A road remote warning method and device

Abstract

The embodiment of the present application discloses a road remote warning method and device, which includes: obtaining road abnormality warning information; the road abnormality warning information includes object factors and index factors; inputting the road abnormality warning information into a weight calculation model to calculate the weight value of the object factor in the index factor; inputting the calculated weight value into a preset multi-factor comprehensive grading model to calculate the warning level of the object factor; determining the warning range that needs to be warned according to the warning level, and sending the warning information data to the target checkpoint within the warning range through a preset communication method to warn of faults through the target checkpoint. Through this embodiment scheme, real-time, effective, and accurate road condition warnings are achieved, reducing the occurrence of road accidents, and providing a technical basis for accelerating the processing of road faults.

Description

Technical Field

[0001] The present invention relates to road monitoring technology, and more particularly to a road remote early warning method and device. Background Art

[0002] With the continuous development of relevant technologies in the security industry, road monitoring and early warning systems have gradually improved. Traditional road monitoring and early warning systems rely on bayonet cameras to identify road vehicles and pedestrians, and conduct behavioral analysis through the background intelligent analysis server to obtain corresponding illegal, violation or alarm data, and query and handle corresponding abnormal illegal behaviors through the video image management platform. However, with the continuous three-dimensional development of the road network and the gradual increase in the number of vehicles, this method has become difficult to more efficiently control complex traffic conditions, especially in terms of emergency real-time warning of road conditions. It is difficult to send warnings to specific target vehicles or personnel in real time, effectively and accurately by relying solely on traditional road monitoring and early warning systems.

[0003] Therefore, how to achieve real-time, effective and accurate road condition warning is the key direction of current intelligent transportation development. Summary of the invention

[0004] The embodiments of the present application provide a road remote warning method and device, which can achieve real-time, effective and accurate road condition warning, reduce the occurrence of road accidents, and provide a technical basis for accelerating the speed of road fault processing.

[0005] The present application provides a road remote warning method, which may include:

[0006] Acquiring road abnormality warning information; the road abnormality warning information may include object factors and indicator factors;

[0007] Inputting the road abnormality warning information into a preset weight calculation model, and calculating the weight value of the object factor in the index factor through the weight calculation model;

[0008] Inputting the calculated weight value into a preset multi-factor comprehensive grading model, and calculating the warning level of the object factor through the multi-factor comprehensive grading model;

[0009] The warning range that requires warning is determined according to the warning level, and the warning information data is sent to the target checkpoint within the warning range through 5G communication, so as to issue a fault warning through the target checkpoint.

[0010] In an exemplary embodiment of the present application, the object factor may include: an alarm type; and calculating the weight value of the object factor in the indicator factor by using the weight calculation model may include:

[0011] constructing a first pairwise comparison matrix between different factors of the road abnormality warning information based on a 1-9 scale method;

[0012] Performing a consistency check on the first pairwise comparison matrix using a canonical column average method, and calculating weight vectors W1 of different factors when the consistency check of the first pairwise comparison matrix passes;

[0013] constructing a second pairwise comparison matrix between the alarm types for different factors based on the 1-9 scale method;

[0014] The plurality of second pairwise comparison matrices obtained are respectively checked for consistency by using the canonical column average method, and when each second pairwise comparison matrix passes the consistency check, the factor weight vector corresponding to the corresponding factor is calculated, and the calculated plurality of factor weight vectors are combined into a weight matrix W2;

[0015] The weight value W of the object factor in the index factor is calculated according to the weight vector W1 and the weight matrix W2.

[0016] In an exemplary embodiment of the present application, the first pairwise comparison matrix between different factors of the road abnormality warning information constructed based on the 1-9 scale method may include:

[0017] Determine the factor vector X1 of different factors; X1 = {x 11 ,x 12 ,x 13 ,…x 1n};

[0018] Get any two factors x from the factor vector X1 1i and x 1j , according to the 1-9 scale method, the factor x 1i and x 1j The ratio of the influence value of 1i,1j is expressed as a natural number from 1 to 9 or the reciprocal of a natural number from 1 to 9; wherein the natural number from 1 to 9 and the reciprocal of the natural number from 1 to 9 are used to represent the factor x 1i and x 1j The scale of impact;

[0019] The ratio of all impact values ​​represented by the 1-9 scale method is a 1i,1j forming the first pairwise comparison matrix;

[0020] and / or,

[0021] The constructing of the second pairwise comparison matrix between the alarm types for different factors based on the 1-9 scale method may include:

[0022] Get the factor vector of each factor separately;

[0023] The multiple evaluation subjects contained in each factor vector are compared in pairs to obtain the ratio of multiple influence values ​​corresponding to the factor; the ratio of the multiple influence values ​​is expressed as a natural number from 1 to 9 or the reciprocal of the natural number from 1 to 9; wherein the natural number from 1 to 9 and the reciprocal of the natural number from 1 to 9 are used to characterize the influence scale between any two evaluation subjects contained in the factor; for any factor, the ratio of all influence values ​​represented by the 1-9 scale method constitutes a second pairwise comparison matrix corresponding to the factor; and multiple second pairwise comparison matrices are obtained accordingly for multiple factors.

[0024] In an exemplary embodiment of the present application, the step of performing a consistency check on the first pairwise comparison matrix using the canonical column average method, and calculating the weight vector W1 of different factors when the consistency check of the first pairwise comparison matrix passes may include:

[0025] Calculate the first maximum characteristic root λ of the first pairwise comparison matrix according to the canonical column average method 1max ;

[0026] According to the first maximum characteristic root λ 1max and detecting whether the consistency of the first pairwise comparison matrix is ​​within an allowable range using a preset consistency detection strategy;

[0027] When the consistency of the first pairwise comparison matrix is ​​within the allowable range, the first maximum characteristic root λ is calculated. 1max The first row of normalized vectors obtained in the process is used as the weight vector W1;

[0028] and / or,

[0029] The step of performing consistency check on the plurality of second pairwise comparison matrices obtained by using the canonical column average method, and calculating the factor weight vector corresponding to the corresponding factor when each second pairwise comparison matrix passes the consistency check may include:

[0030] The second largest characteristic root λ of each second pairwise comparison matrix is ​​calculated according to the canonical column average method 2max ;

[0031] According to the second largest characteristic root λ 2max and detecting whether the consistency degree of the corresponding second pairwise comparison matrix is ​​within an allowable range using a preset consistency detection strategy;

[0032] When the consistency of the second pairwise comparison matrix is ​​within the allowable range, the second maximum characteristic root λ is calculated. 2max The second row of normalized vectors obtained in the process is used as the factor weight vector of the corresponding factor of the current second pairwise comparison matrix.

