CN116340749A - Building energy abnormality monitoring system and method based on big data - Google Patents
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Abstract
The invention discloses a building energy anomaly monitoring system and method based on big data, and belongs to the technical field of energy monitoring. When the situation mapping unit predicts that an instruction is tampered, the early warning unit can rapidly send early warning when receiving a signal to inform staff to verify whether the instruction is correct, and when the analysis unit receives the signal, the prediction situation is analyzed to upgrade and maintain the building energy system in a targeted manner, so that information transmission is safer, and abnormal building energy is monitored more comprehensively.
Description
Technical Field
The invention relates to the technical field of energy monitoring, in particular to a building energy abnormality monitoring system and method based on big data.
Background
People can eat and wear the body shadow of the building, the building can occupy the important position in the building, if the building can be abnormal, certain potential safety hazard exists in the building, and certain resource waste exists at the same time.
However, the monitoring of the building energy is more in monitoring the building energy, and the situation that the building energy cannot be detected by monitoring the building energy system exists, for example, an attacker can implant malicious software into a negative control terminal to obtain terminal authority, directly modify data in a sampling module or tamper a control instruction of a control module, and send the modified data to a master station or remote control equipment through front-end equipment, so that the building energy is abnormal according to the error instruction.
Therefore, the system and the method for monitoring the abnormal energy consumption of the building based on big data can well solve the problems.
Disclosure of Invention
The invention aims to provide a building energy abnormality monitoring system and method based on big data, 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: the building energy abnormality monitoring system based on big data comprises a data acquisition module, a data analysis module, a situation analysis module and an abnormality monitoring module;
the data acquisition module is used for acquiring existing data and historical data;
the data analysis module is used for analyzing the acquired data;
so that the abnormal node for building can be accurately positioned;
the situation analysis module is used for predicting the situation of building energy by analyzing the network situation;
the received instruction is more accurate;
the abnormality monitoring module is used for monitoring whether the building energy is abnormal or not;
the countermeasures are timely made through timely early warning;
the output end of the data acquisition module is connected with the input ends of the data analysis module and the situation analysis module, and the output ends of the data analysis module and the situation analysis module are connected with the input end of the abnormality monitoring module.
According to the technical scheme, the data acquisition module comprises a data acquisition unit, a situation acquisition unit and a storage unit;
the data acquisition unit is used for acquiring the existing data and the historical data of each node of the building energy;
the situation acquisition unit is used for acquiring the existing data and the historical data of the whole network state;
the existing data of the whole network state refers to a Flume log and a Kafka message;
the storage unit is used for storing the acquired data;
and the output ends of the data acquisition unit and the situation acquisition unit are connected with the input end of the storage unit.
According to the technical scheme, the data analysis module comprises a node analysis unit and a comprehensive analysis unit;
the node analysis unit is used for analyzing the energy consumption condition of each node so as to enable the building energy of each node to be more clearly known;
the comprehensive analysis unit is used for comprehensively analyzing the building energy according to the historical conditions, so that the overall building energy is more clearly understood;
the output end of the node analysis unit is connected with the input end of the comprehensive analysis unit.
According to the technical scheme, the situation analysis module comprises a feature extraction unit, a situation understanding unit and a situation mapping unit;
the feature extraction unit is used for extracting abnormal feature data;
the situation understanding unit is used for analyzing abnormal characteristic data;
the situation mapping unit models through the analysis result of the situation understanding unit so as to monitor the building energy;
the monitoring of the building energy is more comprehensive and accurate;
the output end of the situation extraction unit is connected with the input end of the situation understanding unit, and the output end of the situation understanding unit is connected with the input end of the situation mapping unit.
According to the technical scheme, the abnormality monitoring module comprises an early warning unit and an abnormality analysis unit;
the early warning unit is used for early warning the abnormal condition of the building energy monitoring so as to make countermeasures in time;
the abnormality analysis unit is used for analyzing the reasons of the building energy monitoring abnormality, so that the building energy system can be upgraded and maintained in a targeted manner;
the output end of the abnormality analysis unit is connected with the input end of the early warning unit.
