CN117664244A - Multi-sensor fused structure on-line monitoring data processing system - Google Patents
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Abstract
The invention discloses a multi-sensor fused structure on-line monitoring data processing system, in particular to the technical field of treasured monitoring, which is used for solving the problems of how to efficiently process and analyze a large amount of monitoring data and how to accurately ensure sustainable evaluation of the health state of an assembled building structure; the system comprises a data monitoring module, a node evaluation module, a power supply configuration module and a database, wherein the modules are connected through signals; determining the importance degree of the connecting nodes through detecting the deviation of the data and the importance of the nodes, determining the relative importance weight according to the importance degree of each connecting node, determining the standby power supply demand coefficient according to the relative importance weight and the sensor power consumption of each connecting node, and performing cluster analysis according to the power supply demand coefficient and the relative position of each connecting node to select the position suitable for arranging the standby power supply, thereby efficiently and accurately arranging the standby power supply and providing effective guarantee for subsequent continuous efficient monitoring.
Description
Technical Field
The invention relates to the technical field of structural object monitoring, in particular to a multi-sensor fusion structural object on-line monitoring data processing system.
Background
With the continuous advancement of the urban process, the fabricated building is in the brand-new corner in the modern building field. The building mode is assembled on site through prefabricated parts in a factory, so that the building efficiency is improved, the cost is reduced, and the resource waste is reduced. However, due to the specificity of fabricated building structures, on-line monitoring thereof becomes a key challenge to ensure structural safety and health. The rise of the multi-sensor network provides new possibility for solving the problem, and real-time monitoring of the structure is realized by means of equipment such as an inclination sensor, a strain sensor, a temperature sensor and the like, but the technical challenges of how to efficiently process and analyze a large amount of monitoring data and how to accurately ensure sustainable evaluation of the health state of the fabricated building structure are brought forward. This has prompted the development of on-line monitoring data processing systems for fabricated building structures to promote monitoring capabilities, early warning mechanisms, and long-term performance analysis of the building structures to address security and sustainability challenges in complex urban environments.
Disclosure of Invention
In order to achieve the above purpose, the present invention provides the following technical solutions:
the multi-sensor fusion structure on-line monitoring data processing system comprises a data monitoring module, a node evaluation module, a power supply configuration module and a database, wherein the modules are connected through signals;
the data monitoring module is used for monitoring the assembled building in real time through the multi-sensor network, monitoring the structural data of main connecting nodes and key components of the assembled building, and sending the monitoring data to the node evaluation module;
the node evaluation module is used for carrying out state evaluation on the main connection nodes and the key components after receiving the monitoring data sent by the data monitoring module, carrying out early warning notification on the main connection nodes and/or the key components which do not meet the index requirements, calculating the importance degree information of the main connection nodes and the key components by combining the importance of the main connection nodes and the key components, and sending the importance degree information of the main connection nodes and the key components of the assembled building to the power supply configuration module;
after receiving the importance degree information of the main connecting nodes and the key components of the fabricated building, the power configuration module analyzes the standby power requirements of the main connecting nodes and the key components by combining the sensor power of the main connecting nodes and the key components, performs multi-feature cluster analysis on the main connecting nodes and the key components by combining the positions of the main connecting nodes and the key components, classifies the main connecting nodes and the key components, and arranges the standby power according to the feature information of each category and the quantity information in each category;
the database is used for storing processing data generated by the operation of each module and design data and standards of the fabricated building.
In a preferred embodiment, the specific steps of the node evaluation module early warning notification are as follows:
after the monitoring data sent by the data monitoring module are obtained, various monitoring data are respectively compared with standard design indexes, each comparison value is compared with the corresponding design deviation, if the comparison value is larger than the design deviation, an alarm is given, and otherwise, no alarm is given.
