CN114900811A - Intelligent monitoring system for snow melting and ice melting of roads and bridges - Google Patents

Abstract

The invention provides an intelligent monitoring system for snow and ice melting of roads and bridges. The device monitoring module and the data acquisition module are in wired connection with the wireless communication module, the wireless communication module is connected with the cloud through the wireless transmission device, the device monitoring module is in wired connection with the snow and ice melting device, and therefore data transmission, analysis calculation and storage are achieved, and intelligent control over the snow and ice melting device is achieved through an ANP network analysis method. The front end is connected with the cloud end through a wireless network, and remote monitoring and management of the equipment can be achieved.

Description

An intelligent monitoring system for melting snow and ice on roads and bridges

technical field

The invention belongs to the field of intelligent construction, and relates to a technology for melting snow and ice for roads and bridges, in particular to an intelligent monitoring system for melting snow and ice for roads and bridges.

Background technique

As a kind of smart city construction, intelligent monitoring system has been applied to various fields. For example, in the construction of the national-level vehicle networking pilot area in Xiqing District, Tianjin, the intelligent network connection system is used to exchange information with the surrounding environment, realize interaction and interconnection, and build a more dimensional intelligent transportation system. In the Xiangyang and Shenzhen Pingshan IoV projects, the vehicle-road collaborative edge computing platform, autonomous driving cloud platform and traffic cloud control platform have significantly improved traffic travel efficiency, optimized travel experience, and effectively improved driving safety. The invention applies the intelligent monitoring system to the monitoring and management projects of melting snow and ice of roads and bridges. An intelligent monitoring system for melting snow and ice on roads and bridges is designed.

SUMMARY OF THE INVENTION

The purpose of the present invention is to intelligently monitor and manage the intelligent control of snow-melting and ice-melting equipment and whether it is in normal operation through an intelligent monitoring system for snow-melting and ice-melting of roads and bridges.

In order to solve the above-mentioned technical problems, the patent of the present invention adopts the following technical solutions:

An intelligent monitoring system for snow-melting and ice-melting of roads and bridges is characterized in that: it includes an equipment monitoring module, a data acquisition module, a wireless communication module, a cloud, and a snow-melting and ice-melting device with multiple lines;

The equipment monitoring module is used to monitor the running state parameters of the snow melting and ice melting equipment in real time, and upload the data to the cloud through the wireless communication module, and receive and execute the control instructions for the snow melting and ice melting equipment issued by the cloud;

The data collection module is used for real-time collection of environmental parameters that affect road and bridge icing, and uploads the data to the cloud through the wireless communication module; the environmental parameters include temperature, humidity, snowfall, wind speed, atmospheric pressure, water vapor pressure, PM2 .5 and PM10;

The cloud is composed of a server and a database. The server pre-stores a road icing prediction and analysis algorithm based on ANP network analysis method to analyze and calculate whether icing is icing and to issue control instructions to the equipment monitoring module, and the database is used to store data.

Further, the intelligent monitoring system further includes a front end, and the front end is a handheld terminal connected to the cloud through a wireless network, and is used for remote monitoring and management of the snow melting and ice melting equipment.

Further, the equipment monitoring module includes a current monitor, a voltage monitor, an electric energy monitor and an equipment controller, and is used for real-time monitoring of the current, voltage and electric energy of multiple lines of the snow melting and ice melting equipment and control of equipment start and stop. , and transmit the monitoring data to the cloud through the wireless communication module and receive the control instructions from the cloud.

Further, the data acquisition module includes a temperature sensor, a humidity sensor, a snowfall sensor, a wind speed sensor, an atmospheric pressure sensor, a water vapor pressure sensor, an air quality sensor, and a data acquisition card connected with each sensor, and the data acquisition card is connected with the wireless communication module. .

