CN115955491A - Heating power station operation monitoring system based on internet of things - Google Patents
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Abstract
The invention belongs to the field of monitoring, and discloses a heating power station operation monitoring system based on the technology of the Internet of things, which comprises a wireless sensor node, a base station and a monitoring center, wherein the wireless sensor node is connected with the base station; the base station is used for periodically partitioning the monitoring range of the wireless sensor nodes to obtain partitioning results, clustering the wireless sensor nodes according to the partitioning results to obtain clustering results and sending the clustering results to the wireless sensor nodes; the wireless sensor nodes are used for forming a transmission network according to the clustering result; the wireless sensor node is used for acquiring state data of the heating station and sending the state data to the base station through the transmission network; the base station is also used for sending the state data to the monitoring center. The heat station detection system of the invention transmits various state data in the heat station by forming the wireless sensor nodes into a transmission network through the base station, thereby greatly expanding the monitoring range which can be covered by a single base station and effectively saving the monitoring cost.
Description
Technical Field
The invention relates to the field of monitoring, in particular to a heating power station operation monitoring system based on the technology of the Internet of things.
Background
The heating plant of the central heating system is a connection point between a heating network and a heating power user. The heat supply system is used for adjusting and converting the heat medium conveyed by the heat supply network according to the working condition and different conditions of the heat supply network, distributing heat to heat users to meet the needs of the users, and carrying out centralized metering and detecting the parameters and the quantity of the heat medium for heating according to the needs.
In the prior art, in order to monitor a thermal station, various types of sensors are generally arranged to acquire data of various aspects of the thermal station, and then the data are transmitted to a monitoring center to monitor the operation of the thermal station.
However, in an existing thermal station operation monitoring system, for example, in a patent with publication number CN108388210a, a sensor generally communicates with a base station (i.e., a primary data processor) directly during data transmission, which makes a monitoring range that a single base station can cover in the existing thermal station operation monitoring system not large enough, and a plurality of base stations need to be arranged, thereby increasing monitoring cost.
Disclosure of Invention
The invention aims to disclose a heating power station operation monitoring system based on the Internet of things technology, and solves the problem that in the existing heating power station operation monitoring system, the monitoring range which can be covered by a single base station is not large enough, and a plurality of base stations are required to be arranged, so that the monitoring cost is increased.
In order to achieve the purpose, the invention adopts the following technical scheme:
a heating power station operation monitoring system based on the technology of the Internet of things comprises wireless sensor nodes, a base station and a monitoring center;
the base station is used for periodically partitioning the monitoring range of the wireless sensor nodes to obtain partitioning results, clustering the wireless sensor nodes according to the partitioning results to obtain clustering results and sending the clustering results to the wireless sensor nodes;
the wireless sensor nodes are used for forming a transmission network according to the clustering result;
the wireless sensor node is used for acquiring state data of the heating station and sending the state data to the base station through a transmission network;
the base station is also used for sending the state data to the monitoring center.
Optionally, the periodically partitioning the monitoring range of the wireless sensor node includes:
and partitioning the monitoring range of the wireless sensor node according to the attribute information of the wireless sensor node.
Optionally, the attribute information includes remaining energy and communication radius.
Optionally, the partitioning the monitoring range of the wireless sensor node according to the attribute information of the wireless sensor node includes:
acquiring the number N of self-adaptive partitions;
and taking the N as the classified number, calculating the wireless sensor nodes by adopting a classification algorithm to obtain a classification result, and dividing the wireless sensor nodes belonging to the same classification into the same region.
Optionally, the calculating the wireless sensor node by using a classification algorithm to obtain a classification result includes:
s1, randomly selecting N wireless sensor nodes as a classification center;
s2, calculating the distance between other wireless sensor nodes except for serving as classification centers and each classification center;
s3, acquiring the minimum distance corresponding to each wireless sensor node;
s4, dividing the wireless sensor nodes into the classification of the classification center corresponding to the minimum distance;
s5, calculating the average classification coordinate of the wireless sensor nodes in each classification respectively, and taking the wireless sensor node closest to the average classification coordinate as a new classification center;
and S6, judging whether the distance between the new classification center obtained in the S5 and the classification center in the S2 is smaller than a set distance threshold, if so, outputting the classification obtained in the S4, and if not, entering the S2.
