Measurement and control system and measurement and control method based on fusion of multiple sensors

Abstract

The invention provides a measurement and control system and a measurement and control method based on fusion of multiple sensors, and belongs to the technical field of sensor measurement and control. The measurement and control system and the measurement and control method based on the fusion of the multiple sensors comprise a central processing module, a sensor module, a communication module and a user interface module, and node data of the sensor module, the communication module and the user interface module are transmitted to the central processing module. According to the method, Kalman filtering is used in the system to introduce a weighted average method, the accuracy, robustness and flexibility of state estimation can be effectively improved, particularly, the performance is outstanding in a multi-sensor environment, and inconsistent measurement and abnormal values can be effectively processed through reasonable weight distribution, so that the overall performance is improved, and the method is suitable for large-scale popularization and application. And the Kalman filtering algorithm is more efficient and reliable in dynamic system state estimation.

Description

Technical Field

[0001] The present invention relates to the field of sensor measurement and control technology, and in particular to a measurement and control system and a measurement and control method based on the fusion of multiple sensors. Background Art

[0002] Sensor measurement and control is a technology that uses sensors to measure physical quantities and regulates and manages them through control systems. It is widely used in automation, industrial control, environmental monitoring, smart home and other fields. Sensors play a key role in measurement and control systems. They are responsible for collecting various status information of the measured system, and the control system makes decisions and adjustments based on this information, thereby achieving effective management and optimization of the system.

[0003] Sensor measurement and control technology is a key technology that combines sensors, controllers and information processing systems and is widely used in the fields of automation and intelligence. As the core component of information acquisition, sensors can monitor various physical quantities in real time and convert them into electrical signals to provide accurate data. The control system performs real-time analysis and decision-making based on the information fed back by the sensors, thereby realizing automatic control of the equipment or environment. For example, in industrial automation, sensors monitor the temperature and pressure on the production line, and the control system adjusts the operating status of the equipment accordingly to ensure the stability and safety of the production process. With the development of Internet of Things technology, sensor measurement and control systems have developed in the direction of intelligence and networking, realizing remote monitoring and intelligent data analysis, providing important support for fields such as smart manufacturing, smart cities and environmental monitoring, and promoting the digital transformation and efficiency improvement of various industries. By optimizing resource allocation and improving response speed, sensor measurement and control technology has played an important role in improving production efficiency, reducing energy consumption and improving the living environment.

[0004] However, the existing sensor measurement and control systems still have some problems in actual use. For example, the existing sensor measurement and control systems can generally only measure and control a single sensor, that is, a system can only be applied to one sensor and cannot achieve multi-sensor fusion measurement and control, which greatly reduces its practicality. Summary of the invention

[0005] In order to remedy the above deficiencies, the present invention provides a measurement and control system and a measurement and control method based on the fusion of multiple sensors, which overcome the above technical problems or at least partially solve the above problems.

[0006] The present invention is achieved in that:

[0007] The present invention provides a measurement and control system based on the fusion of multiple sensors, comprising a central processing module, a sensor module, a communication module and a user interface module, wherein each node data of the sensor module, the communication module and the user interface module are transmitted to the central processing module;

[0008] The central processing module is responsible for processing various input signals in the system, executing control algorithms, performing data analysis, and coordinating the operation of various parts of the system;

[0009] The sensor module is a sensor for acquiring various physical quantities of a target object or environment;

[0010] The communication module is a module for transmitting data between various components and between external devices;

[0011] The user interface module is an interface for displaying processed data and interacting with users.

[0012] In a preferred solution, the central processing module includes a processor unit, a storage unit, a signal processing unit and a power management;

[0013] The processor unit is used to execute program instructions and is responsible for data processing and control logic. The storage unit is used to store temporary data and variables for use by the processor during operation. The signal processing unit is used to convert analog signals into digital signals for use by the processor. The power management unit is responsible for providing stable power supply to the central processing module and its peripheral devices to ensure the normal operation of the system.

[0014] In a preferred solution, the sensor module includes at least the following sensors: a temperature sensor, a humidity sensor, an acceleration sensor and a GPS sensor, and the sensor module also includes a data acquisition unit and a data fusion unit for data acquisition and fusion between the sensors;

[0015] The temperature sensor is used to measure the ambient temperature, the humidity sensor is used to measure the ambient humidity, the acceleration sensor is used to measure the acceleration of the object, and the GPS sensor is used to obtain the location information of the object.

