An internet-based big data monitoring system and method

Abstract

The application discloses an internet-based big data monitoring system and method, and belongs to the technical field of internet big data monitoring. The system comprises a data acquisition module, a data processing module, a road section avoidance module, a path model establishment module and a path planning algorithm module. The output end of the data acquisition module is electrically connected with the input end of the data processing module. The output ends of the data processing module and the road section avoidance module are electrically connected with the input end of the path model establishment module. The output end of the path model establishment module is electrically connected with the input end of the path planning algorithm module. The application also provides a method for implementing the system. The application monitors various factors of the road sections of urban communities, reasonably plans a path according to the weight influence among the factors, effectively improves the current situation that the work vehicles in urban areas only work along fixed routes, increases the work efficiency of the work vehicles, and improves the work quality of the work vehicles and the satisfaction degree of citizens.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet big data monitoring, and in particular to an Internet-based big data monitoring system and method. Background Art

[0002] There are a large number of monitoring and sensors in today's urban blocks. These devices generate massive amounts of data at all times. The significance of big data technology lies in the professional processing of these massive amounts of data that are generated quickly, have many types of data, and have low value density.

[0003] Data processing is the technical process of analyzing and processing data (both numerical and non-numerical). This includes various methods of processing raw data, such as analysis, organization, calculation, and editing. With the increasing popularity of computers, the combination of the internet and big data can provide better services for smart living. However, there is currently a lack of specific application methods and methods for processing and analyzing comprehensive data. Summary of the Invention

[0004] The purpose of the present invention is to provide an Internet-based big data monitoring system and method to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an Internet-based big data monitoring system, which includes a data acquisition module, a data processing module, a road section avoidance module, a path model establishment module and a path planning algorithm module;

[0006] The output end of the data acquisition module is electrically connected to the input end of the data processing module, the output ends of the data processing module and the road section avoidance module are electrically connected to the input end of the path model establishment module, and the output end of the path model establishment module is electrically connected to the input end of the path planning algorithm module;

[0007] The data acquisition module includes a temperature and humidity acquisition unit, a vehicle flow acquisition unit, and a construction site influencing factor unit;

[0008] The temperature and humidity collection unit is used to collect temperature and humidity data of each road section; the vehicle flow collection unit is used to collect the number and time of vehicles passing through each road section; the construction site influencing factor unit is used to record the number and scale of construction sites around each road section;

[0009] The data processing module includes an urban operation factor receiving unit and an urban operation weight calculation unit;

[0010] The urban operation factor receiving unit is used to receive the temperature and humidity data, vehicle flow data, and construction site data transmitted by the data acquisition module; the urban operation weight calculation unit is used to determine the influence weight of the temperature and humidity data, vehicle flow data, and construction site data on the urban operation demand; the output end of the urban operation factor receiving unit is connected to the input end of the urban operation weight calculation unit;

[0011] The road section avoidance module includes a pedestrian flow acquisition unit and a traffic congestion acquisition unit;

[0012] The pedestrian flow acquisition unit is used to acquire the number of pedestrians on the road section; the traffic congestion unit is used to acquire the traffic congestion situation of the road section;

[0013] The path model building module includes a satellite map image acquisition unit, an urban area operation weight marking unit and an urban area non-operation marking unit;

[0014] The satellite map image acquisition unit is used to collect block image data to establish a road section area network; the urban area operation weight marking unit is used to mark the operation demand weight value of each road section; the urban area non-operation marking unit is used to mark the road section where urban area operation is not performed; the output end of the satellite map image acquisition unit is connected to the input end of the urban area operation weight marking unit, and the output end of the urban area operation weight marking unit is connected to the input end of the urban area non-operation marking unit;

[0015] The path planning algorithm module includes a path restriction unit, a path enumeration unit and a path comparison unit;

[0016] The path restriction unit restricts the working path of the working vehicle according to the principle of not passing through the same block twice, thereby improving the working efficiency of the working vehicle;

[0017] The path listing unit is used to list all restricted paths in the case of restricted paths, then list the operation paths of the sections not included in each restricted path, and finally combine the restricted paths with the operation paths of the sections not included in the restricted paths;

[0018] The path comparison module compares all the listed paths and finds an optimal path as the working path of the working vehicle; the output end of the path restriction unit is connected to the input end of the path listing unit, and the output end of the path listing unit is connected to the input end of the path comparison unit.

