An intelligent dynamic weighing system and its weighing method

Abstract

The present invention relates to the field of weighing measurement and metrology, and discloses an intelligent dynamic weighing system and its weighing method, including: a first-level hierarchical module for realizing local real-time processing through a weighing sensor and a local data acquisition module; a second-level hierarchical module for fusing and cross-verifying multi-sensor data through an edge computing node; and a third-level hierarchical module for performing global optimization and monitoring analysis through a cloud computing node. Through the hierarchical distribution design, the present invention significantly improves the real-time performance and fault tolerance of the system, and solves the problems of accuracy deviation, excessive energy consumption and response delay of traditional systems in high-load and high-frequency dynamic weighing scenarios. The local adaptive algorithm and the global optimization algorithm work together, enabling the system to dynamically adjust weighing parameters in a complex environment to ensure that the weighing accuracy is not affected by external environmental changes. The multi-sensor data fusion algorithm combined with edge computing further improves data reliability and system stability.

Description

Technical Field

[0001] The present invention relates to an intelligent dynamic weighing system and a weighing method thereof, belonging to the technical field of weighing measurement and metrology. Background Art

[0002] In traditional intelligent dynamic weighing systems, a single sensor and a centralized data processing method are usually adopted. This method has the following problems in high-load and high-frequency dynamic weighing scenarios: First, the data acquisition accuracy of a single sensor is easily affected by environmental factors (such as vibration, temperature, and electromagnetic interference), resulting in weighing result deviations; Second, the centralized data processing method needs to transmit a large amount of raw data to the central processor, leading to data transmission delays and excessive energy consumption, making it difficult to meet real-time requirements; Finally, the fault tolerance of the system is poor. Once a sensor or a data processing node fails, the entire system may not operate properly.

[0003] To solve the above problems, the prior art usually adopts the following methods: One is to increase the number of sensors to improve accuracy through multi-sensor data acquisition; the second is to optimize the data transmission protocol to reduce the amount of data transmitted; the third is to introduce redundant design to improve the fault tolerance of the system. However, these methods still have new problems: Although increasing the number of sensors improves the data acquisition accuracy, the fusion and cross-validation of multi-sensor data require complex algorithm support, increasing the computational burden; Although optimizing the data transmission protocol reduces the amount of data, it cannot fundamentally solve the data transmission delay problem; Although redundant design improves the fault tolerance of the system, it increases the complexity and cost of the system. Summary of the Invention

[0004] The present invention provides an intelligent dynamic weighing system and a weighing method thereof, and its main purpose is to solve the problems of accuracy deviation, excessive energy consumption, and response delay in traditional dynamic weighing systems in high-load and high-frequency scenarios.

[0005] To achieve the above object, an intelligent dynamic weighing system provided by the present invention includes: a first-level hierarchical module, including a plurality of weighing sensors and a local data acquisition module. The plurality of weighing sensors are arranged at positions near the weighing points for real-time acquisition of weighing data. The local data acquisition module is connected to the plurality of weighing sensors and is used for local real-time processing of the weighing data, including dynamically adjusting weighing parameters according to the weighing environment through a local adaptive algorithm, and compressing and preprocessing the original weighing data to generate local processed data. A second-level hierarchical module, including a plurality of edge computing nodes, each edge computing node is connected to at least one first-level hierarchical module, and is used for receiving the local processed data, and performing weight allocation and cross-verification on the data from multiple first-level hierarchical modules through a multi-sensor data fusion algorithm to generate fusion data. The multi-sensor data fusion algorithm includes dynamically adjusting weight coefficients according to the historical accuracy data and real-time environment data of each weighing sensor, and the calculation method of the weight coefficients is: , where is the weight coefficient of the th sensor, is the historical accuracy score of the th sensor, is the real-time environment matching degree of the th sensor; is the sensor index, and the value range is from 1 to , is the total number of sensors in the system; and are respectively the historical accuracy score and real-time environment matching degree of the th sensor. A third-level hierarchical module, including a cloud computing node, is used for receiving the fusion data from multiple second-level hierarchical modules and performing global optimization on the fusion data through a global optimization algorithm to generate optimized weighing data. The global optimization algorithm includes dynamically adjusting the weighing model according to external environment parameters, and the external environment parameters include temperature, humidity, and electromagnetic interference intensity. Among them, the first-level hierarchical module, the second-level hierarchical module, and the third-level hierarchical module achieve multi-level linkage through a cross-level collaborative optimization mechanism, including: the second-level hierarchical module sends feedback instructions to the first-level hierarchical module according to the fusion data to dynamically adjust the parameters of the local adaptive algorithm; the third-level hierarchical module sends optimization instructions to the second-level hierarchical module according to the optimized weighing data to dynamically adjust the weight coefficients of the multi-sensor data fusion algorithm.

[0006] As an implementation manner of the present invention, the local adaptive algorithm includes the following steps: real-time monitoring of the vibration intensity, temperature change, and electromagnetic interference intensity of the weighing environment; dynamically adjusting the sampling frequency and filtering parameters of the weighing sensor according to the monitoring results; when the vibration intensity exceeds a preset threshold, automatically increasing the sampling frequency and enhancing the filtering intensity to ensure the accuracy of the weighing data.

[0007] As an implementation manner of the present invention, the multi-sensor data fusion algorithm further includes: dynamically adjusting its weight coefficient according to the real-time signal intensity and noise level of each weighing sensor; when the signal intensity of a certain weighing sensor is lower than the preset threshold or the noise level is higher than the preset threshold, automatically reducing its weight coefficient and increasing the weight coefficients of other sensors to ensure the reliability of the fused data.

[0008] As an implementation manner of the present invention, the global optimization algorithm includes the following steps: real-time monitoring of external environmental parameters, including temperature, humidity, and electromagnetic interference intensity; dynamically adjusting the parameters of the weighing model according to the monitoring results to compensate for the influence of environmental changes on the weighing accuracy; when the temperature change exceeds a preset range, automatically adjusting the temperature compensation coefficient of the weighing model to ensure the stability of the weighing result.