[0033] In an exemplary embodiment of the present application, the first largest characteristic root λ of the first pairwise comparison matrix is ​​calculated according to the canonical column average method. 1max This can include:

[0034] Normalize each column vector of the first pairwise comparison matrix to obtain the first column normalized matrix

[0035] Normalize the first column of the matrix ω 1i,1j Sum by row to get the first vector

[0036] According to the first vector ν 1i Normalize each row vector of the first pairwise comparison matrix to obtain the first row normalized vector

[0037] Calculate the first largest eigenvalue of the first pairwise comparison matrix

[0038] and / or,

[0039] The second largest characteristic root λ of the second pairwise comparison matrix is ​​calculated according to the canonical column average method 2max This can include:

[0040] Normalize each column vector of the second pairwise comparison matrix to obtain a second column normalized matrix

[0041] Normalize the second column of the matrix ω 2i,2j Sum by row to get the second vector

[0042] According to the second vector v 2i Normalize each row vector of the second pairwise comparison matrix to obtain the second row normalized vector

[0043] Calculate the first largest eigenvalue of the first pairwise comparison matrix

[0044] In an exemplary embodiment of the present application, the consistency detection strategy may include:

[0045] Check whether it meets

[0046] When satisfied When , it is determined that the consistency of the comparison matrix is ​​within the allowable range; when it is not satisfied When , it is determined that the consistency degree of the comparison matrix is ​​not within the permissible range;

[0047] in, RI represents the random consistency index. The correspondence between RI values ​​and different k values ​​is provided by a preset mapping table; k is a natural number;

[0048] and / or,

[0049] Calculating the weight value W of the object factor in the index factor according to the weight vector W1 and the weight matrix W2 may include: W=W1×W2.

[0050] In an exemplary embodiment of the present application, calculating the warning level of the object factor by using the multi-factor comprehensive grading model may include:

[0051] Determine the abnormal factors that can cause the road abnormality alarm, and calculate the attribute value of each abnormal factor; the abnormal factor includes the alarm type, and also includes any one or more of the following: traffic volume, road type, abnormal area scene and visibility; wherein the attribute value of the alarm type is calculated according to the weight value W;

[0052] Based on the attribute value of each abnormal factor, the weight of each abnormal factor is determined by using the hierarchical analysis method;

[0053] Calculate the weighted average of all abnormal factors based on the weight and attribute value of each abnormal factor;

[0054] The corresponding warning level is determined according to the calculated weighted average value and a preset first mapping table; the first mapping table contains the correspondence between different weighted average values ​​and different warning levels.

[0055] In an exemplary embodiment of the present application, the method of using the hierarchical analysis method to determine the weight of each abnormal factor for determining the abnormal score based on the attribute value of each abnormal factor may include:

[0056] A comparison matrix for pairwise comparison of all abnormal factors was constructed using the preset 1-9 scale method;

[0057] Normalizing each column vector of the comparison matrix to obtain a normalized matrix;

[0058] The columns of the normalized matrix are added together, and the vector obtained after the addition is divided by the number of abnormal factors to obtain a weight vector; the weight vector contains the weight of each abnormal factor.

[0059] In an exemplary embodiment of the present application, determining the warning range that requires warning according to the warning level may include:

[0060] The warning range is determined according to the calculated warning level and a preset second mapping table; the second mapping table contains the correspondence between different warning levels and different warning ranges.

[0061] An embodiment of the present application provides a road remote warning device, which may include a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, and is characterized in that when the instructions are executed by the processor, any one of the above-mentioned road remote warning methods is implemented.

[0062] Compared with the related art, the embodiment of the present application may include: obtaining road abnormality warning information; the road abnormality warning information includes object factors and index factors; inputting the road abnormality warning information into a preset weight calculation model, and calculating the weight value of the object factor in the index factor through the weight calculation model; inputting the calculated weight value into a preset multi-factor comprehensive grading model, and calculating the warning level of the object factor through the multi-factor comprehensive grading model; determining the warning range that needs to be warned according to the warning level, and sending the warning information data to the target checkpoint within the warning range through a preset communication method, so as to carry out fault warning through the target checkpoint. Through this embodiment scheme, real-time, effective and accurate road condition warning is realized, the occurrence of road accidents is reduced, and a technical basis is provided for accelerating the processing of road faults.

[0063] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by implementing the present application. Other advantages of the present application can be realized and obtained by the schemes described in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The accompanying drawings are used to provide an understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0065] Figure 1 This is a flow chart of the road remote warning method according to an embodiment of the present application;

[0066] Figure 2 A schematic diagram of an alarm reporting scheme for an embodiment of the present application;

[0067] Figure 3 This is a schematic diagram of decomposing all factors that affect the weight calculation model according to different weights from top to bottom into three levels in an embodiment of the present application;

[0068] Figure 4A flow chart of a method for calculating the weight value of the object factor in the index factor by using the weight calculation model in an embodiment of the present application;

[0069] Figure 5 A flow chart of a method for calculating the warning level of the object factor through the multi-factor comprehensive grading model according to an embodiment of the present application;

[0070] Figure 6 This is a block diagram of the road remote warning device according to an embodiment of the present application. DETAILED DESCRIPTION

[0071] The present application describes multiple embodiments, but the description is exemplary rather than restrictive, and it is obvious to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described in the present application. Although many possible feature combinations are shown in the drawings and discussed in the specific embodiments, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.

[0072] The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed in the present application may also be combined with any conventional features or elements to form a unique invention scheme defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other invention schemes to form another unique invention scheme defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in the present application may be implemented individually or in any appropriate combination. Therefore, except for the limitations made according to the attached claims and their equivalents, the embodiments are not subject to other restrictions. In addition, various modifications and changes may be made within the scope of protection of the attached claims.

[0073] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps of the specific order described. As will be understood by those of ordinary skill in the art, other sequences of steps are also possible. Therefore, the specific sequence of the steps set forth in the specification should not be interpreted as a limitation to the claims. In addition, the claims for the method and / or process should not be limited to the steps of performing them in the order written, and those skilled in the art can easily understand that these sequences can be changed and still remain within the spirit and scope of the embodiments of the present application.

[0074] The present application embodiment provides a road remote warning method, such as Figure 1 As shown, the method may include steps S101-S104:

[0075] S101, obtaining road abnormality warning information; the road abnormality warning information may include object factors and indicator factors;

[0076] S102, inputting the road abnormality warning information into a preset weight calculation model, and calculating the weight value of the object factor in the index factor by using the weight calculation model;

[0077] S103, inputting the calculated weight value into a preset multi-factor comprehensive grading model, and calculating the warning level of the object factor through the multi-factor comprehensive grading model;

[0078] S104: Determine a warning range that requires warning according to the warning level, and send warning information data to a target checkpoint within the warning range through a preset communication method, so as to perform a fault warning through the target checkpoint.

