An overload vehicle monitoring and management platform based on the Internet of Things

Abstract

The invention relates to the technical field of overweight vehicle monitoring and management, and specifically discloses an overweight vehicle monitoring and management platform based on the Internet of Things, comprising: an overweight vehicle identification module, a drivable road section judgment module, a drivable monitoring and management module, a non-drivable monitoring and management module, an overweight management report generation module and a database; the invention identifies each overweight vehicle passing through each road confluence node of a target road section, performs drivable monitoring and management or non-drivable monitoring and management on the overweight vehicle, automatically generates an overweight management report by confirming the overweight confluence node prone to overweight, the overweight severity of each type of vehicle and the overweight confluence time period prone to overweight, realizes remote monitoring of overweight vehicles, improves law enforcement efficiency and convenience, reduces traffic congestion, road damage and traffic accident risks, is conducive to the scientificity and systematicness of overweight management work, and helps management departments to understand the overweight vehicle situation on the road section more comprehensively and accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of overweight vehicle monitoring and management, and in particular to an overweight vehicle monitoring and management platform based on the Internet of Things. Background Art

[0002] With the rapid development of the transportation industry, overloaded vehicles (such as overweight, overlength, overwidth, overheight, etc.) pose a serious threat to road safety, bridge structures, and highway service life. Traditional manual monitoring methods have problems such as low efficiency, high cost, and narrow coverage, which are difficult to meet the needs of modern traffic management. Therefore, it is particularly important to develop an overloaded vehicle monitoring and management platform based on the Internet of Things.

[0003] There are still the following problems in the existing monitoring and management methods for overweight vehicles: 1. The current management of overweight vehicles mainly involves setting up checkpoints at key locations on the road and on-site supervision by law enforcement personnel. It is impossible to accurately identify overweight vehicles, which increases the possibility of misjudgment and missed judgment. At the same time, it is impossible to achieve remote monitoring and management of overweight vehicles, which reduces the efficiency and convenience of law enforcement.

[0004] 2. Currently, the main method adopted for overweight vehicles on the road is interception. No analysis is conducted on whether there are other drivable sections for overweight vehicles, and no other drivable sections are provided for overweight vehicles. This increases traffic congestion, aggravates road damage and the risk of traffic accidents, and is not conducive to the scientific and systematic nature of overweight management work.

[0005] 3. Currently, the road management department's understanding of the overweight phenomenon on a road section mainly comes from the data uploaded by law enforcement personnel. It does not automatically generate overweight management reports based on the overweight phenomenon of overweight vehicles, which cannot help the management department to understand the situation of overweight vehicles on the road section and their changing trends more comprehensively and accurately, and cannot provide strong data support for the management department's overweight management. Summary of the invention

[0006] In view of this, in order to solve the problems raised in the above background technology, an overload vehicle monitoring and management platform based on the Internet of Things is proposed.

[0007] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides an overweight vehicle monitoring and management platform based on the Internet of Things, including: an overweight vehicle identification module, which is used to deploy collection equipment at each road confluence node of the target section, collect the driving data of each vehicle passing through each road confluence node of the target section, and identify each overweight vehicle passing through each road confluence node of the target section.

[0008] The drivable road section judgment module is used to extract the corresponding alternative driving sections from the navigation of each overloaded vehicle passing through each road merging node of the target section, and judge whether there is a drivable section for each overloaded vehicle. If there is a drivable section, the drivable monitoring management module is executed; if there is no drivable section, the non-drivable monitoring management module is executed.

[0009] The drivable monitoring and management module is used to analyze the drivability compatibility between each overloaded vehicle and its corresponding drivable road section, and to perform drivable monitoring and management on each overloaded vehicle.

[0010] The non-drivable monitoring and management module is used to monitor and manage the non-drivable status of each overloaded vehicle.

[0011] The overload management report generation module is used to confirm the target road section's prone overload merging nodes, the overload severity of each type of vehicle and the overload merging time period, and automatically generate an overload management report based on the target road section's prone overload merging nodes, the overload severity of each type of vehicle and the overload merging time period, and feed the overload management report back to the target road section's road management department.