Optionally, the S2 includes:
for wireless sensor node a, the calculated function of the distance between wireless sensor node a and the nth classification center is:
wherein dist (a, n) represents the distance between the wireless sensor node a and the nth classification center; x is a radical of a fluorine atom A And y A Respectively represent the abscissa and ordinate, x, of the wireless sensor node A n And y n Respectively representing the abscissa and the ordinate of the nth classification center; n is an element of [1,N]。
Optionally, the S5 includes:
for the nth class, the calculated function of the average class coordinate is:
wherein x is ave,n Abscissa, y, representing the mean classification coordinate ave,n Ordinate, set, representing the mean classification coordinate n Represents the set of all wireless sensor nodes in the nth classification, x i And y i Respectively represent set n The abscissa and the ordinate of the wireless sensor node i in (1); numset n Represents the number of wireless sensor nodes in the nth class, n ∈ [1,N ]]。
Optionally, the S6 includes:
storing the new classification center obtained in S5 into a set S5 Storing the classification centers in S2 into set S2 ；
For set S5 Classification center clsf of (1) S5 Obtaining set S2 Middle distance clsf S5 Nearest Classification center clsf S3 Will clsf S5 And clsf S3 Forming a matching pair;
and if the distance between each matching pair is smaller than the set distance threshold, outputting the classification obtained in the S4, otherwise, entering the S2.
In the process of monitoring the heating station by using the wireless sensor nodes and the base station, the wireless sensor nodes form a transmission network through the base station to transmit various state data in the heating station, so that the monitoring range which can be covered by a single base station is greatly expanded, and the monitoring cost can be effectively saved.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a diagram of an embodiment of a heating power station operation monitoring system based on the internet of things technology.
Fig. 2 is a diagram illustrating an embodiment of calculating a wireless sensor node by using a classification algorithm to obtain a classification result according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention and are not to be construed as limiting the present invention.
As shown in fig. 1, in an embodiment, the invention provides a heating station operation monitoring system based on the technology of internet of things, which includes a wireless sensor node, a base station and a monitoring center;
the base station is used for periodically partitioning the monitoring range of the wireless sensor nodes to obtain partitioning results, clustering the wireless sensor nodes according to the partitioning results to obtain clustering results, and sending the clustering results to the wireless sensor nodes;
the wireless sensor nodes are used for forming a transmission network according to the clustering result;
the wireless sensor node is used for acquiring state data of the heating station and sending the state data to the base station through a transmission network;
the base station is also used for sending the state data to the monitoring center.
As a patent with publication number CN108388210a in the prior art, in the process of transmitting the collected information obtained by the sensors, each sensor needs to be connected with the primary data processor through the internet of things communication module, obviously, such a connection mode may limit the detection range that can be covered by a single primary data processor, and if the detection range needs to be increased, the purpose can be achieved only by a wired communication mode or a means of increasing the transmission power of the internet of things communication module. However, set up a large amount of communication lines, can bring very big pressure for the later maintenance, communication cable's price is not cheap moreover, and if increase thing networking communication module's transmitting power, then can make the probability of communication conflict promote by a wide margin for communication delay between sensor and the elementary data processor improves by a wide margin, and data packet loss's probability also can improve moreover, is unfavorable for carrying out effectual monitoring to the thermal power station.
In the process of monitoring the heating station by using the wireless sensor nodes and the base station, the wireless sensor nodes form a transmission network through the base station to transmit various state data in the heating station, so that the monitoring range which can be covered by a single base station is greatly expanded, and the monitoring cost can be effectively saved.
Optionally, the wireless sensor node may include a wireless noise sensor, a wireless vibration sensor, a wireless temperature and humidity sensor, a wireless smoke sensor, and the like.
Optionally, the status data may include one or more of operating noise, vibration frequency, temperature, humidity of equipment in the thermal power station, and smoke content in an operating environment of the thermal power station.
Optionally, the periodically partitioning the monitoring range of the wireless sensor node includes:
and partitioning the monitoring range of the wireless sensor node according to the attribute information of the wireless sensor node.
And the base station partitions the monitoring range of the wireless sensor node by a fixed period. After partitioning, clustering can be performed again according to the partitioning result.
Optionally, the attribute information includes remaining energy and communication radius.