[0079] In an exemplary embodiment of the present application, the road abnormality alarm information may include any one or more of the following factors: alarm type, alarm time, alarm location, and alarm device type; the road abnormality alarm information may include object factors and indicator factors; the object factors may include: alarm type; the indicator factors may include: alarm time, alarm location, and alarm device type.

[0080] In the exemplary embodiments of the present application, with the mature development and widespread application of new communication technologies (for example, including but not limited to 4G, 5G, 6G, etc.), a road warning system solution based on new communication technologies has been derived. Compared with traditional monitoring systems, such a system has the characteristics of real-time warning, effective information, and accurate positioning. It can send abnormal road condition information to the corresponding target vehicles or personnel in a timely manner, which can largely avoid traffic congestion and accidents and improve the operating efficiency of the entire traffic road network.

[0081] In an exemplary embodiment of the present application, when a road encounters an emergency (for example, pedestrians or animals breaking into a motor vehicle lane, road collapse, obstacles, car accidents, natural disasters, etc.), an intelligent checkpoint can be used to capture the scene and an intelligent analysis server can be used to analyze the scene. After the analysis results are obtained, the warning information will be sent to the corresponding target checkpoint through a preset communication technology (such as 5G communication). Each target checkpoint will then issue a real-time warning to the vehicle terminal traveling on the road, so as to reduce the occurrence of road accidents and speed up the processing time of road faults.

[0082] In an exemplary embodiment of the present application, the smart card port identifies road abnormality warning information. A relatively complete road abnormality warning information may include the following elements: warning type, warning time, warning location, warning device type, warning area scene, warning road type and other information.

[0083] In an exemplary embodiment of the present application, in order to ensure the integrity of the collected road abnormality warning information and the stability and reliability of information transmission, the road abnormality warning information data may be defined as shown in Table 1:

[0084] Table 1

[0085]

[0086] In an exemplary embodiment of the present application, the smart card port can analyze the abnormal road warning information through 5G communication technology and adjust the alarm reporting frequency. After the smart card port identifies the abnormal road warning information, it can calculate the weight according to different card port business area scenarios, warning types, warning times, etc., and then obtain reasonable alarm reporting rules for alarm reporting.

[0087] In an exemplary embodiment of the present application, Figure 2 As shown, the alarm reporting scheme may include: the alarm device identifies abnormal road alarm information, and the alarm type of the alarm device data access layer can be solved by a preset hierarchical analysis model; all the solved alarm types can be weighted by a pre-established weight calculation model; and the warning level is calculated through a multi-factor comprehensive grading model to obtain reasonable alarm reporting rules, and finally processed and reported according to the alarm reporting rules to achieve fault warning.

[0088] In an exemplary embodiment of the present application, the weight calculation model and the multi-factor comprehensive grading model may be established in advance, and the establishment of the two models will be introduced in detail below.

[0089] In an exemplary embodiment of the present application, before the model is established, a model scenario assumption can be first made, assuming that: (1) each alarm received is real-time and true; (2) the alarm device does not miss any alarms; (3) the factors affecting the alarm assessment in the current business scenario (for example, alarm type, alarm level, alarm time, alarm area location, alarm device type, etc.) are reasonable and comprehensive; (4) the weights of all factors in the comprehensive evaluation of the alarm type are stepped; (5) the evaluation of the comprehensive assessment factors of the alarm type must be objective and fair; and (6) it is assumed that the results of the factors at each level in the question will not change in a short period of time.

[0090] In an exemplary embodiment of the present application, when starting to build a model, several important factors (e.g., alarm type, alarm level, alarm time, alarm area location, alarm equipment type, etc.) can be selected for study, so as to accurately formulate a comprehensive assessment of different alarm types.

[0091] In an exemplary embodiment of the present application, Figure 3 As shown in the figure, all factors that affect the model can be decomposed into three levels from top to bottom according to different weights. For example, the alarm type can be at the object level, and the factors at the object level can be called object factors; the alarm level, alarm time, alarm area location, alarm equipment type, etc. can be at the indicator level, and the factors at the indicator level can be called indicator objects; the comprehensive evaluation of the alarm type can be at the target level.

[0092] In the exemplary embodiment of the present application, since the factors in the indicator layer have different proportions in the target measurement, their weights in the target measurement can be determined based on the influence relationship between them. The greater the weight, the greater the influence of the considered factor on the problem.

[0093] In the exemplary embodiment of the present application, it is assumed that there are n (n is a positive integer) factors affecting the evaluation X={x1, x2, x3, ... x n}, we need to compare the influence of these factors on the factors of the previous layer, so as to determine the weight of this layer relative to the previous layer.

[0094] In the exemplary embodiment of the present application, any two factors x can be taken i With x j Compare and compare two factors in the factor vector X. i = 1, 2, ..., n; j = 1, 2, ..., k; i and j are natural numbers.

[0095] In the exemplary embodiment of the present application, a ij Can represent x i and x j The ratio of the impact on the upper level target; A = (a ij ) mn Represents all results (m is a positive integer), that is Then a ij >0,a ij =1 / a ij , it is easy to know (a ii =1,i,j=1,2,…,n).

[0096] In an exemplary embodiment of the present application, when constructing a pairwise comparison matrix (ie, a positive reciprocal matrix) A, the 1-9 scale proposed by TLSaaty can be used, so a ijThe scales can be 1, 2, 3…9 and their reciprocals 1, 1 / 2,…1 / 9. Their meanings can be shown in Table 2:

[0097] Table 2

[0098]

[0099] In an exemplary embodiment of the present application, based on the above principles, a pairwise comparison matrix building module based on the 1-9 scale algorithm may be first constructed when establishing a weight calculation model.

[0100] In an exemplary embodiment of the present application, after constructing the pairwise comparison matrix, it is possible to: ① complete the consistency check of the pairwise comparison matrix of the indicator layer to the target layer; ② find the weight vector and combined consistency check of the lowest layer (object layer) to the indicator layer; ③ finally calculate the weight vector of the object layer to the target layer, and sort the alarm types according to the size of the weight vector.

[0101] In an exemplary embodiment of the present application, When max =k, CI = 0, indicating that A is completely consistent; when CI is larger, the inconsistency of A is more serious. TLSaaty introduces the random consistency index RI for consistency verification. The value of RI can be shown in Table 3:

[0102] Table 3

[0103]

[0104] In the exemplary embodiment of the present application, when , it can be considered that the consistency of the pairwise comparison matrix is ​​within the allowable range.