Further, the road icing prediction analysis algorithm based on ANP network analysis method is as follows:

Set initial thresholds for each environmental parameter and each line operating state parameter of the snow melting and ice melting equipment;

For the pavement state, the initial threshold is used to determine whether the pavement state is icy under the condition of single factor; then the global weight of each environmental parameter under the condition of multiple factors is calculated by the ANP network analysis method, combined with the judgment result under the condition of single factor and the multi-factor condition The global weight under the condition comprehensively judges whether the road surface is icy or not, and the factor is the environmental parameter;

For the operating state of the snow melting and ice melting equipment, if the operating state parameters measured by the equipment monitoring module are not equal to the initial threshold, it means that the equipment is faulty. At this time, regardless of whether the road surface is icy or not, the equipment monitoring module will be issued to shut down the snow melting and ice melting equipment. instruction.

Further, the specific method of the global weight of each environmental parameter under the condition of multiple factors by the ANP network analysis method is as follows:

The first constructed ANP network structure model is divided into two parts: the control layer and the network layer. The control layer is further divided into the target layer and the criterion layer. The target layer is to turn on/off the snow melting and ice melting equipment, and the criterion layer is the road surface state. The network layer It is composed of environment parameters, and each parameter of the environment parameters is defined as an element of the network layer;

For the criterion layer, the weight is calculated by the AHP analytic hierarchy process; for the network layer, the local weight of each influencing factor under multiple criteria needs to be calculated by means of a weighted supermatrix, and the final global weight is obtained by multiplying the criterion layer weight and the local weight of the network layer. Weights.

Further, the method for comprehensively judging whether the road surface is icy is as follows:

For the result of whether the road surface is icy under the single factor condition, the icy road surface state is recorded as +T, the non-icy road surface state is recorded as -T, and T is any positive number;

Under the condition of multiple factors, multiply each factor by the corresponding global weight and add them to obtain the comprehensive index K; if the comprehensive index K is greater than 0, the road surface state is icy; if K is less than 0, the road surface state is not icing; When K is equal to 0, the road surface state is the icing critical state.

Further, the weighted supermatrix is obtained in the following manner:

S1, group the environmental parameters according to the element category, and obtain several element groups,

S2, then compare the importance of each influencing factor within the element group and between the element groups to obtain a judgment matrix D _ij ;

S3, calculate the relative importance M of each row of the judgment matrix by the square root method;

S4, normalize the obtained relative importance M to obtain the eigenvalue w of each row, and finally obtain the eigenvector D ^w _ij of each judgment matrix when D _j is the secondary criterion, where i represents the ith element group, j Represents the jth element as the secondary criterion;

S5. According to the relative importance between elements, combine the eigenvectors of each judgment matrix into an unweighted supermatrix W

n represents the number of element groups, and Wik (i,k= _1,2..n ) represents the elements in the i-th element group to compare the relative importance of the elements in the k-th element group as the secondary criterion, By forming an unweighted supermatrix W through _Wik ;

S6, taking the road surface state as the main criterion, constructing a judgment matrix with each element group as the sub-criteria, comparing the relative importance of each element group, calculating the combination of its eigenvalues a _ij , and obtaining a weighting matrix A;

a _ij represents the eigenvalue obtained by comparing the relative importance between the i-th element group and other element groups when the j-th element group is the secondary criterion, and n is the order of the matrix, that is, the number of groups of element groups ;

S7, the weighted supermatrix W'=AW, the limit supermatrix, that is, when the infinite power of W' exists, each column of the supermatrix will tend to be the same vector, and the column vector of the supermatrix at this time is is the local weight of each element under the road condition criterion; the formula of the limit supermatrix is:

Compared with the prior art, the beneficial effects of the patent of the present invention are:

(1) The present invention establishes an intelligent monitoring system for melting snow and ice on roads and bridges. The system transmits the obtained data to the cloud through the equipment monitoring module and the data acquisition module, and then the cloud first checks the single factor situation through the designed initial threshold. Then, the ANP network analysis method is used to make decisions on the start and stop of equipment under multi-factor conditions and to issue corresponding instructions to the equipment monitoring module. This method takes into account each factor or the difference between adjacent levels. Mutual influence, can be used to solve a variety of quantitative and non-quantitative, rational and irrational decision-making problems.