[0009] As an implementation manner of the present invention, the cross-level collaborative optimization mechanism further includes: the secondary level module sends feedback instructions to the primary level module according to the fused data to dynamically adjust the parameters of the local adaptive algorithm; the tertiary level module sends optimization instructions to the secondary level module according to the optimized weighing data to dynamically adjust the weight coefficients of the multi-sensor data fusion algorithm; through multi-level linkage, ensure the adaptive ability and overall performance of the system in different environments.

[0010] As an implementation manner of the present invention, the edge computing node further includes: a data caching module for temporarily storing the local processing data from the primary level module; a data verification module for cross-verifying the local processing data and eliminating abnormal data; a data fusion module for generating fused data according to the multi-sensor data fusion algorithm.

[0011] As an implementation manner of the present invention, the cloud computing node further includes: a historical data storage module for storing historical weighing data and environmental parameters; a real-time monitoring module for real-time monitoring of the operating states of the weighing sensors, edge computing nodes, and cloud computing nodes; a fault detection module for automatically taking over the functions of the faulty node through redundant nodes when a fault is detected.

[0012] As an implementation manner of the present invention, the first-level hierarchical module further includes: a redundant weighing sensor, which is used to ensure that the weighing data is not affected by data compensation of adjacent nodes when a certain weighing sensor fails; a local data compression module, which is used to compress the weighing data to reduce the data transmission volume and energy consumption; the second-level hierarchical module further includes: a dynamic weight adjustment module, which is used to dynamically adjust its weight coefficient according to the historical accuracy data and real-time environment matching degree of each weighing sensor; a data cross-verification module, which is used to cross-verify the data from multiple first-level hierarchical modules to ensure the reliability and consistency of the data; the third-level hierarchical module further includes: a machine learning module, which is used to perform predictive analysis on the fusion data to generate a weighing trend prediction result; a global optimization module, which is used to dynamically adjust the parameters of the global optimization algorithm according to the prediction result to ensure the long-term stability and accuracy of the system in different environments.

[0013] As an implementation manner of the present invention, the weighing method corresponding to the intelligent dynamic weighing system includes the following steps: Step 1, data acquisition and local processing: Weighing data is collected in real time through a plurality of weighing sensors arranged at positions near the weighing point; the local data acquisition module is used to perform local real-time processing on the collected weighing data, including dynamically adjusting weighing parameters according to the vibration intensity, temperature change, and electromagnetic interference intensity of the weighing environment through a local adaptive algorithm, and compressing and preprocessing the original weighing data to generate local processed data; Step 2, data fusion and cross-verification: Transmit the local processed data to the edge computing node; perform weight assignment and cross-verification on the data from multiple first-level hierarchical modules through a multi-sensor data fusion algorithm to generate fusion data; the multi-sensor data fusion algorithm includes dynamically adjusting the weight coefficient according to the historical accuracy data and real-time environment data of each weighing sensor, and the calculation formula of the weight coefficient is: , where is the weight coefficient of the th sensor, is the historical accuracy score of the th sensor, is the real-time environment matching degree of the th sensor, is the sensor index, and are respectively the The historical accuracy score of the sensors and the real-time environmental matching degree; Step 3, Global optimization and monitoring analysis: Transmit the fused data to the cloud computing node; Perform global optimization on the fused data through a global optimization algorithm to generate optimized weighing data; The global optimization algorithm includes dynamically adjusting the weighing model according to external environmental parameters, and the external environmental parameters include temperature, humidity, and electromagnetic interference intensity; Step 4, Cross-level collaborative optimization: The secondary level module sends feedback instructions to the primary level module according to the fused data to dynamically adjust the parameters of the local adaptive algorithm; The tertiary level module sends optimization instructions to the secondary level module according to the optimized weighing data to dynamically adjust the weight coefficients of the multi-sensor data fusion algorithm; Through multi-level linkage, ensure the adaptive ability and overall performance of the system in different environments.

[0014] Compared with the problems described in the background art, the beneficial effects of the present invention are as follows: Through the local real-time processing of the first-level hierarchical module, the data fusion and cross-verification of the second-level hierarchical module, and the global optimization of the third-level hierarchical module, the system realizes a hierarchical distribution of data processing. This architecture not only reduces data transmission latency but also ensures the stable operation of the system even when some nodes fail through redundant design and local computing, significantly improving the real-time performance and fault tolerance of the system. For example, when a weighing sensor fails, the system can ensure that the weighing data is not affected through data compensation from adjacent nodes; the local adaptive algorithm of the first-level hierarchical module can dynamically adjust the sampling frequency and filtering parameters of the weighing sensor according to the vibration intensity, temperature change, and electromagnetic interference intensity of the weighing environment, while the global optimization algorithm of the third-level hierarchical module dynamically adjusts the parameters of the weighing model according to the deviation between historical data and real-time data. This local and global collaborative optimization mechanism enables the system to maintain high-precision weighing under different environments and loads, solving the problem of precision deviation of traditional systems in complex environments; the second-level hierarchical module assigns weights and cross-verifies the data from multiple first-level hierarchical modules through a multi-sensor data fusion algorithm, combined with the real-time processing ability of the edge computing node, significantly improving the reliability and weighing accuracy of the data. For example, the algorithm can dynamically adjust its weight coefficient according to the real-time working state of each weighing sensor (such as signal strength, noise level, and failure rate) to ensure the accuracy and stability of the weighing data, and through the cross-level collaborative optimization mechanism between the first-level hierarchical module, the second-level hierarchical module, and the third-level hierarchical module, the system can achieve multi-level linkage and dynamically adjust the sampling interval, data processing method, and fusion strategy of the weighing sensor. This mechanism not only improves the adaptive ability of the system but also ensures the efficiency and stability of the system in different application scenarios, solving the problems of response latency and excessive energy consumption of traditional systems in high-load and high-frequency dynamic weighing scenarios. The third-level hierarchical module conducts predictive analysis on the fused data through machine learning algorithms to generate weighing trend prediction results and dynamically adjusts the parameters of the global optimization algorithm according to the prediction results. At the same time, the real-time monitoring module monitors the operating states of the weighing sensors, edge computing nodes, and cloud computing nodes, and automatically takes over the functions of the faulty nodes through redundant nodes when a fault is detected. This design significantly improves the intelligence level and reliability of the system, enabling it to operate efficiently in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the architecture of the intelligent dynamic weighing system of the present invention.

