Vehicle-mounted weighing cloud platform monitoring system based on ZigBee technology

Abstract

The invention discloses a vehicle-mounted weighing cloud platform monitoring system based on a Zigbee technology. The system comprises a hardware part and a software part. The hardware part is a sensor network which comprises a resistance strain gauge, a vehicle-mounted navigation terminal, a ZigBee terminal controller and a ZigBee coordinator. The embedded gateway comprises an STM32 main controller and a BC26 module; and the executing mechanism comprises a relay module. The software part comprises a front-end interface for providing user login and data query guidance; and the back-end database responds to a request of a front-end user or hardware equipment through an API interface and writes or reads information into or from the cloud server database. The vehicle-mounted weighing cloud platform monitoring system provided by the invention can remotely monitor the load and the position of the vehicle in real time through highly integrated hardware information acquisition equipment in combination with a software graphical retrieval interface, so that the monitoring blind area is greatly reduced while the cost and the precision are taken into account, and the monitoring efficiency is improved. The method has a wide application prospect in the field of internet-of-things transportation.

Description

Technical Field

[0001] The present invention relates to the field of vehicle-mounted load-bearing technology, and in particular to a vehicle-mounted weighing cloud platform monitoring system based on ZigBee technology. Background Art

[0002] With the booming Internet of Things (IoT) transportation industry, vehicle overloading has become a major road safety issue. To address the potential road safety hazards posed by overloaded and over-limited transport vehicles, transportation regulators have installed static weighing devices and vehicle speed measuring devices at fixed locations. However, these devices only record the status of vehicles as they pass through the detection devices and cannot track the vehicle's progress in real time during transportation, resulting in significant regulatory blind spots.

[0003] The traditional weighing method currently in widespread use is weighing on a scale. This requires vehicles to first arrive at a designated location for weighing, empty, and then loaded with cargo. While this process offers high precision, it is time-consuming, labor-intensive, and inefficient. Furthermore, weighing on a scale can only be performed on a small scale, leaving some drivers evading overloading regulations by changing routes or loading cargo mid-flight, often slipping through the cracks. To address these drawbacks, some companies are adopting dynamic onboard weighing systems. These systems utilize detection equipment installed on axles or other critical stress points to detect structural deformation and derive the vehicle's load. However, existing onboard weighing solutions typically involve attaching sensors directly to the vehicle frame or replacing certain body components with detection equipment. This not only complicates the installation process but also alters the vehicle's structure, potentially posing safety risks. Furthermore, the equipment is susceptible to mechanical wear during loading and unloading, shortening its lifespan, reducing measurement accuracy, and increasing maintenance costs.

[0004] In summary, the current on-board weighing system is difficult to strike a balance in terms of accuracy, cost, reliability and timeliness. It also has problems such as cumbersome installation, short equipment life, and potential safety hazards. Therefore, it can only be used in the aftermarket of small-scale freight digital platforms and cannot be promoted on a large scale to meet the ever-expanding market demand. Summary of the Invention

[0005] The purpose and workflow of this invention can be summarized as follows: To address the shortcomings of existing on-board weighing technology, a cloud-based monitoring system for on-board weighing based on ZigBee technology is proposed. This system calibrates vehicle weight using a sensor network and Kalman filtering, and confirms vehicle location in conjunction with a Beidou vehicle navigation terminal. This information is transmitted to a cloud server via a ZigBee embedded gateway, and a remote monitoring platform is configured to issue data query or update commands. This enables real-time monitoring of vehicle weight and location, significantly reducing blind spots in monitoring and more effectively preventing safety hazards caused by vehicle overloading.

[0006] The specific technical solution of the present invention is: a vehicle-mounted weighing cloud platform monitoring system based on ZigBee technology, which includes two aspects: hardware and software. The hardware aspect mainly includes a sensor network module, an embedded gateway and an actuator. The sensor network module collects relevant data and then sends it to the embedded gateway through the ZigBee wireless transmission network. The software part is a remote monitoring cloud platform, which is responsible for receiving, storing and displaying vehicle location information and vehicle-mounted weight information. It can display the monitored vehicle data and historical data in real time in the form of charts, which is convenient for users to query. In order to achieve the above technical solutions adopted by the present invention are:

[0007] The hardware system architecture is as follows:

[0008] Architecture 1: Sensor network module construction. Four strain gauge resistors, constructed as a Wheatstone full-bridge circuit, are mounted symmetrically on the vehicle axles for deformation detection. The strain gauges utilize a PEEK substrate. A differential amplifier and analog-to-digital converter (ADC) circuits are then configured for digital-to-analog conversion. Finally, a ZigBee terminal controller acquires data. The vehicle navigation terminal connects to the BC26 module via a separate data cable, communicating vehicle location data with the embedded gateway based on commands from the main controller.