[0012] The database is used to store the restricted vehicle weight, restricted vehicle width, restricted vehicle height and restricted vehicle length of vehicles passing through each road section, and store the severity of overload corresponding to each overload severity coefficient.

[0013] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention can accurately identify overweight vehicles by identifying each overweight vehicle passing through each road merging node of the target road section based on the driving data of each vehicle passing through each road merging node of the target road section, thereby reducing the possibility of misjudgment and missed judgment, and at the same time realizing remote monitoring and management of overweight vehicles, thereby improving law enforcement efficiency and convenience.

[0014] (2) The present invention determines whether there are drivable sections for each overloaded vehicle, and monitors and manages whether the overloaded vehicles can be driven or not, and provides other drivable sections for the overloaded vehicles, thereby reducing traffic congestion, road damage and traffic accident risks, and is conducive to the scientific and systematic management of overloaded vehicles.

[0015] (3) The present invention helps the management department to understand the situation of overweight vehicles and changing trends on the road section more comprehensively and accurately by identifying the nodes where overweight vehicles are likely to merge on the target road section, the severity of overweight of each type of vehicle and the time period where overweight vehicles are likely to merge on the target road section, and automatically generating an overweight management report, thereby providing strong data support for the management department's overweight management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 It is a schematic diagram of the system module structure connection of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 As shown, the present invention provides an overload vehicle monitoring and management platform based on the Internet of Things, including: an overload vehicle identification module, a drivable road section judgment module, a drivable monitoring and management module, a non-drivable monitoring and management module, an overload management report generation module and a database.

[0020] The overloaded vehicle identification module is connected to the drivable road section judgment module, the drivable monitoring management module and the non-drivable monitoring management module are both connected to the drivable road section judgment module, the overloaded vehicle identification module is connected to the overload management report generation module, and the overloaded vehicle identification module, the drivable road section judgment module and the overload management report generation module are all connected to the database.

[0021] The overload vehicle identification module is used to deploy collection equipment at each road confluence node of the target section, collect the driving data of each vehicle passing through each road confluence node of the target section, and identify each overload vehicle passing through each road confluence node of the target section.

[0022] In a specific embodiment of the present invention, the driving data includes vehicle weight, vehicle width, vehicle height, vehicle length, vehicle model and passing time point.

[0023] It should be noted that the vehicle weight is collected by an installed floor scale, the vehicle width, height, length and vehicle model are all identified from vehicle images captured by an installed high-definition camera, and the passing time point is collected from an installed time sensor.

[0024] In a specific embodiment of the present invention, the specific process of identifying each overloaded vehicle passing through each road merging node of the target section is: A1, extracting vehicle weight, vehicle width, vehicle height and vehicle length from the driving data of each vehicle passing through each road merging node of the target section.

[0025] A2. Extract the vehicle weight limit, vehicle width limit, vehicle height limit and vehicle length limit for vehicles passing through each road section from the database, and extract the vehicle weight limit, vehicle width limit, vehicle height limit and vehicle length limit for vehicles passing through the target road section.

[0026] A3. The vehicle weight, width, height and length of each vehicle passing through each road merging node of the target section are respectively compared with the restricted vehicle weight, restricted vehicle width, restricted vehicle height and restricted vehicle length of vehicles passing through the target section. If any one of the vehicle weight, vehicle width, vehicle height and vehicle length of a certain vehicle is greater than the restricted vehicle weight, restricted vehicle width, restricted vehicle height and restricted vehicle length, the vehicle is recorded as an over-limited vehicle, thereby obtaining the over-limited vehicles passing through each road merging node of the target section.

[0027] The embodiment of the present invention can accurately identify overweight vehicles passing through each road merging node of the target road section based on the driving data of each vehicle passing through each road merging node of the target road section, reduce the possibility of misjudgment and missed judgment, and at the same time realize remote monitoring and management of overweight vehicles, thereby improving law enforcement efficiency and convenience.

[0028] The drivable road section judgment module is used to extract the corresponding alternative driving sections from the navigation of each overloaded vehicle passing through each road merging node of the target section, and judge whether there is a drivable road section for each overloaded vehicle. If there is a drivable road section, the drivable monitoring management module is executed; if there is no drivable road section, the non-drivable monitoring management module is executed.

