Vehicle tracking and monitoring method and device based on Internet of Things technology, equipment and medium

Abstract

The invention provides a vehicle tracking and monitoring method, device and equipment based on the Internet of Things technology, and a medium. The method comprises the steps that GPS equipment information, waybill information and wagon balance information are acquired; preprocessing the acquired information including the GPS equipment information, the waybill information and the wagon balance information; constructing a transportation plan generation model, and optimizing the transportation plan generation model to obtain an optimized transportation plan generation model; based on the preprocessed GPS equipment information, waybill information and wagon balance information, utilizing the optimized transportation plan generation model to obtain an optimal transportation plan; wherein the transportation plan comprises a vehicle distribution plan, a driving route plan and a loading and unloading time plan.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics optimization, and in particular to a vehicle tracking and monitoring method, device, equipment and medium based on the Internet of Things technology. Background Art

[0002] With the rapid development of Internet of Things technology, vehicle tracking and monitoring systems have been widely used in the fields of logistics and transportation. The development of this technology enables the real-time collection and analysis of vehicle, cargo and their status information during transportation, greatly improving transportation efficiency and management level.

[0003] Traditional vehicle management methods usually rely on manual recording and manual transmission of information, which has problems such as delayed information feedback, low data processing efficiency, and high costs. Faced with the growing logistics demand and complex transportation environment, traditional methods can no longer meet the increasing requirements of modern logistics systems for real-time, accuracy, and flexibility.

[0004] Therefore, the present invention provides a vehicle tracking and monitoring method, device, equipment and medium based on Internet of Things technology. Summary of the invention

[0005] The main purpose of the present invention is to solve the technical problem that the vehicle management method in the prior art cannot meet the increasingly high requirements of modern logistics systems for real-time performance, accuracy and flexibility.

[0006] The first aspect of the present invention provides a vehicle tracking and monitoring method based on Internet of Things technology, comprising: Obtain GPS device information, freight bill information, and weighbridge information; Pre-process the acquired GPS device information, freight bill information and weighbridge information; Constructing a transportation plan generation model, and optimizing the transportation plan generation model to obtain an optimized transportation plan generation model; Based on the pre-processed GPS equipment information, freight bill information and weighbridge information, the optimized transportation plan generation model is used to obtain the optimal transportation plan; The transportation plan includes: vehicle allocation plan, driving route plan and loading and unloading time plan.

[0007] Optionally, in a first implementation of the first aspect of the present invention, the obtaining includes GPS device information, freight bill information, and weighbridge information, including: Obtain GPS device information through a GPS device, wherein the GPS device information includes a license plate number, longitude, latitude, speed, direction, and signal status information; Obtaining weighbridge information through a weighbridge, wherein the weighbridge information includes weighing data, weighing time, and weight information; The freight bill information is obtained by scanning the barcode or QR code on the goods through a PDA collector. The freight bill information includes the freight bill number, stopover points, specified departure, arrival time, specified mileage, license plate and warehouse location.

[0008] Optionally, in a second implementation of the first aspect of the present invention, the preprocessing of the acquired GPS device information, freight bill information, and weighbridge information includes: Performing data cleaning on the acquired GPS device information, freight bill information and weighbridge information to obtain cleaned GPS device information, freight bill information and weighbridge information; The cleaned GPS device information, freight order information and weighbridge information are stored based on a distributed architecture.

[0009] Optionally, in a third implementation of the first aspect of the present invention, the step of constructing a transportation plan generation model and optimizing the transportation plan generation model to obtain an optimized transportation plan generation model includes: Construct a transportation plan generation model based on optimization objectives and constraints; A set of initial transportation plans is randomly generated, and the transportation plan generation model is iteratively optimized using an optimization algorithm until the preset stopping condition is met to obtain the optimized transportation plan generation model.

[0010] Optionally, in a fourth implementation of the first aspect of the present invention, the method further includes: A transport plan generation model optimized based on different optimization objectives and corresponding constraints is used to obtain multiple optimal transport plans; wherein the multiple optimal transport plans include multiple vehicle allocation plans; A greedy algorithm or a heuristic algorithm is used to perform the final vehicle allocation based on multiple vehicle allocation plans.