The building energy abnormality monitoring method based on big data comprises the following steps:
s1, acquiring existing data and historical data by utilizing a data acquisition module;
s2, analyzing the building energy situation by utilizing a data analysis module;
s3, analyzing the building energy system by using a situation analysis module;
s4, monitoring building energy abnormality by using an abnormality monitoring module.
According to the technical scheme, the acquisition of the existing data and the historical data is performed by utilizing the data acquisition unit and the situation acquisition unit, and the acquired existing data and historical data are stored by utilizing the storage unit;
the data acquisition unit is used for acquiring the data of the building energy of each node and acquiring the historical data of the building energy of each node by utilizing the big data; the situation acquisition unit is used for acquiring data and historical data of the current whole network state;
the collected data of the building energy of each node are put into a set M= { a 1 ，a 2 ，a 3 ，…，a m (wherein a) 1 、a 2 、a 3 、…，a m The data of the 1 st node building energy, the data of the 2 nd node building energy, the data of the 3 rd node building energy, … and the data of the m-th node building energy are respectively represented, and m represents m nodes and is limited.
According to the technical scheme, the analysis of the building energy consumption condition refers to node analysis and comprehensive analysis; the node analysis is to analyze the building energy consumption condition of each node;
analyzing according to the historical data of the building energy of each node to obtain the normal range of the use condition of the building energy, and putting the historical data of the building energy of each node into a set B= { B 1 ，b 2 ，b 3 ，…，b m }；
The normal historical data refers to historical data with abnormal building energy removed;
wherein b 1 、b 2 、b 3 、…、b m Normal history data set expressed as 1 st node, normal history of 2 nd node, respectivelyA data set, a normal history data set of a 3 rd node, …, a normal history data set of an m-th node;
putting the maximum building energy value of the node into a set E= { d 1 ，d 2 ，d 3 ，…，d m Put the minimum building energy value of the node into the set H= { H } 1 ，h 2 ，h 3 ，…，h m }；
Wherein d is m Represents the maximum building energy value of the mth node, h m Representing the minimum building energy value of the mth node;
comparing the elements in set M with the elements in set E and set H, respectively;
when h m ≤a m ≤d m When the building energy of the node is normal, the building energy of the node is proved;
when a is m ≥d m Or a m ≤h m When the building energy of the node is abnormal, the building energy of the node is proved;
the comprehensive analysis refers to analysis of building energy in an integral form;
the integral building energy calculation formula is as follows:
T m ＝a 1 +a 2 +a 3 +…+a m ；
wherein T is m A is integral building energy 1 、a 2 、a 3 、…，a m Respectively representing the data of the building energy of the 1 st node, the data of the building energy of the 2 nd node, the data of the building energy of the 3 rd node, … and the data of the building energy of the m th node, wherein m represents that m nodes are limited, alpha is the minimum value of the whole building energy, and beta is the maximum value of the whole building energy;
when alpha is<T m <Beta, the whole condition of the building energy is proved to be normal;
otherwise, the overall condition of the building energy is proved to be abnormal.
According to the technical scheme, the situation analysis refers to analyzing the acquired data of the current whole network state, and the detection of the building energy is realized through the prediction of the network dangerous event, and the situation analysis comprises the following specific steps:
l1, identifying abnormal characteristic data by utilizing a characteristic extraction unit and extracting environmental elements;
the environmental element refers to context information, such as network topology, vulnerability information and the like, for providing the suspicious activities;
l2, building an ontology model according to the relation of each node by using a situation understanding unit so as to understand the intention behind the suspicious activity;
the building of the ontology model is that the prior art is not described in detail herein, and the manner of understanding the intention behind the suspicious activity also belongs to the prior art, and is not described in detail herein;
l3, building a prediction model by using a situation mapping unit according to the analysis result in the S2, so as to realize monitoring of building energy;
the establishment of the prediction model is to establish the model by using a Bayesian graph theory analysis method;
the Bayesian graph theory analysis method is a probabilistic network model, and is a graph network realized by performing probabilistic reasoning based on a Bayesian formula.