[0019] A big data monitoring method based on the Internet, the method comprising the following steps:

[0020] S1. Collect data on each road section that affects urban operation needs;

[0021] S2. Processing data that affects urban operational needs;

[0022] S3. Use satellite maps to establish a road section regional network, mark the weight of each road section and the road sections that cannot be operated in urban areas;

[0023] S4. List all restricted paths, list the urban operation paths of the remaining sections of the restricted paths, and combine the restricted paths with the corresponding urban operation paths of the remaining sections;

[0024] S5. Compare all paths and find the one with the highest efficiency and the best results in urban areas;

[0025] In the present invention, the capacity of the work vehicle is limited, and the number of road sections that the work vehicle passes through at one time is relatively small. Therefore, within a block of suitable size, the algorithm using the exhaustive method with limited conditions requires less workload and is easier to implement. By setting higher weights for sections that are more in need of urban operations and limiting the number of times the same section is passed through, the effect and efficiency of urban operations are taken into account.

[0026] In step S1, the various data collected that affect the demand for urban operations on each road section are divided into two groups: one group is factors that require urban operations, including temperature and humidity data, vehicle flow data, and factors affecting the construction site; the other group is factors that do not require urban operations, including pedestrian flow data and traffic congestion data;

[0027] In step S2, the weights of the factors requiring urban operations are divided into levels 1, 2, 3, 4, and 5. The levels of the temperature and humidity data weight a, the traffic flow data weight b, and the construction site influencing factor weight c are determined respectively. The total weight level of the factors requiring urban operations for the road section is recorded as w, where w = a × b × c.

[0028] Among the factors requiring urban operations, temperature and humidity data affect the comfort level of road sections. The processing module assigns a higher weight (a) to road sections where temperature and humidity have a greater impact on operations. The processing module also assigns a higher weight (b) to road sections with greater traffic volume. Furthermore, the processing module assigns a higher weight (c) to road sections with a greater number of surrounding construction sites and larger scales.

[0029] The pedestrian flow data and traffic congestion data included in the urban operation factors are not required. When the pedestrian flow data or traffic congestion data of a road section reaches the threshold, the road section will be omitted;

[0030] In step S3, a remote sensing satellite map of the road section is obtained, the total weight level of the road sections that require urban operations is marked on the map, the sections with excessive pedestrian flow and traffic congestion are marked, and the path model of all road sections is established;

[0031] In step S4, according to the path model established in S3, each intersection is regarded as a vertex, the connected road sections are regarded as the edges of the vertex, and the vertex is marked as D i , i>0, and i is an integer, i is represented by the sequence D i The number of columns, mark the road segment as R j , j>0, and j is an integer, j is represented by the sequence R j The number of columns, the number of vertices with odd edges is recorded as N, N ≥ 0, N is an integer and N is not equal to 1, the number of restricted paths is recorded as R l , the number of paths that pass through the sections of the path model except the restricted path is recorded as R s , the total number of paths in the path model is recorded as R a ;

[0032] If N=0, list all restricted paths, then R s The number of paths is 0, and the total number of paths is equal to the number of restricted paths, that is, R a =R l ;

[0033] If N=2, list all restricted paths, then R s The number of paths is 0, and the total number of paths is equal to the number of restricted paths, that is, R a =R l ;

[0034] If N>2, mark and omit some sections so that N=2, list all restricted paths, restore the omitted sections, and for each restricted path, list all paths that complete the remaining sections. The total number of paths in the path model is equal to the number of restricted paths multiplied by the number of paths that traverse the sections in the path model excluding the restricted paths, that is, R a =R l ×R s ;

[0035] If the path is not restricted, the enumerated sections will have the problem of high work quality but low work efficiency. According to mathematical knowledge, only when the number of vertices with an odd number of edges is equal to 0 or 2, can all sections be walked without repetition or omission. When the number of vertices with an odd number of edges is greater than 2, an edge adjacent to a vertex with an odd number of edges can be omitted to make N equal to 2. After enumerating the restricted paths, the path problem of the omitted sections can be considered. The restricted paths have vertices with an odd number of edges as endpoints.

[0036] In step S5, the total weight level of all the total paths listed in S4 is calculated;

[0037] In the path model, starting from a path, each time you pass a section, you get the weight level of that section. Except for the section with the smallest weight level, the levels of the remaining sections are reduced by 1. When the weight levels of all sections are reduced to the same as the section with the smallest weight level, the weight level of the section will no longer decrease.

