Weighing device and control system thereof

Abstract

The invention relates to the technical field of metallurgical equipment control, in particular to a weighing device and a control system thereof, the weighing device comprises a data acquisition module, a communication module, a data processing module, a dynamic control module and a feedback module, the dynamic control module triggers a standby unit to access and adjust the sampling frequency through a load prediction model; the control system comprises a control center, a cloud platform and a user terminal, the control center decrypts and stores the weight data, the cloud platform analyzes historical data based on a statistical model to generate a visual report, and the user terminal remotely adjusts parameters. Stress distribution of a weighing hopper lining plate is optimized through closed-loop control, abrasion caused by material impact is reduced, the flow speed of a gate is dynamically adjusted, the mine return rate and dust dissipation are reduced, instruction reliability is guaranteed through a redundant communication link and encryption transmission, deviation and tearing of a belt conveyor are prevented in combination with foreign matter detection and a load balancing mechanism, and the service life of the belt conveyor is prolonged. And the efficiency and the service life of the weighing system are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of metallurgical equipment control, and particularly to a weighing device and its control system. Background Art

[0002] Conventional weighing devices in the traditional metallurgical industry generally have structural design defects, resulting in insufficient equipment operation stability and material control accuracy. Specifically, the conventional weighing hopper uses a lining plate made of cast steel, which is prone to wear and deformation under the long-term impact and corrosion of materials, and spare parts need to be frequently replaced; at the same time, the collision caused by free fall during the material unloading process exacerbates the crushing of sintered ore, resulting in an increase in the return ore volume and dust emission problems. In addition, the existing gate structure lacks effective control over the material flow rate and direction, easily causes eccentric loading, deviation and abnormal wear of the belt conveyor during material receiving, and cannot effectively isolate foreign objects such as mixed scrap steel, posing a risk of belt tearing. The above problems restrict the overall efficiency and service life of the weighing system, and there is an urgent need for technological breakthroughs through structural optimization and intelligent control means. Summary of the Invention

[0003] Aiming at the deficiencies of the prior art, the present invention provides a weighing device and its control system, which solve the problems that the lining plate of the weighing hopper of the traditional weighing device is prone to wear, the material crushing return rate is high, the dust emission is serious due to structural design defects, and the gate structure lacks control over the material flow rate and foreign object isolation, resulting in risks of belt conveyor deviation, wear and tearing, and restricting the efficiency and service life of the weighing system.

[0004] To solve the above technical problems, the specific technical solutions of the present invention are as follows: In the first aspect, a weighing device provided by the present invention includes: A data acquisition module, configured to obtain the weight signal of the object to be measured in real time through a sensor and convert the weight signal into a digital signal; A communication module, connected to the data acquisition module, configured to transmit the digital signal to a control center based on a preset wireless communication protocol; A data processing module, connected to the control center, configured to receive the digital signal from the control center, parse it into weight data, and calibrate the weight data according to a linear regression model based on environmental interference parameters, where the environmental interference parameters include temperature, humidity, and vibration intensity; A dynamic control module, connected to the data processing module, configured to generate a control instruction according to the calibrated weight data and adjust the sampling frequency or filtering threshold of the weighing device based on the control instruction; A feedback module, connected to the dynamic control module, configured to feedback the adjusted sampling frequency or filtering threshold to the data acquisition module to adjust the signal acquisition process of the sensor and form a closed-loop control.

[0005] Furthermore, for the weighing device of the present invention, the communication module is further configured to: After the data acquisition module completes the conversion of the weight signal, trigger the construction of a dynamic ad-hoc network topology with adjacent weighing devices; Based on the dynamic ad-hoc network topology, transmit the digital signal to the control center through data synchronization among multiple nodes; During the transmission process, dynamically divide the weight data into real-time data and non-real-time data according to the network load, and preferentially transmit the real-time data to the control center based on a preset priority policy.

[0006] Furthermore, for the weighing device of the present invention, the construction of the dynamic ad-hoc network topology includes: Periodically detect the communication signal strength and location information of adjacent weighing devices through the communication module; Calculate the node connection weight based on the signal strength and location information, where the signal strength and location information generate a link stability score through a weighted algorithm; Select the node with the highest link stability score as the relay node to establish redundant communication links.