[0105] In an exemplary embodiment of the present application, where λ represents the characteristic root of the pairwise comparison matrix, λ max represents the maximum eigenvalue of the pairwise comparison matrix, ω i represents the combined weight vector (or simply the weight vector), CI represents the consistency index, RI represents the random consistency index, and CR represents the consistency ratio.

[0106] In the exemplary embodiment of the present application, based on the above principles, a weight calculation model based on CI, RI, CR and λ can be constructed when establishing the weight calculation model. max A pairwise comparison matrix consistency detection module.

[0107] In an exemplary embodiment of the present application, based on the construction of a pairwise comparison matrix construction module and a pairwise comparison matrix consistency detection module, the pairwise comparison matrix can be calculated based on the input road abnormality warning information, the maximum eigenvalue root of the pairwise comparison matrix can be calculated based on the pairwise comparison matrix and the canonical column average method (which can be abbreviated as the sum method), and the eigenvector (as a weight vector) can be determined based on the maximum eigenvalue root and the pairwise comparison matrix consistency detection module.

[0108] In an exemplary embodiment of the present application, the maximum eigenvalue of the pairwise comparison matrix may be calculated using the following scheme:

[0109] 1. Normalize each column vector of A to obtain a column normalized matrix

[0110] 2. For ω ij Sum by row, calculate column vector

[0111] 3. After row normalization, obtain the row normalized vector ω=(ω1,ω2,…,ω n ) T That is the approximate eigenvector;

[0112] 4. Calculation As an approximation of the largest eigenvalue;

[0113] The largest characteristic root of A is λ max , can be substituted into the consistency index In which, the value of k and the value of RI can be obtained by querying Table 3, so that Confirm that the pairwise comparison matrix passes the consistency test; at this time, the calculated λ max The eigenvector ω calculated when is used as the combined weight vector W1, or the weight vector.

[0114] In an exemplary embodiment of the present application, based on the above principles, a first weight vector calculation module based on the canonical column averaging method can be constructed when establishing a weight calculation model.

[0115] In an exemplary embodiment of the present application, the pairwise comparison matrix construction module established above may be composed of two submodules: a first pairwise comparison matrix construction submodule and a second pairwise comparison matrix construction submodule.

[0116] In an exemplary embodiment of the present application, the first pairwise comparison matrix construction submodule can be used to construct a first pairwise comparison matrix based on factors affecting the comprehensive evaluation of the target, for example, all factors in the input road abnormality warning information (for example, warning type, warning time, warning location, and warning device type).

[0117] In an exemplary embodiment of the present application, according to the different degrees of influence of various factors in the model assumptions, corresponding reasonable data can be provided to test the consistency of the pairwise comparison matrix and calculate its weight vector.

[0118] In an exemplary embodiment of the present application, the second pairwise comparison matrix construction submodule can be used to compare the alarm factors (for example, alarm type, alarm time, alarm location, and alarm device type) one by one according to the factors of the object layer (object factors) that affect the indicator layer, such as each different alarm type in the alarm type, to obtain the importance matrix of each different alarm type in each alarm factor, that is, to construct a second pairwise comparison matrix.

[0119] In an exemplary embodiment of the present application, based on the above principles, a second weight vector calculation module based on a weight vector combination algorithm can be constructed when establishing a weight calculation model, for calculating the weight vector (for example, ) can form the weight matrix W2.

[0120] In an exemplary embodiment of the present application, after the second weight vector calculation module calculates the weight vector W1 and the second weight vector calculation module calculates the weight matrix W2, the weight value W of the object factor in the index factor can be calculated by W=W1×W2.

[0121] In an exemplary embodiment of the present application, according to W=W1×W2, a third weight vector calculation module of the weight calculation model can be constructed.

[0122] In an exemplary embodiment of the present application, through the above scheme, a weight calculation model can be constructed, and the weight calculation model can include: a first pairwise comparison matrix construction submodule, a second pairwise comparison matrix construction submodule, a pairwise comparison matrix consistency detection module, a first weight vector calculation module, a second weight vector calculation module and a third weight vector calculation module.

[0123] In an exemplary embodiment of the present application, the weight value of the alarm type in the index factor can be calculated through the weight calculation model. The road abnormality alarm information is analyzed according to the weight calculation model, and the weight results are calculated based on various alarm types in different regional application scenarios. The alarm frequencies are arranged according to the weight results. For alarms with higher frequencies, they are reported first and quickly, and the user is prompted to make processing.

[0124] In an exemplary embodiment of the present application, a method for constructing a multi-factor comprehensive grading model is described in detail below.

[0125] In an exemplary embodiment of the present application, the intelligent analysis server may perform analysis according to different weights based on the transmitted abnormal alarm information and the calculated weight values ​​to obtain the warning level of the abnormal warning.

[0126] In an exemplary embodiment of the present application, a multi-factor comprehensive grading model is constructed, mainly to determine abnormal results based on the size of each factor, and ultimately determine the warning level.

[0127] In an exemplary embodiment of the present application, according to the abnormal road conditions, the following factors can be extracted as abnormal factors for rating:

[0128] 1. Abnormal alarm type (i.e. alarm type): In evaluating the actual road abnormality level, the abnormal alarm type is the key factor in determining the road abnormality level. It specifically includes: traffic accidents, natural disasters, pedestrians or animals intruding, road congestion or collapse, and others, and each type determines the direct score of the abnormal alarm type.

[0129] 2. Traffic flow: When an abnormal situation occurs on the road, the amount of traffic flow on the road can also affect the abnormality level. When the traffic flow is large, the road abnormality may cause more serious traffic congestion or accidents. Therefore, the abnormality level in this case will be relatively high, and vice versa.

[0130] 3. Road type: In my country, road types can be divided into different levels according to different dimensions. Here we analyze according to the general classification, that is, road types include urban roads, highways, national highways, provincial highways and others. For example, when an abnormal situation occurs on an urban road or highway, the level of the abnormal situation needs to be relatively increased.

[0131] 4. Key area scenarios: Key areas refer to the regional nature of abnormal locations, such as urban transportation hubs, schools, hospitals, commercial centers and other areas. Among them, transportation hubs are the most urgent when road abnormalities occur, and the abnormality level will also be the highest.

[0132] 5. Visibility: Road visibility will have a dynamic performance due to the influence of factors such as weather, morning and evening. When analyzing abnormal road conditions in real time, visibility is also an important factor in our analysis model.

