CN114900811B - An intelligent monitoring system for road and bridge snow and ice melting - Google Patents

Abstract

The present invention provides an intelligent monitoring system for snow and ice melting of roads and bridges, which includes an equipment monitoring module, a data acquisition module, a wireless communication module, a cloud, a front end, and snow and ice melting equipment. The equipment monitoring module and the data acquisition module are connected to the wireless communication module by wire, the wireless communication module is connected to the cloud by a wireless transmission device, and the equipment monitoring module is connected to the snow and ice melting equipment by wire, so as to realize data transmission, analytical calculation, and storage, and then realize intelligent control of the snow and ice melting equipment by ANP network analysis method. The front end is connected to the cloud by a wireless network, so as to realize remote monitoring and management of the equipment.

Description

An intelligent monitoring system for road and bridge snow and ice melting

Technical Field

The present invention belongs to the field of smart construction, and relates to a road and bridge snow and ice melting technology, and specifically to an intelligent monitoring system for road and bridge snow and ice melting.

Background Art

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

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 operating normally through an intelligent monitoring system for road and bridge snow-melting and ice-melting.

In order to solve the above technical problems, the present invention adopts the following technical solutions:

An intelligent monitoring system for snow and ice melting on roads and bridges, characterized by comprising: an equipment monitoring module, a data acquisition module, a wireless communication module, a cloud, and snow and ice melting equipment with multiple lines;

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

The data acquisition module is used to collect environmental parameters that affect road and bridge icing in real time, and upload 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 consists of a server and a database. The server pre-stores a road icing prediction and analysis algorithm based on the ANP network analysis method to analyze and calculate whether there is ice and issue control instructions to the equipment monitoring module. The database is used to store data.

Furthermore, the intelligent monitoring system also includes a front end, which is a handheld terminal connected to the cloud via a wireless network and is used to remotely monitor and manage the snow-melting and ice-melting equipment.

Furthermore, the equipment monitoring module includes a current monitor, a voltage monitor, a power monitor and an equipment controller, which are used to perform real-time monitoring of the current, voltage and power of multiple lines of the snow and ice melting equipment and control the start and stop of the equipment, and transmit the monitoring data to the cloud through the wireless communication module and receive control instructions from the cloud.

Furthermore, 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 and an air quality sensor, and a data acquisition card connected to each sensor, and the data acquisition card is connected to the wireless communication module.

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

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

For the road surface condition, firstly, the initial threshold is used to judge whether the road surface condition is icy under the single factor condition; then the global weight of each environmental parameter under the multi-factor condition is analyzed by the ANP network analysis method, and the judgment result under the single factor condition and the global weight under the multi-factor condition are combined to comprehensively judge whether the road surface condition is icy, wherein the factor is the environmental parameter;

Regarding the operating status of the snow-melting and ice-melting equipment, if the operating status parameter measured by the equipment monitoring module is not equal to the initial threshold, it indicates that the equipment is faulty. At this time, regardless of whether the road surface is icy or not, the equipment monitoring module will be instructed to shut down the snow-melting and ice-melting equipment.

Furthermore, the specific method of using the ANP network analysis method to analyze the global weight of each environmental parameter under multiple factors is as follows:

Firstly, the ANP network structure model is divided into two parts: control layer and network layer. The control layer is further divided into target layer and 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 status. The network layer is composed of environmental parameters, and each parameter of the environmental parameters is defined as an element of the network layer.

For the criterion layer, the AHP hierarchy analysis method is used to calculate its weight; for the network layer, the local weight of each influencing factor under multiple criteria needs to be calculated through the weighted super matrix, and the final global weight is obtained by multiplying the criterion layer weight and the local weight of the network layer.

Furthermore, 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, and the non-icy road surface state is recorded as -T, where T is an arbitrary positive number;

Under multi-factor conditions, each factor is multiplied by the corresponding global weight and added 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 icy; if K is equal to 0, the road surface state is in the critical state of icing.