[0007] Furthermore, for the weighing device of the present invention, the error compensation model is generated in the following manner: Collect environmental interference parameters in historical weighing data through the data acquisition module, where the environmental interference parameters include temperature, humidity, and vibration intensity; Input the environmental interference parameters and the weight signal into a linear regression model for correlation analysis to generate an error compensation coefficient; Correct the real-time weighing data based on the error compensation coefficient.

[0008] Furthermore, for the weighing device of the present invention, the dynamic control module is further configured to: predict the working load of the weighing device through a load prediction model according to the calibrated weight data output by the data processing module; When the working load exceeds a preset threshold, generate an alarm instruction and send a standby request to the control center through the communication module; Receive the standby weighing unit identifier assigned by the control center, and establish a communication link with the standby weighing unit based on the identifier.

[0009] Furthermore, for the weighing device of the present invention, the access of the standby weighing unit includes: sending a standby request to the control center through the communication module; Receive the standby unit identifier assigned by the control center, and establish a communication link with the standby weighing unit based on the identifier.

[0010] Furthermore, for the weighing device of the present invention, the feedback module is further configured to: Compare the adjusted weighing parameters with the target parameters to generate a deviation value; If the deviation value exceeds the allowable range, trigger the data processing module to recalibrate the weight data.

[0011] Furthermore, for the weighing device of the present invention, the device further includes: An encryption module, configured to encrypt the weight data using an asymmetric encryption algorithm before the communication module transmits the data; A decryption module, configured to decrypt the encrypted data at the receiving end and verify the data integrity.

[0012] Furthermore, for the weighing device of the present invention, the encryption module is further configured to: Generate a dynamic key according to the device identifier of the weighing device, and transmit the dynamic key bound to the weight data; In the decryption module, complete the decryption by verifying the matching between the dynamic key and the device identifier.

[0013] In a second aspect, the present invention provides a control system for a weighing device, which is applied to the weighing device. The system includes: a control center, connected to the communication module of the weighing device, configured to receive the encrypted weight data, decrypt it through the decryption module, verify the data integrity, and then store it in the local database, and issue it to the weighing device based on the control instruction generated by the dynamic control module; A cloud platform, connected to the control center, configured to extract historical weight data from the local database, perform trend analysis and anomaly detection on the weight data based on a preset statistical model, and generate a visualization report including a load prediction result; A user terminal, connected to the cloud platform, configured to receive the visualization report in real time, and issue an adjustment instruction to the dynamic control module through the control center according to the load prediction result to update the sampling frequency or filtering threshold of the weighing device.

[0014] Advantages of the present invention; The present invention effectively improves the structural defects of traditional weighing devices through modular design and intelligent control mechanisms. The data acquisition module uses vibration-resistant sensors combined with ceramic coating protection to reduce the stress concentration of the lining caused by material impact. Combined with the closed-loop adjustment of the sampling frequency and filtering threshold by the dynamic control module, the force distribution of the weighing hopper is optimized, and local wear and deformation of the lining are inhibited. The error compensation model dynamically calibrates the weight data based on environmental interference parameters, improves the control accuracy of the gate, stabilizes the material flow rate within the set threshold, and reduces the breakage of sintered ore and the return ore amount caused by free-fall collision. The dynamic self-organizing network topology constructs redundant communication links, preferentially transmits real-time control instructions to the gate actuator, combines the electromagnetic iron removal device with the foreign object detection mechanism, accurately adjusts the material landing position and intercepts mixed foreign objects, and synchronously triggers the standby unit to cooperate through the load prediction model to balance the load distribution of the belt conveyor and reduce the risks of deviation and tearing. The encryption module uses asymmetric encryption algorithms and dynamic key binding transmission to ensure the integrity of control instructions and prevent secondary dust diffusion caused by abnormal vibration. The above technical solutions form a systematic optimization, achieving the comprehensive effects of improving weighing accuracy, reducing equipment wear, and enhancing operation safety. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the drawings.

[0016] Figure 1 It is a system architecture diagram of a control system for a weighing device provided by an embodiment of the present invention.

[0017] Figure 2 It is a structural schematic diagram of a weighing device provided by an embodiment of the present invention. Detailed Embodiments

[0018] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention. The technical solutions provided by each embodiment of the present invention will be described in detail below in conjunction with the drawings. To better understand the objectives of the present invention, the present invention will be further described in detail below.