Optionally, the partitioning the monitoring range of the wireless sensor node according to the attribute information of the wireless sensor node includes:
acquiring the number N of self-adaptive partitions;
and taking the N as the number of classification, calculating the wireless sensor nodes by adopting a classification algorithm to obtain a classification result, and dividing the wireless sensor nodes belonging to the same classification into the same region.
The partitioning result is the number of wireless sensor nodes contained in each area.
In the invention, the number of the partitions is self-adaptive and is associated with the current state of the wireless sensor node in a self-adaptive manner, so that a more reasonable partition number is obtained.
Optionally, obtaining the adaptive partition number N includes:
the number of adaptive partitions is calculated using the following function:
wherein N represents the number of adaptive partitionsQuantity, bsnum represents a partition number reference value, wsnset represents a set of wireless sensor nodes, nwtset represents the total number of wireless sensor nodes contained in wsnset, enrlft represents the total number of wireless sensor nodes contained in wsnset k Represents the residual energy of the wireless sensor node k, enrfc represents the residual energy comparison value, currad k The communication radius of a wireless sensor node k is represented, currast represents an average communication radius comparison value, and lambda 1 、λ 2 Representing a weight parameter.
In the invention, the number of the adaptive partitions is related to the residual energy and the communication radius of the wireless sensor nodes, the larger the difference of the residual energy between the wireless sensor nodes is, the smaller the average communication radius of the wireless sensor nodes is, the larger the number of the adaptive partitions is, the smaller the difference of the residual energy between the wireless sensor nodes is, and the larger the average communication radius of the wireless sensor nodes is, the smaller the number of the adaptive partitions is. The larger the difference of the residual energy is, the more unbalanced the energy distribution is, the more the residual energy is in the part of the wireless sensor nodes, and the too little residual energy is in the part of the wireless sensor nodes, which affects the average monitoring time of the wireless sensor nodes. And when the average communication radius is larger, the communication capability of the wireless sensor node is reflected from another aspect, and the larger the communication capability is, the larger the communication power can be used for communication, so that the self-adaptive partition number is obtained by comprehensively calculating from two different aspects, and the reasonability of the obtained partition number is improved.
Optionally, as shown in fig. 2, the calculating the wireless sensor node by using the classification algorithm to obtain a classification result includes:
s1, randomly selecting N wireless sensor nodes as a classification center;
s2, calculating the distance between other wireless sensor nodes except for serving as the classification center and each classification center;
s3, acquiring the minimum distance corresponding to each wireless sensor node;
s4, dividing the wireless sensor nodes into the classification of the classification center corresponding to the minimum distance;
s5, calculating the average classification coordinate of the wireless sensor nodes in each classification respectively, and taking the wireless sensor node closest to the average classification coordinate as a new classification center;
and S6, judging whether the distance between the new classification center obtained in the S5 and the classification center in the S2 is smaller than a set distance threshold, if so, outputting the classification obtained in the S4, and if not, entering the S2.
In the invention, a cyclic calculation mode is adopted for classification, when the change amplitude of the classification result is small, namely whether the distance in S6 is smaller than a set distance threshold value or not, the classification is stopped, and the classification result is output, otherwise, the cyclic calculation is continued according to a newly selected classification center. By setting the distance threshold, the invention does not need to wait for two times of calculation to obtain the wireless sensor nodes which are used as the classification centers and output the classification results only when the wireless sensor nodes are completely consistent. In the invention, the classification is carried out based on the wireless sensor nodes which are sparsely distributed, and the center of the classification is not necessarily exactly positioned on the wireless sensor nodes, so that if the classification result is output only by selecting complete consistency, the classification result may be trapped in local circulation and the result cannot be obtained, therefore, the arrangement of the invention can ensure that the classification result can be obtained smoothly.