[0029] In a specific embodiment of the present invention, the specific process of judging whether there is a drivable road section for each overloaded vehicle is as follows: B1. Matching and comparing each alternative driving section corresponding to each overloaded vehicle with the restricted vehicle weight, restricted vehicle width, restricted vehicle height and restricted vehicle length of vehicles passing through each section stored in a database, obtaining the restricted vehicle weight, restricted vehicle width, restricted vehicle height and restricted vehicle length of vehicles passing through each alternative driving section corresponding to each overloaded vehicle, and respectively comparing them with the vehicle weight, vehicle width, vehicle height and vehicle length of each overloaded vehicle.

[0030] B2. If any one of the weight, width, height and length of an overloaded vehicle is greater than the weight limit, width limit, height limit and length limit of vehicles passing through the corresponding alternative driving sections, it means that there is no drivable section for the overloaded vehicle.

[0031] B3. If any one of the weight, width, height and length of an overloaded vehicle is smaller than the weight limit, width limit, height limit and length limit for vehicles passing through a corresponding alternative driving section, then the alternative driving section will be used as the drivable section for the overloaded vehicle, thereby obtaining the drivable sections corresponding to each overloaded vehicle.

[0032] The drivability monitoring and management module is used to analyze the drivability compatibility between each overloaded vehicle and its corresponding drivable road sections, and to perform drivability monitoring and management on each overloaded vehicle.

[0033] In a specific embodiment of the present invention, the specific process of analyzing the driving compatibility between each overloaded vehicle and each corresponding drivable road section is as follows: C1, the vehicle weight limit, vehicle width limit, vehicle height limit and vehicle length limit of each overloaded vehicle in each drivable road section corresponding to each overloaded vehicle are recorded as , , and ,in, Indicates the number of the overloaded vehicle. , Indicates the number of the drivable road section. .

[0034] C2. Record the weight, width, height and length of each overloaded vehicle as , , and .

[0035] C3. Calculate the driving compatibility between each overloaded vehicle and its corresponding drivable road section , ,in, , , and They represent the vehicle weight deviation, vehicle width deviation, vehicle height deviation and vehicle length deviation of the reference setting respectively. , , and They represent the weights of the set vehicle weight deviation, vehicle width deviation, vehicle height deviation and vehicle length deviation corresponding to the driving adaptability evaluation. , Represents a natural constant.

[0036] In a specific embodiment of the present invention, The setting value of is 0.25. The setting value of is 0.25. The setting value of is 0.25. The setting value of is 0.25. When actually calculating the driving suitability, the above four factors should be considered comprehensively. It is impossible to judge which deviation factor is more important alone, because each factor may have an important impact on the driving suitability.

[0037] In a specific embodiment of the present invention, the specific method of monitoring and managing the drivability of each overweight vehicle is as follows: extracting the maximum value from the driving fitness between each overweight vehicle and its corresponding drivable road section, and using the drivable road section with the maximum driving fitness as the optimal driving section corresponding to each overweight vehicle, sending the optimal driving section corresponding to each overweight vehicle to the navigation of each overweight vehicle, and tracking the driving path of each overweight vehicle in real time through navigation. If an overweight vehicle enters its corresponding optimal driving section, it indicates that the overweight vehicle complies with the drivability monitoring management. If an overweight vehicle does not enter its corresponding optimal driving section, the driving position of the overweight vehicle is sent to the road management department of the target section.

[0038] The non-drivable monitoring and management module is used to perform non-drivable monitoring and management on each overloaded vehicle.

[0039] In a specific embodiment of the present invention, the specific method of performing non-drivable monitoring management on each overloaded vehicle is: issuing a parking warning to each overloaded vehicle, and sending the parking position of the overloaded vehicle to the road management department of the target section.

[0040] The embodiment of the present invention determines whether there is a drivable road section for each overloaded vehicle, and performs drivable monitoring management or non-drivable monitoring management on the overloaded vehicles, provides other drivable road sections for the overloaded vehicles, reduces traffic congestion, road damage and traffic accident risks, and is conducive to the scientific and systematic nature of overload management work.