[0016] In a preferred solution, the communication module includes a wireless communication unit and a wired communication unit;

[0017] The wireless communication unit can transmit via Bluetooth and WIFI, and the wired communication unit can transmit via a serial port, I2C or CAN bus.

[0018] In a preferred solution, the user interface module includes a display unit and a control unit;

[0019] The display unit is used to display sensor data and system status in real time, and the control unit is used for user input and system control.

[0020] In a preferred solution, the data acquisition unit and the data fusion unit use the Kalman filter algorithm to collect and fuse data. The specific algorithm formula is:

[0021] x k =F k x k-1 +B k u k +w k

[0022] In the above, xk is the current state vector, Fk is the state transfer matrix, which describes the process of the system state transferring from time k-1 to time k, B is the control input matrix, which describes the influence of the control input on the state, uk is the control input, and Wk is the process noise, which is assumed to be Gaussian white noise with a mean of 0.

[0023] In a preferred solution, the data acquisition unit and the data fusion unit further use a weighted average method to assign weights to the outputs of different sensors and calculate the weighted average to fuse the data. The specific formula is:

[0024]

[0025] Among them, wi is the weight, xi is the data collected by each sensor, and n is the number of sensors.

[0026] A measurement and control method based on multiple sensor fusion, characterized in that it includes the following steps:

[0027] S1: Data acquisition: The sensor module obtains the required physical quantity information through sensors such as temperature, humidity, acceleration, and GPS;

[0028] S2: Data conversion: The data acquisition unit converts analog signals into digital signals for subsequent processing and analysis;

[0029] S3: Data fusion: The data fusion processing unit uses algorithms such as Kalman filtering to fuse data from different sensors, eliminate noise, and improve measurement accuracy;

[0030] S4: Data transmission: Through the communication module, the processed data can be transmitted between modules within the system and with external devices;

[0031] S5: Data display and interaction: The user interface visualizes the processed data, and users can monitor the system status in real time and perform control operations.

[0032] The present invention provides a measurement and control system and a measurement and control method based on multiple sensor fusion, and its beneficial effects include:

[0033] 1. After the data acquisition unit and data fusion unit in the sensor module collect and fuse the data between each sensor, the data is transmitted to the central processing module through the communication module for data integration and storage. In addition, the operator can perform real-time detection of the data of each sensor and control each sensor through the display unit and control unit in the user interface module, thereby achieving the purpose of real-time detection and control of multiple sensors using one system.

[0034] 2. The use of Kalman filtering and the introduction of weighted averaging in the system can effectively improve the accuracy, robustness and flexibility of state estimation, especially in multi-sensor environments. By reasonably allocating weights, inconsistent measurements and outliers can be effectively handled, thereby improving the overall performance and making the Kalman filtering algorithm more efficient and reliable in dynamic system state estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 It is an overall architecture diagram provided by an embodiment of the present invention;

[0037] Figure 2 A schematic diagram of a data transmission and fusion architecture provided for an embodiment of the present invention;

[0038] Figure 3 A schematic diagram of a sensor measurement and control process provided by an embodiment of the present invention;

[0039] Figure 4 Schematic diagram of the central processing module architecture provided for an embodiment of the present invention

[0040] Figure 5 A schematic diagram of the overall workflow provided for an implementation manner of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0042] Reference Figure 1-Figure 4 , the present invention provides a technical solution: a measurement and control system based on the fusion of multiple sensors, including a central processing module, a sensor module, a communication module and a user interface module, wherein each node data of the sensor module, the communication module and the user interface module are transmitted to the central processing module;

[0043] The central processing module is responsible for processing various input signals in the system, executing control algorithms, performing data analysis, and coordinating the operation of various parts of the system;

[0044] The central processing module includes a processor unit, a storage unit, a signal processing unit and a power management;

[0045] The processor unit is used to execute program instructions and is responsible for data processing and control logic. The storage unit is used to store temporary data and variables for use by the processor during operation. The signal processing unit is used to convert analog signals into digital signals for use by the processor. The power management unit is responsible for providing a stable power supply for the central processing module and its peripheral devices to ensure the normal operation of the system. When in use, the data acquisition unit and the data fusion unit in the sensor module collect and fuse the data between each sensor, and then transmit the data to the central processing module through the communication module for data integration and storage. The operator can use the display unit and the control unit in the user interface module to detect the data of each sensor in real time and operate each sensor.