[0016] Figure 2 It is a schematic diagram of the cross-level collaborative optimization mechanism of the intelligent dynamic weighing system of the present invention.

[0017] The implementation, functional features, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0018] It should be understood that the specific embodiments described herein are merely used to explain the present invention and are not used to limit the present invention.

[0019] An embodiment of the present application provides an intelligent dynamic weighing system and a weighing method thereof. It includes: a first-level hierarchical module, including a plurality of weighing sensors and a local data acquisition module. The plurality of weighing sensors are arranged at positions near the weighing point for real-time acquisition of weighing data; the local data acquisition module is connected to the plurality of weighing sensors for local real-time processing of the weighing data, including dynamically adjusting weighing parameters according to the weighing environment through a local adaptive algorithm, and compressing and preprocessing the original weighing data to generate local processed data; a second-level hierarchical module, including a plurality of edge computing nodes, each edge computing node is connected to at least one first-level hierarchical module for receiving the local processed data, and performing weight allocation and cross-verification on the data from multiple first-level hierarchical modules through a multi-sensor data fusion algorithm to generate fusion data; the multi-sensor data fusion algorithm includes dynamically adjusting weight coefficients according to the historical accuracy data and real-time environment data of each weighing sensor, and the calculation method of the weight coefficients is: , where is the weight coefficient of the th sensor, is the historical accuracy score of the th sensor, is the real-time environment matching degree of the th sensor; is the sensor index, and the value range is from 1 to , is the total number of sensors in the system; and are respectively the historical accuracy score and real-time environment matching degree of the th sensor; a third-level hierarchical module, including a cloud computing node, for receiving the fusion data from multiple second-level hierarchical modules and performing global optimization on the fusion data through a global optimization algorithm to generate optimized weighing data; the global optimization algorithm includes dynamically adjusting the weighing model according to external environment parameters, and the external environment parameters include temperature, humidity and electromagnetic interference intensity; wherein, the first-level hierarchical module, the second-level hierarchical module and the third-level hierarchical module achieve multi-level linkage through a cross-level collaborative optimization mechanism, including: the second-level hierarchical module sends feedback instructions to the first-level hierarchical module according to the fusion data to dynamically adjust the parameters of the local adaptive algorithm; the third-level hierarchical module sends optimization instructions to the second-level hierarchical module according to the optimized weighing data to dynamically adjust the weight coefficients of the multi-sensor data fusion algorithm.

[0020] As an embodiment of the present invention, the local adaptive algorithm includes the following steps: continuously monitor the vibration intensity, temperature change, and electromagnetic interference intensity of the weighing environment; dynamically adjust the sampling frequency and filtering parameters of the weighing sensor according to the monitoring results; when the vibration intensity exceeds a preset threshold, automatically increase the sampling frequency and enhance the filtering intensity to ensure the accuracy of the weighing data.

[0021] As an embodiment of the present invention, the multi-sensor data fusion algorithm further includes: dynamically adjust the weight coefficient according to the real-time signal intensity and noise level of each weighing sensor; when the signal intensity of a certain weighing sensor is lower than the preset threshold or the noise level is higher than the preset threshold, automatically reduce its weight coefficient and increase the weight coefficients of other sensors to ensure the reliability of the fused data.

[0022] As an embodiment of the present invention, the global optimization algorithm includes the following steps: continuously monitor the external environmental parameters, including temperature, humidity, and electromagnetic interference intensity; dynamically adjust the parameters of the weighing model according to the monitoring results to compensate for the influence of environmental changes on the weighing accuracy; when the temperature change exceeds a preset range, automatically adjust the temperature compensation coefficient of the weighing model to ensure the stability of the weighing result.

[0023] As an embodiment of the present invention, the cross-level collaborative optimization mechanism further includes: the secondary-level module sends feedback instructions to the primary-level module according to the fused data to dynamically adjust the parameters of the local adaptive algorithm; the tertiary-level module sends optimization instructions to the secondary-level module according to the optimized weighing data to dynamically adjust the weight coefficients of the multi-sensor data fusion algorithm; through multi-level linkage, ensure the adaptive ability and overall performance of the system in different environments.

[0024] As an embodiment of the present invention, the edge computing node further includes: a data caching module for temporarily storing the local processing data from the primary-level module; a data verification module for cross-verifying the local processing data and eliminating abnormal data; a data fusion module for generating fused data according to the multi-sensor data fusion algorithm.

[0025] As an embodiment of the present invention, the cloud computing node further includes: a historical data storage module for storing historical weighing data and environmental parameters; a real-time monitoring module for real-time monitoring of the operating states of the weighing sensors, edge computing nodes, and cloud computing nodes; a fault detection module for automatically taking over the functions of the faulty node through redundant nodes when a fault is detected.

[0026] As an implementation manner of the present invention, the first-level hierarchical module further includes: a redundant weighing sensor, which is used to ensure that the weighing data is not affected by data compensation from adjacent nodes when a certain weighing sensor fails; a local data compression module, which is used to compress the weighing data to reduce the data transmission volume and energy consumption; the second-level hierarchical module further includes: a dynamic weight adjustment module, which is used to dynamically adjust its weight coefficient according to the historical accuracy data and real-time environment matching degree of each weighing sensor; a data cross-verification module, which is used to perform cross-verification on the data from multiple first-level hierarchical modules to ensure the reliability and consistency of the data; the third-level hierarchical module further includes: a machine learning module, which is used to perform predictive analysis on the fused data to generate a weighing trend prediction result; a global optimization module, which is used to dynamically adjust the parameters of the global optimization algorithm according to the prediction result to ensure the long-term stability and accuracy of the system in different environments.