(2) Compared with other icing identification methods, the ANP network analysis method used in the present invention calculates the weight of each influencing factor through an empirical method, and does not need to collect a large number of samples to establish a simulation model, which is simple and efficient, and the judgment matrix can be changed. Constantly revised according to the judgment results.

(3) Compared with the previous control system, the present invention can not only realize the intelligent start-stop function of the equipment, but also monitor the environmental parameters of the road surface and various data of the equipment in real time, and diagnose and troubleshoot the faults of the equipment. The device can automatically disconnect the line. Secondly, the monitored data can be transmitted to the front end through the cloud. If the road surface reaches the critical point of freezing or the equipment fails, the control system will send information to the front end to warn in advance. Through this function, managers can be reminded to check various environmental parameters of the road surface and the operation of equipment and repair them in time.

Description of drawings

FIG. 1 is a schematic structural diagram of an intelligent monitoring system according to an embodiment of the present invention.

FIG. 2 is an internal structure diagram of a device monitoring module according to an embodiment of the present invention.

FIG. 3 is an internal structure diagram of a data acquisition module according to an embodiment of the present invention.

FIG. 4 is a diagram of an internal structure of a cloud according to an embodiment of the patent of the present invention.

FIG. 5 is a flow chart of the intelligent control of the embodiment of the patent of the present invention.

FIG. 6 is a flow chart of the ANP network analysis method according to the embodiment of the patent of the present invention.

FIG. 7 is a structural model diagram of an ANP network analysis method according to an embodiment of the present invention.

In the picture: 1-equipment monitoring module, 2-data acquisition module, 3-wireless communication module, 4-cloud, 5-front-end, 6-snow and ice-melting equipment, 7-temperature sensor, 8-humidity sensor, 9-snowfall sensor , 10-wind speed sensor, 11-barometric pressure sensor, 12-water vapor pressure sensor, 13-air quality sensor, 14-current monitor, 15-voltage monitor, 16-energy monitor, 17-server, 18-database, 19- device controller, 20- data acquisition card.

Detailed ways

The patent of the present invention will be described in detail below in conjunction with the drawings and embodiments. The embodiments shown in the drawings are only used to explain the patent of the present invention, but not to limit the patent of the present invention.

As shown in Figures 1-7, the patent of the present invention is an intelligent monitoring system for snow melting and ice melting for roads and bridges, including equipment monitoring module 1, data acquisition module 2, wireless communication module 3, cloud 4, front end 5, snow melting Ice equipment 6 consists of six parts. The equipment monitoring module 1, the data acquisition module 2 and the wireless communication module 3 are connected through RVV wires, the wireless communication module 3 and the cloud 4 are wirelessly connected through a communication protocol, and the equipment monitoring module 1 and the snow melting and ice melting equipment 6 are connected through the BVR The cables are connected to form a closed loop to realize intelligent control. The front end 5 and the cloud 4 are wirelessly connected through a communication protocol.

The equipment monitoring module 1 includes a current monitor 14, a voltage monitor 15, an electric energy monitor 16 and an equipment controller 19. The monitors can perform real-time monitoring and fault diagnosis on the current, voltage and electric energy of the six lines of the snow melting and ice melting equipment. , and transmit diagnostic data to the cloud4.

The data acquisition module 2 uses a temperature sensor 7, a humidity sensor 8, a snowfall sensor 9, a wind speed sensor 10, an atmospheric pressure sensor 11, a water vapor pressure sensor 12, an air quality sensor 13 and a data acquisition card 20 connected to each sensor. Data collection and real-time monitoring of various environmental parameters affecting icing are carried out, and these data are transmitted to the cloud4.

The wireless communication module 3 is a 4g communication device for data transmission.

The cloud 4 includes a server 17 and a database 18 . The server 17 is used to parse and calculate the data, and the database 18 is used to store the data.