In a preferred embodiment, the node assessment module combines the importance of the primary connection nodes and critical components of the fabricated building to comprehensively determine the importance of the primary connection nodes and critical components of the fabricated building as follows:
a1, obtaining comparison values of all monitoring data, and obtaining load data of all main connecting nodes and key components from a database;
step A2, linear normalization is adopted for the obtained comparison value of each monitoring data and the load data of each main connecting node and the key component, and the comparison value of each monitoring data and the load data of each main connecting node and the key component are mapped to a section [0, 1];
step A3, after normalizing the comparison value of each monitoring data and the load data of each main connecting node and the key component, determining the importance degree score of each main connecting node and the key component through weighted summation;
and A4, respectively carrying out weight assignment on importance degree scores of the main connection nodes and the key components according to a priority diagram method, determining relative weight values of the main connection nodes and the key components, setting the relative weight values of the main connection nodes and the key components as the visibility information, and sending the visibility information to a power supply configuration module.
In a preferred embodiment, the specific steps of the power configuration module are as follows:
step B1, obtaining importance degree information of main connection nodes and key components of the fabricated building, and obtaining total power consumption of sensors arranged at the positions of the main connection nodes and the key components from a database;
step B2, mapping the obtained importance degree information and the total power consumption of the sensors arranged at the main connection nodes and the key components to intervals [0, 1] by adopting linear normalization;
step B3, after normalizing the importance degree information and the total power consumption of the sensors arranged at the main connecting nodes and the key components, determining the standby power supply demand coefficients of the main connecting nodes and the key components through weighted summation;
step B4, dividing the main connecting nodes and the key components into different categories based on the standby power supply demand coefficients and the position distance data of the main connecting nodes and the key components through a K-means clustering algorithm;
and step B5, determining the arrangement rule of the standby power supply according to the category information of each main connection node and the key component.
In a preferred embodiment, the K-means clustering algorithm comprises the following specific steps:
step B41, normalizing the data;
step B42, randomly selecting a plurality of initial center points;
step B43, calculating the distance between each main connection node and each key component and each central point, and distributing the main connection nodes and the key components to clusters which are closest to the central point; updating the central point of each cluster, and calculating the average value of the nodes belonging to the cluster in each dimension;
step B44, repeating the iterative calculation step until no significant change in the center point occurs or a predetermined number of iterations is reached.
In a preferred embodiment, in step B5, the number of the standby power supply to be put in is determined according to the number information of the categories in which the main connection nodes and the key components are located and the average value information of the standby power supply demand coefficient, and the placement position of the standby power supply is determined according to the average value of the position distance values of the categories in which the main connection nodes and the key components are located.
The invention has the technical effects and advantages that:
according to the invention, through multi-direction analysis of the state data of each main connecting node in the fabricated building, the real-time monitoring of the fabricated structure can be ensured
The invention also comprehensively determines the importance degree of the connection nodes through the deviation of the sensor detection data and the importance of the connection nodes, then determines the relative importance weight according to the importance degree of each connection node, finally determines the standby power supply demand coefficient according to the relative importance weight and the sensor power consumption of each connection node, and then performs cluster analysis according to the power supply demand coefficient and the relative position of each connection node to select the position suitable for arranging the standby power supply, thereby being capable of efficiently and accurately arranging the standby power supply and providing effective guarantee for the follow-up continuous efficient monitoring.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a diagram of a multi-sensor fused structure on-line monitoring data processing system framework in the present invention.
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.
According to the invention, corresponding sensors are arranged at the connecting nodes, monitoring data are acquired, the importance degree of the connecting nodes is comprehensively determined according to the deviation of the detection data and the importance of the connecting nodes, the relative importance weight is determined according to the importance degree of each connecting node, the standby power demand coefficient is determined according to the relative importance weight and the sensor power consumption of each connecting node, and the clustering analysis is performed according to the power demand coefficient and the relative position of each connecting node, so that the position suitable for arranging the standby power is selected, therefore, on one hand, the structural state of the main connecting nodes of each assembled building can be analyzed in real time, and on the other hand, effective guarantee can be provided for subsequent continuous efficient monitoring, and the influence of power interruption on a monitoring system is avoided.
Examples
The multi-sensor fused structure on-line monitoring data processing system, as shown in fig. 1, comprises: the system comprises a data monitoring module, a node evaluation module, a power supply configuration module and a database, wherein the modules are connected through signals.
The functions of each module are as follows:
the data monitoring module is used for realizing real-time monitoring of the fabricated building through the multi-sensor network, and is particularly used for monitoring main connecting nodes and key components of the fabricated building, so that structural data of the main connecting nodes and the key components of the fabricated building are obtained, and the monitoring data are sent to the node evaluation module.
