CN117252335A - Machine learning-based municipal equipment facility intelligent management method and system - Google Patents
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Abstract
The application is applicable to the technical field of data processing, and provides a municipal equipment facility intelligent management method and system based on machine learning, wherein the method comprises the following steps: acquiring each related data of municipal facility equipment as basic data; processing the basic data to obtain standard data; and constructing a decision tree according to the standard data, and then carrying out running situation early warning on municipal facility equipment. The method and the system can find the abnormal condition of facility equipment in advance, perform early warning, and effectively respond to emergency, so that the efficiency and accuracy of urban facility management are remarkably improved, the flow and decision of municipal management are optimized, and the city is more intelligent, efficient and beneficial.
Description
Technical Field
The application belongs to the technical field of data analysis and processing, and particularly relates to an intelligent management method and system for municipal equipment and facilities based on machine learning.
Background
Municipal facility equipment is taken as an important component of urban development and construction, including roads, street lamps, bridges and the like, the position of the municipal facility equipment in national economic construction is increasingly prominent, and in recent years, the number of municipal facility equipment is continuously increased along with the continuous development of cities.
At present, management of municipal facility equipment mainly adopts a management mode of manual inspection, problem finding and dispatch solving.
Obviously, the management method of municipal facility equipment is difficult to discover abnormal conditions of the facility equipment in advance and perform early warning, so that emergency response capability is insufficient.
Disclosure of Invention
In view of this, the embodiment of the application provides a municipal equipment and facility intelligent management method and system based on machine learning, so as to solve the technical problems.
The first aspect of the application provides an intelligent management method for municipal equipment facilities based on machine learning, which comprises the following steps:
acquiring each related data of municipal facility equipment as basic data;
processing the basic data to obtain standard data;
constructing a decision tree according to the standard data, and further carrying out running situation early warning on municipal facility equipment, wherein the decision tree construction comprises the following steps:
dividing standard data into a plurality of classes through dissimilarity calculation;
and selecting two classes with the smallest dissimilarity value from all the classes to train to construct a support vector machine, generating a non-leaf node, merging the two classes to form a new class, carrying out dissimilarity calculation on the new class and the rest classes again, selecting the two classes with the smallest dissimilarity value again to train to construct another support vector machine, generating another non-leaf node, and continuously repeating the steps until all the classes are traversed, thus completing the construction of a decision tree based on the support vector machine.
The second aspect of the application provides a municipal equipment facility intelligent management system based on machine learning, comprising:
the acquisition module is used for acquiring all relevant data of municipal facility equipment as basic data;
the processing module is used for processing the basic data to obtain standard data;
the decision module is used for constructing a decision tree according to the standard data, and further carrying out running situation early warning on municipal facility equipment, wherein the decision tree construction comprises the following steps:
dividing standard data into a plurality of classes through dissimilarity calculation;
and selecting two classes with the smallest dissimilarity value from all the classes to train to construct a support vector machine, generating a non-leaf node, merging the two classes to form a new class, carrying out dissimilarity calculation on the new class and the rest classes again, selecting the two classes with the smallest dissimilarity value again to train to construct another support vector machine, generating another non-leaf node, and continuously repeating the steps until all the classes are traversed, thus completing the construction of a decision tree based on the support vector machine.
A third aspect of the present application provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to the first aspect when executing the computer program.
A fourth aspect of the present application provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method according to the first aspect.
From the above, compared with the prior art, the present application has at least the following advantages:
1. according to the method, large data analysis is performed on all relevant data based on municipal facility equipment, and operation situation early warning is performed on the municipal facility equipment by constructing a decision tree, so that abnormal conditions of the facility equipment can be found in advance, early warning is performed, and effective emergency response capability is achieved, so that the efficiency and accuracy of municipal facility management are remarkably improved, the process and decision of municipal management are optimized, and the city is more intelligent, efficient and beneficial.