[0038] The total weight levels of all total paths are compared, and the road section with the largest total weight level is the operation path with high operation efficiency and good results in the urban area.

[0039] Compared with the existing technology, the beneficial effects achieved by the present invention are: using big data technology to process the collected real-time data of road sections, establishing a path model, and planning an operation path with excellent operation efficiency and operation effect according to the set technology, thereby improving the satisfaction of residents in the block. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0041] Figure 1 This is a structural diagram of an Internet-based big data monitoring system of the present invention;

[0042] Figure 2 This is a schematic diagram of the steps of an Internet-based big data monitoring method of the present invention;

[0043] Figure 3 It is a schematic diagram of an embodiment of an Internet-based big data monitoring system and method of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] See also Figure 1-Figure 2 , the present invention provides a technical solution: a big data monitoring system based on the Internet, the system includes a data acquisition module, a data processing module, a road section avoidance module, a path model establishment module and a path planning algorithm module;

[0046] The output end of the data acquisition module is electrically connected to the input end of the data processing module, the output ends of the data processing module and the road section avoidance module are electrically connected to the input end of the path model establishment module, and the output end of the path model establishment module is electrically connected to the input end of the path planning algorithm module;

[0047] The data acquisition module includes a temperature and humidity acquisition unit, a vehicle flow acquisition unit, and a construction site influencing factor unit;

[0048] The temperature and humidity collection unit is used to collect temperature and humidity data of each road section; the vehicle flow collection unit is used to collect the number and time of vehicles passing through each road section; the construction site influencing factor unit is used to record the number and scale of construction sites around each road section;

[0049] The data processing module includes an urban operation factor receiving unit and an urban operation weight calculation unit;

[0050] The urban operation factor receiving unit is used to receive the temperature and humidity data, vehicle flow data, and construction site data transmitted by the data acquisition module; the urban operation weight calculation unit is used to determine the influence weight of the temperature and humidity data, vehicle flow data, and construction site data on the operation demand; the output end of the urban operation factor receiving unit is connected to the input end of the urban operation weight calculation unit;

[0051] The road section avoidance module includes a pedestrian flow acquisition unit and a traffic congestion acquisition unit;

[0052] The pedestrian flow acquisition unit is used to acquire the number of pedestrians on the road section; the traffic congestion unit is used to acquire the traffic congestion situation of the road section;

[0053] The path model building module includes a satellite map image acquisition unit, an urban area operation weight marking unit and an urban area non-operation marking unit;

[0054] The satellite map image acquisition unit is used to collect block image data to establish a road section area network; the urban area operation weight marking unit is used to mark the operation demand weight value of each road section; the urban area non-operation marking unit is used to mark blocks where urban area operations are not performed; the output end of the satellite map image acquisition unit is connected to the input end of the urban area operation weight marking unit, and the output end of the urban area operation weight marking unit is connected to the input end of the urban area non-operation marking unit;

[0055] The path planning algorithm module includes a path restriction unit, a path enumeration unit and a path comparison unit;

[0056] The path restriction unit restricts the working path of the working vehicle according to the principle of not passing through the same block twice, thereby improving the working efficiency of the working vehicle;

[0057] The path listing unit is used to list all restricted paths in the case of restricted paths, then list the operation paths of the sections not included in each restricted path, and finally combine the restricted paths with the operation paths of the sections not included in the restricted paths;

[0058] The path comparison module compares all the listed paths and finds an optimal path as the urban operation path of the operation vehicle; the output end of the path restriction unit is connected to the input end of the path listing unit, and the output end of the path listing unit is connected to the input end of the path comparison unit.

[0059] A big data monitoring method based on the Internet, the method comprising the following steps:

[0060] S1. Collect data on each road section that affects urban operation needs;

[0061] S2. Processing data that affects urban operational needs;

[0062] S3. Use satellite maps to establish a road section regional network, mark the weight of each road section and the road sections that cannot be operated in urban areas;

[0063] S4. List all restricted paths, list the urban operation paths of the remaining sections of the restricted paths, and combine the restricted paths with the corresponding urban operation paths of the remaining sections;

[0064] S5. Compare all paths and find the one with the highest efficiency and the best working effect;

[0065] In step S1, the collected data affecting the operation requirements of each road section are divided into two groups: one group is factors that require urban operation, including temperature and humidity data, traffic flow data, and factors affecting the construction site; the other group is factors that do not require urban operation, including pedestrian flow data and traffic congestion data;

[0066] In step S2, the weights of the factors requiring urban operations are divided into levels 1, 2, 3, 4, and 5. The levels of the temperature and humidity data weight a, the traffic flow data weight b, and the construction site influencing factor weight c are determined respectively. The total weight level of the factors requiring urban operations for the road section is recorded as w, where w = a × b × c.