[0011] Optionally, in a fifth implementation of the first aspect of the present invention, the method further comprises: acquiring vehicle, cargo and traffic information in real time, and preprocessing the vehicle, cargo and traffic information acquired in real time to obtain preprocessed vehicle, cargo and traffic information; Based on the pre-processed vehicle, cargo and traffic information, the traffic congestion situation is analyzed using big data analysis methods; the probability of vehicle failure is predicted using machine learning algorithms; The predicted traffic congestion and vehicle failure probability are displayed through a visual interface; Set corresponding adjustment strategies based on the predicted traffic congestion and vehicle failure probability, and adjust the current transportation plan based on the adjustment strategies.

[0012] Optionally, in a sixth implementation of the first aspect of the present invention, the method further includes: Obtain GPS device information through a GPS device, wherein the GPS device information includes a license plate number, longitude, latitude, speed, direction, and signal status information; The vehicle's operating status is judged based on the acquired GPS device information. When any data in the GPS device information does not meet the preset requirements, an intelligent alarm is triggered.

[0013] A second aspect of the present invention provides a vehicle tracking and monitoring device based on Internet of Things technology, comprising: Information acquisition module, used to obtain GPS device information, freight bill information and weighbridge information; A preprocessing module is used to preprocess the acquired GPS device information, freight bill information and weighbridge information; A model building module is used to build a transportation plan generation model and optimize the transportation plan generation model to obtain an optimized transportation plan generation model; The transportation plan generation module is used to obtain the optimal transportation plan based on the pre-processed GPS device information, freight order information and weighbridge information using the optimized transportation plan generation model; The transportation plan includes: vehicle allocation plan, driving route plan and loading and unloading time plan.

[0014] Optionally, in a first implementation of the second aspect of the present invention, the information acquisition module includes: Obtain GPS device information through a GPS device, wherein the GPS device information includes a license plate number, longitude, latitude, speed, direction, and signal status information; Obtaining weighbridge information through a weighbridge, wherein the weighbridge information includes weighing data, weighing time, and weight information; The freight bill information is obtained by scanning the barcode or QR code on the goods through a PDA collector. The freight bill information includes the freight bill number, stopover points, specified departure, arrival time, specified mileage, license plate and warehouse location.

[0015] Optionally, in a second implementation of the second aspect of the present invention, the preprocessing module includes: Performing data cleaning on the acquired GPS device information, freight bill information and weighbridge information to obtain cleaned GPS device information, freight bill information and weighbridge information; The cleaned GPS device information, freight order information and weighbridge information are stored based on a distributed architecture.

[0016] Optionally, in a third implementation of the second aspect of the present invention, the model building module includes: Construct a transportation plan generation model based on optimization objectives and constraints; A set of initial transportation plans is randomly generated, and the transportation plan generation model is iteratively optimized using an optimization algorithm until the preset stopping condition is met to obtain the optimized transportation plan generation model.

[0017] Optionally, in a fourth implementation of the second aspect of the present invention, the device further includes: an optimization vehicle allocation module, which is used to generate a transportation plan model based on different optimization objectives and corresponding constraints to obtain multiple optimal transportation plans; wherein the multiple optimal transportation plans include multiple vehicle allocation plans; and a greedy algorithm or a heuristic algorithm is used to perform final vehicle allocation based on multiple vehicle allocation plans.

[0018] Optionally, in a fifth implementation of the second aspect of the present invention, the device further includes: a prediction module, used to acquire vehicle, cargo and traffic information in real time, and preprocess the vehicle, cargo and traffic information acquired in real time to obtain preprocessed vehicle, cargo and traffic information; based on the preprocessed vehicle, cargo and traffic information, using a big data analysis method to analyze and acquire traffic congestion conditions; using a machine learning algorithm to predict the probability of vehicle failure; displaying the predicted traffic congestion conditions and vehicle failure probability through a visual interface; setting corresponding adjustment strategies based on the predicted traffic congestion conditions and vehicle failure probability, and adjusting the current transportation plan based on the adjustment strategies.

[0019] Optionally, in a sixth implementation of the second aspect of the present invention, the device further includes: an intelligent alarm module, used to obtain GPS device information through a GPS device, the GPS device information including license plate number, longitude, latitude, speed, direction and signal status information; judging the operating status of the vehicle based on the acquired GPS device information, and triggering an intelligent alarm when any data in the GPS device information does not meet the preset requirements.

[0020] A third aspect of the present invention provides an electronic device, the electronic device comprising a memory and at least one processor, the memory storing instructions; The at least one processor calls the instructions in the memory so that the electronic device executes each step of the above-mentioned vehicle tracking and monitoring method based on Internet of Things technology.