[0009] Architecture 2: Embedded gateway; the STM32F103 main controller uses a Kalman filter algorithm and covariance recursion to eliminate noise interference, and then establishes a multi-level calibration linear model to achieve accurate conversion of payload data; the BC26 module provides global communication coverage. When the BC26 receives data, it sends the corresponding instructions to the STM32 to enable the STM32 to receive the data.

[0010] Architecture 3: Actuator; its function is to coordinate the operation of the STM32 main controller and BC26 module according to the received commands. The core part is the optocoupler isolation relay, which is driven by peripheral circuits.

[0011] The software cloud platform is designed as follows:

[0012] 1. Front-end part: The front-end web design is based on Vue.js, and implements specific functions through routing management, component development, visual design, front-end and back-end communication, performance optimization and other links.

[0013] 2. Backend: Data captured by the device or frontend is converted into a JSON file. This file is then uploaded to a database (a cloud-based server) via a TCP connection to a wireless network. Once uploaded, it is imported into the corresponding tables using Navicat's import function. Users can then query SQL to retrieve data for the corresponding ID. Depending on the system's functional needs, the database stores the following information: user information, vehicle weight, vehicle location, and device status.

[0014] Effects of the present invention:

[0015] The hardware device of the present invention overcomes the defects of the traditional weighing method, such as fixed weighing position, low weighing efficiency and high human resource consumption. The sensor network can collect vehicle load data within an error range of 5%, and combine it with the vehicle position information obtained by the on-board navigation terminal and send it to the cloud server through the Zigbee embedded gateway.

[0016] The remote monitoring platform of the present invention realizes the modularization and visualization of vehicle data, improves the convenience and timeliness of data acquisition, and at the same time, the platform can send monitoring instructions to the on-board hardware equipment and update the vehicle load and location data in the cloud server in real time, greatly reducing the blind spots of supervision. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flowchart of the hardware part of the vehicle weighing cloud platform monitoring system based on ZigBee technology of the present invention;

[0018] Figure 2 This is a front view of the axle with the resistance strain gauge installed in the present invention;

[0019] Figure 3 It is a schematic diagram of the hardware equipment installation layout;

[0020] Figure 4 This is the flow chart of the Kalman filter formula;

[0021] Figure 5 It is a circuit diagram of the optocoupler relay of the actuator of the present invention;

[0022] Figure 6 This is a workflow diagram of the software part of a vehicle-mounted weighing cloud platform monitoring system based on ZigBee technology of the present invention.

[0023] The reference numerals in the figure are as follows: 1-four-piece resistance strain gauge; 2-vehicle navigation terminal; 3-BC26 module; 4-embedded gateway; 5-STM32 main controller; 6-actuator; 7-differential amplifier circuit; 8-AD conversion circuit. DETAILED DESCRIPTION

[0024] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. The embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0025] like Figure 1As shown, the present invention provides a vehicle-mounted weighing cloud platform monitoring system based on ZigBee technology, which is divided into two modules: hardware and software. The figure shows the workflow of the hardware part, which mainly includes a sensor network, an embedded gateway and an actuator. When the goods are loaded onto the vehicle, the axle will be deformed, which in turn causes the voltage at the output end of the Wheatstone full-bridge circuit composed of four strain resistors to change. After undergoing a differential amplifier circuit, zero adjustment and attenuation circuit to improve the accuracy, it is collected by the ZigBee terminal controller after AD conversion, and packaged and sent to the ZigBee wireless communication network with the cooperation of the ZigBee coordinator. The embedded gateway receives the data sent from the ZigBee coordinator, calculates the vehicle weight through the Kalman filter algorithm, and transmits it to the server using the NB-IOT protocol. During this period, the actuator receives the command from the main controller to control the response of the equipment under different circumstances.