[0067] In step S3, a remote sensing satellite map of the road section is obtained, the total weight level of the road sections that require urban operations is marked on the map, the sections with excessive pedestrian flow and traffic congestion are marked, and the path model of all road sections is established;

[0068] In step S4, according to the path model established in S3, each intersection is regarded as a vertex, the connected road sections are regarded as the edges of the vertex, and the vertex is marked as D i , i>0, and i is an integer, i is represented by the sequence D i The number of columns, mark the road segment as R j , j>0, and j is an integer, j is represented by the sequence R jThe number of columns, the number of vertices with odd edges is recorded as N, N ≥ 0, N is an integer and N is not equal to 1, the number of restricted paths is recorded as R l , the number of paths that pass through the sections of the path model except the restricted path is recorded as R s , the total number of paths in the path model is recorded as R a ;

[0069] If N=0, list all restricted paths, then R s The number of paths is 0, and the total number of paths is equal to the number of restricted paths, that is, R a =R l ;

[0070] If N=2, list all restricted paths, then R s The number of paths is 0, and the total number of paths is equal to the number of restricted paths, that is, R a =R l ;

[0071] If N>2, mark and omit some sections so that N=2, list all restricted paths, restore the omitted sections, and for each restricted path, list all paths that complete the remaining sections. The total number of paths in the path model is equal to the number of restricted paths multiplied by the number of paths that traverse the sections in the path model excluding the restricted paths, that is, R a =R l ×R s ;

[0072] In step S5, the total weight level of all the total paths listed in S4 is calculated;

[0073] In the path model, starting from a path, each time you pass a section, you get the weight level of that section. Except for the section with the smallest weight level, the levels of the remaining sections are reduced by 1. When the weight levels of all sections are reduced to the same as the section with the smallest weight level, the weight level of the section will no longer decrease.

[0074] Compare the total weight levels of all total paths. The section with the largest total weight level is the operation path with high operation efficiency and good results.

[0075] See also Figure 3 , in this embodiment:

[0076] Taking smart cities as an example, existing urban operation vehicles all operate according to set routes, with low operation efficiency and quality, and even affecting road traffic and pedestrians on the roadside. The operation route cannot achieve both efficiency and quality, and cannot improve the satisfaction of urban residents. In this embodiment, taking the watering operation route of a sprinkler truck as an example, an Internet-based big data monitoring data analysis is performed. The established path model has 9 intersections, marked as D1 to D9, and 12 road sections, marked as R1 to R 12 ;

[0077] After collecting and processing data information, we obtained the following levels of the temperature and humidity data weight a, traffic flow data weight b, and construction site influencing factor weight c for the 12 intersections:

[0078] Section R1: a=3, b=3, c=3;

[0079] Section R2: a=2, b=2, c=2;

[0080] Section R3: a=2, b=3, c=3;

[0081] Section R4: a=4, b=2, c=5;

[0082] Section R5: a=3, b=1, c=2;

[0083] Section R6: a=2, b=2, c=2;

[0084] Section R7: a=4, b=3, c=2;

[0085] Section R8: a=1, b=3, c=4;

[0086] Section R9: a=3, b=5, c=1;

[0087] Section R 10 : a=2, b=3, c=3;

[0088] Section R 11 : a=3, b=3, c=3;

[0089] Section R 12 : a=2, b=1, c=4;

[0090] Therefore, according to the formula:

[0091] w=a×b×c

[0092] It turns out that the total weight level of each road segment is as follows:

[0093] Section R1: 27;

[0094] Section R2: 8;

[0095] Section R3: 18;

[0096] Section R4: 40;

[0097] Section R5: 6;

[0098] Section R6: 8;

[0099] Section R7: 24;

[0100] Section R8: 12;

[0101] Section R9: 15;

[0102] Section R 10 :18;

[0103] Section R 11 :27;

[0104] Section R 12 :8;

[0105] Road sections with heavy traffic: Section R3; Road sections with heavy traffic: None;

[0106] The vertices with odd edges are: D1, D2, D6, D8, N = 4;

[0107] Try omitting section R1. The vertices with odd edges are: D6, D8, N=2;

[0108] The following are examples of restricted paths:

[0109] Restricted path 1: R5—R2—R4—R7—R 10 —R 12 —R9—R6—R8—R 11 ;

[0110] A total path after limiting path 1: R5—R2—R4—R7—R 10 —R 12 —R9—R6—R8—R 11 —R9—R4—R1; the total weight of the total path is: 6+(8-1)+(40-2)+(24-3)+(18-4)+(8-2)+(15-6)+(8-2)+(12-6)+(27-9)+(27-12)=146;

[0111] Restricted path 2: R7-R6-R8-R 11 —R 12 —R 10 —R5—R2—R4—R9;

[0112] A total path after limiting path 2: R7-R6-R8-R11 —R 12 —R 10 —R5—R2—R4—R9—R 11 —R8—R6—R4—R1; the total weight of the total path is: 24+(8-1)+(12-2)+(27-3)+(8-2)+(18-5)+(6)+(8-2)+(40-8)+(15-9)+(27-14)=147;

[0113] Comparing the total path of restriction path 1 and restriction path 2, it can be seen that the total accumulated weight of the total path of restriction path 2 is larger. If it is used as a watering path, it is more efficient and has a better effect.

[0114] List all restricted paths and their corresponding total paths, calculate the total weight of all total paths, and after comparison, select the total path with the largest total weight as the watering operation path.

[0115] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0116] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. An Internet-based big data monitoring system, characterized by: It includes data acquisition module, data processing module, road section avoidance module, path model building module and path planning algorithm module; The data acquisition module is used to collect data on factors affecting the demand for urban operations on each road section, including temperature and humidity information, traffic flow information, and construction site location information in the urban area, and summarize the data; the data processing module is used to receive information transmitted by the data acquisition module, calculate the weight of the influence of each factor, and calculate the weight level of each road section requiring urban operations based on the weight of the influence of each factor; the road section avoidance module is used to monitor road sections with large passenger flow and traffic congestion; the path model establishment module is used to receive information from the data processing module and the road section avoidance module and construct a block model for path planning; the path planning algorithm module is used to apply a set algorithm in the path model to plan the operation path of the urban operation vehicle; The path planning algorithm module includes a path restriction unit, a path listing unit and a path comparison unit; the path restriction unit constrains the working path of the urban operation vehicle according to the principle of not passing through the same block twice; the path listing unit, under the constraint of the path restriction unit, analyzes the number of odd degree vertices in the block model, and if the number of odd degree zero points is less than or equal to two, lists all Euler paths of the block model as restricted paths; if the number of odd degree zero points is greater than two, then by ignoring some road sections, the number of odd degree vertices is made less than or equal to two, and all Euler paths of the block model after ignoring some road sections are listed as restricted paths; based on each of the restricted paths, all urban operation paths not included in the restricted path are listed, and the restricted path is combined with the urban operation path whose restricted path does not include the road section. A total path is formed; the path comparison module calculates the total weight of all the total paths, including walking from the beginning of each total path, obtaining the weight level of each road section passed through, and reducing the levels of the remaining road sections by 1 except for the road section with the smallest weight level. When the weight level of the remaining road section is reduced to be equal to the road section with the smallest weight level, the weight level of the road section will no longer be reduced; the weight level of the road section that is passed through repeatedly is not repeatedly obtained; at the end of the total path, all the obtained weight levels are summed to obtain the total weight level of the total path; the total weight levels of all the total paths are compared, and the total path with the largest total weight level is the optimal path for urban operations; the output end of the path restriction unit is connected to the input end of the path enumeration unit, and the output end of the path enumeration unit is connected to the input end of the path comparison unit; The output end of the data acquisition module is electrically connected to the input end of the data processing module, the output ends of the data processing module and the road section avoidance module are electrically connected to the input end of the path model establishment module, and the output end of the path model establishment module is electrically connected to the input end of the path planning algorithm module; The data acquisition module includes a temperature and humidity acquisition unit, a vehicle flow acquisition unit, and a construction site influencing factor unit; The temperature and humidity collection unit is used to collect temperature and humidity data of each road section; the vehicle flow collection unit is used to collect the number and time of vehicles passing through each road section; and the construction site influencing factor unit is used to record the number and scale of construction sites around each road section.