[0133] 6. Others.

[0134] In the exemplary embodiment of the present application, the key to designing a multi-factor comprehensive grading model lies in determining the attributes of multiple abnormal factors and the weights of each abnormal factor. Therefore, the construction of the multi-factor comprehensive grading model can be divided into the following steps (the first step to the fourth step):

[0135] The first step is to evaluate the model of each abnormal factor

[0136] Here, only abnormal alarm type, traffic flow and visibility are taken as examples for evaluation. Other abnormal factors can be evaluated by referring to these models.

[0137] 1. Abnormal alarm type model

[0138] The abnormal alarm type can be evaluated according to its score. The larger the attribute value, the higher the warning level. The model can be obtained by normalizing it.

[0139] Among them, f(x 1i ) indicates the abnormal alarm type attribute value, x 1i Indicates the abnormal alarm type, a 1i Indicates the abnormality type score of abnormal alarm type i, a 1max The maximum score of the abnormal type attribute. The abnormal type score of abnormal alarm type i can be obtained according to the weight value calculated by the weight calculation model in the above solution.

[0140] 2. Traffic flow model

[0141] Because the exact data value of traffic flow can be obtained in real time every time, the larger the value, the higher the warning level, so it is directly normalized here to obtain the model

[0142] Among them, f(x 2i ) represents the traffic flow attribute value, x 2i represents the traffic volume, a 2i represents the actual traffic flow of abnormal alarm type i, a 2max Indicates the ideal maximum value of traffic flow.

[0143] 3. Visibility Model

[0144] Road visibility is also an exact value, but the larger the value, the lower the warning level. Here, we normalize it to get the model

[0145] Among them, f(x 3i ) represents the visibility attribute value, x 3i Indicates visibility, a 3i Indicates the actual visibility of abnormal alarm type i, a 2min Indicates the ideal minimum visibility.

[0146] The second step is to use the analytic hierarchy process (AHP) to determine the weight of each abnormal factor in determining the abnormal score. The specific model construction method can be as follows:

[0147] 1. Use each abnormal factor to construct a comparison matrix: compare each abnormal factor two by two, and represent the abnormal alarm type, traffic flow, key area, road type, visibility, and other factors as a1...a i Factor, compare the result of comparing the i-th factor with the j-th factor, that is, the matrix element a ij The value of can be given by Santy's 1-9 scale method. Here, the comparison matrix a of the six abnormal factors affecting the abnormal results can be shown in Table 4 below:

[0148] Table 4

[0149] a1 a2 a3 a4 a5 a6 a1 1 3 4 6 7 9 a2 1 / 3 1 2 5 7 8 a3 1 / 4 1 / 2 1 2 4 7 a4 1 / 6 1 / 5 1 / 2 1 3 5 a5 1 / 7 1 / 7 1 / 4 1 / 3 1 3 a6 1 / 9 1 / 8 1 / 7 1 / 5 1 / 3 1

[0150] 2. Weight calculation

[0151] Here we can use the arithmetic mean method to calculate the weights. Each column in the comparison matrix a approximately reflects the distribution of weights, so the arithmetic mean of all column vectors can be used to estimate the weight vector, that is,

[0152] Above In , first, the elements of matrix a are normalized by column; the normalized columns are added; and the added vector is divided by N to obtain the weight vector.

[0153] The third step is to calculate the multi-factor weighted arithmetic mean to obtain the abnormal analysis results:

[0154] Where f(x i ) represents the attribute value of the i-th abnormal factor, W i represents the weight of the i-th abnormal factor, and S represents the abnormal analysis result.

[0155] In the fourth step, different scores S can correspond to different warning levels, and the warning levels can be defined as follows according to Table 5:

[0156] Table 5

[0157] S Warning level 0-0.2 1 0.2-0.5 2 0.5-0.8 3 0.8-1 4

[0158] In the exemplary embodiment of the present application, a weight calculation model and a multi-factor comprehensive grading model are constructed through the above scheme.

[0159] In an exemplary embodiment of the present application, after obtaining road abnormality warning information through a smart card port, the road abnormality warning information can be input into a preset weight calculation model, and the weight value of the object factor in the index factor is calculated by the weight calculation model.

[0160] In an exemplary embodiment of the present application, Figure 4 As shown, the calculating the weight value of the object factor in the index factor by the weight calculation model may include steps S201-S205:

[0161] S201. Construct a first pairwise comparison matrix between different factors of the road abnormality warning information based on a 1-9 scaling method.

[0162] In an exemplary embodiment of the present application, the first pairwise comparison matrix between different factors of the road abnormality warning information constructed based on the 1-9 scale method may include:

[0163] Determine the factor vector X1 of different factors; X1 = {x 11 ,x 12 ,x 13 ,…x 1n};

[0164] Get any two factors x from the factor vector X1 1i and x 1j , according to the 1-9 scale method, the factor x 1i and x 1j The ratio of the influence value of 1i,1j is expressed as a natural number from 1 to 9 or the reciprocal of a natural number from 1 to 9; wherein the natural number from 1 to 9 and the reciprocal of the natural number from 1 to 9 are used to represent the factor x 1i and x 1j The scale of impact;

[0165] The ratio of all impact values ​​represented by the 1-9 scale method is a 1i,1j The first pairwise comparison matrix is ​​formed.

[0166] In an exemplary embodiment of the present application, the above steps can be completed by a first pairwise comparison matrix construction submodule.

[0167] In an exemplary embodiment of the present application, for example, it can be assumed that the four most important factors affecting the comprehensive evaluation of the target in the business scenario of the school include: alarm type, alarm area (or alarm location), alarm time, and alarm device type; the importance relationship between them can be obtained according to the 1-9 scale algorithm. The relationship table between the important factors affecting the comprehensive evaluation of the target and their importance is shown in Table 6 (wherein B1 represents the alarm type, B2 represents the alarm area, B3 represents the alarm time, and B4 represents the alarm device type):

[0168] Table 6

[0169]

[0170] In an exemplary embodiment of the present application, the first pairwise comparison matrix can be obtained according to Table 6 as follows:

[0171] S202: Perform a consistency check on the first pairwise comparison matrix using a canonical column average method, and calculate weight vectors W1 of different factors when the consistency check on the first pairwise comparison matrix passes.