[0027] As an implementation manner of the present invention, the weighing method corresponding to the intelligent dynamic weighing system includes the following steps: Step 1, data acquisition and local processing: Weighing data is collected in real time through multiple weighing sensors arranged near the weighing point; the local data acquisition module is used to perform local real-time processing on the collected weighing data, including dynamically adjusting weighing parameters according to the vibration intensity, temperature change, and electromagnetic interference intensity of the weighing environment through a local adaptive algorithm, and compressing and preprocessing the original weighing data to generate locally processed data; Step 2, data fusion and cross-verification: Transmit the locally processed data to the edge computing node; perform weight assignment and cross-verification on the data from multiple first-level hierarchical modules through a multi-sensor data fusion algorithm to generate fused data; the multi-sensor data fusion algorithm includes dynamically adjusting the weight coefficient according to the historical accuracy data and real-time environment data of each weighing sensor, and the calculation formula of the weight coefficient is: , where is the weight coefficient of the th sensor, is the historical accuracy score of the th sensor, is the real-time environment matching degree of the th sensor, is the sensor index, and are respectively the The historical accuracy score of each sensor and the matching degree with the real-time environment; Step 3, Global optimization and monitoring analysis: Transmit the fused data to the cloud computing node; Perform global optimization on the fused data through a global optimization algorithm to generate optimized weighing data; The global optimization algorithm includes dynamically adjusting the weighing model according to external environment parameters, and the external environment parameters include temperature, humidity, and electromagnetic interference intensity; Step 4, Cross-level collaborative optimization: The secondary level module sends feedback instructions to the primary level module according to the fused data to dynamically adjust the parameters of the local adaptive algorithm; The tertiary level module sends optimization instructions to the secondary level module according to the optimized weighing data to dynamically adjust the weight coefficients of the multi-sensor data fusion algorithm; Through multi-level linkage, ensure the adaptive ability and overall performance of the system in different environments.

[0028] Embodiment 1: The present invention relates to the field of intelligent dynamic weighing technology, and specifically relates to a dynamic weighing system based on a distributed multi-level architecture. For example, in the logistics and warehousing scenario, high-frequency and high-load dynamic weighing requirements are extremely common. Traditional centralized weighing systems often struggle to meet the weighing requirements in complex environments due to relying on a single sensor and a central processor. Taking a large logistics center as an example, it needs to process thousands of goods weighings every day. Traditional weighing systems are prone to problems such as accuracy deviation, response delay, and excessive energy consumption under high load. In the embodiment of the present invention, the system includes a primary level module, a secondary level module, and a tertiary level module. The primary level module consists of multiple weighing sensors and a local data acquisition module. The weighing sensors are arranged at key positions on the goods conveyor belt to collect real-time goods weight data. The local data acquisition module dynamically adjusts the sampling frequency and filtering parameters of the weighing sensors through a local adaptive algorithm according to the vibration intensity, temperature change, and electromagnetic interference intensity of the weighing environment to generate local processed data. For example, when the environmental vibration intensity is high, the system automatically increases the sampling frequency and adjusts the filtering parameters to ensure the accuracy of the weighing data. The secondary level module consists of multiple edge computing nodes. Each edge computing node is connected to multiple primary level modules, receives the local processed data, and performs data fusion and cross-verification through a multi-sensor data fusion algorithm. The algorithm dynamically adjusts its weight coefficient according to the real-time working state of each weighing sensor (such as signal strength, noise level, and failure rate) to generate fused data. For example, when the signal strength of a certain weighing sensor decreases due to a failure, the system automatically reduces its weight coefficient to ensure the reliability of the weighing data. The tertiary level module consists of cloud computing nodes, receives the fused data from multiple secondary level modules, and performs global optimization and monitoring analysis through a global optimization algorithm. The global optimization algorithm dynamically adjusts the parameters of the weighing model according to the deviation between the historical weighing data and the real-time weighing data to ensure the stability of the weighing accuracy in different environments. For example, when the system detects that the temperature change causes a weighing deviation, it automatically adjusts the parameters of the weighing model to ensure that the weighing result is not affected.

[0029] Example 2: In an industrial production scenario, such as a steel plant that needs to weigh thousands of tons of steel every day, the traditional weighing system has problems of accuracy deviation, response delay, and excessive energy consumption in high-load and high-frequency scenarios. In this example, the system includes a first-level hierarchical module, a second-level hierarchical module, and a third-level hierarchical module. The first-level hierarchical module consists of multiple weighing sensors and a local data acquisition module. The weighing sensors are arranged at key positions on the steel conveyor belt to collect steel weight data in real time. The local data acquisition module uses a local adaptive algorithm to dynamically adjust the sampling frequency and filtering parameters of the weighing sensors according to the vibration intensity, temperature change, and electromagnetic interference intensity of the weighing environment, and generates local processed data. For example, when the environmental vibration intensity is high, the system automatically increases the sampling frequency and adjusts the filtering parameters to ensure the accuracy of the weighing data. The second-level hierarchical module consists of multiple edge computing nodes. Each edge computing node is connected to multiple first-level hierarchical modules, receives the local processed data, and performs data fusion and cross-verification through a multi-sensor data fusion algorithm. The algorithm dynamically adjusts its weight coefficient according to the real-time working state of each weighing sensor (such as signal strength, noise level, and failure rate), and generates fusion data. For example, when the signal strength of a certain weighing sensor decreases due to a failure, the system automatically reduces its weight coefficient to ensure the reliability of the weighing data. The third-level hierarchical module consists of cloud computing nodes, receives the fusion data from multiple second-level hierarchical modules, and performs global optimization and monitoring analysis through a global optimization algorithm. The global optimization algorithm dynamically adjusts the parameters of the weighing model according to the deviation between the historical weighing data and the real-time weighing data, and ensures the stability of the weighing accuracy in different environments. For example, when the system detects that the temperature change causes a weighing deviation, it automatically adjusts the parameters of the weighing model to ensure that the weighing result is not affected.