Attribute state node S using Bayesian network reasoning method i The dynamic posterior probability model is:
wherein, p (o|r=r) is a posterior probability, p (r=r) is a priori probability, and p (O) is a joint probability of the current attribute node O and all parent node sets;
arbitrary node variable S i The prior probability distribution of (a) is as formula
Wherein the joint probability of the current attribute node and all the parent node sets thereof is shown as the formula:
wherein pa (S) i ) Is an attribute node S i Is set of parent nodes S i N represents n father node set, i represents i attribute nodes;
the p (r=r) is a priori probability, which means probability estimation of the event r=r according to historical data;
the p (o|r=r) posterior probability refers to probability estimation of the event r=r after the point of evidence O;
the evidence point O can be any node; the father node refers to an arc from node X to Y, then X is the father node of Y, which can also be said to be a direct precursor, and Y is a child node or successor;
attribute state node S using bayesian network reasoning method i The dynamic posterior probability model enables the attack of the building energy system by hand to be predicted, and the abnormal building energy is detected by predicting the probability of tampering instructions.
And L4, substituting the data analyzed in the step S2 into a prediction model to perform probability prediction.
According to the technical scheme, the monitoring of building energy abnormality is realized by using the early warning unit and the analysis unit, and the analysis results of the node analysis unit, the comprehensive analysis unit and the situation mapping unit are transmitted to the early warning unit and the analysis unit;
when the early warning unit receives abnormal energy consumption of the node building, automatically informing node staff responsible for the abnormal energy consumption of the building in a telephone and short message mode;
when the early warning unit receives that the whole energy consumption data is abnormal, automatically informing a worker responsible for the project in an early warning mode of telephone, short message and display equipment, wherein the display equipment refers to a computer, a mobile phone and the like;
when the early warning unit receives the prediction result of situation mapping, a worker is informed to verify whether the received instruction is correct or not in a telephone and short message mode, abnormal building energy is avoided to a certain extent, and loss is reduced;
analyzing by using an analysis unit according to the abnormal results of the building energy of the node analysis unit and the comprehensive analysis unit, so as to adjust the building energy scheme; and upgrading and perfecting the building energy system by utilizing the analysis unit according to the prediction result of the situation mapping unit, sequencing the prediction result according to the magnitude of the prediction probability, and carrying out targeted maintenance and upgrading according to the behavior purpose of the prediction time.
Through the technical scheme, the abnormal node position for the building can be accurately positioned, the energy scheme for the building can be timely adjusted according to the condition of the whole energy for the building, and meanwhile, the instruction can be timely verified, so that the received instruction is more accurate, the abnormal condition for the building is avoided to a certain extent, and the energy system for the building is upgraded and maintained in a targeted mode.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention is provided with the node analysis unit and the comprehensive analysis unit, and can analyze the energy consumption of each node building, so that the abnormal node position of the building energy consumption can be accurately positioned, the condition of the building energy consumption can be more clearly understood by analyzing the whole building energy consumption, and the scheme of the building energy consumption can be timely adjusted.
2. The situation mapping unit is arranged, the information attack behavior can be predicted by establishing a prediction model for situation analysis, and the instruction can be verified in time, so that the received instruction is more accurate, the abnormal situation of building energy is avoided to a certain extent, and some potential risk factors can be detected.
3. The invention is provided with the early warning unit and the analysis unit, the early warning can be carried out through the early warning unit, the staff can be informed of solving the problem in time, meanwhile, the energy consumption scheme of the building can be adjusted in time through the analysis unit, and the energy consumption system of the building can be upgraded and maintained in a targeted mode.