[0172] In an exemplary embodiment of the present application, the step of performing a consistency check on the first pairwise comparison matrix using the canonical column average method, and calculating the weight vector W1 of different factors when the consistency check of the first pairwise comparison matrix passes may include:

[0173] Calculate the first maximum characteristic root λ of the first pairwise comparison matrix according to the canonical column average method 1max ;

[0174] According to the first maximum characteristic root λ 1max and detecting whether the consistency of the first pairwise comparison matrix is ​​within an allowable range using a preset consistency detection strategy;

[0175] When the consistency of the first pairwise comparison matrix is ​​within the allowable range, the first maximum characteristic root λ is calculated. 1max The first row of normalized vectors obtained in the process is used as the weight vector W1.

[0176] In an exemplary embodiment of the present application, the first maximum characteristic root λ of the first pairwise comparison matrix is ​​calculated according to the canonical column average method. 1max This can include:

[0177] Normalize each column vector of the first pairwise comparison matrix to obtain the first column normalized matrix

[0178] Normalize the first column of the matrix ω 1i,1j Sum by row to get the first vector

[0179] According to the first vector v 1i Normalize each row vector of the first pairwise comparison matrix to obtain the first row normalized vector

[0180] Calculate the first largest eigenvalue of the first pairwise comparison matrix

[0181] In an exemplary embodiment of the present application, the consistency detection strategy may include:

[0182] Check whether it meets

[0183] When satisfied When , it is determined that the consistency of the comparison matrix is ​​within the allowable range; when it is not satisfied When , it is determined that the consistency degree of the comparison matrix is ​​not within the permissible range;

[0184] in, RI represents a random consistency index, and the correspondence between RI values ​​and different k values ​​is provided by a preset mapping table.

[0185] In an exemplary embodiment of the present application, the above solution can be completed by a first weight vector calculation module.

[0186] In an exemplary embodiment of the present application, for example, The largest characteristic root of A is λ max =4.0710, its consistency index is:

[0187]

[0188] When k = 4, the random consistency index RI value table shows RI = 0.9, the consistency ratio It can be shown that the first pairwise comparison matrix passes the consistency test;

[0189] The calculated λ max The eigenvalues ​​and eigenvectors of are calculated using the sum method:

[0190] ω=(0.636, 0.2833, 0.4483, 0.163); ω can be used as the weight vector W1.

[0191] S203: construct a second pairwise comparison matrix between the alarm types for different factors based on the 1-9 scale method.

[0192] The constructing of the second pairwise comparison matrix between the alarm types for different factors based on the 1-9 scale method may include:

[0193] Get the factor vector of each factor separately;

[0194] The multiple evaluation subjects contained in each factor vector are compared in pairs to obtain the ratio of multiple influence values ​​corresponding to the factor; the ratio of the multiple influence values ​​is expressed as a natural number from 1 to 9 or the reciprocal of the natural number from 1 to 9; wherein the natural number from 1 to 9 and the reciprocal of the natural number from 1 to 9 are used to characterize the influence scale between any two evaluation subjects contained in the factor; for any factor, the ratio of all influence values ​​represented by the 1-9 scale method constitutes a second pairwise comparison matrix corresponding to the factor; and multiple second pairwise comparison matrices are obtained accordingly for multiple factors.

[0195] In an exemplary embodiment of the present application, for example, the alarm type may include three evaluation subjects: traffic accident, pedestrian intrusion and congestion. The above three evaluation subjects can be compared one by one in each factor (alarm type, alarm time, alarm location and alarm device type). The following is an example of the three evaluation subjects for the alarm type factor, and the importance matrix of each indicator factor can be obtained as shown in Table 7:

[0196] Table 7

[0197]

[0198]

[0199] In an exemplary embodiment of the present application, the second pairwise comparison matrix A can be obtained according to Table 5: B1 :

[0200] And obtain the corresponding weight vector and maximum eigenvalue, which can be expressed as: λ1=3.086.

[0201] In the exemplary embodiment of the present application, similarly for the warning area B2, A can be obtained. B2 matrix:

[0202] λ2=3.004.

[0203] In the exemplary embodiment of the present application, similarly for the alarm time B3, A can be obtained. B3 matrix:

[0204] λ3=3.054.

[0205] In the exemplary embodiment of the present application, similarly for the alarm device type B4, A can be obtained. B4 matrix:

[0206] λ4=3.039.

[0207] S204. Use the canonical column average method to perform consistency check on the multiple second pairwise comparison matrices obtained respectively, and calculate the factor weight vector corresponding to the corresponding factor when each second pairwise comparison matrix passes the consistency check, and combine the calculated multiple factor weight vectors into a weight matrix W2.

[0208] In an exemplary embodiment of the present application, the step of performing consistency check on the obtained plurality of second pairwise comparison matrices respectively by using the canonical column average method, and calculating the factor weight vector corresponding to the corresponding factor when each second pairwise comparison matrix passes the consistency check may include:

[0209] The second largest characteristic root λ of each second pairwise comparison matrix is ​​calculated according to the canonical column average method 2max ;

[0210] According to the second largest characteristic root λ 2max and detecting whether the consistency degree of the corresponding second pairwise comparison matrix is ​​within an allowable range using a preset consistency detection strategy;

[0211] When the consistency of the second pairwise comparison matrix is ​​within the allowable range, the second maximum characteristic root λ is calculated. 2max The second row of normalized vectors obtained in the process is used as the factor weight vector of the corresponding factor of the current second pairwise comparison matrix.

[0212] In an exemplary embodiment of the present application, the second largest characteristic root λ of the second pairwise comparison matrix is ​​calculated according to the canonical column average method. 2max This can include:

[0213] Normalize each column vector of the second pairwise comparison matrix to obtain a second column normalized matrix

[0214] Normalize the second column of the matrix ω 2i,2j Sum by row to get the second vector

[0215] According to the second vector v 2i Normalize each row vector of the second pairwise comparison matrix to obtain the second row normalized vector

[0216] Calculate the first largest eigenvalue of the first pairwise comparison matrix

[0217] In an exemplary embodiment of the present application, a plurality of second pairwise comparison matrices A obtained based on the above-mentioned embodiment scheme are B1 , A B2 , A B3 , AB4 The calculated factor weight vector The weight matrix W2 can be formed.

[0218] In an exemplary embodiment of the present application, for a plurality of second pairwise comparison matrices A B1 , A B2 , A B3 , A B4 And multiple factor weight vectors After sorting, we can obtain the weight vector consistency verification table shown in Table 8:

[0219] Table 8

[0220]

[0221]

[0222] In an exemplary embodiment of the present application, where λ k Including λ1, λ2, λ3, λ4.

[0223] In an exemplary embodiment of the present application, the above solution can be completed by the second weight vector calculation module.

[0224] S205 , calculating the weight value W of the object factor in the index factor according to the weight vector W1 and the weight matrix W2 .