2. The method and the device adopt a mode of constructing the decision tree from bottom to top, so that each class has only one classification path, training time is shortened, time cost is saved, and accuracy, rapidness and reliability of a prediction result of the decision tree can be ensured.
3. According to the method and the device, the dimension reduction processing is carried out on the basic data, so that the data dimension and the required storage space can be reduced, the training calculation time can be saved, and the efficiency of predicting the running situation of municipal facility equipment can be effectively improved.
4. The method and the device can eliminate noise interference and show the periodic trend of the data because the data is smoothed.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for intelligent management of municipal equipment facilities based on machine learning according to an embodiment of the application;
fig. 2 is a schematic structural diagram of a municipal equipment facility intelligent management system based on machine learning according to an embodiment of the application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
In particular implementations, the electronic devices described in embodiments of the present application include, but are not limited to, other portable devices such as mobile phones, laptop computers, or tablet computers having a touch-sensitive surface (e.g., a touch screen display and/or a touch pad). It should also be appreciated that in some embodiments, the device is not a portable communication device, but a desktop computer having a touch-sensitive surface (e.g., a touch screen display and/or a touch pad).
In the following discussion, an electronic device including a display and a touch-sensitive surface is described. However, it should be understood that the electronic device may include one or more other physical user interface devices such as a physical keyboard, mouse, and/or joystick.
The electronic device supports various applications, such as one or more of the following: drawing applications, presentation applications, word processing applications, website creation applications, disk burning applications, spreadsheet applications, gaming applications, telephony applications, video conferencing applications, email applications, instant messaging applications, workout support applications, photo management applications, digital camera applications, digital video camera applications, web browsing applications, digital music player applications, and/or digital video player applications.
Various applications that may be executed on the electronic device may use at least one common physical user interface device such as a touch-sensitive surface. One or more functions of the touch-sensitive surface and corresponding information displayed on the electronic device may be adjusted and/or changed between applications and/or within the corresponding applications. In this way, a common physical architecture (e.g., touch-sensitive surface) of the electronic device may support various applications with user interfaces that are intuitive and transparent to the user.
It should be understood that the sequence number of each step in this embodiment does not mean the sequence of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.
In order to illustrate the technical solutions described in the present application, the following description is made by specific examples.
Referring to fig. 1, fig. 1 is a flow chart of a method for intelligent management of municipal equipment facilities based on machine learning provided in an embodiment of the application, and as shown in fig. 1, the method for intelligent management of municipal equipment facilities based on machine learning includes the following contents:
acquiring each related data of municipal facility equipment as basic data;
processing the basic data to obtain standard data;
and constructing a decision tree according to the standard data, and then carrying out running situation early warning on municipal facility equipment.
Obviously, the scheme is characterized in that the big data analysis is performed based on all relevant data of municipal facility equipment, and the operation situation early warning is performed on the municipal facility equipment by constructing a decision tree, so that the abnormal condition of the facility equipment can be found in advance, the early warning is performed, the effective emergency response capability is achieved, the efficiency and the accuracy of urban facility management are remarkably improved, the flow and the decision of municipal management are optimized, and the city is more intelligent, efficient and beneficial.
In some embodiments, each relevant data of the municipal facility equipment is obtained as basic data, where each relevant data includes attribute data, geographical location data, operation data, maintenance management data, and the like of the municipal facility equipment, and may be specifically set according to needs, which is not limited herein.
In some embodiments, the base data is processed to obtain standard data, including the following:
and performing dimension reduction processing on the basic data to obtain standard data.
Obviously, through carrying out dimension reduction processing on basic data, not only can reduce data dimension and required storage space, but also training calculation time can be saved, and then the efficiency of municipal facility equipment operation situation prediction is effectively improved.