The front end 5 is a handheld terminal, such as a mobile phone, a computer, a tablet, or any other device that can be connected to the Internet. It is used to view various data of the snow melting and ice melting equipment 6 and control it remotely.

The equipment monitoring module 1 and the data acquisition module 2 transmit the obtained data to the wireless communication module 3 through the MODBUS communication protocol, and then the wireless communication module 3 transmits the obtained data to the cloud 4 through the TCP/IP communication protocol.

As a preferred embodiment, the equipment monitoring module 1 receives and executes the instructions issued by the cloud 4 through the wireless communication module, thereby realizing the intelligent control of the snow melting and ice melting equipment.

As a preferred embodiment, the front end 5 establishes a remote connection with the cloud 4 through the HTTP communication protocol, and calls the data in the database through the remote connection, thereby realizing remote monitoring and management.

As a preferred embodiment, the front end 5 can display various environmental parameters of the road surface, as well as a number of data such as current, voltage, electrical energy, start-stop status, and running status of the equipment.

The cloud 4 first designed a set of initial thresholds for temperature (0°C), humidity (80%), snowfall (0.25mm), wind speed (2m/s), atmospheric pressure (101.3Kpa), and water vapor pressure (0.1Kpa). , PM2.5 (25.0), PM10 (37.0), current (100A), voltage (230V), electric energy (23kw.h), as a preliminary judgment on whether the road surface is icing and whether the snow melting and ice melting equipment is operating under single factor conditions. normal basis.

For the pavement state, first determine whether the pavement state is icy under the condition of single factor through the initial threshold, and then use the ANP network analysis method to calculate the global weight of each environmental parameter under the condition of multiple factors, and combine the judgment results under the condition of single factor and multiple factors. The global weight under the condition comprehensively judges whether the road surface is icy or not, and the factor is the environmental parameter;

For the operating status of the snow melting and ice melting equipment, if the current, voltage and electric energy measured by the monitor are not equal to the initial thresholds, it means that the equipment is faulty. Device 6 instructions. The equipment monitoring module 1 receives and executes the instructions issued by the cloud 4 through the wireless communication module 3 , so as to realize the intelligent control of the snow melting and ice melting equipment 6 .

As a preferred embodiment, the comprehensive judgment method is:

For the result of whether the road surface is icy under the single factor condition, the icy road surface state is recorded as +T, the non-icy road surface state is recorded as -T, and T is any positive number;

Under the condition of multiple factors, multiply each factor by the corresponding global weight and add them to obtain the comprehensive index K; if the comprehensive index K is greater than 0, the road surface state is icy; if K is less than 0, the road surface state is not icing; When K is equal to 0, the road surface state is the icing critical state.

As a preferred embodiment, the ANP network analysis method is to make decisions on the target under the influence of multiple factors. The first constructed ANP network structure model is divided into two parts: control layer and network layer, in which the control layer is further divided into target layer and criterion layer. For the criterion layer, its weight can be calculated by the AHP analytic hierarchy process. For the network layer, it is necessary to calculate the local weight of each influencing factor under multiple criteria by means of a weighted supermatrix. The final global weight can be obtained by multiplying the criterion layer weight and the local weight of the network layer, and finally the influencing factors are sorted.

The following takes specific data as an example to illustrate the judgment process of the present invention.

In this embodiment, the target layer is to turn on/off the snow melting and ice melting equipment (A), and the criterion layer only considers the road surface state (B1). Because there is only one factor in the criterion layer, the AHP can not be used to calculate the weight.

The network layer is composed of a plurality of element groups, and the element group contains a plurality of elements, and the elements are interconnected and interdependent. Dependency and feedback relationships also exist between groups of elements. The first element group of the network layer in this embodiment is the weather influencing factor (C1), the second element group is the atmospheric influencing factor (C2), and the third element group is the air quality influencing factor (C3). The elements in element group one are temperature (D1), humidity (D2), snowfall (D3), and wind speed (D4). The elements in element group two are atmospheric pressure (D5) and water vapor pressure (D6). The elements in element group three are PM2.5 (D7) and PM10 (D8). Then through the form of expert survey and group discussion to study the relationship between the influencing factors or according to the experience value, give the degree of mutual influence between the elements, and make a table of influencing factors. The final ANP model is shown in Figure 7.