Furthermore, the weighted super matrix is obtained as follows:

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

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

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

S4, normalizing the obtained relative importance M to obtain the eigenvalue w of each row, and finally obtaining the eigenvector D ^w _ij of each judgment matrix when D _j is used as the sub-criterion, where i represents the i-th element group, and j represents the j-th element as the sub-criterion;

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

n represents the number of element groups, _Wik (i,k=1,2..n) represents the comparison of the relative importance of the elements in the i-th element group with the elements in the k-th element group as the secondary criterion, and the unweighted super matrix W is formed by _Wik ;

S6, taking the road surface condition as the main criterion and each element group as the secondary criterion to construct a judgment matrix, compare the relative importance of each element group, calculate the eigenvalue a _ij combination, and obtain the weighted matrix A;

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

S7, weighted supermatrix W'=AW, the limit supermatrix, that is, when W' is infinitely powered, each column of the supermatrix will tend to the same vector, and the column vector of the supermatrix is the local weight of each element under the road surface condition criterion; the formula of the limit supermatrix is:

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

(1) The present invention establishes an intelligent monitoring system for snow and ice melting on roads and bridges. The system transmits the obtained data to the cloud through the equipment monitoring module and the data acquisition module. Then, the cloud first judges the road surface state and equipment operation status under the single factor condition through the designed initial threshold, and then makes decisions on the start and stop of the equipment under the multi-factor condition through the ANP network analysis method and issues corresponding instructions to the equipment monitoring module. This method takes into account the mutual influence between various factors or adjacent levels and can be used to solve various quantitative and non-quantitative, rational and non-rational decision-making problems.

(2) Compared with other icing identification methods, the ANP network analysis method used in the present invention calculates the weights of various influencing factors in an empirical manner, does not require the collection of a large number of samples to establish a simulation model, is simple and efficient, and the judgment matrix can be continuously modified according to the judgment results.

(3) Compared with previous control systems, the present invention can not only realize the intelligent start and stop function of the equipment, but also monitor the environmental parameters of the road surface and various data of the equipment in real time, diagnose the failure of the equipment and automatically disconnect the line of the failed equipment. Secondly, the monitoring data can be transmitted to the front end through the cloud. If the road surface condition reaches the critical point of freezing or the equipment fails, the control system will send information to the front end for early warning. Through this function, the manager can be reminded in time to check the environmental parameters of the road surface and the operation status of the equipment and conduct maintenance.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of the structure of an intelligent monitoring system according to an embodiment of the present invention.

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

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

FIG4 is a diagram of the internal structure of the cloud in accordance with an embodiment of the present invention.

FIG5 is a flow chart of intelligent control according to an embodiment of the present invention.

FIG6 is a flow chart of the ANP network analysis method of an embodiment of the present invention.

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

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

DETAILED DESCRIPTION

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

As shown in Figures 1-7, the patent of the present invention is an intelligent monitoring system for snow and ice melting on roads and bridges, which includes six parts: equipment monitoring module 1, data acquisition module 2, wireless communication module 3, cloud 4, front end 5, and snow and ice melting equipment 6. The equipment monitoring module 1, data acquisition module 2 and wireless communication module 3 are connected through RVV wires, the wireless communication module 3 and cloud 4 are wirelessly connected through a communication protocol, and the equipment monitoring module 1 and snow and ice melting equipment 6 are connected through a BVR cable, thereby forming a closed loop to realize intelligent control. The front end 5 and 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 monitor can monitor the current, voltage and electric energy of the six lines of the snow-melting and ice-melting equipment in real time and diagnose faults, and transmit the diagnostic data to the cloud 4.

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. The sensor can collect data and monitor in real time various environmental parameters that affect icing, and transmit the data to the cloud 4.

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 analyze and calculate data, and the database 18 is used to store 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, for viewing various data of the snow-melting and ice-melting device 6 and remotely controlling it.

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

As a preferred embodiment, the equipment monitoring module 1 receives and executes instructions from the cloud 4 via the wireless communication module, thereby realizing intelligent control of 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 and multiple data such as the current, voltage, electric energy, start/stop status, and operation status of the equipment.

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

For the road surface condition, firstly, the initial threshold is used to judge whether the road surface condition is icy under the single factor condition, and then the global weight of each environmental parameter under the multi-factor condition is analyzed by the ANP network analysis method, and the judgment result under the single factor condition and the global weight under the multi-factor condition are combined to comprehensively judge whether the road surface condition is icy, wherein the factor is the environmental parameter;

For the operation 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 threshold value, it indicates that the equipment is faulty. At this time, regardless of whether the road surface is icy or not, the equipment monitoring module 1 will be instructed to shut down the snow-melting and ice-melting equipment 6. The equipment monitoring module 1 receives and executes the instructions issued by the cloud 4 through the wireless communication module 3, thereby realizing 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, and the non-icy road surface state is recorded as -T, where T is an arbitrary positive number;

Under multi-factor conditions, each factor is multiplied by the corresponding global weight and added 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 icy; if K is equal to 0, the road surface state is in the critical state of icing.