[0019] In a first aspect, a weighing device provided by the present invention includes: A data acquisition module for obtaining the weight signal of the object to be measured in real time through a sensor and converting the weight signal into a digital signal; A communication module, connected to the data acquisition module, for transmitting the digital signal to a control center based on a preset wireless communication protocol; A data processing module, connected to the control center, for receiving the digital signal from the control center, parsing it into weight data, and calibrating the weight data according to a linear regression model based on environmental interference parameters, where the environmental interference parameters include temperature, humidity, and vibration intensity; A dynamic control module, connected to the data processing module, for generating a control instruction according to the calibrated weight data and adjusting the sampling frequency or filtering threshold of a weighing device based on the control instruction; A feedback module, connected to the dynamic control module, for feeding back the adjusted sampling frequency or filtering threshold to the data acquisition module to adjust the signal acquisition process of the sensor and form a closed-loop control.

[0020] The data acquisition module real-time collects the weight signal of the material in the weighing hopper through a high-precision sensor. The sensor adopts a strain gauge structure and integrates an anti-electromagnetic interference circuit, and inputs the analog signal into an analog-to-digital converter to generate a digital signal. The surface of the sensor is covered with a ceramic coating to reduce the thermal expansion effect in a high-temperature environment, and high-frequency vibration noise is filtered out through a low-pass filter. The communication module constructs a wireless transmission link based on the ZigBee protocol, triggers a data encapsulation process after analog-to-digital conversion is completed, and encapsulates the digital signal into a data frame conforming to the IEEE 802.15.4 standard. The frame header contains a timestamp and device identification information. The communication module performs differential transmission on real-time control instructions and non-real-time log data according to a preset priority policy. Real-time data adopts a short frame structure and enables a fast retransmission mechanism, and non-real-time data adopts a long frame structure and enables a compression algorithm to reduce network load.

[0021] After receiving the data frame from the control center, the data processing module parses the frame header information to verify the legality of the data source, and then extracts the weight data and environmental interference parameters. The environmental interference parameters are synchronously collected through temperature sensors, humidity sensors, and vibration sensors distributed on the weighing device. The temperature sensor adopts a PT100 platinum resistance structure, and the vibration sensor is based on a MEMS accelerometer. The multiple linear regression model generates the weight coefficients of each environmental parameter through historical data training. For example, the influence coefficient of temperature on the sensor output is α, the humidity influence coefficient is β, and the vibration intensity influence coefficient is γ. Substitute the real-time collected environmental parameters into the model to calculate the compensation value, and perform addition or multiplication correction on the original weight data. The calibrated weight data is output to the dynamic control module and stored in the local buffer for periodic update of the model parameters.

[0022] The dynamic control module constructs a time series data set based on the calibrated weight data and analyzes the current workload trend through the sliding window algorithm. The load prediction model uses the exponentially weighted moving average method to calculate the predicted values for future time periods. When the predicted value exceeds 90% of the preset threshold, an instruction packet containing the alarm level and standby request parameters is generated. The instruction packet is sent to the control center through the communication module. After the control center responds, it assigns the MAC address of the standby weighing unit and establishes a communication channel based on AES-256 encryption. After the standby unit is connected, the dynamic control module synchronizes the current sampling frequency, filtering threshold, and error compensation coefficient, and maintains the state consistency of the primary and standby units through the heartbeat mechanism to achieve redundant control.

[0023] The feedback module receives the adjustment parameters output by the dynamic control module in real time, calculates the difference between the actual sampling frequency and the target value, and triggers the calibration process reset when the difference exceeds the preset range. The reset signal is transmitted to the data processing module to force an update of the acquisition period of the environmental interference parameters and recalculate the compensation coefficient of the linear regression model. The closed-loop control logic is achieved by adjusting the sensor parameters of the data acquisition module. For example, when the sampling frequency increases, the sensor acquisition interval shortens, the clock frequency of the analog-to-digital converter is synchronized to increase, and the cut-off frequency of the low-pass filter is dynamically adjusted to match the new sampling rate. The feedback data is also used to optimize the transmission strategy of the dynamic ad hoc network. For example, when the network load is high, the transmission frequency of non-real-time data is reduced to give priority to ensuring the transmission bandwidth of control instructions.

[0024] Before data transmission, the encryption module calls the elliptic curve encryption algorithm to generate a public-private key pair, combines the device identifier and the current timestamp to generate a dynamic key seed, and generates a unique key through the HMAC-SHA256 algorithm. The encrypted data packet contains encrypted weight data, a dynamic key, and a digital signature. The digital signature is generated by encrypting the data hash value with the private key. The decryption module extracts the dynamic key at the control center after verifying the signature validity with the public key, restores the original data using the symmetric decryption algorithm, and verifies the data integrity through CRC check. The key life cycle is set to 30 minutes, and the key update process is automatically triggered after timeout to prevent replay attacks.