The calculation method of the local weight of each influencing factor under the multi-criteria is as follows:

In this embodiment, the unweighted supermatrix W and the weighted matrix A are first established based on the road state (B1), and then the unweighted supermatrix W and the weighted matrix A are multiplied to obtain the weighted supermatrix W'. Finally, by calculating the limit supermatrix W' ^∞ , the local weight of each element under the criterion of road surface state (B1) can be obtained.

The unweighted super-matrix W is to establish an unweighted super-matrix that takes the road state (B1) as the main criterion, one element is the sub-criteria, and compares the relative importance of other elements. First, the importance of each influencing factor within and between groups should be compared to obtain a judgment matrix.

When D1, D2, D3, and D4 in C1 take D _j (j=1, 2, .

When D5 and D6 in C2 take D _j (j=1, 2, .

When D7 and D8 in C3 take D _j (j=1, 2, . . . , 8) as the secondary criterion, eight 2×2 initial judgment matrices D can be obtained.

The initial judgment matrix D can calculate the relative importance M _i of each row through the square root method, and after normalizing it, the eigenvector w _i of each matrix can be obtained, and the judgment matrix with the temperature in the element group 1 as the secondary criterion For example, the judgment matrix and eigenvector w are shown in Table 1:

The initial judgment matrix D ₁₁ with temperature as the criterion in the first element group of Table 1

In the table is the relative importance between two elements, the relative importance M _i of each row of the judgment matrix is calculated by the square root method; the obtained relative importance M _i is normalized to obtain the eigenvalue _wi of each row,

The square root method is to calculate the n-th root of the product of the elements of each row of the judgment matrix to obtain the relative importance M _i (i=1, 2....n) of each row, and the relative importance M _i (i=1 , 2....n) to form a relative importance vector M, where n is the matrix level. Then, the obtained relative importance M is normalized to obtain the eigenvalues _wi (i=1, 2....n) of each row, that is, the weight, and the eigenvalues _wi of each row form an eigenvalue vector w. Finally, the maximum eigenvalue λ _max is calculated. The formulas of M, w, and λ _max are shown in formulas (1), (2), and (3), respectively. In formula (3), _wi is the eigenvalue of the ith row relative importance M _i normalized.

d _ij is the element in the i-th row and the j-th column in the initial judgment matrix D ₁₁ , n is the order of the initial judgment matrix D ₁₁ , and n=4 for D ₁₁ .

For example, for the first row,

M _i is the relative importance of the i-th row in the judgment matrix D.

For example, for the first row,

Then, the maximum eigenvalue is calculated by formula (3) and (4) and the consistency test is carried out. The final result obtained is CR<0.1, so it meets the requirements.

In the above formula, Si is the ith row value of the test vector S, and the test vector is equal to the initial judgment matrix D multiplied by the eigenvalue vector w, that is, S=Dw.

In the consistency check, first, the consistency ratio CR=CI/RI is calculated. When CR<0.1, the requirements are met, and when CR>0.1, the judgment matrix needs to be readjusted and corrected. Among them, RI can be obtained by looking up the table,

得到以元素组一中温度为次准则的判断矩阵为例所得到的特征向量D^w ₁₁＝w，即：The eigenvalue vector w of the initial judgment matrix D ₁₁ is used as the judgment matrix

The eigenvector D ^w ₁₁ =w obtained by taking the judgment matrix with temperature as the secondary criterion in element group 1 as an example is obtained, namely:

Similarly, the judgment matrix between elements in other groups can be calculated by this method. Finally, the eigenvector of each judgment matrix can be obtained when D _j is the secondary criterion, that is, the weight value D ^w _ij , where i represents the i-th (i=1, 2, 3) element group, and j represents the j-th (j=1) element group. ,2,...,8) elements are secondary criteria.