[0046] The sensor module is a sensor for acquiring various physical quantities of a target object or environment;

[0047] The sensor module includes at least the following sensors: a temperature sensor, a humidity sensor, an acceleration sensor and a GPS sensor. The sensor module also includes a data acquisition unit and a data fusion unit, which are used for data acquisition and fusion between various sensors, wherein each sensor is responsible for acquiring specific physical quantities or environmental information, the data acquisition unit amplifies and conditions the signal through an adaptation circuit to ensure data quality, and the data fusion unit realizes data fusion, and applies Kalman filtering and weighted average algorithm to process data from different sensors to improve measurement accuracy and stability;

[0048] The temperature sensor is used to measure the ambient temperature, the humidity sensor is used to measure the ambient humidity, the acceleration sensor is used to measure the acceleration of the object, and the GPS sensor is used to obtain the location information of the object.

[0049] The sensors can be replaced or various sensors can be installed to meet different measurement needs, which greatly improves the practicality and applicability of the system.

[0050] The data acquisition unit and the data fusion unit use the Kalman filter algorithm to collect and fuse data. The specific algorithm formula is:

[0051] x k =F k x k-1 +B k u k +w k

[0052] In the above, xk is the current state vector, Fk is the state transfer matrix, which describes the process of the system state transferring from time k-1 to time k, B is the control input matrix, which describes the influence of the control input on the state, uk is the control input, and Wk is the process noise, which is assumed to be Gaussian white noise with a mean of 0.

[0053] The operation process of the Kalman filter algorithm can be summarized as follows: initialization: setting the initial state estimate and error covariance; prediction step: predicting the next state and its covariance based on the system model; update step: updating the state estimate and covariance using new measurement data; loop: repeating prediction and update until the end, and finally uploading the latest data.

[0054] Through the prediction and update steps, Kalman filtering can effectively reduce system noise and improve the accuracy of state estimation. The Kalman filter algorithm can recursively update the state each time new data is acquired, using the state estimate of the previous moment and the current measurement data. This system can process data in real time and does not need to retain all historical data, thereby saving storage and computing resources. Moreover, under the assumption of Gaussian noise, Kalman filtering provides a minimum mean square error estimate and can optimally fuse measurement data from different sensors to obtain the most accurate state estimate. Kalman filtering can effectively handle measurement noise and process noise, and takes into account the statistical characteristics of noise in state estimation, so that good performance can be maintained even in noisy environments.

[0055] The data acquisition unit and the data fusion unit also use a weighted average method to assign weights to the outputs of different sensors and calculate the weighted average to fuse the data. The specific formula is:

[0056]

[0057] Among them, wi is the weight, xi is the data collected by each sensor, and n is the number of sensors.

[0058] The specific operation process of the weighted average method is as follows:

[0059] 1. Determine the data set: list all data points and their corresponding weights;

[0060] 2. Verify weights: ensure that the sum of all weights is 1;

[0061] 3. Calculate weighted average: Use formula xf to calculate the weighted average;

[0062] 4. Get the result: Output the calculated weighted average.

[0063] Assume the following data and weights: data points: [15, 25, 35], weights: [0.1, 0.3, 0.6], follow the above steps: verify weight: 0.1+0.3+0.6=1; calculate weighted average: =(0.1x15)+(0.3x25)+(0.6x35)=1.5+7.5+21=30; the final weighted average is 30. The weighted average method effectively combines the importance of different data points.

[0064] The above algorithms help improve data quality, achieve accurate fusion and effective analysis, thereby supporting subsequent decision-making, not only ensuring the validity of the data but also improving the efficiency of data processing.

[0065] The combination of Kalman filter algorithm and weighted averaging method plays an important role in sensor fusion. Their respective advantages complement each other, thereby improving the accuracy and reliability of data processing. Dynamic update: The dynamic update mechanism of Kalman filter combined with the simplicity of weighted averaging method can process and weight the sensor data in real time, thereby improving the accuracy of fusion results. Noise processing: Kalman filter can effectively handle noise. By dynamically adjusting state estimation, combined with weighted averaging method, it can further optimize the measurement value at each update and reduce the impact of unreliable data on the final result. Resistance to outliers: The weighted averaging method allows the weights of different sensors to be adjusted. In the presence of outliers or faults, In the case of multiple sensors, their impact on the fusion results can be reduced, thereby enhancing the robustness of the system. Multi-sensor trust mechanism: Through the state estimation of the Kalman filter and the weight adjustment of the weighted average method, a dynamic evaluation of the sensor trust can be formed to better deal with sensor failures or performance degradation problems; Flexible configuration: In a multi-sensor environment, the weighting strategy can be dynamically adjusted according to environmental changes and needs, combined with the state prediction of the Kalman filter, it has strong adaptability and can process diversified data: After combining the two, it can process heterogeneous data from different sensors, and use the model adaptability of the Kalman filter and the simplicity and intuitiveness of the weighted average method to enhance the system's adaptability to complex scenarios.