[0021] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed by a processor, implement the various steps of the vehicle tracking and monitoring method based on the Internet of Things technology as described above.

[0022] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention ensures real-time tracking of vehicle status and cargo information during transportation through the combined use of GPS equipment, weighbridge and PDA collector; this real-time monitoring reduces human errors and improves the accuracy and reliability of data; 2. The present invention makes transportation scheduling more scientific and intelligent by constructing and optimizing the transportation plan generation model; by using the optimization algorithm, it can quickly generate the optimal transportation plan under the transportation objectives and constraints, including reasonable vehicle allocation, driving routes and loading and unloading time arrangements, thereby minimizing transportation costs and time; 3. The present invention can acquire and analyze the information of vehicles, goods and roads in real time, and timely warn of potential traffic congestion and vehicle failure risks; through dynamic adjustment strategies based on real-time data, the transportation plan can respond quickly according to the current environmental changes to ensure the smooth progress of the transportation process; 4. The present invention supports multiple optimization objectives and corresponding constraints, and can flexibly respond to changes in actual conditions during operation; such as weather changes, traffic conditions, temporary orders, etc., and can adjust transportation strategies in a timely manner to ensure effective response to various uncertainties. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings: Figure 1 A first flow chart of a vehicle tracking and monitoring method based on Internet of Things technology provided in an embodiment of the present invention.

[0024] Figure 2 A second flow chart of a vehicle tracking and monitoring method based on Internet of Things technology provided in an embodiment of the present invention.

[0025] Figure 3 A third flow chart of the vehicle tracking and monitoring method based on Internet of Things technology provided in an embodiment of the present invention.

[0026] Figure 4 A fourth flow chart of the vehicle tracking and monitoring method based on Internet of Things technology provided in an embodiment of the present invention.

[0027] Figure 5 A fifth flow chart of a vehicle tracking and monitoring method based on Internet of Things technology provided in an embodiment of the present invention.

[0028] Figure 6 A schematic diagram of the structure of a vehicle tracking and monitoring device based on Internet of Things technology provided by an embodiment of the present invention.

[0029] Figure 7 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The embodiment of the present invention provides a vehicle tracking and monitoring method, device, equipment and medium based on the Internet of Things technology, which obtains GPS device information, freight bill information and weighbridge information; pre-processes the acquired GPS device information, freight bill information and weighbridge information; constructs a transportation plan generation model, and optimizes the transportation plan generation model to obtain an optimized transportation plan generation model; based on the pre-processed GPS device information, freight bill information and weighbridge information, uses the optimized transportation plan generation model to obtain the optimal transportation plan; wherein the transportation plan includes: vehicle allocation plan, driving route plan and loading and unloading time plan. The present invention solves the technical problem that the vehicle management method in the prior art cannot meet the increasingly high requirements of modern logistics systems for real-time, accuracy and flexibility.

[0031] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 The first embodiment of the vehicle tracking and monitoring method based on the Internet of Things technology in the embodiment of the present invention includes: 101. Obtain GPS device information, freight bill information and weighbridge information; In this embodiment, GPS device information is obtained through a GPS device, and the GPS device information includes a license plate number, longitude, latitude, speed, direction, and signal status information; Obtaining weighbridge information through a weighbridge, wherein the weighbridge information includes weighing data, weighing time, and weight information; The freight bill information is obtained by scanning the barcode or QR code on the goods through a PDA collector. The freight bill information includes the freight bill number, stopover points, specified departure, arrival time, specified mileage, license plate and warehouse location.

[0033] This embodiment ensures real-time tracking of vehicle status and cargo information during transportation through the combined use of GPS equipment, weighbridge and PDA collector. This real-time monitoring reduces human errors and improves the accuracy and reliability of data.

[0034] 102. Preprocess the acquired GPS device information, freight bill information and weighbridge information; In this embodiment, the acquired GPS device information, freight bill information and weighbridge information are cleaned to obtain cleaned GPS device information, freight bill information and weighbridge information; The cleaned GPS device information, freight order information and weighbridge information are stored based on a distributed architecture.

[0035] This embodiment can effectively eliminate noise and redundant information by cleaning the acquired data.

[0036] 103. Construct a transportation plan generation model, and optimize the transportation plan generation model to obtain an optimized transportation plan generation model; In this embodiment, a transportation plan generation model is constructed based on the optimization objectives and constraints; A set of initial transportation plans is randomly generated, and the transportation plan generation model is iteratively optimized using an optimization algorithm until the preset stopping condition is met to obtain the optimized transportation plan generation model.