As a preferred embodiment, the ANP network analysis method is to make decisions on the target under the influence of multiple factors. The ANP network structure model constructed first is divided into two parts: the control layer and the network layer, wherein the control layer is further divided into the target layer and the criterion layer. The weight of the criterion layer can be calculated by the AHP hierarchical analysis method. For the network layer, the local weights of each influencing factor under multiple criteria need to be calculated by the weighted super matrix. The final global weight can be obtained by multiplying the weight of the criterion layer and the local weight of the network layer, and finally the influencing factors are sorted.

The judgment process of the present invention is described below by taking specific data as an example.

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 the criterion layer has only one factor, the weight can be calculated without the AHP analytic hierarchy process.

The network layer is composed of multiple element groups, and there are multiple elements inside the element group, and the elements are interconnected and interdependent. There is also a relationship of dependence and feedback between the element groups. The element group one of the network layer of the present embodiment is the weather influencing factor (C1), the element group two is the atmospheric influencing factor (C2), and the element group three is the air quality influencing factor (C3). The elements in the element group one are temperature (D1), humidity (D2), snowfall (D3), and wind speed (D4). The elements in the element group two are atmospheric pressure (D5) and water vapor pressure (D6). The elements in the element group three are PM2.5 (D7) and PM10 (D8). Then, the relationship between the influencing factors is studied in the form of expert surveys and group discussions, or according to empirical values, the degree of mutual influence between the elements is given, and an influencing factor table is made. The final ANP model is shown in Figure 7.

The local weight calculation method of each influencing factor under multiple criteria is as follows:

In this embodiment, the unweighted supermatrix W and the weighted matrix A are first established based on the road surface condition (B1), and then the weighted supermatrix W' is obtained by multiplying the unweighted supermatrix W and the weighted matrix A. Finally, the local weights of each element based on the road surface condition (B1) can be obtained by calculating the limit supermatrix ^W'∞ .

The unweighted supermatrix W is an unweighted supermatrix that takes the road surface condition (B1) as the main criterion, one element as the secondary criterion, and compares the relative importance of other elements. First, the importance of each influencing factor within and between groups is compared pairwise to obtain a judgment matrix.

When D1, D2, D3, and D4 in C1 use D _j (j=1, 2, ..., 8) as the secondary criterion, 8 4×4 initial judgment matrices D can be obtained.

When D5 and D6 in C2 use D _j (j=1, 2, ..., 8) as the secondary criteria, 8 2×2 initial judgment matrices D can be obtained.

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

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

Table 1 Initial judgment matrix D ₁₁ based on temperature in element group 1

The table shows the relative importance between two elements. The relative importance of each row of the judgment matrix is calculated by the square root method _. The relative importance of each _row is normalized to obtain the eigenvalue of each row w _i .

The square root method is to calculate the nth root of the product of the elements of each row of the judgment matrix to obtain the relative importance of each row _Mi (i=1, 2....n), and the relative importance of each row _Mi (i=1, 2....n) constitutes a relative importance vector M, where n is the matrix hierarchy. Then the obtained relative importance M is normalized to obtain the eigenvalues w _i (i=1, 2....n) of each row, that is, the weight, and the eigenvalues w _i of each row constitute 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), w _i is the normalized eigenvalue of the relative importance _Mi of the i-th row.

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

For example, for the first row,

_Mi 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 using formula (3) (4) and a consistency check is performed. The final result CR is less than 0.1, thus meeting the requirements.

In the above formula, Si is the i-th 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.

The consistency test first calculates the consistency ratio CR = CI/RI. When CR < 0.1, the requirement is met. When CR > 0.1, the judgment matrix needs to be readjusted and corrected. RI can be obtained by looking up the table.

The eigenvalue vector w of the initial judgment matrix D ₁₁ is used as the judgment matrix Taking the judgment matrix with temperature as the secondary criterion in element group 1 as an example, the eigenvector D ^w ₁₁ =w is obtained, that is:

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

The judgment matrices with each element in element group 1 as sub-criteria are:

and

The judgment matrices with the elements in element group 2 as sub-criteria are:

and

The judgment matrices with the elements in element group three as sub-criteria are:

and

The above judgment matrix is composed into an unweighted supermatrix W, which is composed as follows:

_Wij (i,j=1,2,3) represents the comparison of the relative importance of the elements in the i-th element group with the elements in the j-th element group as the secondary criterion. The unweighted supermatrix W is constructed by _Wij .