[0041] The overload management report generation module is used to confirm the target road section's prone overload merging nodes, the overload severity of each type of vehicle and the overload merging time period, and automatically generate an overload management report based on the target road section's prone overload merging nodes, the overload severity of each type of vehicle and the overload merging time period, and feed the overload management report back to the road management department of the target road section.

[0042] In a specific embodiment of the present invention, the specific method of confirming the target road section's easy-to-overload confluence node is as follows: D1, accumulating the number of overloaded vehicles passing through each road confluence node of the target road section to obtain the number of overloaded vehicles at each road confluence node of the target road section, and recording it as ,in, Indicates the number of the node where the road merges into. .

[0043] D2. Calculate the proportion of overloaded vehicles at each road merging node on the target road section , .

[0044] D3. Compare the proportion of overweight vehicles at each road confluence node of the target section with the proportion of overweight vehicles of the set reference. If the proportion of overweight vehicles at a road confluence node is greater than or equal to the proportion of overweight vehicles of the set reference, then record the road confluence node as a confluence node prone to overweight, thereby obtaining the confluence node prone to overweight of the target section.

[0045] In a specific embodiment of the present invention, the specific process of confirming the severity of overloading of various types of vehicles on the target road section is as follows: E1, extracting the vehicle model and passing time point from the driving data of each vehicle passing through each road confluence node of the target road section, and extracting the vehicle model and passing time point of each overloaded vehicle passing through each road confluence node.

[0046] E2. Compare the models of the overloaded vehicles passing through the nodes on each road, and count the number of overloaded vehicles corresponding to each model, recorded as ,in, Indicates the number of each type of vehicle. .

[0047] E3. Calculate the vehicle overload rate for each vehicle type , .

[0048] E4. Compare the passing time points of each overloaded vehicle passing through each road confluence node, count the passing time points of each overloaded vehicle corresponding to each vehicle type, and take the interval between the first passing time point and the last passing time point as the overload interval time corresponding to each vehicle type, and record it as .

[0049] E5. Calculate the frequency of over-limit for each type of vehicle , , Indicates the allowed over-limit frequency for the set over-limit vehicle.

[0050] E6. Calculate the overload severity coefficient for each vehicle type , ,in, and They represent the weights of the set vehicle overload rate and overload frequency corresponding to the overload severity coefficient assessment, .

[0051] In a specific embodiment of the present invention, The setting value of is 0.5. The value of is set to 0.5. When calculating the overload severity coefficient for each type of vehicle, both the vehicle overload rate and overload frequency play an important role. The overload rate provides an overall overview of the overload problem, while the overload frequency reveals the persistence and recurrence of the problem. The two complement each other and together constitute a comprehensive and in-depth assessment of the overload problem.

[0052] E7. Compare the overload severity coefficients corresponding to each type of vehicle model with the overload severity levels corresponding to each overload severity coefficient stored in the database, so as to obtain the overload severity levels of each type of vehicle model.

[0053] In a specific embodiment of the present invention, the specific method of confirming the time period for merging into a target section that is prone to overloading is as follows: the passing time points of each overloading vehicle passing through each road merging node are respectively marked on the time axis to obtain the overloading time axis corresponding to each road merging node, and the time period between the first passing time point and the last passing time point is used as the overloading merging time period corresponding to each road merging node; finally, the overloading merging time periods corresponding to each road merging node are overlapped, and the time period between the first passing time point and the last passing time point after the overlap is used as the time period for merging into the target section that is prone to overloading.

[0054] The embodiment of the present invention helps the management department to understand the overload vehicle situation and change trend of the road section more comprehensively and accurately by confirming the target road section's prone overload merging nodes, the overload severity of each type of vehicle and the overload merging time period, and automatically generating an overload management report, and provides strong data support for the management department's overload management.

[0055] The database is used to store the restricted vehicle weight, restricted vehicle width, restricted vehicle height and restricted vehicle length of vehicles passing through each road section, and to store the overload severity corresponding to each overload severity coefficient.

[0056] The data sources in the database of this embodiment are shown in Table 1 below.