[0037] This embodiment makes transportation scheduling more scientific and intelligent by constructing and optimizing the transportation plan generation model. By using the optimization algorithm, the optimal transportation plan can be quickly generated under the transportation goals and constraints, including reasonable vehicle allocation, driving routes and loading and unloading time arrangements, thereby minimizing transportation costs and time.

[0038] 104. Based on the pre-processed GPS device information, freight bill information and weighbridge information, the optimized transportation plan generation model is used to obtain the optimal transportation plan; The transportation plan includes: vehicle allocation plan, driving route plan and loading and unloading time plan.

[0039] This embodiment can make more scientific and reasonable transportation management decisions based on the optimized transportation plan generation model, which not only improves the management efficiency, but also promotes the overall logistics management to develop in the direction of intelligence.

[0040] See also Figure 2 The second embodiment of the vehicle tracking and monitoring method based on the Internet of Things technology in the embodiment of the present invention includes: 201. Obtain GPS device information, freight bill information, and weighbridge information; 202. Preprocessing the acquired GPS device information, freight bill information, and weighbridge information; In this embodiment, the acquired GPS device information, freight bill information and weighbridge information are cleaned and converted, and then the processed information is stored in a database for subsequent query and analysis. Data storage usually adopts a distributed architecture to store data in multiple databases or servers to improve data availability and scalability. At the same time, a data redundancy mechanism is involved to ensure that the data can still maintain integrity when some nodes fail.

[0041] The data cleaning includes removing duplicate, invalid or erroneous data to ensure data quality and accuracy. The processed and analyzed data can provide decision support for logistics companies and improve transportation efficiency and safety.

[0042] 203. Construct a transportation plan generation model, and optimize the transportation plan generation model to obtain an optimized transportation plan generation model; In this embodiment, the optimization objectives and constraints are determined, such as minimizing transportation cost and time, while considering constraints such as vehicle capacity, travel time, loading and unloading time, and constructing a transportation plan generation model; Initialize the transportation plan, such as randomly generating a set of initial transportation plans; Use optimization algorithms to perform iterative optimization and continuously improve the transportation plan until a stopping condition is met, such as reaching a certain number of iterations or finding a satisfactory solution.

[0043] 204. Based on the pre-processed GPS device information, freight bill information and weighbridge information, the optimized transportation plan generation model is used to obtain the optimal transportation plan; the optimal transportation plan is output, including vehicle allocation, driving route, loading and unloading time, etc.; 205. A transportation plan generation model is optimized based on different optimization objectives and corresponding constraints to obtain multiple optimal transportation plans; wherein the multiple optimal transportation plans include multiple vehicle allocation plans; and a greedy algorithm or a heuristic algorithm is used to perform final vehicle allocation based on the multiple vehicle allocation plans.

[0044] This embodiment supports multiple optimization objectives and corresponding constraints, and can flexibly respond to changes in actual conditions during operation, such as weather changes, traffic conditions, temporary orders, etc., and can adjust transportation strategies in a timely manner to ensure effective response to various uncertainties.

[0045] See also Figure 3 The third embodiment of the vehicle tracking and monitoring method based on the Internet of Things technology in the embodiment of the present invention includes: 301. Obtain GPS device information, freight bill information, and weighbridge information; 302. Preprocessing the acquired GPS device information, freight bill information, and weighbridge information; In this embodiment, according to the express business process, in accordance with the required data resources and data resource lists, data item resources existing in the form of database, file, API interface, and domain name transmission, the scattered and multiple data are stored together through the decoding, extraction, conversion, and loading process from the source end, and cleaned and converted to improve data quality; Data decoding includes: parsing the received raw data to extract useful information. For example, for GPS positioning data, it is necessary to decode longitude, latitude, speed and other information. Then, data analysis is performed, and statistical, machine learning and other methods are used to conduct in-depth analysis of the data to extract valuable information. For example, by analyzing vehicle driving data, it is possible to determine whether the current vehicle is driving normally to ensure transportation safety.