[0030] Example 3: The intelligent dynamic weighing system in this example still adopts a distributed multi-level architecture, including three-level modules: a first-level module, a second-level module, and a third-level module. By introducing an improved edge computing algorithm and a data caching mechanism in the second-level module, the real-time performance and fault tolerance of the system are enhanced. First-level module: It includes multiple weighing sensors and a local data acquisition module. The sensors are located at key positions and collect weighing data in real time. The local data acquisition module dynamically adjusts the sampling frequency and filtering parameters according to environmental monitoring data (such as vibration intensity, temperature change, etc.). In order to reduce the data transmission volume and energy consumption, all original weighing data is compressed and local processed data is generated. Second-level module: It includes multiple edge computing nodes, and each node is responsible for receiving data from multiple first-level modules. The data caching and cross-verification functions are implemented inside each edge node. Specifically, the data caching module temporarily stores the data from the first-level module, while the data verification module eliminates abnormal data to ensure data accuracy. Third-level module: Global optimization analysis is performed in the cloud. Combining historical data and real-time data, the parameters of the weighing model are dynamically adjusted through a global optimization algorithm. The cloud module is also responsible for real-time monitoring and fault detection. Once a system fault is detected, data takeover is automatically performed through redundant nodes to ensure that the system does not interrupt.

[0031] The core advantage of this example lies in its cross-level collaborative optimization mechanism, which not only improves the weighing accuracy but also significantly reduces data transmission delay and energy consumption. Local adaptive algorithm: This algorithm monitors the vibration intensity, temperature change, and electromagnetic interference intensity in the environment in real time, and automatically adjusts the sampling frequency and filtering parameters of the weighing sensors according to environmental changes. For example, when the system detects that the vibration intensity exceeds the set threshold, it automatically increases the sampling frequency and strengthens the filtering to ensure the accuracy and stability of data collection; Multi-sensor data fusion: The edge computing nodes in the second-level module adopt a multi-sensor data fusion algorithm, and dynamically adjust their weight coefficients according to the real-time signal intensity and noise level of each sensor. The weight coefficient calculation formula is as follows: , where, is the weight coefficient of the th sensor, is the historical accuracy score of the th sensor, is the real-time environmental matching degree of the th sensor, is the total number of sensors in the system, and are respectively the The historical accuracy score of each sensor and the real-time environmental matching degree; Global optimization algorithm: The three-level module uses the global optimization algorithm in the cloud to combine external environmental parameters (such as temperature, humidity, magnetic field interference, etc.) to dynamically adjust the parameters of the weighing model. When the external environmental parameters change, the system automatically adjusts the compensation coefficient in the model to cope with the impact of environmental changes on the weighing accuracy; Cross-level collaborative optimization: The second-level module sends feedback instructions to the first-level module according to the fused data to adjust the parameters of the local adaptive algorithm. The third-level module sends optimization instructions to the second-level module according to the optimized data to adjust the weight coefficients of the multi-sensor data fusion algorithm. Through this mechanism, the system can achieve adaptive adjustment according to different environmental and load conditions to ensure the optimization of the overall performance. In specific implementation, the local adaptive algorithm and the multi-sensor data fusion algorithm can be implemented through existing embedded systems and computing platforms. Each edge computing node internally includes a data cache module, a data verification module, and a data fusion module, which can complete the real-time processing of data with low latency. When implementing, first, sensors need to be arranged according to the application scenario to ensure that the weighing sensors cover key positions and configure the data acquisition module. Secondly, configure the edge computing nodes to process and optimize the data. Finally, through the cloud module, the entire system is monitored in real time and globally optimized to achieve a distributed architecture through the three-level module, enabling the system to achieve efficient data processing and optimization in a high-load and high-frequency dynamic weighing environment.

[0032] Embodiment 4: In this embodiment, the optimization focus of the local adaptive algorithm is to monitor the vibration intensity, temperature change, and electromagnetic interference intensity in the weighing environment in real time, and dynamically adjust the sampling frequency and filtering parameters of the weighing sensor according to this real-time data. To improve the accuracy of the weighing data, we added a threshold judgment mechanism for vibration intensity and temperature change. When the system detects that the environmental vibration intensity exceeds the set threshold, it will automatically increase the sampling frequency of the weighing sensor and enhance the filtering intensity to ensure the accuracy of the data; when the temperature change exceeds the preset range, the system will automatically adjust the temperature compensation coefficient of the sensor to ensure the stability of the weighing result. This optimization step is executed by the embedded computing module, which can respond to environmental changes in real time to ensure the reliability and accuracy of the weighing data. For the optimization of the multi-sensor data fusion algorithm, we introduced a more accurate environmental matching degree model to monitor the working state of each weighing sensor in detail. When the signal intensity of the sensor is lower than the preset threshold or the noise level exceeds the specified range, the system will automatically reduce its weight in the data fusion and increase the weight of other sensors. The dynamic adjustment of this weight is based on the historical accuracy score ( ) and the real-time environmental matching degree ( ). Specifically, the calculation of the weight coefficient is based on the following two main parameters: the historical accuracy score ( )reflects the performance of the sensor in past data, and the real-time environment matching degree ( )characterizes the adaptability of the current environment to the sensor output data. The weight coefficient is dynamically adjusted through the following path: calculate the historical accuracy score and real-time environment matching degree of each sensor. Dynamically adjust the sensor weights according to the historical accuracy score and environment matching degree to ensure that the actual operating status of each sensor and the current environmental factors are taken into account during data fusion.