It should be noted that, through many sensor networks, can carry out health condition evaluation and early warning to the prefabricated building. The multi-sensor network specifically comprises a tilt sensor, a strain sensor, a temperature sensor and the like. Which are all arranged on the main connection nodes and key components of the fabricated building according to the self-monitoring targets. The data monitoring module acquires multidimensional data such as deformation, temperature and vibration of the assembled building structure in real time through the multi-sensor network, and sends the multidimensional data such as the deformation, the temperature and the vibration of the assembled building structure to the node evaluation module.
The node evaluation module is used for carrying out state evaluation on the main connection nodes and the key components of the assembled building after receiving the monitoring data sent by the data monitoring module, carrying out early warning notification on the connection nodes and the key components which do not meet the index requirements, comprehensively determining the importance of the main connection nodes and the key components of the assembled building by combining the importance of the main connection nodes and the key components of the assembled building, and sending the importance information of the main connection nodes and the key components of the assembled building to the power supply configuration module.
After receiving the importance degree information of the main connection nodes and the key components of the fabricated building, the power supply configuration module analyzes the power requirements of the main connection nodes and the key components by combining the sensor power of the main connection nodes and the key components, performs multi-feature cluster analysis on the main connection nodes and the key components by combining the relative positions of the main connection nodes and the key components, classifies the main connection nodes and the key components, and arranges a standby power supply according to the feature information of each category and the total number of the main connection nodes and the key components.
The database is used for storing processing data generated by the operation of each module and design data and standards of the assembly type building, and meanwhile, the data in the database can be called by other modules.
The specific process of the node evaluation module early warning notification in this embodiment is as follows:
after the monitoring data sent by the data monitoring module are obtained, various monitoring data are respectively compared with standard design indexes, each comparison value is compared with corresponding design deviation, if the comparison value is larger than the design deviation, the fact that the structural safety of the main connecting node or the key component is greatly different is indicated, and accordingly alarming is conducted, and otherwise, no alarming is conducted.
It should be noted that, comparing various monitoring data with standard design indexes may specifically be performed in various different comparison manners according to actual situations, for example, ratio operation or difference operation may be adopted, which is not limited herein.
The node evaluation module combines the importance of main connection nodes and key components of the fabricated building, and the specific steps for comprehensively determining the importance degree of the main connection nodes and the key components of the fabricated building are as follows:
a1, obtaining comparison values of all monitoring data, and obtaining load data of all main connecting nodes and key components from a database;
step A2, the obtained comparison value of each monitoring data and the load data of each main connection node and the key component are mapped to the interval [0, 1] by adopting linear normalization]The formula is:. In the method, in the process of the invention,is the value after normalization.
It should be noted that, the normalization formula given in this embodiment is a general formula, and the temperature information obtained by the data monitoring module in this application is not related, and the process of normalizing the temperature information is consistent with the general formula, which is not described herein.
Step A3, after normalizing the comparison value of each monitoring data and the load data of each main connection node and each key component, marking the load data L (i) of each main connection node and each key component, and marking the comparison value as D (i), wherein i represents the serial numbers of each main connection node and each key component, determining the importance degree of each main connection node and each key component through weighted summation, and specifically, the importance degree scoring calculation formula of each main connection node and each key component can be as follows: i (I) =d (I) +l (I). Wherein, I (I) is the importance degree score of each main connecting node and key component;
the larger the load data of the main connection node and the key member is, the larger the load the node and the member bear is, and the higher the importance of the structure is, therefore, even if the comparison value of a certain main connection node is smaller, the importance of the main connection node is higher if the load the main connection node bears is large, that is, the importance of the main connection node still needs to be paid attention to.
It should be noted that, in the embodiment, the importance degree scoring of each main connection node and the key component is to integrate the comparison value of the monitored data of each main connection node and the monitored data of each key component and the load data of each main connection node and the monitored data of each key component, and weight and sum the two to determine an overall importance degree, where the weights of the two can be designed by themselves according to the actual situation, and the calculation formula of the embodiment only uses the weights of the two as examples, and is not repeated herein.