[0057] Table 1 Detailed description of data sources in the database

[0058]

[0059] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. An overload vehicle monitoring and management platform based on the Internet of Things, characterized in that: include: An overload vehicle identification module is used to deploy collection equipment at each road confluence node of the target road section, collect the driving data of each vehicle passing through each road confluence node of the target road section, and identify each overload vehicle passing through each road confluence node of the target road section; The drivable road section judgment module is used to extract the corresponding alternative driving sections from the navigation of each overloaded vehicle passing through each road confluence node of the target section, and judge whether there is a drivable section for each overloaded vehicle. If there is a drivable section, the drivable monitoring management module is executed; if there is no drivable section, the non-drivable monitoring management module is executed; The drivability monitoring and management module is used to analyze the drivability compatibility between each overloaded vehicle and its corresponding drivable road sections, and to perform drivability monitoring and management on each overloaded vehicle; The non-drivable monitoring and management module is used to monitor and manage the non-drivable vehicles that exceed the limit; An overload management report generation module is used to confirm the target road section's easy overload merging nodes, the overload severity of each type of vehicle and the easy overload merging time period, and automatically generate an overload management report based on the target road section's easy overload merging nodes, the overload severity of each type of vehicle and the easy overload merging time period, and feed the overload management report back to the road management department of the target road section; A database for storing the vehicle weight limit, vehicle width limit, vehicle height limit and vehicle length limit of vehicles passing through each road section, and storing the severity of overload corresponding to each overload severity coefficient; The specific method of confirming the target section's easy-to-overload merging node is as follows: D1. Accumulate the number of overloaded vehicles passing through each road confluence node of the target section to obtain the number of overloaded vehicles passing through each road confluence node of the target section, and record it as ,in, Indicates the number of the node where the road merges into. ; D2. Calculate the proportion of overloaded vehicles at each road merging node on the target road section , ; D3. Compare the ratio of the number of overweight vehicles at each road confluence node of the target road section with the ratio of the number of overweight vehicles of the set reference. If the ratio of the number of overweight vehicles at a road confluence node is greater than or equal to the ratio of the number of overweight vehicles of the set reference, record the road confluence node as a confluence node prone to overweight, thereby obtaining the confluence node prone to overweight of the target road section; The specific process of confirming the severity of overload of various types of vehicles on the target road section is as follows: E1. Extracting the vehicle type and passing time point from the driving data of each vehicle passing through each road confluence node of the target road section, and extracting the vehicle type and passing time point of each overloaded vehicle passing through each road confluence node; E2. Compare the models of the overloaded vehicles passing through the nodes on each road, and count the number of overloaded vehicles corresponding to each model, recorded as ,in, Indicates the number of each type of vehicle. ; E3. Calculate the vehicle overload rate for each vehicle type , ; E4. Compare the passing time points of each overloaded vehicle passing through each road confluence node, count the passing time points of each overloaded vehicle corresponding to each vehicle type, and take the interval between the first passing time point and the last passing time point as the overload interval time corresponding to each vehicle type, and record it as ; E5. Calculate the frequency of over-limit for each type of vehicle , , Indicates the allowed over-limit frequency of the set over-limit vehicle; E6. Calculate the overload severity coefficient for each vehicle type , ,in, and They represent the weights of the set vehicle overload rate and overload frequency corresponding to the overload severity coefficient assessment, ; E7. Compare the overload severity coefficients corresponding to each type of vehicle model with the overload severity corresponding to each overload severity coefficient stored in the database, thereby obtaining the overload severity of each type of vehicle model; The specific method for confirming the time period for merging into the target road section that is prone to overloading is as follows: marking the passing time points of each overloaded vehicle passing through each road merging node on the time axis respectively, so as to obtain the overloaded time axis corresponding to each road merging node, and taking the time period between the first passing time point and the last passing time point as the overloaded merging time period corresponding to each road merging node; finally, overlapping the overloaded merging time periods corresponding to each road merging node, and taking the time period between the first passing time point and the last passing time point after the overlap as the time period for merging into the target road section that is prone to overloading.

2. According to the Internet of Things-based overload vehicle monitoring and management platform of claim 1, it is characterized by: The driving data includes vehicle weight, vehicle width, vehicle height, vehicle length, vehicle model and passing time point.