[0025] The cloud platform extracts historical weight data from the control center database, performs data cleaning, and then inputs it into the statistical model. The trend analysis module uses the ARIMA algorithm to predict future load changes, and the anomaly detection module identifies data points deviating from the normal distribution based on the Isolation Forest algorithm. The visualization report integrates the load prediction curve, anomaly warning events, and the fluctuation trend of environmental parameters, and pushes them to the user terminal through the WebSocket protocol. The user terminal is built with a parameter configuration interface, and the operator can set the sampling frequency threshold, load warning level, and calibration period. The configuration instructions are forwarded to the dynamic control module through the control center, triggering real-time parameter updates. The dynamic control module responds to the instructions within 200 milliseconds, pauses the current control process through the interruption mechanism, loads the new parameters, and then resumes operation.

[0026] Specifically, for the weighing device described in the present invention, the communication module is further used for: After the data acquisition module completes the conversion of the weight signal, triggering the construction of a dynamic self-organizing network topology with adjacent weighing devices; Based on the dynamic self-organizing network topology, transmitting the digital signal to the control center through data synchronization among multiple nodes; During the transmission process, dynamically dividing the weight data into real-time data and non-real-time data according to the network load, and preferentially transmitting the real-time data to the control center based on a preset priority strategy.

[0027] After the data acquisition module completes the conversion of the weight signal, the communication module starts the detection process of adjacent nodes, and obtains the signal strength and position coordinates of surrounding weighing devices by periodically sending beacon frames. The beacon frame contains the transmission power, receiving sensitivity, and the unique identifier of the node. The receiving node calculates the relative distance according to the signal attenuation model, and generates a link quality evaluation parameter in combination with the received signal strength indication value. The communication module inputs the link quality parameter and the position stability coefficient into the weighted scoring algorithm, calculates the comprehensive connection weight of each adjacent node, selects the node with the highest score as the relay node to establish the main transmission path, and at the same time retains the second-best node as the backup link.

[0028] After the construction of the dynamic self-organizing network topology is completed, the communication module divides the digital signal into data packets of a fixed length, attaches a sequence number and a check code to each data packet, and transmits them in parallel through the main path and the backup path. During the transmission process, a timestamp synchronization mechanism is adopted to coordinate the data sending timings among multiple nodes to avoid channel conflicts. After receiving the data packet, the relay node verifies the integrity of the check code, discards the incorrect data packet and requests retransmission, and the complete data packet is forwarded to the control center through multi-hop. The communication module real-time monitors the transmission delay and packet loss rate of each path, and dynamically adjusts the data packet distribution ratio to keep the load rate of the main path below 70% and the load rate of the backup path not exceeding 30%.

[0029] In the network load dynamic partitioning stage, the communication module is built with a traffic monitoring unit to statistically calculate the channel utilization rate and the data queue length in real time. When the network load exceeds the preset threshold, the data classification engine is activated, and the weight data is classified into real-time control instructions and non-real-time status information according to the type identifier in the packet header. The real-time data is encapsulated into high-priority frames, allocated dedicated transmission time slots, and forward error correction coding is enabled; the non-real-time data is encapsulated into low-priority frames, and a batch aggregation transmission mode is adopted and data compression is enabled. The priority policy is executed through the weighted round-robin scheduling algorithm, and the scheduling weight of the real-time data frames is set to 3 times that of the non-real-time data to ensure that high-priority data can obtain transmission resources first in case of congestion.

[0030] During the transmission process, the communication module continuously collects network state parameters, including the channel noise level, the remaining energy of the node, and the link stability index. The state parameters are input into the adaptive adjustment algorithm to dynamically adjust the frame length and modulation method: in a low-noise environment, higher-order modulation is adopted to improve the transmission efficiency, and in a high-noise environment, it is switched to lower-order modulation to enhance the anti-interference ability. The non-real-time data reduces power consumption through the sleep mechanism during the transmission gap period, and the real-time data adopts the continuous listening mode to maintain low-latency transmission. The heartbeat message is used between nodes to maintain the topological connection state. After the detection timeout of the faulty node, the topological reconstruction process is automatically triggered, the relay node is re-elected, and the routing table is updated.

