indicates an abnormal event set corresponding to the current time, a sub-event set associated with a corresponding abnormal factor contains the g-th abnormal event of mr,
W (gr，mr) representing the binding ratio of the element mr in the abnormal factor associated sub-event set corresponding to the abnormal event gr,
dgr indicates an abnormal event set corresponding to the current time, and an abnormal influence sweep value corresponding to the abnormal event gr;
and when an abnormal time fault investigation sequence corresponding to the current time is generated in the step S3, arranging elements in a union set of the obtained sub-event sets associated with each abnormal factor according to the sequence from the large value to the small value of the corresponding crowd risk influence characteristic value, wherein the obtained sequence is used as the abnormal event fault investigation sequence corresponding to the current time.
7. An artificial intelligence based intelligent building two-bus monitoring system using the intelligent building two-bus monitoring method of any one of claims 1-6, characterized in that the system comprises the following modules:
the abnormal event influence and analysis module monitors the running states of the numbered devices in the building in real time through a sensor, and extracts a set constructed by the abnormal events corresponding to the numbered devices of the building in abnormal states at the same time as an abnormal event set at the corresponding time; event influence information of positions corresponding to elements in the abnormal event set in the historical monitoring data is obtained, and abnormal influence swept values corresponding to each element in the abnormal event set are analyzed; the equipment and the sensors arranged in the building are connected with a building two-bus;
the sub-event influence correlation analysis module is used for extracting each correlation sub-event corresponding to each abnormal event by combining historical monitoring data in the building monitoring database; analyzing influence association coefficients among associated sub-events in different abnormal events, and generating an abnormal factor associated sub-event set corresponding to each abnormal event respectively by combining the obtained influence association coefficients;
the fault investigation sequence optimization module is used for acquiring each abnormal event set corresponding to the current time and a corresponding abnormal factor association sub-event set, extracting a union set of the obtained abnormal factor association sub-event sets, analyzing the obtained union set to obtain a cluster intelligence risk influence characteristic value corresponding to each element, and generating an abnormal event fault investigation sequence corresponding to the current time;
the main line control and early warning module marks the equipment corresponding to the corresponding abnormal event according to the abnormal event fault investigation sequence corresponding to the current time in sequence, sends the marked result and the abnormal event fault investigation sequence corresponding to the current time to an administrator as early warning information, and controls the power supply state of marking equipment connected with the building two buses in the fault investigation process of the administrator.
8. An intelligent building two-bus monitoring system based on artificial intelligence as in claim 7, wherein: the abnormal event influence wave and analysis module comprises an abnormal event set construction module, an event information acquisition module and an abnormal influence analysis module,
the abnormal event set construction module monitors the running states of the numbered devices in the building in real time through a sensor, and extracts abnormal event constructed sets corresponding to the numbered devices of the building in abnormal states at the same time as abnormal event sets at corresponding time;
the event information acquisition module acquires event influence information at positions corresponding to elements in the abnormal event set in the historical monitoring data,
the abnormal influence analysis module analyzes abnormal influence swept values corresponding to each element in the abnormal event set by combining the data acquisition result in the event information acquisition module.
9. An intelligent building two-bus monitoring system based on artificial intelligence as in claim 7, wherein: the sub-event influence association analysis module comprises an association sub-event acquisition module, an influence association coefficient acquisition module and an abnormal factor association sub-event set generation module,
the related sub-event acquisition module is used for extracting each related sub-event corresponding to each abnormal event by combining historical monitoring data in the building monitoring database;
the influence correlation coefficient acquisition module analyzes influence correlation coefficients between associated sub-events in different abnormal events,
and the abnormal factor associated sub-event set generation module is used for generating an abnormal factor associated sub-event set corresponding to each abnormal event respectively by combining the obtained influence associated coefficients.
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