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 diagram of the module composition structure of a building energy anomaly monitoring system based on big data;
FIG. 2 is a schematic flow chart of steps of a building energy abnormality monitoring method based on big data;
FIG. 3 is a schematic diagram of a connection structure of the building energy abnormality monitoring system based on big data.
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.
As shown in fig. 1 to 3, the present invention provides the following technical solutions: the building energy abnormality monitoring system based on big data comprises a data acquisition module, a data analysis module, a situation analysis module and an abnormality monitoring module;
the data acquisition module is used for acquiring existing data and historical data;
the data analysis module is used for analyzing the acquired data;
so that the abnormal node for building can be accurately positioned;
the situation analysis module is used for predicting the situation of building energy by analyzing the network situation;
the received instruction is more accurate;
the abnormality monitoring module is used for monitoring whether the building energy is abnormal or not;
the countermeasures are timely made through timely early warning;
the output end of the data acquisition module is connected with the input ends of the data analysis module and the situation analysis module, and the output ends of the data analysis module and the situation analysis module are connected with the input end of the abnormality monitoring module.
The data acquisition module comprises a data acquisition unit, a situation acquisition unit and a storage unit;
the data acquisition unit is used for acquiring the existing data and the historical data of each node of the building energy;
the situation acquisition unit is used for acquiring the existing data and the historical data of the whole network state, for example, the current whole network state is acquired by data, the acquisition of the jump log is realized by adopting a distributed method to collect log data generated in various devices, systems and applications, and a plurality of useful information and other modes are often hidden;
the existing data of the whole network state refers to a Flume log and a Kafka message;
the storage unit is used for storing the acquired data;
and the output ends of the data acquisition unit and the situation acquisition unit are connected with the input end of the storage unit.
The data analysis module comprises a node analysis unit and a comprehensive analysis unit;
the node analysis unit is used for analyzing the energy consumption condition of each node so as to enable the building energy of each node to be more clearly known;
the comprehensive analysis unit is used for comprehensively analyzing the building energy according to the historical conditions, so that the overall building energy is more clearly understood;
the output end of the node analysis unit is connected with the input end of the comprehensive analysis unit.
The situation analysis module comprises a feature extraction unit, a situation understanding unit and a situation mapping unit;
the feature extraction unit is used for extracting abnormal feature data;
the situation understanding unit is used for analyzing abnormal characteristic data;
the situation mapping unit models through the analysis result of the situation understanding unit so as to monitor the building energy;
the monitoring of the building energy is more comprehensive and accurate;
the output end of the situation extraction unit is connected with the input end of the situation understanding unit, and the output end of the situation understanding unit is connected with the input end of the situation mapping unit.
The abnormality monitoring module comprises an early warning unit and an abnormality analysis unit;
the early warning unit is used for early warning the abnormal condition of the building energy monitoring so as to make countermeasures in time;
the abnormality analysis unit is used for analyzing the reasons of the building energy monitoring abnormality, so that the building energy system can be upgraded and maintained in a targeted manner;
the output end of the abnormality analysis unit is connected with the input end of the early warning unit.
The building energy abnormality monitoring method based on big data comprises the following steps:
s1, acquiring existing data and historical data by utilizing a data acquisition module;
s2, analyzing the building energy situation by utilizing a data analysis module;
s3, analyzing the building energy system by using a situation analysis module;
s4, monitoring building energy abnormality by using an abnormality monitoring module.