[0225] In an exemplary embodiment of the present application, the calculating the weight value W of the object factor in the index factor according to the weight vector W1 and the weight matrix W2 may include: W=W1×W2.

[0226] In an exemplary embodiment of the present application, based on Table 8, the weight vector W of the object layer to the target layer is calculated:

[0227]

[0228] In an exemplary embodiment of the present application, the above solution can be completed by a third weight vector calculation module.

[0229] In an exemplary embodiment of the present application, Figure 5 As shown, calculating the warning level of the object factor by the multi-factor comprehensive grading model may include steps S301-S304:

[0230] S301. Determine abnormal factors that can cause abnormal road warnings, and calculate the attribute value of each abnormal factor; the abnormal factors include the warning type, and also include any one or more of the following: traffic volume, road type, abnormal area scene, and visibility; wherein the attribute value of the warning type is calculated based on the weight value W.

[0231] S302: Based on the attribute value of each abnormal factor, the analytic hierarchy process is used to determine the weight of each abnormal factor.

[0232] In an exemplary embodiment of the present application, the method of using the hierarchical analysis method to determine the weight of each abnormal factor for determining the abnormal score based on the attribute value of each abnormal factor may include:

[0233] A comparison matrix for pairwise comparison of all abnormal factors was constructed using the preset 1-9 scale method;

[0234] Normalizing each column vector of the comparison matrix to obtain a normalized matrix;

[0235] The columns of the normalized matrix are added together, and the vector obtained after the addition is divided by the number of abnormal factors to obtain a weight vector; the weight vector contains the weight of each abnormal factor.

[0236] S303: Calculate the weighted average of all abnormal factors according to the weight and attribute value of each abnormal factor.

[0237] S304: Determine a corresponding warning level according to the calculated weight average value and a preset first mapping table; the first mapping table contains a correspondence between different weight average values ​​and different warning levels.

[0238] In an exemplary embodiment of the present application, determining the warning range that requires warning according to the warning level may include:

[0239] The warning range is determined according to the calculated warning level and a preset second mapping table; the second mapping table contains the correspondence between different warning levels and different warning ranges.

[0240] In an exemplary embodiment of the present application, the intelligent analysis server can determine the target checkpoints that need to be warned according to the warning level obtained by analysis, that is, define different warning ranges according to different warning levels, that is, the warning information data can be sent to all target checkpoints within a certain coordinate range. The corresponding configuration correspondence can be shown in Table 9:

[0241] Table 9

[0242]

[0243] In an exemplary embodiment of the present application, after all target checkpoints are acquired, all target checkpoints related to the abnormal information are stored in the database so that a release instruction can be sent again when the abnormal information is released.

[0244] In an exemplary embodiment of the present application, after obtaining all warning target checkpoints, the server combines the abnormal information with the target checkpoint to form warning information and sends it to each target checkpoint. The sending data format may be as shown in Table 10:

[0245] Table 10

[0246] WarningData A complete road warning information WarningMsgCode Warning message code TroubleData Abnormal information data WarningKKName Warning target checkpoint name WarningKKCode Warning target card gate coding WarningTime Warning time

[0247] In an exemplary embodiment of the present application, after receiving the warning information, the intelligent target checkpoint can start to identify vehicles and users passing through the target checkpoint, and send the warning information to the corresponding vehicle terminal and / or user terminal.

[0248] In an exemplary embodiment of the present application, when the smart checkpoint detects that the road abnormality has been resolved, it can send data to the intelligent analysis server in accordance with the road abnormality information protocol, and then the intelligent analysis server sends an abnormality resolution instruction to the target checkpoint, and the target checkpoint resolves the abnormal warning state.

[0249] In the exemplary embodiments of the present application, the embodiments of the present application can apply 5G communication technology to road traffic warning, establish a set of reliable abnormal alarm information reporting and warning data protocols and a scientific warning level algorithm, which can provide real-time and effective warning of abnormal road conditions.

[0250] In the exemplary embodiments of the present application, the embodiments of the present application include at least the following innovative points:

[0251] 1. The remote road condition analysis and warning system based on a preset communication method (such as 5G communication) and a smart card slot camera in the embodiment of the present application may include a multi-channel integrated 5G signal transceiver module smart card slot camera, an intelligent analysis server, a communication transmission system (such as a 5G transmission system), and an intelligent warning terminal. After the card slot camera and the intelligent analysis server obtain abnormal road condition alarm information, they design corresponding alarm reporting rules to report the alarm information. After receiving the alarm information, the server analyzes the abnormal alarm information and needs to warn the level and other information, and sends the warning information to the target smart card slot camera within a certain range of the surrounding area through 5G. After receiving the warning information, the target smart card slot camera sends the warning information to the vehicle or pedestrian terminal within the card slot range through the integrated 5G module for real-time warning.

[0252] 2. Calculate the comprehensive evaluation value for different application scenarios and alarm types, and obtain the reporting frequency of different alarms. By designing alarm reporting rules, the problems of resource waste and untimely alarms in existing alarm reporting rules are solved.

[0253] 3. Based on the roads and specific abnormal conditions actually required for warning of road abnormalities, a set of transmission protocol formats for reporting and warning of road abnormality information has been developed.

[0254] 4. The definition of road abnormality information warning level is based on the influencing factors of the road abnormality, setting a certain score according to its attributes, and calculating the score of the abnormality according to the weights of different influencing factors, thereby establishing a grading method for road abnormality warning levels.

[0255] 5. The reporting and warning of abnormal information are sent and received using new communication technologies (for example, 4G, 5G, 6G, etc.), which can ensure real-time warning of abnormal road information.

[0256] The present application embodiment provides a road remote warning device 1, such as Figure 6 As shown, it may include a processor 11 and a computer-readable storage medium 12, wherein the computer-readable storage medium 12 stores instructions, and when the instructions are executed by the processor, any one of the above-mentioned road remote warning methods is implemented.