Optionally, the S2 includes:
for wireless sensor node a, the distance between wireless sensor node a and the nth classification center is calculated as:
wherein dist (a, n) represents the distance between the wireless sensor node a and the nth classification center; x is the number of A And y A Respectively represent the abscissa and ordinate, x, of the wireless sensor node A n And y n Respectively representing the abscissa and the ordinate of the nth classification center; n is an element of [1,N]。
Optionally, the S5 includes:
for the nth class, the calculated function of the average class coordinate is:
wherein x is ave,n Abscissa, y, representing the mean classification coordinate ave,n Ordinate, set, representing the mean classification coordinate n Represents the set of all wireless sensor nodes in the nth class, x i And y i Respectively represent set n The abscissa and the ordinate of the wireless sensor node i in (1); numset n Represents the number of wireless sensor nodes in the nth classification, n ∈ [1,N]。
Optionally, the S6 includes:
the new classification center obtained in S5 is stored in the set S5 Storing the classification centers in S2 into set S2 ；
For set S5 Classification center clsf of (1) S5 Acquiring set S2 Middle distance clsf S5 Nearest Classification center clsf S3 Will clsf S5 And clsf S3 Forming a matching pair;
and if the distance between each matching pair is smaller than the set distance threshold, outputting the classification obtained in the S4, otherwise, entering the S2.
In the present invention, the classification is terminated when each matching pair meets the requirements.
Optionally, clustering the wireless sensor nodes according to the partition result to obtain a clustering result, including:
for partition, the communication radius of the wireless sensor node in partition is updated by adopting the following function:
wherein, currad partition Indicates the updated communication radius of the wireless sensor node in partition, bascur indicates the set reference communication radius, wsnnum par titi on Representing the number of wireless sensor nodes in partition, area par titi on The monitoring range of the wireless sensor nodes in the partition is represented, the denssta represents a set distribution density reference value, the aveennr represents an average value of residual energy of the wireless sensor nodes in the partition, and the fullerene represents an average value of initial energy of the wireless sensor nodes;
calculating the number of cluster head nodes according to the updated communication radius:
wherein, numclst partition Representing the number of cluster head nodes in partition, phi represents the adjustment coefficient, phi>1.1；
Respectively calculating the initial probability of each wireless sensor node in partition becoming cluster head by using HEED algorithm, and calculating the first numclst with the maximum initial probability partition The wireless sensor nodes are used as cluster head nodes in the partition, and the rest wireless sensor nodes in the partition are used as member nodes and are added into a cluster where the cluster head nodes are located according to the principle of minimum distance;
and taking the serial number of the cluster head node of each cluster, the serial number of the member node and the updated communication radius as a clustering result.
In the invention, after each clustering, the communication radius of the member node is recalculated, thereby achieving the purpose of prolonging the average monitoring time of the wireless sensor node. In a place where the distribution is dense, the communication radius of the wireless sensor node is set to be small, and in a specified range, the smaller the average value of the remaining energy of the wireless sensor node is, the smaller the communication radius is set. And calculating the number of the cluster head nodes by the updated communication radius, wherein the smaller the updated communication radius is, the larger the monitoring range is, the larger the number of the cluster head nodes is, so that the number of the cluster head nodes is adaptively changed along with the change of the updated communication radius.
Optionally, forming a transmission network according to the clustering result includes:
after receiving the clustering result, the wireless sensor node determines whether the wireless sensor node belongs to the cluster head node or the member node according to the number of the wireless sensor node, then the member node communicates with the cluster head node of the cluster to which the wireless sensor node belongs according to the transmitting power corresponding to the updated communication radius contained in the clustering result, the cluster head node is not limited by the updated communication radius, and the cluster head node communicates with the base station to form a hierarchical transmission network.
The cluster head nodes and the base station are communicated with each other in a single-hop or multi-hop mode. The single hop is directly communicated with the base station, and the hop is communicated with the base station through the relay of other cluster head nodes.
Optionally, sending the status data to the base station through a transmission network includes:
after the member nodes acquire the state data, the state data are sent to the corresponding cluster head nodes, and then the cluster head nodes speak the state data and forward the state data to the base station.
Optionally, the monitoring center includes a storage module and a monitoring module;
the storage module is used for storing the state data sent by the base station;
the monitoring module is used for judging whether the state data exceeds a set size range or not, and if the state data exceeds the set size range, alarming to the staff of the monitoring center.
The invention being thus described by way of example, it will be obvious that the same may be practiced otherwise than as specifically described,
the invention is in the scope of protection only if various improvements are made by the method conception and the technical scheme of the invention, or the method is directly applied to other occasions without improvement.