和

The judgment matrix with each element in element group 1 as the sub-criteria is as follows:

and

和

The judgment matrix with its elements in element group 2 as the secondary criterion is as follows:

and

和

The judgment matrix with its elements in element group 3 as the secondary criterion is as follows:

and

The above judgment matrix is formed into an unweighted supermatrix W, and the composition method is as follows:

W _ij (i, j=1, 2, 3) represents that the elements in the i-th element group are compared with the elements in the j-th element group as the sub-criteria to compare their relative importance. The unweighted _supermatrix W is formed by Wij.

The weighting matrix A is based on the road state (B1) as the main criterion, and each element group is used as the sub-criteria to construct an initial judgment matrix, compare the relative importance of each element group, and calculate the combination of its eigenvalues a _ij made. a _ij represents the eigenvalue a obtained by comparing the relative importance between the i-th element group and other element groups when the j-th element group is the secondary criterion. The element groups in this example are weather influencing factors (C1), atmospheric influencing factors (C2), and air quality influencing factors (C3). The relative importance of each pair is compared, and the eigenvalues a ₁₁ , a ₂₁ , and a ₃₁ are obtained by the square root method. As shown in Table 2 below. In the same way, the eigenvalues of the judgment matrix between each element group are calculated with C2 and C3 as the sub-criteria respectively, and the weighting matrix A is finally obtained.

Table 2 Initial judgment matrix between element groups when weather influencing factors are secondary criteria

weather C1 C2 C3 M a C1 1 3 5 2.466212074 0.637 C2 1/3 1 3 1 0.258 C3 1/5 1/3 1 0.405480133 0.105

a ₁₁ =0.637

a ₂₁ =0.258

a ₃₁ = 0.105

In the same way, by constructing the initial judgment matrix between the element groups when the atmospheric influence factor is the secondary criterion, and calculating the eigenvalues a ₁₂ , a ₂₂ , and a ₃₂ .

By constructing the initial judgment matrix between element groups with air quality influencing factors as the secondary criterion, and calculating the eigenvalues a ₁₃ , a ₂₃ , a ₃₃ .

Combining the weighting matrix A is as follows:

The weighted supermatrix W'=aW, that is:

The limit supermatrix, that is, when the infinite power of W' exists, each column of the supermatrix will tend to be the same vector, and the column vector of the supermatrix is the local part of each element under the road condition criterion. Weights. The formula for this limit supermatrix is:

The local weights of each influencing element in this example are temperature (0.23), humidity (0.204), snowfall (0.144), wind speed (0.109), atmospheric pressure (0.143), water vapor pressure (0.065), PM2.5 (0.052), PM10 (0.053).

The global weight is obtained by multiplying the local weight of each influencing element of the network layer with the weight of the corresponding criterion layer. In this embodiment, the criterion layer only has one item of road surface state, so the weight of the criterion layer is (1.0), and the global weight of each influencing element is the same as the local weight.

The decision-making process, that is, programming through Python, considers the judgment value of each influencing factor as icing under single-factor conditions as "1"; the judgment value of no icing is regarded as "-1", and then passes Multiplying the judgment value of , and the global weight of the corresponding influencing factors obtained under the multi-factor condition after calculation by the ANP network analysis method, the judgment value of each influencing factor under the multi-factor condition can be obtained, and adding these judgment values can make multiple influence factors The judgment of whether the road surface state is icy under the factor, if the result > 0, it means that the road surface state is "icy"; if the result < 0, it means that the road surface state is "not icy". In this embodiment, the initial thresholds of each influencing factor are set as temperature (0°C), humidity (80%), snowfall (0.25mm), wind speed (2m/s), atmospheric pressure (101.3Kpa), and water vapor pressure (0.1Kpa). ), PM2.5 (25.0), PM10 (37.0). Now take two groups of examples to verify the ANP network analysis method, as shown in the following table

Table 3 Decision table

The results obtained by ANP network analysis are the same as those of the two sets of examples. Therefore, the network analysis method can better reflect the complex internal relationship between the influencing factors in the actual situation.