The acquisition of the existing data and the historical data is performed by utilizing a data acquisition unit and a situation acquisition unit, and the acquired existing data and the historical data are stored by utilizing a storage unit;
the data acquisition unit is used for acquiring the data of the building energy of each node and acquiring the historical data of the building energy of each node by utilizing the big data; the situation acquisition unit is used for acquiring data and historical data of the current whole network state;
the collected data of the building energy of each node are put into a set M= { a 1 ，a 2 ，a 3 ，…，a m (wherein a) 1 、a 2 、a 3 、…，a m Respectively represent section 1The data of the point building energy, the data of the 2 nd node building energy, the data of the 3 rd node building energy, … and the data of the M th node building energy, M represents M nodes and is limited, for example, each node building data collected is put into a set of m= {35, 40, 28, …,30}.
The analysis of the building energy consumption condition refers to node analysis and comprehensive analysis; the node analysis is to analyze the building energy consumption condition of each node;
analyzing according to the historical data of the building energy of each node to obtain the normal range of the use condition of the building energy, and putting the historical data of the building energy of each node into a set B= { B 1 ，b 2 ，b 3 ，…，b m For example, after eliminating the history data of abnormal building energy of points, the history data of normal building energy of each node is put into a set b=
{{30，34，…，40}，{29，34，…，32}，…，{33，35，…，29}}；
The normal historical data refers to historical data with abnormal building energy removed;
wherein b 1 、b 2 、b 3 、…、b m The normal history data set of the 1 st node, the normal history data set of the 2 nd node, the normal history data set of the 3 rd node, … and the normal history data set of the m th node are respectively expressed;
putting the maximum building energy value of the node into a set E= { d 1 ，d 2 ，d 3 ，…，d m Put the minimum building energy value of the node into the set H= { H } 1 ，h 2 ，h 3 ，…，h m }；
Wherein d is m Represents the maximum building energy value of the mth node, h m Representing the minimum building energy value of the mth node;
comparing the elements in set M with the elements in set E and set H, respectively;
when h m ≤a m ≤d m When it is, the building energy of said node is provedNormal;
when a is m ≥d m Or a m ≤h m When the building energy of the node is abnormal, the building energy of the node is proved;
the comprehensive analysis refers to analysis of building energy in an integral form;
the integral building energy calculation formula is as follows:
T m ＝a 1 +a 2 +a 3 +…+a m ；
wherein T is m A is integral building energy 1 、a 2 、a 3 、…，a m Respectively representing the data of the building energy of the 1 st node, the data of the building energy of the 2 nd node, the data of the building energy of the 3 rd node, … and the data of the building energy of the m th node, wherein m represents that m nodes are limited, alpha is the minimum value of the whole building energy, and beta is the maximum value of the whole building energy;
when alpha is<T m <Beta, the whole condition of the building energy is proved to be normal;
otherwise, the overall condition of the building energy is proved to be abnormal.
The situation analysis refers to analyzing the acquired data of the current whole network state, and detecting the building energy through predicting the network dangerous event, and the specific steps of the situation analysis are as follows:
l1, identifying abnormal characteristic data by utilizing a characteristic extraction unit and extracting environmental elements;
the environmental element refers to context information, such as network topology, vulnerability information and the like, for providing the suspicious activities; for example, the network topology technology mainly adopts an active scanning mode to continuously update the network topology structure of the industrial control system, and the vulnerability discovery technology mainly adopts an active scanning mode to continuously update vulnerability information of the industrial control system assets and services.
L2, building an ontology model according to the relation of each node by using a situation understanding unit so as to understand the intention behind the suspicious activity;
the building of the ontology model is that the prior art is not described in detail herein, and the manner of understanding the intention behind the suspicious activity also belongs to the prior art, and is not described in detail herein;
l3, building a prediction model by using a situation mapping unit according to the analysis result in the S2, so as to realize monitoring of building energy;
the establishment of the prediction model is to establish the model by using a Bayesian graph theory analysis method;
the Bayesian graph theory analysis method is a probabilistic network model, and is a graph network realized by performing probabilistic reasoning based on a Bayesian formula.