Specifically, the basic data is processed to obtain standard data, which includes the following contents:
forming N rows and M columns of matrix X from the basic data according to columns;
zero-equalizing each row of the matrix X;
calculating a covariance matrix C of the averaging matrix X;
obtaining the eigenvalue and corresponding eigenvector of covariance matrix C;
arranging the eigenvectors into a matrix according to the corresponding eigenvalue from top to bottom, and taking the first k rows to form a matrix P;
standard data y=px after the dimension reduction to k dimensions is calculated.
Wherein, the above content can be specifically expressed as:
the basic data form N rows and M columns of matrix X:
X＝[x1,x2,...,xm]
[x1,x2,...,xm]
...
[x1,x2,...,xm]
zero-equalizing each row of matrix X:
x_mean=average (X, axis=0) (average by column)
X_centered＝X-X_mean
Calculating a covariance matrix C of the mean matrix X:
C＝(X_centered.T@X_centered)/(N-1)
the eigenvalue and corresponding eigenvector of covariance matrix C are obtained:
eig_values, eig_vectors=eigenvalue decomposition (C)
The eigenvectors are arranged into a matrix according to the corresponding eigenvalue from top to bottom, and the first k rows are taken to form a matrix P:
P＝eig_vectors[:k,:]
standard data y=px after the dimension reduction to k dimensions is calculated:
Y＝X_centered@P
the operator @ in the above formula represents a matrix multiplication, and axis=0 represents operation along the column direction.
In some embodiments, the base data is processed to obtain standard data, including the following:
and smoothing the basic data to obtain standard data.
Because of factors such as data sent by a sensor or manually collected data in each related data of municipal facility equipment, instability of the sensor, uncertainty of the manually collected data and the like, the situation that individual data are inconsistent with actual data may exist, namely: since abnormal data or disturbance data exists, noise disturbance can be eliminated and a periodic trend of data can be developed by smoothing the data.
Specifically, the smoothing process is performed by the following formula:
v t ＝βv t-1 +(1-β)θ t
in the above, θ t The parameter value is obtained at the moment t; v t Is a smoothed value; beta is the weight parameter (0<β<1)。
In some embodiments, a decision tree is constructed according to the standard data, and then running situation pre-warning is carried out on municipal facility equipment, wherein the decision tree construction comprises the following contents:
dividing standard data into a plurality of classes through dissimilarity calculation;
and selecting two classes with the smallest dissimilarity value from all the classes to train to construct a support vector machine, generating a non-leaf node, merging the two classes to form a new class, carrying out dissimilarity calculation on the new class and the rest classes again, selecting the two classes with the smallest dissimilarity value again to train to construct another support vector machine, generating another non-leaf node, and continuously repeating the steps until all the classes are traversed, thus completing the construction of a decision tree based on the support vector machine.
Obviously, through the scheme, the whole training process is realized, the constructed decision tree is an optimal binary tree between one-to-one and one-to-other multi-classifier, the execution efficiency is high, the prediction result is accurate, and the cost is low.
Specifically, a decision tree is constructed according to standard data, and then running situation early warning is carried out on municipal facility equipment, wherein the decision tree construction comprises the following contents:
dividing standard data into N classes through dissimilarity calculation to obtain an initial set F= { N1, N2, …, nn } with N binary trees;
calculating the dissimilarity between classes corresponding to each tree in the initial set F, and constructing a dissimilarity matrix; according to the dissimilarity matrix, selecting two subtrees corresponding to the class with the smallest dissimilarity value as left and right subtrees of the newly constructed binary tree, and training the class corresponding to the left and right subtrees to obtain a support vector machine; deleting the two subtrees from the set F, and adding a new binary tree into the set F; the steps are repeated until there is only one binary tree in set F.
Obviously, by constructing the decision tree from bottom to top, each class has only one classification path, so that the training time is reduced, the time cost is saved, and the accuracy, the rapidness and the reliability of the prediction result of the decision tree can be ensured.
In some embodiments, a decision tree is constructed according to standard data, and then running situation pre-warning is performed on municipal facility equipment, including the following contents:
and screening early warning indexes, constructing a decision tree according to standard data, and further carrying out running situation early warning on municipal facility equipment.