[0026] like Figure 2 As shown, the core of the sensor network is a Wheatstone full-bridge circuit composed of strain gauge resistors. Four strain gauge resistors are placed symmetrically in the vertical and horizontal directions around the axle, forming the Wheatstone full-bridge circuit. Four pins extend from the Wheatstone full-bridge circuit, connecting to the positive and negative power inputs and two voltage signal outputs.

[0027] From the inside out, the resistance strain gauge consists of a base, a sensitive grid, leads, and a cover. The sensitive grid is the core component. The leads are metal wires extending from the sensitive grid. The base and cover secure the sensitive grid and leads relative to each other and protect them. The base is made of PEEK film with low water absorption and is manufactured into a fully sealed structure. This provides excellent heat resistance and low moisture absorption, while also enabling both temperature and creep self-compensation.

[0028] When the vehicle is loaded with cargo, the deformation of the axle will be detected by the resistance strain gauge. Assume that the power supply of the Wheatstone full-bridge circuit is VO, the coefficient of the strain gauge is K, the strains of the four strain gauges are δ1, δ2, δ3, and δ4 respectively, and the circuit output voltage is VE1. They will satisfy the following relationship:

[0029]

[0030] The Wheatstone full-bridge circuit uses four resistance strain gauges for comprehensive measurement. Its output has the advantage of eliminating interference from bending, shearing, lateral or longitudinal torsional stress. The circuit output voltage is first differentially amplified, and then adjusted by zero adjustment and attenuation circuits to improve measurement accuracy. The differential amplifier circuit structure is as follows: Figure 3As shown, V1-V2 is the output voltage value VE1 of the Wheatstone full-bridge circuit, Ral-Ra2 are proportional resistors with the same resistance value, Rf is the proportional reference resistor, Rp is the matching resistor, and the voltage after differential amplification is VE2. They will satisfy the following relationship:

[0031]

[0032] The differentially amplified voltage is converted by the AD circuit and then received by the ZigBee terminal controller. The ZigBee terminal controller primarily consists of a CC2530 core board and a controller baseboard. The CC2530 core board includes the CC2530F256RHAR chip and the external circuitry required for the driver chip's operation. The controller baseboard includes a communication interface for connecting to the Wheatstone full-bridge circuit and system debugging. The hardware of the ZigBee coordinator is essentially the same as that of the ZigBee terminal node. Its main function is to establish a ZigBee wireless communication network and transmit the information converted by the AD circuit to the embedded terminal via serial communication for processing.

[0033] The embedded gateway consists of an STM32 main controller and a BC26 module. The STM32 main controller receives analog-to-digital conversion data from the ZigBee coordinator, calculates vehicle weight using a Kalman filter algorithm, and then transmits the result to the server via the BC26 module. The main controller MCU is an STM32F103C6T6, and the external circuits include a pulse clock circuit, a reset circuit, and a power supply.

[0034] The flow chart of Kalman filter formula is as follows Figure 4 As shown, after the MCU receives the information converted by the AD circuit, it uses it as the measurement value MC, compares it with the signal predicted by the Kalman filter algorithm and calculates the covariance, and then obtains the next prediction signal, and so on. Each prediction signal constitutes an output sequence, and then according to the linear relationship established by multi-level calibration, the linear relationship established during calibration is finally used to obtain vehicle load data with high accuracy, and the data error range can be controlled within 5%. Kalman filtering is a processing technology that eliminates noise and restores actual data. The specific implementation method is to estimate the state of the dynamic system from a series of data with measurement noise when the measurement variance is known. Let the Kalman gain be GA, the current estimate value be PE, the previous estimate value be LE, the current estimated covariance be PCV, the next estimate value be NCV, the measurement value be MC, the current measurement covariance be PMC, the next measurement covariance be NMC, and the estimated change ratio be EMC. Then the Kalman filter formula can be expressed as:

[0035] PE=(MC-LE)*GA+EMC*LE

[0036]

[0037] The BC26 module is based on the MT2625 chip, adopts the NB-IOT protocol, and supports global frequency bands, enabling global communication with a single module. The BC26 is connected to an STM32 via a serial port. The STM32 sends AT commands to the BC26 to control the BC26's network connection and data transmission and reception. When the BC26 receives data, it sends corresponding commands to the STM32 to adjust the STM32's reception of the data.