Claims (8)
1. A heating power station operation monitoring system based on the technology of the Internet of things is characterized by comprising wireless sensor nodes, a base station and a monitoring center;
the base station is used for periodically partitioning the monitoring range of the wireless sensor nodes to obtain partitioning results, clustering the wireless sensor nodes according to the partitioning results to obtain clustering results, and sending the clustering results to the wireless sensor nodes;
the wireless sensor nodes are used for forming a transmission network according to the clustering result;
the wireless sensor node is used for acquiring state data of the heating station and sending the state data to the base station through a transmission network;
the base station is also used for sending the state data to the monitoring center.
2. The thermal station operation monitoring system based on internet of things technology as claimed in claim 1, wherein the periodically partitioning the monitoring range of the wireless sensor node comprises:
and partitioning the monitoring range of the wireless sensor node according to the attribute information of the wireless sensor node.
3. The system for monitoring the operation of the thermal station based on the technology of the internet of things as claimed in claim 2, wherein the attribute information comprises residual energy and communication radius.
4. The thermal station operation monitoring system based on the internet of things technology as claimed in claim 3, wherein the partitioning of the monitoring range of the wireless sensor node according to the attribute information of the wireless sensor node comprises:
acquiring the number N of self-adaptive partitions;
and taking the N as the number of classification, calculating the wireless sensor nodes by adopting a classification algorithm to obtain a classification result, and dividing the wireless sensor nodes belonging to the same classification into the same region.
5. The thermal station operation monitoring system based on the internet of things technology as claimed in claim 4, wherein the calculating the wireless sensor nodes by using the classification algorithm to obtain the classification result comprises:
s1, randomly selecting N wireless sensor nodes as a classification center;
s2, calculating the distance between other wireless sensor nodes except for serving as the classification center and each classification center;
s3, acquiring the minimum distance corresponding to each wireless sensor node;
s4, dividing the wireless sensor nodes into the classification of the classification center corresponding to the minimum distance;
s5, calculating the average classification coordinate of the wireless sensor nodes in each classification respectively, and taking the wireless sensor node closest to the average classification coordinate as a new classification center;
and S6, judging whether the distance between the new classification center obtained in the S5 and the classification center in the S2 is smaller than a set distance threshold, if so, outputting the classification obtained in the S4, and if not, entering the S2.
6. The thermal station operation monitoring system based on internet of things technology as claimed in claim 5, wherein the S2 comprises:
for wireless sensor node a, the distance between wireless sensor node a and the nth classification center is calculated as:
wherein dist (a, n) represents the distance between the wireless sensor node a and the nth classification center; x is the number of A And y A Respectively represent the abscissa and ordinate, x, of the wireless sensor node A n And y n Respectively representing the abscissa and the ordinate of the nth classification center; n is an element of [1,N]。
7. The thermal station operation monitoring system based on internet of things technology as claimed in claim 5, wherein the S5 comprises:
for the nth class, the calculated function of the average class coordinate is:
wherein x is ave,n Abscissa, y, representing the mean classification coordinate ave,n Ordinate, set, representing the mean classification coordinate n Represents the set of all wireless sensor nodes in the nth class, x i And y i Respectively represent set n The abscissa and the ordinate of the wireless sensor node i in (1); numset n Represents the number of wireless sensor nodes in the nth class, n ∈ [1,N ]]。
8. The thermal station operation monitoring system based on internet of things technology as claimed in claim 5, wherein the S6 comprises:
the new classification center obtained in S5 is stored in the set S5 Storing the classification centers in S2 into set S2 ；
For set S5 Classification center clsf of (1) S5 Acquiring set S2 Middle distance clsf S5 Nearest Classification center clsf S3 Will clsf S5 And clsf S3 Forming a matching pair;
and if the distance between each matching pair is smaller than the set distance threshold value, outputting the classification obtained in the S4, otherwise, entering the S2.
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| CN116388401A (en) * | 2023-05-31 | 2023-07-04 | 国网吉林省电力有限公司辽源供电公司 | Substation equipment control method and system |
| CN116388401B (en) * | 2023-05-31 | 2023-08-25 | 国网吉林省电力有限公司辽源供电公司 | Substation equipment control method and system |
| CN118111020A (en) * | 2024-02-01 | 2024-05-31 | 山东和同信息科技股份有限公司 | Heat exchange station operation monitoring intelligent control system based on multidimensional data |
| CN118111020B (en) * | 2024-02-01 | 2024-09-24 | 山东和同信息科技股份有限公司 | Heat exchange station operation monitoring intelligent control system based on multidimensional data |
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