The weighted matrix A is composed of the following: the road surface state (B1) is the main criterion, and each element group is used as the secondary criterion to construct the initial judgment matrix, compare the relative importance of each element group, and calculate the eigenvalue a _ij . _{a ij} represents the eigenvalue a obtained by comparing the relative importance of the i-th element group with other element groups when the j-th element group is used as the secondary criterion. The element groups in this example are weather influencing factors (C1), atmospheric influencing factors (C2), and air quality influencing factors (C3). Taking the weather influencing factors (C1) as the secondary criterion, a judgment matrix is constructed to compare the relative importance of each element group, and the eigenvalues a ₁₁ , a ₂₁ , and a ₃₁ are obtained by the square root method. As shown in Table 2 below. Similarly, the eigenvalues of the judgment matrix between each element group are calculated using C2 and C3 as the secondary criteria, and the weighted 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

_a21 ＝0.258

_a31 ＝0.105

Similarly, by constructing the initial judgment matrix between element groups with the atmospheric influence factor as the secondary criterion, the eigenvalues a ₁₂ , a ₂₂ , and a ₃₂ are calculated.

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

The weighted matrix A is obtained by combination as follows:

The weighted super matrix W'=aW, that is:

The limit supermatrix, that is, when W' is infinite, each column of the supermatrix will tend to the same vector. At this time, the column vector of the supermatrix is the local weight of each element under the road surface condition criterion. The formula of the limit supermatrix is:

In this embodiment, the local weights of the influencing elements 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), and PM10 (0.053).

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

The decision is made through programming in Python, and the judgment value of each influencing factor under the single factor condition is considered to be "1" when it is judged as icing; the judgment value of non-icing is considered to be "-1", and then the judgment value under the single factor condition is multiplied by the global weight of the corresponding influencing factor obtained under the multi-factor condition after calculation by the ANP network analysis method, so as to obtain the judgment value of each influencing factor under the multi-factor condition. These judgment values are added together to make a judgment on whether the road surface state is icy under multiple influencing factors. If the result is greater than 0, it means that the road surface state is "iced"; if the result is less than 0, it means that the road surface state is "non-iced". In this embodiment, the initial thresholds of each influencing factor are set to temperature (0°C), humidity (80%), snowfall (0.25mm), wind speed (2m/s), atmospheric pressure (101.3Kpa), water vapor pressure (0.1Kpa), PM2.5 (25.0), and PM10 (37.0). Two groups of examples are taken 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 groups of examples. Therefore, network analysis can better reflect the complex internal relationship between various influencing factors in actual situations.

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, electric energy, equipment switch and whether the equipment is operating normally of the snow-melting and ice-melting equipment 6, and remotely monitor and manage the 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 only after passing the consistency check. During use, the initial matrix can be corrected in turn according to the actual road and bridge pavement result status (such as manual verification), continuously improved, and the prediction accuracy can be improved.

In the description of the present invention, it should be understood that all terms described in the present invention have the same meaning as those generally understood by technicians in the technical field of the present invention, and are only used to facilitate the description of the embodiments described in the patent of the present invention, and cannot limit the present invention.

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 monitors that can monitor whether the equipment is faulty can also be used. In addition to the above 8 factors, the influencing elements taken by the ANP network analysis method in this embodiment can also be any other factors that affect 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 in the wireless communication module 3 in this embodiment, other wireless communication methods may also be used, and the present invention is not limited thereto.

The above-mentioned embodiments and examples are only for a more detailed and specific understanding of the present invention and cannot be understood as a limitation of the patent of the present invention. It should be pointed out that for ordinary technicians in this field, any modification, variation, combination or equivalent replacement of the description and drawings of the present invention is included in the scope of patent protection of the present invention.

Claims (7)