[0038] Vehicle positioning is implemented by the on-board navigation terminal, which consists of a Beidou terminal host, information processing equipment, an interactive display device, and on-board terminal software. The Beidou terminal host connects to the public network via a wireless public network access module, enabling data exchange with Beidou satellites and recording the vehicle's latitude and longitude. The interactive display device and information processing equipment are integrated into one unit, connected to the BC26 module via a dedicated data cable, enabling bidirectional data transmission and reception with the embedded gateway. The information processing equipment also features a wireless hotspot module and a monitoring and alarm module. The wireless hotspot module ensures stable wireless public network coverage throughout the vehicle, while the monitoring and alarm module automatically triggers in emergencies, quickly transmitting device error or danger information to the embedded gateway. The gateway then uploads the data to the server and, based on its interpretation of the data and instructions from the server, controls actuators and adjusts device status.

[0039] The function of the actuator is to coordinate the work of the STM32 main controller and the BC26 module according to the received commands to ensure that the corresponding operations are realized. The core structure is the relay module. The relay module structure is as follows Figure 5 As shown, an optocoupler isolation relay is used and driven by a peripheral circuit. The relay input end is connected to the STM32 main controller, and the output end is connected to the controlled part.

[0040] like Figure 6 As shown, the present invention provides a vehicle-mounted weighing cloud platform monitoring system based on ZigBee technology, which is divided into two modules: hardware and software. The figure shows the workflow of the software part, which mainly designs a remote monitoring platform, including the front end, the back end and the database. The front end uses the Vue.js framework to write the page, and sends HTTP requests to the back end through Axios. The back end uses the API interface written in the Express framework of Node.js to process the front end request, query or write information in the MySQL database, and return the data in JSON format. The front end then renders the data on the page. At the same time, the back end also processes the information sent by the detection equipment and stores it in the database in a table form.

[0041] On the front-end, we first used Vue Router to configure routing parameters and wildcards, perform path mapping and query functions, implement web page routing management, and divide the page into multiple independent, highly reusable functional modules. Data interaction is achieved through parent-child component communication. After the page routing is completed, the Element-UI component library is used for page UI design to achieve intuitive display of interactive user interface content.

[0042] The monitoring platform uses the Axios library to facilitate data exchange between the frontend and backend. Specifically, the frontend retrieves environmental data, device status, and other information through HTTP requests, and renders pages based on the JSON data returned by the backend. Using VueRouter, the system loads components based on user interactions, enabling efficient switching between login, registration, and homepage display.

[0043] After the front-end programming is complete, use the Webpack tool to package the source code and optimize the project's static resources into submodules that are loaded on demand. After the back-end is started, use nginx as a reverse proxy to complete the front-end deployment.

[0044] On the backend, first convert the data from the device or front-end into a JSON file, then connect to the wireless network via TCP, send the file to the corresponding API interface, and upload it to the MySQL database. After the upload is complete, use Navicat's import function to fill it into the corresponding form. After the user logs in to the account, he can access the database through the SQL query language to obtain the data of the corresponding ID. This embodiment uses Alibaba Cloud Server ECS as the cloud database and interactive platform environment, with a CPU of 2 cores, 4GB of memory, and a Windows operating system. Create a table to store information in the database, specifically:

[0045] The user information table stores user accounts and passwords, and provides login and registration functions. Users can only obtain access to the database after logging in.

[0046] The vehicle weight table stores the vehicle weight data measured by the equipment in tons.

[0047] The vehicle location table stores the vehicle's latitude and longitude data measured by the device.

[0048] The device status table receives updates on device data. After logging in, users can refresh the device status to obtain real-time information about the vehicle. After logging in, users can query the vehicle's most recently measured weight and location information based on the vehicle's ID.

[0049] After completing the backend programming, use the Webpack tool to package the source code. Configure FileZilla to transfer the packaged files to the Alibaba Cloud server. This requires obtaining permissions for the target server folder in advance and ensuring that the firewall does not restrict FTP traffic. After the transfer is complete, use the npm manager to install dependencies and PM2. Use PM2 to start the backend and use nginx as a reverse proxy to complete the frontend deployment.

[0050] In summary, the present invention discloses a vehicle-mounted weighing cloud platform monitoring system based on ZigBee technology. In terms of hardware, it balances various aspects such as measurement accuracy, manufacturing cost, installation convenience, and equipment life. In terms of software, it mainly integrates a remote monitoring platform, which can monitor the vehicle's load changes and travel path online in real time, meet the growing demand for overload and over-limit supervision in the transportation industry, greatly reduce the blind spots of supervision, and have high practical application value.