[0033] The key to the global optimization algorithm lies in further optimizing the fusion data from each level of module through the cloud computing node. The optimization process mainly includes adjusting the parameters of the weighing model according to external environmental parameters (such as temperature, humidity, electromagnetic interference intensity, etc.). In this embodiment, we introduce a real-time environment monitoring module that can automatically update the optimization parameters when the environment changes. For example, when the temperature change causes a weighing deviation, the system will automatically adjust the temperature compensation coefficient through the real-time optimization algorithm and dynamically adjust the weighing model according to the deviation between historical data and real-time data to ensure the weighing accuracy and stability under different environments. To further improve the real-time performance and fault tolerance of the system, this embodiment refines the cross-level collaborative optimization mechanism. In the secondary level module, the edge computing node is not only responsible for data fusion and cross-verification, but also can send instructions to the primary level module through the feedback mechanism to dynamically adjust the parameters of the local adaptive algorithm. At the same time, the tertiary level module can send optimization instructions to the secondary level module according to the optimized weighing data to dynamically adjust the weight coefficients of the multi-sensor data fusion algorithm. Through this cross-level collaborative optimization, the system can dynamically adjust the operation parameters of each level under complex environments to ensure high efficiency and stability under different load conditions. And to further improve the fault tolerance of the system, this embodiment adds redundant weighing sensors and redundant computing nodes. When a weighing sensor fails, the system can ensure the continuity and stability of the weighing data through data compensation from adjacent nodes. When a computing node fails, the redundant computing node will automatically take over the task to ensure that the system will not degrade in performance due to a single point of failure. The specific implementation path of this embodiment specifically includes, hardware requirements: multiple high-precision weighing sensors and edge computing nodes need to be configured, and at the same time support the integration of the cloud computing platform; data acquisition and processing: the weighing data is processed in real time through the local data acquisition module and adjusted through the adaptive algorithm to ensure data accuracy and real-time performance; edge computing and cloud collaboration: the local data is processed in real time through the edge computing node, and the data is further optimized in the cloud through the global optimization algorithm to ensure the accuracy and stability of the weighing result; algorithm and module configuration: ensure that the configuration of each module can achieve data flow and collaborative work, and the feedback mechanism and instruction transmission between modules can achieve real-time adjustment and optimization.

[0034] Example 5: This example further optimizes the fault tolerance, real-time performance, and accuracy of multi-sensor data fusion of the system. In this example, the intelligent dynamic weighing system still adopts a three-level hierarchical module architecture, namely the first-level hierarchical module, the second-level hierarchical module, and the third-level hierarchical module. Each module will continue to allocate tasks according to the original design, but the cooperation mode between the modules of each layer is optimized to ensure the rapid transmission and processing of data. Enhancement of the first-level hierarchical module: The first-level module consists of multiple weighing sensors and a local data acquisition module. The weighing sensors are arranged at key positions to achieve real-time data acquisition. The local data acquisition module further introduces an adaptive adjustment algorithm to dynamically detect and adjust weighing parameters. Especially in an environment with high vibration intensity, temperature change, and electromagnetic interference intensity, the system will automatically increase the sampling frequency of the sensors and optimize the filtering parameters to improve the accuracy and stability of data acquisition. Optimization of the second-level hierarchical module: The second-level module consists of multiple edge computing nodes. Each edge computing node receives local processing data from multiple first-level hierarchical modules. To further optimize the efficiency of data fusion and cross-verification, the second-level module will use an improved multi-sensor data fusion algorithm. By analyzing the signal strength and noise level of signals from multiple sensors in real time, the system will dynamically adjust the weight coefficients of the sensors according to the historical accuracy scores and environmental matching degrees of each sensor. The implementation path of this process is as follows: Data is transmitted from the first-level module to the second-level module. The second-level module calculates the real-time signal strength and noise level of each sensor. Dynamically adjust the weight coefficients according to the real-time data to enhance the data weight of sensors with higher signal strength and lower noise. Improvement of the third-level hierarchical module: The cloud computing node of the third-level module receives the fused data from the second-level hierarchical module and further processes the data through a global optimization algorithm. The global optimization algorithm will consider the influence of external environmental parameters (such as temperature, humidity, and electromagnetic interference, etc.) on the weighing model to ensure that when the environment changes, the system can automatically adjust the compensation coefficient to maintain the stability of the weighing result.

[0035] To ensure the stability of the system in high-load and high-frequency dynamic weighing scenarios, this example particularly strengthens the fault tolerance mechanism. The first-level hierarchical module introduces a redundant sensor design. Once a weighing sensor fails, the system can automatically ensure that the weighing data is not affected through data compensation by adjacent sensors. The redundant design can ensure that the entire system can still work normally without obvious data deviation in the case of sensor failure. The second-level module further strengthens the fault tolerance processing and adds functions such as data caching, abnormal data detection, and automatic adjustment. The data caching module will cache the data from the first-level module to prevent calculation interruption caused by data loss. The data verification module conducts cross-verification on the received data, eliminates abnormal data, and transfers reliable data to the next-level module.