[0046] 303. Construct a transportation plan generation model, and optimize the transportation plan generation model to obtain an optimized transportation plan generation model; 304. Based on the pre-processed GPS device information, freight bill information and weighbridge information, an optimized transportation plan generation model is used to obtain an optimal transportation plan; wherein the transportation plan includes: a vehicle allocation plan, a driving route plan and a loading and unloading time plan; 305. A transportation plan generation model is optimized based on different optimization objectives and corresponding constraints to obtain multiple optimal transportation plans; wherein the multiple optimal transportation plans include multiple vehicle allocation plans; and a greedy algorithm or a heuristic algorithm is used to perform final vehicle allocation based on the multiple vehicle allocation plans; 306. Use big data analysis methods to analyze traffic congestion; use machine learning algorithms to predict the probability of vehicle failure; In this embodiment, the traffic congestion situation is analyzed and obtained using a big data analysis method based on the pre-processed GPS device information, freight order information, and weighbridge information; The probability of vehicle failure is predicted using machine learning algorithms based on pre-processed GPS device information, freight order information, and weighbridge information; The predicted traffic congestion and vehicle failure probability are displayed through a visual interface, including map display, vehicle status monitoring, and transportation plan execution; Set corresponding adjustment strategies based on the predicted traffic congestion and vehicle failure probability, and adjust the current transportation plan based on the adjustment strategies.

[0047] When special situations arise that require human intervention, dispatchers operate through a visual interface.

[0048] Provide auxiliary decision support, such as recommending feasible adjustment plans and evaluating the impact of adjustment plans.

[0049] The dispatcher makes adjustment decisions based on auxiliary decision support and implements the adjustment plan through the system.

[0050] Transportation plans and scheduling results are updated in real time to reflect the impact of human intervention.

[0051] See also Figure 4 The fourth embodiment of the vehicle tracking and monitoring method based on the Internet of Things technology in the embodiment of the present invention includes: 401. Obtain GPS device information, freight bill information, and weighbridge information; 402. Preprocessing the acquired GPS device information, freight bill information, and weighbridge information; 403. Construct a transportation plan generation model, and optimize the transportation plan generation model to obtain an optimized transportation plan generation model; 404. Based on the pre-processed GPS device information, freight bill information and weighbridge information, an optimized transportation plan generation model is used to obtain an optimal transportation plan; wherein the transportation plan includes: a vehicle allocation plan, a driving route plan and a loading and unloading time plan; 405. A transportation plan generation model is optimized based on different optimization objectives and corresponding constraints to obtain multiple optimal transportation plans; wherein the multiple optimal transportation plans include multiple vehicle allocation plans; and a greedy algorithm or a heuristic algorithm is used to perform final vehicle allocation based on the multiple vehicle allocation plans; 406. Based on the pre-processed GPS equipment information, freight order information and weighbridge information, the traffic congestion situation is analyzed using big data analysis methods; the probability of vehicle failure is predicted using machine learning algorithms; 407. Determine the running status of the vehicle based on the acquired GPS device information. When any data in the GPS device information does not meet the preset requirements, an intelligent alarm is triggered; In this embodiment, by collecting data and cleaning existing data, basic data is integrated through data analysis to establish intelligent alarms; By monitoring the vehicle transportation data transmitted by GPS and processing the data, the current operating status of the vehicle can be determined, such as: low speed, abnormal parking, line deviation, etc., so that business personnel can promptly discover vehicle abnormalities and deal with them.

[0052] This embodiment can obtain and analyze the information of vehicles, goods and roads in real time, and timely warn of potential traffic congestion and vehicle failure risks. Through dynamic adjustment strategies based on real-time data, the transportation plan can respond quickly to changes in the current environment to ensure the smooth progress of the transportation process.

[0053] See also Figure 5 The fifth embodiment of the vehicle tracking and monitoring method based on the Internet of Things technology in the embodiment of the present invention includes: 501. Express information data collection; In this embodiment, data information is obtained by obtaining GPS equipment, weighbridges, PDAs and other equipment; GPS device information: including license plate number, longitude, latitude, speed, direction, signal status, etc.

[0054] Freight bill information: including freight bill number, stopover points, specified departure time, arrival time, specified mileage, license plate and storage space, etc.

[0055] Scale information: including weighing data, weighing time, weight, etc.

[0056] PDA data information: Use a PDA collector to scan the barcode / QR code on the cargo, and establish an association with the vehicle during the cargo unloading and loading scanning stages.

[0057] 502. Data storage and data collection cleaning processing; According to the express business process, the required data resources and data resource lists are transmitted through databases, files, API interfaces, and domain names. The data is decrypted, extracted, converted, and loaded from the source. The main purpose is to centrally store scattered and multiple data, clean and convert them, so as to improve data quality.