Specifically, the specific steps of the operation situation pre-warning include:
acquiring all information transmission paths of the equipment, and acquiring path data corresponding to each information transmission path, wherein the path data comprises passing equipment information;
dividing the path data into a preset number of clusters by using a clustering algorithm according to the equipment information in the path data, wherein each cluster at least comprises one path data;
according to the equipment information of each equipment, the corresponding relation with the clusters and a preset security situation formula, calculating the equipment security situation value of each equipment, and carrying out weight summation to obtain the network security situation value of the network;
if the security situation value of the network exceeds a preset security threshold range, judging that the network has security risk;
the running situation early warning is obtained through the following formula:
wherein M is as described above i For the value of the device security situation of the device i, the class coefficient v of the device i i And the security value E of the cluster y y The method comprises the steps of obtaining the device, path data and a corresponding topological relation among clusters, and obtaining a legal access ratio of each device obtained in advance, wherein at least one path data comprising device information of the device i exists in the clusters y.
In this embodiment, the early warning indicators include an equipment hidden danger indicator, a production environment hidden danger indicator, a management hidden danger indicator, and a human behavior hidden danger indicator.
Based on the same inventive concept, the embodiment of the present application provides an intelligent management system for municipal equipment and facilities based on machine learning, as shown in fig. 2, including:
an acquisition module 21, configured to acquire each relevant data of municipal facility equipment as basic data;
a processing module 22, configured to process the basic data to obtain standard data;
the decision module 23 is configured to construct a decision tree according to the standard data, and further perform operational situation early warning on municipal facility equipment, where the decision tree construction includes:
dividing standard data into a plurality of classes through dissimilarity calculation;
and selecting two classes with the smallest dissimilarity value from all the classes to train to construct a support vector machine, generating a non-leaf node, merging the two classes to form a new class, carrying out dissimilarity calculation on the new class and the rest classes again, selecting the two classes with the smallest dissimilarity value again to train to construct another support vector machine, generating another non-leaf node, and continuously repeating the steps until all the classes are traversed, thus completing the construction of a decision tree based on the support vector machine.
Obviously, the scheme is characterized in that the big data analysis is performed based on all relevant data of municipal facility equipment, and the operation situation early warning is performed on the municipal facility equipment by constructing a decision tree, so that the abnormal condition of the facility equipment can be found in advance, the early warning is performed, the effective emergency response capability is achieved, the efficiency and the accuracy of urban facility management are remarkably improved, the flow and the decision of municipal management are optimized, and the city is more intelligent, efficient and beneficial.
Fig. 3 is a structural diagram of an electronic device provided in an embodiment of the present application, and as shown in the drawing, the electronic device 4 of the embodiment includes: at least one processor 40 (only one is shown in fig. 3), a memory 41 and a computer program 42 stored in the memory 41 and executable on the at least one processor 40, the processor 40 implementing the steps in any of the various method embodiments described above when executing the computer program 42.
The electronic device 4 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The electronic device 4 may include, but is not limited to, a processor 40, a memory 41. It will be appreciated by those skilled in the art that fig. 3 is merely an example of the electronic device 4 and is not meant to be limiting of the electronic device 4, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the electronic device may also include input-output devices, network access devices, buses, etc.