[0257] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

Claims

1. A road remote warning method, characterized in that: The method comprises: Acquiring road abnormality warning information; the road abnormality warning information includes object factors and indicator factors; Inputting the road abnormality warning information into a preset weight calculation model, and calculating the weight value of the object factor in the index factor through the weight calculation model; Input the calculated weight value into a preset multi-factor comprehensive grading model, and calculate the warning level of the object factor through the multi-factor comprehensive grading model, including: determining the abnormal factors that can cause road abnormality alarms, and calculating the attribute value of each abnormal factor; the abnormal factors include alarm types, and also include any one or more of the following: traffic volume, road type, abnormal area scene and visibility; wherein the attribute value of the alarm type is calculated according to the weight value W; based on the attribute value of each abnormal factor, the weight of each abnormal factor is determined by the hierarchical analysis method; according to the weight size and attribute value of each abnormal factor, the weight average value of all abnormal factors is calculated; according to the calculated weight average value and the preset first mapping table, the corresponding warning level is determined; the first mapping table contains the corresponding relationship between different weight average values ​​and different warning levels; Determine the warning range that needs warning according to the warning level, and send the warning information data to the target checkpoint within the warning range through a preset communication method, so as to perform fault warning through the target checkpoint; Among them, the object factors include the alarm type, and the indicator factors include the alarm time, alarm location and alarm device type; The step of calculating the weight value of the object factor in the indicator factor by using the weight calculation model includes: constructing a first pairwise comparison matrix between different factors of the road abnormality warning information based on a 1-9 scale method; Performing a consistency check on the first pairwise comparison matrix using a canonical column average method, and calculating weight vectors W1 of different factors when the consistency check of the first pairwise comparison matrix passes; constructing a second pairwise comparison matrix between the alarm types for different factors based on the 1-9 scale method; The plurality of second pairwise comparison matrices obtained are respectively checked for consistency by using the canonical column average method, and when each second pairwise comparison matrix passes the consistency check, the factor weight vector corresponding to the corresponding factor is calculated, and the calculated plurality of factor weight vectors are combined into a weight matrix W2; The weight value W of the object factor in the index factor is calculated according to the weight vector W1 and the weight matrix W2.

2. The road remote warning method according to claim 1, characterized in that: The first pairwise comparison matrix between different factors of the road abnormality warning information constructed based on the 1-9 scale method includes: Determine the factor vector X1 of different factors; ; Get any two factors from the factor vector X1 and , according to the 1-9 scale method, the factors and The ratio of the impact value Expressed as a natural number from 1 to 9 or the reciprocal of a natural number from 1 to 9; wherein the natural number from 1 to 9 and the reciprocal of the natural number from 1 to 9 are used to represent the factor and The scale of impact; The ratio of all impact values ​​represented by the 1-9 scale forming the first pairwise comparison matrix; and / or, The second pairwise comparison matrix between the alarm types for different factors constructed based on the 1-9 scale method includes: Get the factor vector of each factor separately; The multiple evaluation subjects contained in each factor vector are compared in pairs to obtain the ratio of multiple influence values ​​corresponding to the factor; the ratio of the multiple influence values ​​is expressed as a natural number from 1 to 9 or the reciprocal of the natural number from 1 to 9; wherein the natural number from 1 to 9 and the reciprocal of the natural number from 1 to 9 are used to characterize the influence scale between any two evaluation subjects contained in the factor; for any factor, the ratio of all influence values ​​represented by the 1-9 scale method constitutes a second pairwise comparison matrix corresponding to the factor; and multiple second pairwise comparison matrices are obtained accordingly for multiple factors.

3. The road remote warning method according to claim 2, characterized in that: The step of performing consistency check on the first pairwise comparison matrix by using the canonical column average method, and calculating the weight vector W1 of different factors when the consistency check of the first pairwise comparison matrix passes includes: Calculate the first maximum eigenvalue of the first pairwise comparison matrix according to the canonical column average method ; According to the first maximum characteristic root and detecting whether the consistency of the first pairwise comparison matrix is ​​within an allowable range using a preset consistency detection strategy; When the consistency of the first pairwise comparison matrix is ​​within the allowable range, the first maximum eigenvalue is calculated. The first row of normalized vectors obtained in the process is used as the weight vector W1; and / or, The step of performing consistency check on the plurality of second pairwise comparison matrices obtained by using the canonical column average method, and calculating the factor weight vector corresponding to the corresponding factor when each second pairwise comparison matrix passes the consistency check includes: The second largest eigenvalue of each second pairwise comparison matrix is ​​calculated according to the canonical column average method. ; According to the second largest characteristic root and detecting whether the consistency degree of the corresponding second pairwise comparison matrix is ​​within an allowable range using a preset consistency detection strategy; When the consistency of the second pairwise comparison matrix is ​​within the allowable range, the second maximum eigenvalue is calculated. The second row of normalized vectors obtained in the process is used as the factor weight vector of the corresponding factor of the current second pairwise comparison matrix.

4. The road remote warning method according to claim 3, characterized in that: The first maximum eigenvalue of the first pairwise comparison matrix is ​​calculated according to the canonical column average method. include: Normalize each column vector of the first pairwise comparison matrix to obtain the first column normalized matrix ; Normalize the first column of the matrix Sum by row to get the first vector ; According to the first vector Normalize each row vector of the first pairwise comparison matrix to obtain the first row normalized vector ; Calculate the first largest eigenvalue of the first pairwise comparison matrix ; and / or, The second largest eigenvalue of the second pairwise comparison matrix is ​​calculated according to the canonical column average method. include: Normalize each column vector of the second pairwise comparison matrix to obtain a second column normalized matrix ; Normalize the second column of the matrix Sum by row to get the second vector ; According to the second vector Normalize each row vector of the second pairwise comparison matrix to obtain the second row normalized vector ; Calculate the first largest eigenvalue of the first pairwise comparison matrix .

5. The road remote warning method according to claim 3, characterized in that: The consistency detection strategy includes: Check whether it meets ; When satisfied When , it is determined that the consistency of the comparison matrix is ​​within the allowable range; when it is not satisfied When , it is determined that the consistency degree of the comparison matrix is ​​not within the permissible range; in, , represents the random consistency index, The correspondence between the numerical value and different k values ​​is provided by a preset mapping table; K is a natural number; and / or, The calculation of the weight value W of the object factor in the index factor according to the weight vector W1 and the weight matrix W2 includes: W= × .

6. The road remote warning method according to any one of claims 1 to 5, characterized in that: The method of using the analytic hierarchy process to determine the weight of each abnormal factor in determining the abnormal score based on the attribute value of each abnormal factor includes: A comparison matrix for pairwise comparison of all abnormal factors was constructed using the preset 1-9 scale method; Normalizing each column vector of the comparison matrix to obtain a normalized matrix; The columns of the normalized matrix are added together, and the vector obtained after the addition is divided by the number of abnormal factors to obtain a weight vector; the weight vector contains the weight of each abnormal factor.

7. The road remote warning method according to any one of claims 1 to 5, characterized in that: Determining the warning scope that needs warning according to the warning level includes: The warning range is determined according to the calculated warning level and a preset second mapping table; the second mapping table contains the correspondence between different warning levels and different warning ranges.

8. A road remote warning device, comprising a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, characterized in that: When the instruction is executed by the processor, the road remote warning method as described in any one of claims 1-7 is implemented.

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