And A4, respectively carrying out weight assignment on importance degree scores of the main connection nodes and the key components according to a priority diagram method, determining relative weight values of the main connection nodes and the key components, setting the relative weight values of the main connection nodes and the key components as the visibility information, and sending the visibility information to a power supply configuration module.
For example, the present embodiment performs weight assignment on importance scores of each main connection node and key member, respectively, as shown in the following table 1,
| first main connection point | Second main connection point | Third main connection point | Key component | TTL index | Relative weight value | |
| Relative importance of first primary connection point | 0.5 | 1 | 1 | 1 | 3.5 | 0.4325 |
| The relative importance of the second main connection point | 0 | 0.5 | 1 | 1 | 2.5 | 0.3125 |
| Third primary connection point relative importance | 0 | 0 | 0.5 | 1 | 1.5 | 0.1875 |
| Relative importance of critical components | 0 | 0 | 0 | 0.5 | 0.5 | 0.0625 |
TABLE 1
Note that in table 1, Q, W, E, R is merely an example of the primary connection nodes and the key components, and a plurality of primary connection nodes and/or key components may be actually set to perform weight assignment according to actual situations.
According to the method, firstly, the sensor monitoring data of the main connecting nodes and the key components are analyzed, the main connecting nodes and the key components which do not meet the standard are warned, the structural safety of the fabricated building is ensured, then the deviation of the main connecting nodes and the key components from the standard and the load data of the main connecting nodes and the key components are comprehensively analyzed, the importance degree of the main connecting nodes and the key components is determined, and accordingly the importance degree required by the main connecting nodes and the key components is determined.
The specific working process of the power supply configuration module is as follows:
step B1, obtaining importance degree information (relative weight value) of main connection nodes and key components of the fabricated building, and obtaining total power consumption of sensors arranged at the positions of the main connection nodes and the key components from a database;
step B2, regarding the obtained importance degree information and each main connection nodeThe total power consumption of the sensors arranged at the points and the key components maps the importance information and the total power consumption of the sensors arranged at the main connecting nodes and the key components to the intervals [0, 1] by adopting linear normalization]The formula is:. In the method, in the process of the invention,is the value after normalization;
step B3, after normalizing the importance degree information and the total power consumption of the sensors arranged at the positions of each main connecting node and the key component, marking the relative weight value of each main connecting node and the key component as W (i), and marking the total power consumption as P (i), wherein i represents the serial numbers of each main connecting node and the key component, determining the standby power demand coefficients of each main connecting node and the key component through weighted summation, and specifically, the calculation formula of the standby power demand coefficients of each main connecting node and the key component can be as follows: r (i) =w (i) +p (i). Wherein R (i) is a standby power demand coefficient of each primary connection node and key component;
it should be noted that, similar to the importance degree, the relative weight values of the main connection nodes and the key components and the weights corresponding to the total power consumption of the sensors disposed at the main connection nodes and the key components can be designed according to the actual situation, and the calculation formula of the embodiment only takes the weights of the two as examples, which are not repeated here.
Step B4, dividing the main connecting nodes and the key components into different categories based on the standby power supply demand coefficients and the position distance data of the main connecting nodes and the key components through a K-means clustering algorithm;
the specific steps of the K-means clustering algorithm are as follows:
step B41, standardizing the data to ensure that the dimensions of all dimensions are the same;
step B42, randomly selecting a plurality of initial center points;
step B43, calculating the distance between each main connection node and each key component and each central point, and distributing the main connection nodes and the key components to clusters which are closest to the central point; updating the central point of each cluster, namely calculating the average value of the nodes belonging to the cluster in each dimension;
step B44, repeating the iterative calculation step until no significant change in the center point occurs or a predetermined number of iterations is reached.
It should be noted that, the K-means clustering algorithm in this embodiment considers two features, namely, a standby power demand coefficient and position distance data of each main connection node and a key member, where the position distance data refers to a spatial position difference or a relative position of each main connection node and the key member in the fabricated building structure. Such distances may be represented by coordinate values of the primary connection nodes and the key member in a certain coordinate system.
For example, a three-dimensional coordinate system, a rectangular coordinate system, or the like can be considered.
In this embodiment, in order to facilitate confirmation of the positional relationship between each main connection node and the key member, rectangular coordinate system is selected to confirm positional distance data between each main connection node and the key member. In this embodiment, each wall of a specific project is used as a unit, and the standby power supply is arranged.