[0058] Data decoding: This part parses the received raw data and extracts useful information. For example, for GPS positioning data, it is necessary to decode longitude, latitude, speed and other information. Then, data analysis is performed, and statistical, machine learning and other methods are used to conduct in-depth analysis of the data and extract valuable information. For example, by analyzing vehicle driving data, it is possible to determine whether the current vehicle is driving normally and ensure transportation safety.

[0059] Data storage: The processed data is stored in the database for subsequent query and analysis. Data storage usually adopts a distributed architecture to store data in multiple databases or servers to improve data availability and scalability. At the same time, a data redundancy mechanism is designed to ensure that the data can still maintain integrity when some nodes fail. Including vehicle information, driver information, transportation order information, etc., to provide support for visual management and data analysis.

[0060] The data processing process generally includes steps such as data collection, data decoding, data cleaning, data analysis and data storage.

[0061] Data cleaning is to remove duplicate, invalid or erroneous data to ensure data quality and accuracy. The processed and analyzed data can provide decision support for logistics companies and improve transportation efficiency and safety.

[0062] 503. Intelligent scheduling based on cleaned data; The transportation system can automatically generate a transportation plan for the next five days based on vehicle routes and cargo volume information or users can add a temporary transportation plan. Users can bind vehicles to transportation plans. Establish an intelligent dispatching center to centrally manage and monitor vehicles, goods and traffic information during transportation; Dispatchers can use the visual interface of the dispatch center to understand the transportation situation in real time, conduct manual intervention and adjust the dispatch plan; Specifically, it includes: regularly obtaining vehicle routes, cargo volume and traffic information from data sources; cleaning and preprocessing data to remove outliers and erroneous data; converting data into a format suitable for algorithm processing, such as building data matrices, charts, etc.; Determine the optimization objectives and constraints, such as minimizing transportation costs and time, while considering constraints such as vehicle capacity, driving time, loading and unloading time, etc. Initialize the transportation plan, such as randomly generating a set of initial transportation plans. Use optimization algorithms to iteratively optimize and continuously improve the transportation plan until the stopping conditions are met, such as reaching a certain number of iterations or finding a satisfactory solution. Output the optimal transportation plan, including vehicle allocation, driving routes, loading and unloading times, etc.

[0063] The user selects a vehicle on the system interface and binds it to the transportation plan; the system automatically verifies the availability and suitability of the vehicle, such as checking whether the vehicle's capacity meets the cargo requirements and whether the vehicle's status is normal.

[0064] According to the binding results, the vehicles are assigned to the corresponding transportation tasks. Greedy algorithms, heuristic algorithms, etc. can be used for vehicle allocation to improve vehicle utilization and transportation efficiency.

[0065] In this embodiment, it also includes: obtaining vehicle, cargo and traffic information in real time, and updating the information in real time through sensors, GPS and other technologies.

[0066] Use big data analysis and machine learning algorithms to analyze and predict information, such as using time series analysis to predict traffic congestion and using machine learning algorithms to predict the probability of vehicle failure.

[0067] The analysis and prediction results are displayed on a visual interface, including map display, vehicle status monitoring, transportation plan execution status, etc.

[0068] When special situations arise that require human intervention, dispatchers operate through a visual interface.

[0069] The system provides auxiliary decision support, such as recommending feasible adjustment plans and evaluating the impact of adjustment plans.

[0070] The dispatcher makes adjustment decisions based on auxiliary decision support and implements the adjustment plan through the system.

[0071] The system updates transportation plans and scheduling results in real time to reflect the impact of human intervention.

[0072] 504. Early warning applications based on collected and preprocessed data; In this embodiment, by cleaning the collected data and integrating the basic data through data analysis, intelligent alarm and transportation visualization are established, so that the business can observe the overall operation status of the vehicle in a timely manner and maximize the value of the data; Intelligent alarm: By monitoring the vehicle transportation data transmitted by GPS, after data processing and using model data algorithm, the operating status of the vehicle at this moment is determined, such as: low speed, abnormal parking, line deviation, etc., so that business personnel can promptly deal with abnormal vehicle conditions and handle them.

[0073] Trajectory visualization: Users can obtain the transportation plan and GPS time binding node by locking the vehicle (PDA operation) and other actions as the starting node of the transportation plan trajectory, and understand the location, speed and other information of the vehicle loaded with the freight bill in real time, and display it through a visual interface; when the transportation of the transportation plan trajectory is completed, the user can trigger the end of GPS monitoring by unlocking the vehicle (PDA operation) and other actions.