1. An intelligent monitoring system for snow and ice melting on roads and bridges, characterized by comprising an equipment monitoring module, a data acquisition module, a wireless communication module, a cloud and snow and ice melting equipment with multiple lines; The equipment monitoring module is used to monitor the operating status parameters of the snow-melting and ice-melting equipment in real time, upload the data to the cloud through the wireless communication module, and receive and execute the control instructions of the snow-melting and ice-melting equipment issued by the cloud; The data acquisition module is used to collect environmental parameters that affect road and bridge icing in real time, and upload 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 consists of a server and a database. The server pre-stores a road icing prediction and analysis algorithm based on the ANP network analysis method to analyze and calculate whether there is icing and issue control instructions to the equipment monitoring module. The database is used to store data. The road icing prediction analysis algorithm based on the ANP network analysis method is as follows: Setting initial thresholds for each environmental parameter and each line operating status parameter of the snow and ice melting equipment; For the road surface condition, firstly, the initial threshold is used to judge whether the road surface condition is icy under the single factor condition; then the global weight of each environmental parameter under the multi-factor condition is analyzed by the ANP network analysis method, and the judgment result under the single factor condition and the global weight under the multi-factor condition are combined to comprehensively judge whether the road surface condition is icy, wherein the factor is the environmental parameter; For the operation status of the snow-melting and ice-melting equipment, if the operation status parameter measured by the equipment monitoring module is not equal to the initial threshold value, it indicates that the equipment is faulty. At this time, the equipment monitoring module will be instructed to shut down the snow-melting and ice-melting equipment regardless of whether the road surface is icy or not. The specific method of using ANP network analysis to determine the global weight of each environmental parameter under multi-factor conditions is as follows: Firstly, the ANP network structure model is divided into two parts: control layer and network layer. The control layer is further divided into target layer and 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 status. The network layer is composed of environmental parameters, and each parameter of the environmental parameters is defined as an element of the network layer. For the criterion layer, the AHP hierarchy analysis method is used to calculate its weight; for the network layer, the local weight of each influencing factor under multiple criteria needs to be calculated through the weighted super matrix, and the final global weight is obtained by multiplying the criterion layer weight and the local weight of the network layer. 2. According to claim 1, the intelligent monitoring system for road and bridge snow and ice melting is characterized in that it also includes a front end, which is a handheld terminal connected to the cloud via a wireless network and is used to remotely monitor and manage the snow and ice melting equipment. 3. According to claim 1, the intelligent monitoring system for snow and ice melting on roads and bridges is characterized in that: the equipment monitoring module includes a current monitor, a voltage monitor, an electric energy monitor and an equipment controller, which is used to monitor the current, voltage and electric energy of multiple lines of the snow and ice melting equipment in real time and control the start and stop of the equipment, and transmit the monitoring data to the cloud through the wireless communication module and receive control instructions from the cloud. 4. The intelligent monitoring system for road and bridge snow and ice melting according to claim 1 is characterized in that 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 and an air quality sensor, and a data acquisition card connected to each sensor, and the data acquisition card is connected to the wireless communication module. 5. The intelligent monitoring system for road and bridge snow and ice melting according to claim 4 is characterized in that the data acquisition module and the equipment monitoring module transmit data to the wireless communication module through the MODBUS communication protocol. 6. The intelligent monitoring system for road and bridge snow and ice melting according to claim 1 is characterized in that: 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, and the non-icy road surface state is recorded as -T, where T is an arbitrary positive number; Under multi-factor conditions, each factor is multiplied by the corresponding global weight and added 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 icy; if K is equal to 0, the road surface state is in the critical state of icing. 7. The intelligent monitoring system for road and bridge snow and ice melting according to claim 1 is characterized in that: the weighted super matrix is obtained as follows: S1, group the environmental parameters according to the element categories to obtain several element groups, S2, then compare the importance of each influencing factor within and between element groups to obtain the judgment matrix _Dij ; S3, calculate the relative importance M of each row of the judgment matrix by the square root method; S4, normalizing the obtained relative importance M to obtain the eigenvalue w of each row, and finally obtaining the eigenvector D ^w _ij of each judgment matrix when D _j is used as the sub-criterion, where i represents the i-th element group, and j represents the j-th element as the sub-criterion; S5. According to the relative importance of the elements, the eigenvectors of each judgment matrix are combined into an unweighted supermatrix W n represents the number of element groups, _Wik (i,k=1,2..n) represents the comparison of the relative importance of the elements in the i-th element group with the elements in the k-th element group as the secondary criterion, and the unweighted supermatrix W is formed by _Wik ; S6, taking the road surface condition as the main criterion and each element group as the secondary criterion to construct a judgment matrix, compare the relative importance of each element group, calculate the eigenvalue aij combination, and obtain the weighted matrix A; aij represents the eigenvalue obtained by comparing the relative importance of the i-th element group with other element groups when the j-th element group is used as the secondary criterion, and n is the matrix order, that is, the number of element groups; S7, weighted supermatrix W'=AW, when W' exists to an infinite power, the columns of the weighted supermatrix W' will tend to the same vector, and a limit supermatrix will be obtained. At this time, the column vector of the limit supermatrix is the local weight of each element under the road condition criterion; the formula of the limit supermatrix is: .