[0066] By adding weighted averaging to the Kalman filter algorithm and applying different weights to measurement data from different sources, information can be better fused. In particular, when data from multiple sensors have different reliabilities, higher weights can be assigned to measurements with higher signal-to-noise ratios or smaller errors, thereby improving the accuracy of the overall estimate. Through the weighted averaging method, weights can be dynamically adjusted based on the historical performance or uncertainty of the measurement, thereby effectively reducing the impact of inaccurate measurements on the final estimate. The combination of weighted averaging and Kalman filtering can better utilize the advantages of both. The combination of the predictive ability of Kalman filtering and the flexibility of the weighted averaging method can provide more stable and accurate state estimates in dynamic environments.

[0067] The introduction of weighted averaging in Kalman filtering can effectively improve the accuracy, robustness and flexibility of state estimation, especially in multi-sensor environments. By reasonably allocating weights, inconsistent measurements and outliers can be effectively handled, thereby improving the overall performance and making the Kalman filter algorithm more efficient and reliable in dynamic system state estimation.

[0068] The communication module is a module for transmitting data between various components and between external devices, and the communication module includes a wireless communication unit and a wired communication unit;

[0069] Among them, the data acquisition unit collects data from each sensor and can pre-process the data. The pre-processing method is data cleaning, and then the processed data of each sensor is transmitted in the MQTT data format. The transmitted data is then received by the data fusion unit, and the data is fused and analyzed. Finally, the results are output to the communication module. Finally, the final data is transmitted to the user interface module through the communication module to facilitate the operator to perform real-time detection of the data between each sensor and carry out effective control.

[0070] In addition, the transmission process between the data acquisition unit and the data fusion unit includes multiple steps such as data acquisition, preprocessing, sending, receiving, fusion, analysis and result output. By rationally designing this process, efficient transmission of data from acquisition to fusion can be ensured, thus providing reliable support for subsequent data analysis and decision-making.

[0071] The MQTT data format used in the above has the following advantages: Lightweight and low overhead: The header overhead of MQTT is very small, usually only 2 bytes, which is suitable for use on devices with limited resources and in bandwidth-constrained environments, and can effectively reduce the burden of data transmission; Reliability: Three quality service levels: QoS 0: at most once (no guarantee of delivery), QoS1: at least once (message delivery is guaranteed, but may be repeated), QoS2: only once (message delivery is guaranteed and not repeated); Security: MQTT can be securely transmitted through TLS / SSL encryption to protect data privacy and integrity, and supports user authentication to ensure that only authorized users can use it, thereby effectively increasing data security.

[0072] When in use, the control unit in the user interface module activates the start button to turn on each sensor and sends a command to each sensor. Then each sensor feeds back the collected data to the data acquisition unit for data processing and analysis, and then the data is fused. Finally, the data is transmitted to the central processing module through the signal transmission module for integration and storage. It can be observed and controlled by the user-end module. When the value collected by one of the sensors exceeds the standard, an alarm will be issued to remind the operator to respond quickly to improve the safety of the system.

[0073] The wireless communication unit can be transmitted via Bluetooth and WIFI, and the wired communication unit can be transmitted via a serial port, I2C or CAN bus. The communication module supports data encryption to ensure the security of data transmission and prevent data leakage. Commonly used communication protocols are: CAN bus: widely used in automotive electronics and industrial control, I2C and SPI: used for short-distance communication, connecting multiple sensors and microcontrollers, wireless communication: such as Zigbee, LoRa and Wi-Fi, etc., suitable for remote data transmission, so that the device as a whole can be applied to a variety of usage environments, so as to be applied to different fields and different equipment, which greatly increases the practicality and scope of application of the device as a whole;

[0074] The user interface module is used to display the processed data and to interact with the user, and the user interface module includes a display unit and a control unit;

[0075] The display unit is used to display sensor data and system status in real time, and the control unit is used for user input and system control. The display unit can be a visualization device such as a display screen and an instrument panel, and the control unit can be a device such as a control button or a touch screen.