[0036] The local adaptive algorithm monitors the vibration intensity, temperature, and electromagnetic interference of the weighing environment in real time. It dynamically adjusts the sampling frequency and filtering parameters of the sensor. If the vibration intensity exceeds the preset threshold, the sampling frequency is increased and the filtering intensity is enhanced. When data deviation occurs due to environmental changes, the sensor sampling frequency and filtering parameters are automatically adjusted to ensure the accuracy of the data. In the secondary module, the multi-sensor data fusion algorithm dynamically adjusts the weight coefficients of the sensors by analyzing the real-time signal intensity and noise level of each sensor. When the signal intensity of a certain sensor is lower than the set threshold or the noise level exceeds the preset range, the system will automatically reduce the weight coefficient of this sensor and increase the weights of other sensors to ensure the accuracy and stability of the data fusion result. The global optimization algorithm realizes global optimization through the cloud computing node. According to the deviation between the historical data and the real-time data, it dynamically adjusts the compensation coefficient of the weighing model. When there are large fluctuations in the environmental temperature, the system will automatically adjust the temperature compensation coefficient to ensure the stability of the weighing result under different environmental conditions. At the same time, when implementing this intelligent dynamic weighing system, multiple high-precision weighing sensors need to be configured and reasonably arranged at key positions, such as on the conveyor belt of goods, to ensure that each object can be weighed accurately and in real time when passing through the conveyor belt. Each sensor needs to be connected to the local data acquisition module, and the data acquisition module processes the data collected by the sensor through the adaptive algorithm and adjusts the relevant parameters in real time. In the secondary hierarchical module, multiple edge computing nodes need to be configured to receive the local data from the primary module and perform data fusion and verification. The data fusion algorithm will dynamically adjust the weight coefficients based on the historical accuracy scores of the sensors and the real-time environmental data to ensure the accuracy and reliability of the weighing data. The cloud computing node will further optimize the data from the secondary module and adjust the model parameters through the global optimization algorithm to ensure the stability of the weighing accuracy. In addition, the system also needs to implement redundant sensor design and fault detection mechanisms to ensure that the system can automatically switch and continue to provide accurate weighing data in case of a failure.

[0037] Example 6: In this example, the data weight assignment and cross-validation steps in the multi-sensor data fusion algorithm are optimized to ensure that the data fusion process of the system is more accurate, reliable, and has efficient real-time processing capabilities. For example, when the system is running, multiple weighing sensors are respectively arranged at different positions to collect weighing data in real time. These sensor data are affected by environmental factors (such as temperature, humidity, vibration, etc.) and the performance of the sensors themselves. Therefore, they need to be preliminarily processed by the local data acquisition module. This module dynamically adjusts the sampling frequency and filtering parameters for the working environment of each sensor through a local adaptive algorithm. For example, when the sensor is in a high-vibration environment, its sampling frequency will increase and the filtering intensity will be enhanced to remove more noise and ensure the accuracy of the data. The sensor data collected by each first-level hierarchical module will be further processed and fused by the edge computing node. The key step in data fusion is weight assignment, that is, to adjust the importance of each sensor in the fusion process according to its historical accuracy score and real-time environment matching degree; historical accuracy score ( ): It reflects the accuracy of the sensor in past work and can be obtained through the analysis of system historical data; real-time environment matching degree ( ): Based on the real-time monitored data, it represents the working matching degree of the sensor under the current state; the calculation of the weight coefficient is achieved through the following method: First, according to the historical accuracy score and real-time environment matching degree of each sensor, a weight is assigned to each sensor respectively; when the historical accuracy of the sensor is high and the current environmental impact is small (i.e., the real-time environment matching degree is high), its weight will increase accordingly, otherwise it will decrease; in order to ensure the reliability and consistency of the data, this embodiment introduces a cross-validation mechanism. The specific process of cross-validation is as follows: Perform consistency checks on the data from multiple first-level hierarchical modules to ensure that the differences between sensor data do not exceed a preset threshold; if the data of a certain sensor deviates from the data of other sensors beyond the set range, then determine that the data is abnormal and automatically eliminate it. At this time, the system will estimate and fill in the missing values according to the data of other sensors to ensure the integrity of the data; this process is automatically executed by an algorithm without manual intervention, thus ensuring the efficient processing of the data. After the fusion data is completed, the data that has passed cross-validation will be further input into the third-level hierarchical module (i.e., the cloud computing node) for global optimization. This step uses a global optimization algorithm to finely adjust the fusion data to compensate for the impact of environmental factors on the weighing accuracy. For example, the system will dynamically adjust the optimization coefficient according to parameters such as environmental temperature and humidity to ensure that the system can always maintain high-precision weighing under different environmental conditions. And the system ensures the dynamic feedback and adaptive adjustment of each hierarchical module through a cross-level collaborative optimization mechanism: After the second-level hierarchical module performs data fusion and cross-validation, it will feedback the data to the first-level hierarchical module to adjust the filtering parameters and sampling frequency in the local data acquisition module. The third-level hierarchical module sends adjustment instructions to the second-level hierarchical module according to the optimized weighing data through a feedback mechanism to optimize the weight assignment algorithm. This embodiment ensures that data with higher accuracy can be preferentially considered in a multi-sensor environment by dynamically adjusting the weight coefficient of the sensor, thereby improving the data fusion accuracy of the entire system. Its cross-validation mechanism: Introduce a cross-validation algorithm to effectively eliminate abnormal data and ensure the consistency and reliability of the final fusion data; real-time environment adaptability: Through real-time monitoring and adjustment of system parameters, it ensures that the system can dynamically adjust to cope with external changes under different environmental conditions.