Specifically, a certain side wall angle is taken as an origin of a rectangular coordinate system, coordinate axes are taken at two sides of the wall, coordinate information of other main connecting nodes and key components is confirmed, position distance values of the main connecting nodes and the key components relative to the origin are calculated through Euclidean distance formulas, and the position distance values of the main connecting nodes and the key components relative to the origin are marked as position distance data. And if the position distance values are similar, the fact that the main connecting node or the key component is positioned on a certain circular arc close to the origin is indicated, so that the standby power supply can conveniently supply power to any sensor.
The present embodiment can be exemplified as follows:
assuming that the number of primary connection nodes and critical components is five, each is represented as a two-dimensional vector, where the first dimension is the backup power demand factor and the second dimension is the location distance data of the primary connection nodes or critical components.
Suppose that the data for five nodes is as follows:
node 1: [1, 3];
node 2, [2, 5];
node 3 [2.5, 4];
node 4 [1.37, 2];
node 5 [2.93, 6];
selecting two center points, and observing the center point of each cluster to obtain two types of center points which are respectively: center point of category 1: center point of [1.37, 2] with category 2: [2.38, 4.6]. Wherein 1.37 and 2.38 are standby power demand coefficient averages of the respective categories, and 2 and 4.6 are position distance value averages of the respective categories.
Class 1 includes nodes 1 and 4, which are relatively low in standby power demand factor and are 2 in number, and class 2 includes nodes 2, 3, and 5, which are relatively high in standby power demand factor and are 3 in number.
Thereby classifying the five primary connection nodes and critical components into two categories. The two classes of standby power supply demand coefficients are similar and the position distance values relative to the origin are similar, namely, the demand of the configured standby power supplies are similar and the respective positions are relatively concentrated together.
And step B5, determining the arrangement rule of the standby power supply according to the category information of each main connection node and the key component.
Specifically, the throwing quantity of the standby power supply is comprehensively determined according to the quantity information of the categories of the main connecting nodes and the key components and the standby power supply demand coefficient average value information, and meanwhile, the placement position of the standby power supply is determined through the position distance value average value of the categories of the main connecting nodes and the key components, namely, the standby power supply is placed on an arc taking a corner as a circle center and the position distance value average value of the categories of the main connecting nodes and the key components as a radius.
It should be noted that, according to the number information of the categories of each main connection node and the key component and the average value information of the demand coefficient of the standby power, the specific steps of determining the throwing number of the standby power are as follows:
and step B51, setting the number of categories of the main connection nodes and the key components as S (j), and setting the average value of the standby power demand coefficients of the categories of the main connection nodes and the key components as RR (j), wherein j is the serial number of the category. The calculation formula of the estimation coefficient of the throwing quantity of each category can be specifically: a (j) =s (j) +rr (j). Wherein A (j) is a delivery quantity evaluation coefficient of each class.
It should be noted that, the calculation formulas of the estimation coefficients of the number of drops in each type of embodiment only take the weights of the two as examples, and are not described herein.
And step B52, comparing the estimation coefficient of the number of the throwing of each category with a threshold value to determine the throwing number.
For example, a first threshold and a second threshold are set, if the evaluation coefficient of the number of the released devices is smaller than the first threshold, the standby power devices are not required to be released, if the evaluation coefficient of the number of the released devices is larger than or equal to the first threshold and smaller than the second threshold, the number of the released devices is 1, and if the evaluation coefficient of the number of the released devices is larger than or equal to the second threshold, the number of the released devices is 2.
It should be noted that, the specific delivery rule may be designed according to the actual situation, and is not limited herein, and this embodiment is merely an example of one of the possible embodiments.