[0076] Reference Figure 5 , a measurement and control method based on multiple sensor fusion, comprising the following steps:

[0077] S1: Data acquisition: The sensor module obtains the required physical quantity information through sensors such as temperature, humidity, acceleration, and GPS;

[0078] S2: Data conversion: The data acquisition unit converts analog signals into digital signals for subsequent processing and analysis;

[0079] S3: Data fusion: The data fusion processing unit uses algorithms such as Kalman filtering to fuse data from different sensors, eliminate noise, and improve measurement accuracy;

[0080] S4: Data transmission: Through the communication module, the processed data can be transmitted between modules within the system and with external devices;

[0081] S5: Data display and interaction: The user interface visualizes the processed data, and users can monitor the system status in real time and perform control operations.

Claims

1. A measurement and control system based on multiple sensor fusion, characterized in that: It includes a central processing module, a sensor module, a communication module and a user interface module, and each node data of the sensor module, the communication module and the user interface module are transmitted to the central processing module; The central processing module is responsible for processing various input signals in the system, executing control algorithms, performing data analysis, and coordinating the operation of various parts of the system; The sensor module is a sensor for acquiring various physical quantities of a target object or environment; The communication module is a module for transmitting data between various components and between external devices; The user interface module is an interface for displaying processed data and interacting with users.

2. A measurement and control system based on multiple sensor fusion according to claim 1, characterized in that: The central processing module includes a processor unit, a storage unit, a signal processing unit and a power management unit; The processor unit is used to execute program instructions and is responsible for data processing and control logic. The storage unit is used to store temporary data and variables for use by the processor during operation. The signal processing unit is used to convert analog signals into digital signals for use by the processor. The power management unit is responsible for providing stable power supply to the central processing module and its peripheral devices to ensure the normal operation of the system.

3. A measurement and control system based on multiple sensor fusion according to claim 1, characterized in that: The sensor module includes at least the following sensors: a temperature sensor, a humidity sensor, an acceleration sensor and a GPS sensor. The sensor module also includes a data acquisition unit and a data fusion unit for data acquisition and fusion between the sensors. The temperature sensor is used to measure the ambient temperature, the humidity sensor is used to measure the ambient humidity, the acceleration sensor is used to measure the acceleration of the object, and the GPS sensor is used to obtain the location information of the object.

4. A measurement and control system based on multiple sensor fusion according to claim 1, characterized in that: The communication module includes a wireless communication unit and a wired communication unit; The wireless communication unit can transmit via Bluetooth and WIFI, and the wired communication unit can transmit via a serial port, I2C or CAN bus.

5. The measurement and control system based on multiple sensor fusion according to claim 1 is characterized in that: The user interface module includes a display unit and a control unit; The display unit is used to display sensor data and system status in real time, and the control unit is used for user input and system control.

6. The measurement and control system based on multiple sensor fusion according to claim 1 is characterized in that: The data acquisition unit and the data fusion unit use the Kalman filter algorithm to collect and fuse data. The specific algorithm formula is: x k =F k x k-1 +B k u k +w k In the above, xk is the current state vector, Fk is the state transfer matrix, which describes the process of the system state transferring from time k-1 to time k, B is the control input matrix, which describes the influence of the control input on the state, uk is the control input, and Wk is the process noise, which is assumed to be Gaussian white noise with a mean of 0.

7. The measurement and control system based on multiple sensor fusion according to claim 1 is characterized in that: The data acquisition unit and the data fusion unit also use a weighted average method to assign weights to the outputs of different sensors and calculate the weighted average to fuse the data. The specific formula is: Among them, wi is the weight, xi is the data collected by each sensor, and n is the number of sensors.

8. A measurement and control method based on multiple sensor fusion, the method being applicable to a measurement and control system based on multiple sensor fusion as claimed in any one of claims 1 to 7, comprising the following steps: S1: Data acquisition: The sensor module obtains the required physical quantity information through sensors such as temperature, humidity, acceleration, and GPS; S2: Data conversion: The data acquisition unit converts analog signals into digital signals for subsequent processing and analysis; S3: Data fusion: The data fusion processing unit uses algorithms such as Kalman filtering to fuse data from different sensors, eliminate noise, and improve measurement accuracy; S4: Data transmission: Through the communication module, the processed data can be transmitted between modules within the system and with external devices; S5: Data display and interaction: The user interface visualizes the processed data, and users can monitor the system status in real time and perform control operations.

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