The front end 5 establishes a remote connection with the cloud 4 through the HTTP communication protocol. Through the remote connection, the front end 5 can call the data of the database 18 to check the current, voltage, electrical energy, device switch and whether the device is running normally. , and remote monitoring and management of snow melting and ice melting equipment 6.

It should be noted that all the above-mentioned initial judgment matrices of the present invention are determined by empirical values, or obtained through historical data analysis, and are adopted after passing the consistency check. Verification), in turn, revise the initial matrix to continuously improve and improve the prediction accuracy.

In the description of the present invention, it should be understood that all terms described in the present invention have the same meaning as commonly understood by those skilled in the technical field of the present invention, and are only used for the convenience of describing the embodiments described in the patent of the present invention, and cannot The invention is limited.

It should be understood that, in addition to the sensors and monitors used in this embodiment, other environmental parameter sensors that affect icing and any monitor that can monitor whether the equipment is faulty may also be used. In addition to the above 8 kinds of influence elements, the influence elements taken in the ANP network analysis method in this embodiment may also be any other factors that influence road icing.

It should be understood that the limit supermatrix of the ANP network analysis method in this embodiment needs to be calculated by PYTHON.

It should be understood that, in addition to the 4g communication used by the wireless communication module 3 in this embodiment, other wireless communication methods can also be used, which cannot limit the present invention.

The above-mentioned embodiments and examples are only for a more detailed and specific understanding of the present invention, and should not be construed as a limitation on the patent of the present invention. It should be pointed out that for those of ordinary skill in the art, any modifications, variations, combinations or equivalent replacements made to the description and drawings of the present invention are included in the scope of patent protection of the present invention.

Claims (9)