Attribute state node S using Bayesian network reasoning method i The dynamic posterior probability model is:
wherein, p (o|r=r) is a posterior probability, p (r=r) is a priori probability, and p (O) is a joint probability of the current attribute node O and all parent node sets;
arbitrary node variable S i The prior probability distribution of (a) is as formula
Wherein the joint probability of the current attribute node and all the parent node sets thereof is shown as the formula:
wherein pa (S) i ) Is an attribute node S i Is set of parent nodes S i N represents n father node set, i represents i attribute nodes;
the p (r=r) is a priori probability, which means probability estimation of the event r=r according to historical data;
the p (o|r=r) posterior probability refers to probability estimation of the event r=r after the point of evidence O;
the evidence point O can be any node;
the father node refers to an arc from node X to Y, then X is the father node of Y, which can also be said to be a direct precursor, and Y is a child node or successor;
l4, substituting the data analyzed in the step S2 into a prediction model to perform probability prediction;
attribute state node S using bayesian network reasoning method i The dynamic posterior probability model predicts the attack of the building energy system, and detects the abnormal building energy by predicting the probability of tampering the instruction, for example, the situation mapping unit predicts that the probability of tampering one instruction reaches 73%, for example, the execution of the command can cause the abnormal building energy, and the Yu Jiang unit can quickly perform early warning after receiving the signal to inform the staff to verify whether the instruction is correct.
The monitoring of building energy abnormality is realized by using an early warning unit and an analysis unit, and analysis results of the node analysis unit, the comprehensive analysis unit and the situation mapping unit are transmitted to the early warning unit and the analysis unit;
when the early warning unit receives abnormal energy consumption of the node building, automatically informing node staff responsible for the abnormal energy consumption of the building in a telephone and short message mode;
when the early warning unit receives that the whole energy consumption data is abnormal, automatically informing a worker responsible for the project in an early warning mode of a telephone, a short message and a display device; the display equipment refers to a computer, a mobile phone and the like;
when the early warning unit receives the prediction result of situation mapping, a worker is informed to verify whether the received instruction is correct or not in a telephone and short message mode, abnormal building energy is avoided to a certain extent, and loss is reduced;
analyzing by using an analysis unit according to the abnormal results of the building energy of the node analysis unit and the comprehensive analysis unit, so as to adjust the building energy scheme; the building energy system is upgraded and perfected by the analysis unit according to the prediction result of the situation mapping unit, the prediction result is ordered according to the prediction probability, and targeted maintenance and upgrading are carried out according to the behavior purpose of the prediction time, for example, the situation mapping unit predicts that the probability of tampering of one data is up to 80%, and then the analysis unit carries out targeted upgrading according to the event.
The method has the advantages that the abnormal node position for building can be accurately positioned, the energy scheme for building can be timely adjusted according to the condition of the whole energy for building, and meanwhile, the instruction can be timely verified, so that the received instruction is more accurate, the abnormal condition for building is avoided to a certain extent, and the energy system for building is pertinently upgraded and maintained.
Examples
Each node building data acquired through the acquisition unit is put into a set M= {35, 40, 28, …,30} for storage, a node maximum building energy value set E= {40, 37, 38, …,40} and a node minimum building energy value is put into a set H= {10, 20, 12, …,25};
comparing the elements in the set M with the elements in the set E and the set H respectively, and knowing that the 2 nd node building energy is abnormal by 40>37, wherein the other node building energy is in a normal range;
the node analysis unit transmits an abnormal signal to the early warning unit, and the early warning unit timely informs a responsible person of the node of abnormal building energy when receiving the signal;
calculating the overall building energy consumption by using an analysis unit:
T m ＝40+37+38+…+40＝4867；
where α=2000, β=6700, and 2000<4867<6700, the overall building energy use is proved to be within the normal range.