It should be noted that, the above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of acquired data being simulated by software to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (6)
1. The multi-sensor fusion structure on-line monitoring data processing system is characterized by comprising a data monitoring module, a node evaluation module, a power supply configuration module and a database, wherein the modules are connected through signals;
the data monitoring module is used for monitoring the assembled building in real time through the multi-sensor network, monitoring the structural data of main connecting nodes and key components of the assembled building, and sending the monitoring data to the node evaluation module;
the node evaluation module is used for carrying out state evaluation on the main connection nodes and the key components after receiving the monitoring data sent by the data monitoring module, carrying out early warning notification on the main connection nodes and/or the key components which do not meet the index requirements, calculating the importance degree information of the main connection nodes and the key components by combining the importance of the main connection nodes and the key components, and sending the importance degree information of the main connection nodes and the key components of the assembled building to the power supply configuration module;
after receiving the importance degree information of the main connecting nodes and the key components of the fabricated building, the power configuration module analyzes the standby power requirements of the main connecting nodes and the key components by combining the sensor power of the main connecting nodes and the key components, performs multi-feature cluster analysis on the main connecting nodes and the key components by combining the positions of the main connecting nodes and the key components, classifies the main connecting nodes and the key components, and arranges the standby power according to the feature information of each category and the quantity information in each category;
the database is used for storing processing data generated by the operation of each module and design data and standards of the fabricated building.
2. The multi-sensor fusion structure on-line monitoring data processing system according to claim 1, wherein the specific steps of the node evaluation module pre-warning notification are as follows:
after the monitoring data sent by the data monitoring module are obtained, various monitoring data are respectively compared with standard design indexes, each comparison value is compared with the corresponding design deviation, if the comparison value is larger than the design deviation, an alarm is given, and otherwise, no alarm is given.
3. The multi-sensor fusion structure on-line monitoring data processing system of claim 2, wherein the node assessment module combines the importance of the primary connection nodes and the key components of the fabricated building to comprehensively determine the importance of the primary connection nodes and the key components of the fabricated building as follows:
a1, obtaining comparison values of all monitoring data, and obtaining load data of all main connecting nodes and key components from a database;
step A2, linear normalization is adopted for the obtained comparison value of each monitoring data and the load data of each main connecting node and the key component, and the comparison value of each monitoring data and the load data of each main connecting node and the key component are mapped to a section [0, 1];
step A3, after normalizing the comparison value of each monitoring data and the load data of each main connecting node and the key component, determining the importance degree score of each main connecting node and the key component through weighted summation;
and A4, respectively carrying out weight assignment on importance degree scores of the main connection nodes and the key components according to a priority diagram method, determining relative weight values of the main connection nodes and the key components, setting the relative weight values of the main connection nodes and the key components as the visibility information, and sending the visibility information to a power supply configuration module.
4. The multi-sensor fusion fabric online monitoring data processing system of claim 1, wherein the power configuration module comprises the following specific steps:
step B1, obtaining importance degree information of main connection nodes and key components of the fabricated building, and obtaining total power consumption of sensors arranged at the positions of the main connection nodes and the key components from a database;
step B2, mapping the obtained importance degree information and the total power consumption of the sensors arranged at the main connection nodes and the key components to intervals [0, 1] by adopting linear normalization;
step B3, after normalizing the importance degree information and the total power consumption of the sensors arranged at the main connecting nodes and the key components, determining the standby power supply demand coefficients of the main connecting nodes and the key components through weighted summation;
step B4, dividing the main connecting nodes and the key components into different categories based on the standby power supply demand coefficients and the position distance data of the main connecting nodes and the key components through a K-means clustering algorithm;
and step B5, determining the arrangement rule of the standby power supply according to the category information of each main connection node and the key component.
5. The multi-sensor fusion architecture on-line monitoring data processing system of claim 4, wherein the K-means clustering algorithm comprises the following specific steps:
step B41, normalizing the data;
step B42, randomly selecting a plurality of initial center points;
step B43, calculating the distance between each main connection node and each key component and each central point, and distributing the main connection nodes and the key components to clusters which are closest to the central point; updating the central point of each cluster, and calculating the average value of the nodes belonging to the cluster in each dimension;
step B44, repeating the iterative calculation step until no significant change in the center point occurs or a predetermined number of iterations is reached.
6. The multi-sensor fusion structure on-line monitoring data processing system according to claim 4, wherein in step B5, the number of the standby power supply to be put in is determined comprehensively according to the number information of the categories in which the main connection nodes and the key components are located and the standby power supply demand coefficient average value information, and the placement position of the standby power supply is determined through the average value of the position distance values of the categories in which the main connection nodes and the key components are located.
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