[0038] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent dynamic weighing system, characterized in that, including: The first-level hierarchical module includes multiple weighing sensors and a local data acquisition module. The multiple weighing sensors are arranged at positions near the weighing points and are used to collect weighing data in real time. The local data acquisition module is connected to the multiple weighing sensors and is used to perform local real-time processing on the weighing data, including dynamically adjusting weighing parameters according to the weighing environment through a local adaptive algorithm, and compressing and preprocessing the original weighing data to generate local processed data. The second-level hierarchical module includes multiple edge computing nodes. Each edge computing node is connected to at least one first-level hierarchical module and is used to receive the local processed data and perform weight assignment and cross-verification on the data from multiple first-level hierarchical modules through a multi-sensor data fusion algorithm to generate fusion data. The multi-sensor data fusion algorithm includes dynamically adjusting weight coefficients according to the historical accuracy data and real-time environment data of each weighing sensor, and the calculation method of the weight coefficient is: , where is the weight coefficient of the th sensor, is the historical accuracy score of the th sensor, is the real-time environment matching degree of the th sensor; is the sensor index, and its value range is from 1 to , is the total number of sensors in the system; and are respectively the The historical accuracy scores of the sensors and the matching degree with the real-time environment; a three-level hierarchical module, including a cloud computing node, which is used to receive the fusion data from multiple second-level hierarchical modules and perform global optimization on the fusion data through a global optimization algorithm to generate optimized weighing data; the global optimization algorithm includes dynamically adjusting the weighing model according to external environmental parameters, and the external environmental parameters include temperature, humidity, and electromagnetic interference intensity; wherein, the first-level hierarchical module, the second-level hierarchical module, and the third-level hierarchical module achieve multi-level linkage through a cross-level collaborative optimization mechanism, including: the second-level hierarchical module sends feedback instructions to the first-level hierarchical module according to the fusion data to dynamically adjust the parameters of the local adaptive algorithm; the third-level hierarchical module sends optimization instructions to the second-level hierarchical module according to the optimized weighing data to dynamically adjust the weight coefficients of the multi-sensor data fusion algorithm; the local adaptive algorithm includes the following steps: real-time monitoring of the vibration intensity, temperature change, and electromagnetic interference intensity of the weighing environment; dynamically adjusting the sampling frequency and filtering parameters of the weighing sensor according to the monitoring results; when the vibration intensity exceeds the preset threshold, automatically increasing the sampling frequency and enhancing the filtering intensity; the multi-sensor data fusion algorithm further includes: dynamically adjusting its weight coefficient according to the real-time signal intensity and noise level of each weighing sensor; when the signal intensity of a certain weighing sensor is lower than the preset threshold or the noise level is higher than the preset threshold, automatically reducing its weight coefficient and increasing the weight coefficients of other sensors; the global optimization algorithm includes the following steps: real-time monitoring of external environmental parameters, including temperature, humidity, and electromagnetic interference intensity; dynamically adjusting the parameters of the weighing model according to the monitoring results to compensate for the impact of environmental changes on weighing accuracy; when the temperature change exceeds the preset range, automatically adjusting the temperature compensation coefficient of the weighing model.

2. The intelligent dynamic weighing system according to claim 1, characterized in that, The cross - level collaborative optimization mechanism further includes: the secondary - level module sends feedback instructions to the primary - level module according to the fusion data to dynamically adjust the parameters of the local adaptive algorithm; the tertiary - level module sends optimization instructions to the secondary - level module according to the optimized weighing data to dynamically adjust the weight coefficients of the multi - sensor data fusion algorithm; through multi - level linkage, the adaptability and overall performance of the system in different environments are ensured.

3. The intelligent dynamic weighing system according to claim 1, wherein The edge computing node further includes: a data cache module for temporarily storing the local processing data from the primary - level module; a data verification module for cross - verifying the local processing data to eliminate abnormal data; a data fusion module for generating fusion data according to the multi - sensor data fusion algorithm.

4. The intelligent dynamic weighing system according to claim 1, wherein The cloud computing node further includes: a historical data storage module for storing historical weighing data and environmental parameters; a real - time monitoring module for real - time monitoring of the operating states of the weighing sensors, edge computing nodes, and cloud computing nodes; a fault detection module for automatically taking over the functions of the faulty node by the redundant node when a fault is detected.

5. The intelligent dynamic weighing system according to claim 1, wherein The primary - level module further includes: redundant weighing sensors for ensuring that the weighing data is not affected by data compensation from adjacent nodes when a certain weighing sensor fails; a local data compression module for compressing the weighing data to reduce the data transmission volume and energy consumption; the secondary - level module further includes: a dynamic weight adjustment module for dynamically adjusting its weight coefficient according to the historical accuracy data and real - time environment matching degree of each weighing sensor; a data cross - verification module for cross - verifying the data from multiple primary - level modules to ensure the reliability and consistency of the data; the tertiary - level module further includes: a machine learning module for predicting and analyzing the fusion data to generate a weighing trend prediction result; a global optimization module for dynamically adjusting the parameters of the global optimization algorithm according to the prediction result.

6. The intelligent dynamic weighing system according to claim 1, wherein The weighing method corresponding to the intelligent dynamic weighing system includes the following steps: Step 1, data acquisition and local processing: Weighing data is collected in real time through multiple weighing sensors arranged at positions near the weighing point; the locally collected data is processed locally in real time using a local data acquisition module, including dynamically adjusting weighing parameters according to the vibration intensity, temperature change, and electromagnetic interference intensity of the weighing environment through a local adaptive algorithm, and compressing and preprocessing the original weighing data to generate locally processed data; Step 2, data fusion and cross-verification: The locally processed data is transmitted to the edge computing node; through a multi-sensor data fusion algorithm, weight assignment and cross-verification are performed on data from multiple first-level modules to generate fused data; the multi-sensor data fusion algorithm includes dynamically adjusting the weight coefficient according to the historical accuracy data and real-time environment data of each weighing sensor, and the calculation formula for the weight coefficient is: , where is the weight coefficient of the -th sensor, is the historical accuracy score of the -th sensor, is the real-time environment matching degree of the -th sensor, is the sensor index, and are respectively the historical accuracy score and real-time environment matching degree of the -th sensor; Step 3, global optimization and monitoring analysis: The fused data is transmitted to the cloud computing node; through a global optimization algorithm, global optimization is performed on the fused data to generate optimized weighing data; the global optimization algorithm includes dynamically adjusting the weighing model according to external environment parameters, and the external environment parameters include temperature, humidity, and electromagnetic interference intensity; Step 4, cross-level collaborative optimization: The secondary-level module sends feedback instructions to the primary-level module according to the fused data to dynamically adjust the parameters of the local adaptive algorithm; the tertiary-level module sends optimization instructions to the secondary-level module according to the optimized weighing data to dynamically adjust the weight coefficient of the multi-sensor data fusion algorithm.

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