2. The Internet-based big data monitoring system according to claim 1, characterized in that: The data processing module includes an urban operation factor receiving unit and an urban operation weight calculation unit; The urban operation factor receiving unit is used to receive temperature and humidity data, traffic flow data and construction site data transmitted by the data acquisition module; The urban operation weight calculation unit is used to determine the influence weight of temperature and humidity data, traffic flow data and construction site data on urban operation demand; The output end of the urban area operation factor receiving unit is connected to the input end of the urban area operation weight calculation unit.

3. The Internet-based big data monitoring system according to claim 1, characterized in that: The road section avoidance module includes a pedestrian flow acquisition unit and a traffic congestion acquisition unit; The pedestrian flow acquisition unit is used to acquire the number of pedestrians on the road section; the traffic congestion unit is used to acquire the traffic congestion situation of the road section.

4. The Internet-based big data monitoring system according to claim 1, characterized in that: The path model building module includes a satellite map image acquisition unit, an urban area operation weight marking unit and an urban area non-operation marking unit; The satellite map image acquisition unit is used to collect street image data, thereby establishing a road section area network; The urban operation weight marking unit is used to mark the urban operation demand weight value of each road section; the urban non-operation marking unit is used to mark the blocks where urban operations are not performed; The output end of the satellite map image acquisition unit is connected to the input end of the urban area operation weight marking unit, and the output end of the urban area operation weight marking unit is connected to the input end of the urban area non-operation marking unit.

5. A big data monitoring method based on the Internet, characterized by: The method comprises the following steps: S1. Collect data on each road section that affects urban operation needs; S2. Processing data that affects urban operational needs; S3. Use satellite maps to establish a road section regional network, mark the weight of each road section and the road sections that cannot be operated in urban areas; S4. List all restricted paths, list the urban operation paths of the remaining sections of the restricted paths, and combine the restricted paths with the corresponding urban operation paths of the remaining sections; In step S4, according to the path model established in S3, each intersection is regarded as a vertex, the connected road sections are regarded as the edges of the vertex, and the vertex is marked as D i , i>0, and i is an integer, i is represented by the sequence D i The number of columns, mark the road segment as R j , j>0, and j is an integer, j is represented by the sequence R j The number of columns, the number of vertices with odd edges is recorded as N, N ≥ 0, N is an integer and N is not equal to 1, the number of restricted paths is recorded as R l , the number of paths that pass through the sections of the path model except the restricted path is recorded as R s , the total number of paths in the path model is recorded as R a ; If N=0, list all restricted paths, then R s The number of paths is 0, and the total number of paths is equal to the number of restricted paths, that is, R a =R l ; If N=2, list all restricted paths, then R s The number of paths is 0, and the total number of paths is equal to the number of restricted paths, that is, R a =R l ; If N>2, mark and omit some sections so that N=2, list all restricted paths, restore the omitted sections, and for each restricted path, list all paths that complete the remaining sections. The total number of paths in the path model is equal to the number of restricted paths multiplied by the number of paths that traverse the sections in the path model excluding the restricted paths, that is, R a =R l ×R s ; S5. Compare all paths and find the one with the highest efficiency and the best working effect; In step S5, the total weight level of all the total paths listed in S4 is calculated; In the path model, starting from a path, each time a section is passed, the weight level of the section is obtained. Except for the section with the smallest weight level, the levels of the remaining sections are reduced by 1. When the weight level of the remaining sections is reduced to the same as the section with the smallest weight level, the weight level of the section will no longer be reduced. The weight level of the section passed through repeatedly is not obtained repeatedly. At the end of the total path, all the obtained weight levels are summed up to obtain the total weight level of the path. The total weight levels of all total paths are compared, and the total path with the largest total weight level is the optimal path for urban operations.

6. The Internet-based big data monitoring method according to claim 5, characterized in that: In step S1, the collected data affecting the operation requirements of each road section are divided into two groups: one group is factors that require urban operation, including temperature and humidity data, traffic flow data, and factors affecting the construction site; the other group is factors that do not require urban operation, including pedestrian flow data and traffic congestion data; In step S2, the weights of factors requiring urban operations are divided into levels 1, 2, 3, 4, and 5, and the levels of the temperature and humidity data weight a, the traffic flow data weight b, and the construction site influencing factor weight c are determined respectively. The total weight level of the factors requiring urban operations on the road section is recorded as w, then w = a×b×c.

7. The Internet-based big data monitoring method according to claim 5, characterized in that: In step S3, a remote sensing satellite map of the road section is obtained, the total weight level of the road sections that require urban operations is marked on the map, the sections with excessive pedestrian flow and traffic congestion are marked, and the path models of all road sections are established.

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