3. According to claim 2, an overload vehicle monitoring and management platform based on the Internet of Things is characterized by: The specific process of identifying each overloaded vehicle passing through each road merging node of the target road section is as follows: A1. Extracting vehicle weight, vehicle width, vehicle height and vehicle length from the driving data of each vehicle passing through each road confluence node of the target road section; A2. Extracting the vehicle weight limit, vehicle width limit, vehicle height limit and vehicle length limit of vehicles passing through each road section from the database, and extracting the vehicle weight limit, vehicle width limit, vehicle height limit and vehicle length limit of vehicles passing through the target road section; A3. The vehicle weight, width, height and length of each vehicle passing through each road merging node of the target section are respectively compared with the restricted vehicle weight, restricted vehicle width, restricted vehicle height and restricted vehicle length of vehicles passing through the target section. If any one of the vehicle weight, vehicle width, vehicle height and vehicle length of a certain vehicle is greater than the restricted vehicle weight, restricted vehicle width, restricted vehicle height and restricted vehicle length, the vehicle is recorded as an over-limited vehicle, thereby obtaining the over-limited vehicles passing through each road merging node of the target section.

4. According to claim 3, an overload vehicle monitoring and management platform based on the Internet of Things is characterized by: The specific process of determining whether each overloaded vehicle has a drivable section is as follows: B1. Match and compare the alternative driving sections corresponding to the overloaded vehicles with the vehicle weight limits, vehicle width limits, vehicle height limits and vehicle length limits of the vehicles passing through the sections stored in the database, obtain the vehicle weight limits, vehicle width limits, vehicle height limits and vehicle length limits of the vehicles passing through the alternative driving sections corresponding to the overloaded vehicles, and compare them with the vehicle weight, vehicle width, vehicle height and vehicle length of the overloaded vehicles respectively; B2. If any one of the weight, width, height and length of an overloaded vehicle is greater than the weight limit, width limit, height limit and length limit of vehicles passing through the corresponding alternative driving sections, it means that there is no driving section for the overloaded vehicle; B3. If any one of the weight, width, height and length of an overloaded vehicle is smaller than the weight limit, width limit, height limit and length limit for vehicles passing through a corresponding alternative driving section, then the alternative driving section will be used as the drivable section for the overloaded vehicle, thereby obtaining the drivable sections corresponding to each overloaded vehicle.

5. According to claim 4, an overload vehicle monitoring and management platform based on the Internet of Things is characterized by: The specific process of analyzing the driving compatibility between each overloaded vehicle and its corresponding drivable road sections is as follows: C1. The vehicle weight limit, vehicle width limit, vehicle height limit and vehicle length limit of each overloaded vehicle in each drivable road section are recorded as , , and ,in, Indicates the number of the overloaded vehicle. , Indicates the number of the drivable road section. ; C2. Record the weight, width, height and length of each overloaded vehicle as , , and ; C3. Calculate the driving compatibility between each overloaded vehicle and its corresponding drivable road section , ,in, , , and They represent the vehicle weight deviation, vehicle width deviation, vehicle height deviation and vehicle length deviation of the reference setting respectively. , , and They represent the weights of the set vehicle weight deviation, vehicle width deviation, vehicle height deviation and vehicle length deviation corresponding to the driving adaptability evaluation. , Represents a natural constant.

6. The overload vehicle monitoring and management platform based on the Internet of Things according to claim 5 is characterized by: The specific method of carrying out drivable monitoring and management on each overweight vehicle is as follows: extracting the maximum value from the drivable fitness between each overweight vehicle and its corresponding drivable road section, and taking the drivable road section with the maximum drivable fitness as the optimal drivable road section corresponding to each overweight vehicle, sending the optimal drivable road section corresponding to each overweight vehicle to the navigation of each overweight vehicle, and tracking the driving path of each overweight vehicle in real time through the navigation; if an overweight vehicle enters its corresponding optimal drivable road section, it indicates that the overweight vehicle complies with the drivable monitoring and management; if an overweight vehicle does not enter its corresponding optimal drivable road section, the driving position of the overweight vehicle is sent to the road management department of the target road section.

7. The overload vehicle monitoring and management platform based on the Internet of Things according to claim 4 is characterized by: The specific method of performing non-drivable monitoring management on each overloaded vehicle is: issuing a parking warning to each overloaded vehicle, and sending the parking position of the overloaded vehicle to the road management department of the target road section.

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