[0074] The above describes the vehicle tracking and monitoring method based on the Internet of Things technology in the embodiment of the present invention. The following describes the vehicle tracking and monitoring device based on the Internet of Things technology in the embodiment of the present invention. Figure 6 In one embodiment of the present invention, a vehicle tracking and monitoring device based on the Internet of Things technology includes: Information acquisition module 601, used to acquire GPS device information, freight bill information and weighbridge information; In this embodiment, the information acquisition module 601 includes: Obtain GPS device information through a GPS device, wherein the GPS device information includes a license plate number, longitude, latitude, speed, direction, and signal status information; Obtaining weighbridge information through a weighbridge, wherein the weighbridge information includes weighing data, weighing time, and weight information; The freight bill information is obtained by scanning the barcode or QR code on the goods through a PDA collector. The freight bill information includes the freight bill number, stopover points, specified departure, arrival time, specified mileage, license plate and warehouse location.

[0075] A preprocessing module 602 is used to preprocess the acquired GPS device information, freight bill information and weighbridge information; In this embodiment, the preprocessing module 602 includes: Perform data cleaning and format conversion on the acquired GPS device information, freight bill information and weighbridge information to obtain processed GPS device information, freight bill information and weighbridge information; The processed GPS device information, freight order information and weighbridge information are stored based on a distributed architecture.

[0076] The model building module 603 is used to build a transportation plan generation model and optimize the transportation plan generation model to obtain an optimized transportation plan generation model; In this embodiment, the model building module 603 includes: Construct a transportation plan generation model based on optimization objectives and constraints; A set of initial transportation plans is randomly generated, and the transportation plan generation model is iteratively optimized using an optimization algorithm until the preset stopping condition is met to obtain the optimized transportation plan generation model.

[0077] The transportation plan generation module 604 is used to obtain the optimal transportation plan based on the pre-processed GPS device information, freight bill information and weighbridge information using the optimized transportation plan generation model; wherein the transportation plan includes: vehicle allocation plan, driving route plan and loading and unloading time plan.

[0078] The optimized vehicle allocation module 605 is used to generate a transportation plan model based on different optimization objectives and corresponding constraints to obtain multiple optimal transportation plans; wherein the multiple optimal transportation plans include multiple vehicle allocation plans; and a greedy algorithm or a heuristic algorithm is used to perform final vehicle allocation based on multiple vehicle allocation plans.

[0079] The prediction module 606 is used to obtain vehicle, cargo and traffic information in real time, and pre-process the vehicle, cargo and traffic information obtained in real time to obtain pre-processed vehicle, cargo and traffic information; based on the pre-processed vehicle, cargo and traffic information, use big data analysis methods to analyze and obtain traffic congestion conditions; use machine learning algorithms to predict vehicle failure probabilities; display the predicted traffic congestion conditions and vehicle failure probabilities through a visual interface; set corresponding adjustment strategies based on the predicted traffic congestion conditions and vehicle failure probabilities, and adjust the current transportation plan based on the adjustment strategies.

[0080] The intelligent alarm module 607 is used to obtain GPS device information through the GPS device, and the GPS device information includes license plate number, longitude, latitude, speed, direction and signal status information; based on the obtained GPS device information, the operating status of the vehicle is judged, and when any data in the GPS device information does not meet the preset requirements, the intelligent alarm is triggered.

[0081] above Figure 6 The vehicle tracking and monitoring device based on the Internet of Things technology in the embodiment of the present invention is described in detail from the perspective of modular functional entities, and the electronic device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0082] Figure 7 7 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device 700 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 710 (for example, one or more processors) and a memory 720, and one or more storage media 730 (for example, one or more mass storage devices) storing application programs 733 or data 732. Among them, the memory 720 and the storage medium 730 can be temporary storage or permanent storage. The program stored in the storage medium 730 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the electronic device 700. Furthermore, the processor 710 may be configured to communicate with the storage medium 730 to execute a series of instruction operations in the storage medium 730 on the electronic device 700.

[0083] The electronic device 700 may also include one or more power supplies 740, one or more wired or wireless network interfaces 750, one or more input and output interfaces 750, and / or one or more operating systems 731, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 7The structure of the electronic device shown does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine some components, or arrange the components differently.

[0084] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of a vehicle tracking and monitoring method based on Internet of Things technology.