1. an intelligent monitoring system for melting snow and melting ice of roads and bridges, it is characterized in that: comprise equipment monitoring module, data acquisition module, wireless communication module, cloud and the snow melting and ice melting equipment with multiple lines; The equipment monitoring module is used to monitor the running state parameters of the snow melting and ice melting equipment in real time, and upload the data to the cloud through the wireless communication module, and receive and execute the control instructions for the snow melting and ice melting equipment issued by the cloud; The data collection module is used for real-time collection of environmental parameters that affect road and bridge icing, and uploads the data to the cloud through the wireless communication module; the environmental parameters include temperature, humidity, snowfall, wind speed, atmospheric pressure, water vapor pressure, PM2 .5 and PM10; The cloud is composed of a server and a database. The server pre-stores a road icing prediction and analysis algorithm based on ANP network analysis method to analyze and calculate whether icing is icing and to issue control instructions to the equipment monitoring module, and the database is used to store data. 2. The intelligent monitoring system for snow melting and ice melting for roads and bridges according to claim 1, characterized in that: it further comprises a front end, and the front end is a handheld terminal connected to the cloud through a wireless network, and is used for performing the snow melting and ice melting equipment. Remote monitoring and management. 3. The intelligent monitoring system for snow melting and ice melting for roads and bridges according to claim 1, wherein the equipment monitoring module comprises a current monitor, a voltage monitor, an electric energy monitor and a device controller, and is used for snow melting The current, voltage and electric energy of multiple lines of the ice melting equipment are monitored in real time and the start and stop of the equipment is controlled, and the monitoring data is transmitted to the cloud through the wireless communication module and received control instructions from the cloud. 4. The intelligent monitoring system for melting snow and ice of roads and bridges according to claim 1, wherein the data acquisition module comprises a temperature sensor, a humidity sensor, a snowfall sensor, a wind speed sensor, an atmospheric pressure sensor, a water vapor pressure sensor and a The air quality sensor and the data acquisition card connected with each sensor are connected with the wireless communication module. 5 . The intelligent monitoring system for melting snow and ice for roads and bridges according to claim 4 , wherein the data acquisition module and the equipment monitoring module transmit data to the wireless communication module through the MODBUS communication protocol. 6 . 6. The intelligent monitoring system for road and bridge melting snow melting according to any one of claims 1-5, characterized in that: the road icing prediction analysis algorithm based on ANP network analysis method is specifically as follows: Set initial thresholds for each environmental parameter and each line operating state parameter of the snow melting and ice melting equipment; For the pavement state, the initial threshold is used to determine whether the pavement state is icy under the condition of single factor; then the global weight of each environmental parameter under the condition of multiple factors is calculated by the ANP network analysis method, combined with the judgment result under the condition of single factor and the multi-factor condition The global weight under the condition comprehensively judges whether the road surface is icy or not, and the factor is the environmental parameter; For the operating state of the snow melting and ice melting equipment, if the operating state parameters measured by the equipment monitoring module are not equal to the initial threshold, it means that the equipment is faulty. At this time, regardless of whether the road surface is icy or not, the equipment monitoring module will be issued to shut down the snow melting and ice melting equipment. instruction. 7. the intelligent monitoring system for road and bridge melting snow and ice melting according to claim 6, is characterized in that: the concrete manner of the global weight of each environmental parameter under the condition of multiple factors by ANP network analysis method is as follows: The first constructed ANP network structure model is divided into two parts: the control layer and the network layer. The control layer is further divided into the target layer and the criterion layer. The target layer is to turn on/off the snow melting and ice melting equipment, and the criterion layer is the road surface state. The network layer It is composed of environment parameters, and each parameter of the environment parameters is defined as an element of the network layer; For the criterion layer, the weight is calculated by the AHP analytic hierarchy process; for the network layer, the local weight of each influencing factor under multiple criteria needs to be calculated by means of a weighted supermatrix, and the final global weight is obtained by multiplying the criterion layer weight and the local weight of the network layer. Weights. 8. The intelligent monitoring system for melting snow and icing of roads and bridges according to claim 6, is characterized in that: the method for comprehensively judging whether the road surface state is icing is as follows: For the result of whether the road surface is icy under the single factor condition, the icy road surface state is recorded as +T, the non-icy road surface state is recorded as -T, and T is any positive number; Under the condition of multiple factors, multiply each factor by the corresponding global weight and add them to obtain the comprehensive index K; if the comprehensive index K is greater than 0, the road surface state is icy; if K is less than 0, the road surface state is not icing; When K is equal to 0, the road surface state is the icing critical state. 9. the intelligent monitoring system for road and bridge melting snow and ice melting according to claim 7, is characterized in that: the weighted supermatrix obtaining mode is as follows: S1, group the environmental parameters according to the element category to obtain several element groups, S2, then compare the importance of each influencing factor within the element group and between the element groups to obtain a judgment matrix D _ij ; S3, calculate the relative importance M of each row of the judgment matrix by the square root method; S4, normalize the obtained relative importance M to obtain the eigenvalue w of each row, and finally obtain the eigenvector D ^w _ij of each judgment matrix when D _j is the secondary criterion, where i represents the ith element group, j Represents the jth element as the secondary criterion; S5. According to the relative importance between elements, combine the eigenvectors of each judgment matrix into an unweighted supermatrix W

n represents the number of element groups, and Wik (i,k= _1,2..n ) represents the elements in the i-th element group to compare the relative importance of the elements in the k-th element group as the secondary criterion, By forming an unweighted supermatrix W through _Wik ; S6, taking the road surface state as the main criterion, constructing a judgment matrix with each element group as the sub-criteria, comparing the relative importance of each element group, calculating the combination of its eigenvalues a _ij , and obtaining a weighting matrix A;

a _ij represents the eigenvalue obtained by comparing the relative importance between the i-th element group and other element groups when the j-th element group is the secondary criterion, and n is the order of the matrix, that is, the number of groups of element groups ; S7, the weighted supermatrix W'=AW, the limit supermatrix, that is, when the infinite power of W' exists, each column of the supermatrix will tend to be the same vector, and the column vector of the supermatrix at this time is is the local weight of each element under the road condition criterion; the formula of the limit supermatrix is:

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