The processor 40 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the electronic device 4, such as a hard disk or a memory of the electronic device 4. The memory 41 may be an external storage device of the electronic device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 41 is used for storing the computer program and other programs and data required by the electronic device. The memory 41 may also be used for temporarily storing data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
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.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other manners. For example, the apparatus/electronic device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each method embodiment described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
The present application may implement all or part of the flow of the method of the above embodiments, and may also be implemented by a computer program product, which when run on an electronic device, causes the electronic device to execute the steps of the method embodiments described above.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (10)
1. An intelligent management method for municipal equipment facilities based on machine learning is characterized by comprising the following steps:
acquiring each related data of municipal facility equipment as basic data;
processing the basic data to obtain standard data;
constructing a decision tree according to the standard data, and further carrying out running situation early warning on municipal facility equipment, wherein the decision tree construction comprises the following steps:
dividing standard data into a plurality of classes through dissimilarity calculation;
and selecting two classes with the smallest dissimilarity value from all the classes to train to construct a support vector machine, generating a non-leaf node, merging the two classes to form a new class, carrying out dissimilarity calculation on the new class and the rest classes again, selecting the two classes with the smallest dissimilarity value again to train to construct another support vector machine, generating another non-leaf node, and continuously repeating the steps until all the classes are traversed, thus completing the construction of a decision tree based on the support vector machine.
2. The method of claim 1, wherein each related data includes attribute data, geographic location data, operational data, maintenance management data for municipal utility equipment.
3. The method of claim 1, wherein processing the base data to obtain standard data comprises:
and performing dimension reduction processing on the basic data to obtain standard data.
4. A method according to claim 3, wherein said performing a dimension reduction process on the base data to obtain standard data comprises:
forming N rows and M columns of matrix X from the basic data according to columns;
zero-equalizing each row of the matrix X;
calculating a covariance matrix C of the averaging matrix X;
obtaining the eigenvalue and corresponding eigenvector of covariance matrix C;
arranging the eigenvectors into a matrix according to the corresponding eigenvalue from top to bottom, and taking the first k rows to form a matrix P;
standard data y=px after the dimension reduction to k dimensions is calculated.
5. The method of claim 1, wherein processing the base data to obtain standard data comprises:
and smoothing the basic data to obtain standard data.
6. The method of claim 5, wherein the smoothing is performed by the formula:
v t ＝βv t-1 +(1-β)θ t
in the above, θ t The parameter value is obtained at the moment t; v t Is a smoothed value; beta is the weight parameter (0<β<1)。
7. The method according to claim 1, wherein the constructing a decision tree according to the standard data, and further performing operational situation pre-warning on municipal facility equipment, wherein the constructing the decision tree includes:
dividing standard data into N classes through dissimilarity calculation to obtain an initial set F= { N1, N2, …, nn } with N binary trees;
calculating the dissimilarity between classes corresponding to each tree in the initial set F, and constructing a dissimilarity matrix; according to the dissimilarity matrix, selecting two subtrees corresponding to the class with the smallest dissimilarity value as left and right subtrees of the newly constructed binary tree, and training the class corresponding to the left and right subtrees to obtain a support vector machine; deleting the two subtrees from the set F, and adding a new binary tree into the set F; the steps are repeated until there is only one binary tree in set F.
8. The method according to claim 1, wherein the constructing a decision tree according to the standard data, and further performing operational situation pre-warning on municipal facility equipment, comprises:
and screening early warning indexes, constructing a decision tree according to standard data, and further carrying out running situation early warning on municipal facility equipment.
9. The method of claim 8, wherein the pre-warning indicator comprises an equipment hazard indicator, a production environment hazard indicator, a management hazard indicator, and a human behavior hazard indicator.
10. A machine learning-based intelligent management system for municipal equipment facilities, the system comprising:
the acquisition module is used for acquiring all relevant data of municipal facility equipment as basic data;
the processing module is used for processing the basic data to obtain standard data;
the decision module is used for constructing a decision tree according to the standard data, and further carrying out running situation early warning on municipal facility equipment, wherein the decision tree construction comprises the following steps:
dividing standard data into a plurality of classes through dissimilarity calculation;
and selecting two classes with the smallest dissimilarity value from all the classes to train to construct a support vector machine, generating a non-leaf node, merging the two classes to form a new class, carrying out dissimilarity calculation on the new class and the rest classes again, selecting the two classes with the smallest dissimilarity value again to train to construct another support vector machine, generating another non-leaf node, and continuously repeating the steps until all the classes are traversed, thus completing the construction of a decision tree based on the support vector machine.
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