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 (10)
1. Building energy anomaly monitoring system based on big data, its characterized in that: the abnormal monitoring system for building energy comprises a data acquisition module, a data analysis module, a situation analysis module and an abnormal monitoring module;
the data acquisition module is used for acquiring existing data and historical data;
the data analysis module is used for analyzing the acquired data;
the situation analysis module is used for predicting the situation of building energy by analyzing the network situation;
the abnormality monitoring module is used for monitoring whether the building energy is abnormal or not;
the output end of the data acquisition module is connected with the input ends of the data analysis module and the situation analysis module, and the output ends of the data analysis module and the situation analysis module are connected with the input end of the abnormality monitoring module.
2. The building energy anomaly monitoring system based on big data according to claim 1, wherein: the data acquisition module comprises a data acquisition unit, a situation acquisition unit and a storage unit;
the data acquisition unit is used for acquiring the existing data and the historical data of each node of the building energy;
the situation acquisition unit is used for acquiring the existing data and the historical data of the whole network state;
the storage unit is used for storing the acquired data;
and the output ends of the data acquisition unit and the situation acquisition unit are connected with the input end of the storage unit.
3. The building energy abnormality monitoring system based on big data according to claim 2, wherein: the data analysis module comprises a node analysis unit and a comprehensive analysis unit;
the node analysis unit is used for analyzing the energy consumption of each node;
the comprehensive analysis unit is used for comprehensively analyzing the building energy according to the historical conditions;
the output end of the node analysis unit is connected with the input end of the comprehensive analysis unit.
4. A building energy anomaly monitoring system based on big data as claimed in claim 3, wherein: the situation analysis module comprises a feature extraction unit, a situation understanding unit and a situation mapping unit;
the feature extraction unit is used for extracting abnormal feature data;
the situation understanding unit is used for analyzing abnormal characteristic data;
the situation mapping unit models through the analysis result of the situation understanding unit so as to monitor the building energy;
the output end of the situation extraction unit is connected with the input end of the situation understanding unit, and the output end of the situation understanding unit is connected with the input end of the situation mapping unit.
5. The building energy abnormality monitoring system based on big data according to claim 4, wherein: the abnormality monitoring module comprises an early warning unit and an abnormality analysis unit;
the early warning unit is used for early warning the abnormal condition of the building energy monitoring;
the abnormality analysis unit is used for analyzing the reasons of the building energy monitoring abnormality;
the output end of the abnormality analysis unit is connected with the input end of the early warning unit.
6. A building energy abnormality monitoring method based on big data is characterized in that: the building energy abnormality monitoring method comprises the following steps:
s1, acquiring existing data and historical data by utilizing a data acquisition module;
s2, analyzing the building energy situation by utilizing a data analysis module;
s3, analyzing the building energy system by using a situation analysis module;
s4, monitoring building energy abnormality by using an abnormality monitoring module.
7. The building energy abnormality monitoring method based on big data according to claim 6, wherein: the acquisition of the existing data and the historical data is performed by utilizing a data acquisition unit and a situation acquisition unit, and the acquired existing data and the historical data are stored by utilizing a storage unit;
the data acquisition unit is used for acquiring the data of the building energy of each node and acquiring the historical data of the building energy of each node by utilizing the big data; the situation acquisition unit is used for acquiring data and historical data of the current whole network state;
the collected data of the building energy of each node are put into a set M= { a 1 ，a 2 ，a 3 ，…，a m (wherein a) 1 、a 2 、a 3 、…，a m The data of the 1 st node building energy, the data of the 2 nd node building energy, the data of the 3 rd node building energy and the data of the m th node building energy are respectively represented, and m represents m nodes and is limited.