[0085] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0086] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0087] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A vehicle tracking and monitoring method based on Internet of Things technology, characterized in that: include: Obtain GPS device information, freight bill information, and weighbridge information; Pre-process the acquired GPS device information, freight bill information and weighbridge information; Constructing a transportation plan generation model, and optimizing the transportation plan generation model to obtain an optimized transportation plan generation model; Based on the pre-processed GPS equipment information, freight bill information and weighbridge information, the optimized transportation plan generation model is used to obtain the optimal transportation plan; The transportation plan includes: vehicle allocation plan, driving route plan and loading and unloading time plan.

2. The vehicle tracking and monitoring method based on Internet of Things technology according to claim 1 is characterized in that: The acquisition includes GPS device information, freight bill information and weighbridge information, including: Obtain GPS device information through a GPS device, wherein the GPS device information includes a license plate number, longitude, latitude, speed, direction, and signal status information; Obtaining weighbridge information through a weighbridge, wherein the weighbridge information includes weighing data, weighing time, and weight information; The freight bill information is obtained by scanning the barcode or QR code on the goods through a PDA collector. The freight bill information includes the freight bill number, stopover points, specified departure, arrival time, specified mileage, license plate and warehouse location.

3. The vehicle tracking and monitoring method based on Internet of Things technology according to claim 1 is characterized in that: The preprocessing of the acquired GPS device information, freight bill information and weighbridge information includes: Performing data cleaning on the acquired GPS device information, freight bill information and weighbridge information to obtain cleaned GPS device information, freight bill information and weighbridge information; The cleaned GPS device information, freight order information and weighbridge information are stored based on a distributed architecture.

4. The vehicle tracking and monitoring method based on Internet of Things technology according to claim 1 is characterized in that: The transport plan generation model is constructed and optimized to obtain an optimized transport plan generation model, including: Construct a transportation plan generation model based on optimization objectives and constraints; A set of initial transportation plans is randomly generated, and the transportation plan generation model is iteratively optimized using an optimization algorithm until the preset stopping condition is met to obtain the optimized transportation plan generation model.

5. The vehicle tracking and monitoring method based on Internet of Things technology according to claim 4 is characterized in that: The method further comprises: A transport plan generation model optimized based on different optimization objectives and corresponding constraints is used to obtain multiple optimal transport plans; wherein the multiple optimal transport plans include multiple vehicle allocation plans; A greedy algorithm or a heuristic algorithm is used to perform the final vehicle allocation based on multiple vehicle allocation plans.

6. The vehicle tracking and monitoring method based on Internet of Things technology according to claim 1 is characterized in that: The method further comprises: Acquire vehicle, cargo and traffic information in real time, and pre-process the vehicle, cargo and traffic information acquired in real time to obtain pre-processed vehicle, cargo and traffic information; Based on the pre-processed vehicle, cargo and traffic information, the traffic congestion situation is analyzed using big data analysis methods; the probability of vehicle failure is predicted using machine learning algorithms; The predicted traffic congestion and vehicle failure probability are displayed through a visual interface; Set corresponding adjustment strategies based on the predicted traffic congestion and vehicle failure probability, and adjust the current transportation plan based on the adjustment strategies.

7. The vehicle tracking and monitoring method based on Internet of Things technology according to claim 1 is characterized in that: The method further comprises: Obtain GPS device information through a GPS device, wherein the GPS device information includes a license plate number, longitude, latitude, speed, direction, and signal status information; The vehicle's operating status is judged based on the acquired GPS device information. When any data in the GPS device information does not meet the preset requirements, an intelligent alarm is triggered.

8. A vehicle tracking and monitoring device based on Internet of Things technology, characterized in that: include: Information acquisition module, used to obtain GPS device information, freight bill information and weighbridge information; A preprocessing module is used to preprocess the acquired GPS device information, freight bill information and weighbridge information; A model building module is used to build a transportation plan generation model and optimize the transportation plan generation model to obtain an optimized transportation plan generation model; The transportation plan generation module is used to obtain the optimal transportation plan based on the pre-processed GPS device information, freight order information and weighbridge information using the optimized transportation plan generation model; The transportation plan includes: vehicle allocation plan, driving route plan and loading and unloading time plan.

9. An electronic device, comprising a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory so that the electronic device executes each step of the vehicle tracking and monitoring method based on the Internet of Things technology as described in any one of claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the various steps of the vehicle tracking and monitoring method based on the Internet of Things technology as described in any one of claims 1 to 7 are implemented.

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