[0051] The above embodiments are only used as preferred implementation methods to fully illustrate the technical solutions of the present invention. It should be pointed out that, based on the principles of the present invention, equivalent substitutions or modifications made by technicians in this technical field should be regarded as within the scope of protection of the present invention. The specific scope of protection shall be subject to the claims.

Claims

1. A vehicle-mounted weighing cloud platform monitoring system based on ZigBee technology, characterized by The system includes the following modules: a sensor network module for collecting vehicle weight and position data, the sensor network module is composed of a resistance strain gauge, a vehicle navigation terminal, a ZigBee terminal controller and a ZigBee coordinator. The resistance strain gauge collects vehicle weight data, the vehicle navigation terminal collects vehicle position data, the ZigBee terminal controller sends the collected data to the ZigBee coordinator, and the ZigBee coordinator is connected to the embedded gateway via serial communication; The embedded gateway is used to receive data sent by the sensor network module and upload it to the server. The embedded gateway is composed of an STM32 main controller and a BC26 module. The STM32 main controller receives data from the ZigBee coordinator and uploads it to the server through the programmed BC26 module; The server is used to receive and store data uploaded by the embedded gateway. The server is an Alibaba Cloud server ECS with a CPU of 2 cores, 4GB of memory, and a Windows operating system. The remote monitoring platform is used for users to log in and access data in the server, realizing real-time monitoring of vehicle weight and location information and historical data query. The remote monitoring platform adopts a front-end and back-end separation design. The front-end uses the Vue framework to write pages and sends HTTP requests to the API interface written in the Express framework on the back-end through Axios. The back-end processes the request and returns the data in JSON format. The front-end then renders the data on the page.

2. The vehicle-mounted weighing cloud platform monitoring system based on ZigBee technology according to claim 1 is characterized in that: In the sensor network module, four resistance strain gauges form a Wheatstone full-bridge circuit. The resistance strain gauges are composed of a substrate, a sensitive grid, a lead and a cover from the inside to the outside. A PEEK film with low water absorption is selected as the substrate to form a fully sealed structure.

3. The vehicle-mounted weighing cloud platform monitoring system based on ZigBee technology according to claim 1 is characterized in that: In the embedded gateway, the STM32 main controller uses the Kalman filter algorithm to calculate the vehicle weight. The BC26 module is developed based on the MT2625 chip, adopts the NB-IoT protocol, supports global frequency bands, and a single module can achieve global communication coverage. The STM32 main controller sends corresponding AT commands to the BC26 module through the serial port to control the BC26 module's network connection and data transmission and reception. At the same time, when the BC26 module receives data, it sends corresponding instructions to the STM32 main controller to enable the STM32 main controller to receive the data.

4. The vehicle-mounted weighing cloud platform monitoring system based on ZigBee technology according to claim 1 is characterized in that: The remote monitoring platform also includes functional modules such as user management, vehicle management, data reception and storage, real-time status monitoring, and historical data management. Among them: the user management module is responsible for the maintenance and management of user accounts and passwords, and the verification of user login identity, account and password information; the vehicle management module is responsible for entering, maintaining, and deleting vehicle information accessing the system and removing it from the system; the data reception and storage module includes data access and analysis, and database design; the real-time status monitoring module is responsible for displaying the vehicle's load-bearing information and location information in real time; the historical data management module includes data query, download, and visualization functions. System administrators can query and export the status data of a specified vehicle within a specified time period, and can also view the vehicle operation status of the latest time period and the specified time period.

5. The vehicle-mounted weighing cloud platform monitoring system based on ZigBee technology according to claim 1 is characterized in that: The database in the server stores a user information table, a vehicle weight table, a vehicle location table, and a device status table, wherein: the user information table stores the user's account and password, and serves login and registration functions. Only after logging in can the user obtain access to the database; the vehicle weight table stores the vehicle weight data (unit: tons) measured by the device. After the user logs in, the user can query the vehicle's most recently measured weight according to the ID; the vehicle location table stores the vehicle's latitude and longitude data measured by the device. After the user logs in, the user can query the vehicle's most recently measured location according to the ID; the device status table receives updates to device data. After the user logs in, the device status can be refreshed to obtain real-time information about the vehicle.

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