8. The building energy abnormality monitoring method based on big data according to claim 6, wherein: the analysis of the building energy consumption condition refers to node analysis and comprehensive analysis; the node analysis is to analyze the building energy consumption condition of each node;
analyzing according to the historical data of the building energy of each node to obtain the normal range of the use condition of the building energy, and putting the historical data of the building energy of each node into a set B= { B 1 ，b 2 ，b 3 ，…，b m }；
Wherein b 1 、b 2 、b 3 、…、b m The normal history data set of the 1 st node, the normal history data set of the 2 nd node, the normal history data set of the 3 rd node, … and the normal history data set of the m th node are respectively expressed;
putting the maximum building energy value of the node into a set E= { d 1 ，d 2 ，d 3 ，…，d m Put the minimum building energy value of the node into the set H= { H } 1 ，h 2 ，h 3 ，…，h m }；
Wherein d is m Represents the maximum building energy value of the mth node, h m Representing the minimum building energy value of the mth node;
comparing the elements in set M with the elements in set E and set H, respectively;
when h m ≤a m ≤d m When the building energy of the node is normal, the building energy of the node is proved;
when a is m ≥d m Or a m ≤h m When the building energy of the node is abnormal, the building energy of the node is proved;
the comprehensive analysis refers to analysis of building energy in an integral form;
the integral building energy calculation formula is as follows:
T m ＝a 1 +a 2 +a 3 +…+a m ；
wherein T is m A is integral building energy 1 、a 2 、a 3 、…，a m Respectively represent section 1The data of point building energy, the data of 2 nd node building energy, the data of 3 rd node building energy, … and the data of m th node building energy, m represents m nodes and is limited, alpha is the minimum value of the whole building energy, and beta is the maximum value of the whole building energy;
when alpha is<T m <Beta, the whole condition of the building energy is proved to be normal;
otherwise, the overall condition of the building energy is proved to be abnormal.
9. The building energy abnormality monitoring method based on big data according to claim 6, wherein: the situation analysis refers to analyzing the acquired data of the current whole network state, and detecting the building energy through predicting the network dangerous event, and the specific steps of the situation analysis are as follows:
l1, identifying abnormal characteristic data by utilizing a characteristic extraction unit and extracting environmental elements;
l2, building an ontology model according to the relation of each node by using a situation understanding unit so as to understand the intention behind the suspicious activity;
l3, building a prediction model by using a situation mapping unit according to the analysis result in the S2, so as to realize monitoring of building energy;
the establishment of the prediction model is to establish the model by using a Bayesian graph theory analysis method;
attribute state node S using Bayesian network reasoning method i The dynamic posterior probability model is:
wherein, p (o|r=r) is a posterior probability, p (r=r) is a priori probability, and p (O) is a joint probability of the current attribute node O and all parent node sets;
wherein the joint probability of the current attribute node and all the parent node sets thereof is shown as the formula:
wherein pa (S) i ) Is an attribute node S i Is set of parent nodes S i N represents n father node set, i represents i attribute nodes;
the p (r=r) is a priori probability, which means probability estimation of the event r=r according to historical data;
the p (o|r=r) posterior probability refers to probability estimation of the event r=r after the point of evidence O;
and L4, substituting the data analyzed in the step S2 into a prediction model to perform probability prediction.
10. The building energy abnormality monitoring method based on big data according to claim 6, wherein: the monitoring of building energy abnormality is realized by using an early warning unit and an analysis unit, and analysis results of the node analysis unit, the comprehensive analysis unit and the situation mapping unit are transmitted to the early warning unit and the analysis unit;
when the early warning unit receives abnormal energy consumption of the node building, automatically informing node staff responsible for the abnormal energy consumption of the building in a telephone and short message mode;
when the early warning unit receives that the whole energy consumption data is abnormal, automatically informing a worker responsible for the project in an early warning mode of a telephone, a short message and a display device;
when the early warning unit receives the prediction result of situation mapping, notifying a worker to verify whether the received instruction is correct or not in a telephone and short message mode;
analyzing by using an analysis unit according to the abnormal results of the building energy of the node analysis unit and the comprehensive analysis unit, so as to adjust the building energy scheme; and upgrading and perfecting the building energy system by utilizing the analysis unit according to the prediction result of the situation mapping unit.
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