[0031] Specifically, for the weighing device described in the present invention, the construction of the dynamic ad hoc network topology includes: Periodically detecting the communication signal strength and position information of adjacent weighing devices through the communication module; Calculating the node connection weight based on the signal strength and position information, where the signal strength and position information generate a link stability score through a weighted algorithm; Selecting the node with the highest link stability score as the relay node to establish redundant communication links.

[0032] The communication module periodically sends beacon frames containing node identifiers and transmit power parameters to detect the communication signal strength of adjacent weighing devices. The beacon frames adopt the directional broadcast mode and are transmitted at fixed time intervals within the preset frequency band. The receiving node calculates the path loss according to the received signal strength indication value and deduces the relative distance in combination with the transmit power parameters. The position information is collected by the three-axis accelerometer and gyroscope deployed on the weighing device, and the three-dimensional space coordinates of the device are calculated in real time, and the measurement error caused by environmental vibration is eliminated through the coordinate difference algorithm.

[0033] Based on signal strength and location information, the communication module uses a normalization weighting algorithm to calculate the node connection weight. The signal strength parameter is processed through a logarithmic attenuation model and mapped to a link quality coefficient; the location information is converted into a spatial stability coefficient through the Euclidean distance formula. The link quality coefficient and the spatial stability coefficient are respectively given weight ratios of 0.6 and 0.4, and are input into a linear weighting function to generate a link stability score. The score result is stored in the adjacency list, and the score value, communication delay, and historical connection success rate of each adjacent node are recorded in the table.

[0034] The communication module traverses the adjacency list to select the node with the highest link stability score as the primary relay node, and at the same time selects the node with the second highest score as the backup relay node. After the primary relay node is established, the communication module sends a routing request message to it, negotiates the transmission frequency band and modulation method, and exchanges encrypted handshake information to establish a secure channel. The backup relay node maintains a heartbeat detection in the background, periodically receives the status report of the primary relay node, and immediately activates the backup link to take over the data transmission when it detects that the communication of the primary node is interrupted. The primary and backup links use the same data encapsulation format and encryption protocol to achieve seamless switching.

[0035] The transmission process of the redundant communication link adopts a multi-path load balancing mechanism, and data packets are assigned to different paths according to the priority tags. High-priority data packets are transmitted through the primary relay node along the minimum-hop path, and low-priority data packets are multi-hop forwarded through the backup relay node. The communication module monitors the throughput and bit error rate of each path in real time, dynamically adjusts the data packet distribution ratio, and keeps the load rate of the primary path within the optimal range. The link stability score is recalculated every 5 minutes. When the score of the primary relay node drops by more than the threshold, a topology reconstruction process is triggered, and the relay node is re-elected and the routing policy is updated.

[0036] Specifically, for the weighing device described in the present invention, the error compensation model is generated in the following manner: Collect the environmental interference parameters in the historical weighing data through the data acquisition module, and the environmental interference parameters include temperature, humidity, and vibration intensity; Input the environmental interference parameters and the weight signal into a linear regression model for correlation analysis to generate an error compensation coefficient; Correct the real-time weighing data based on the error compensation coefficient.

[0037] The data acquisition module deploys temperature sensors, humidity sensors and vibration sensors at key positions of the weighing device. The temperature sensor is mounted on the side wall of the weighing hopper using a PT100 platinum resistor structure. The humidity sensor is installed at the material entrance based on the capacitive measurement principle. The vibration sensor uses a MEMS three-axis accelerometer fixed to the weighing hopper support frame. The sensor data is transmitted to the acquisition circuit through a shielded cable, and a sliding average filter is performed before the analog-to-digital conversion to eliminate instantaneous interference noise. The historical weighing data is stored in a ring buffer, and a mapping relationship is established between the timestamp and the environmental parameters to form a multidimensional data set containing weight signals, temperature, humidity and vibration intensity.

[0038] The data processing module performs a data cleaning process to remove outliers that exceed the sensor range, and uses linear interpolation of adjacent time points to complete the missing data. The cleaned data set is input into the standardization unit, and the temperature, humidity, and vibration intensity parameters are normalized to a zero-mean unit variance distribution to eliminate the impact of dimensional differences on model training. The linear regression model uses the least squares method to fit the relationship between environmental parameters and weight signal deviations. The generalization ability of the model is evaluated through cross-validation during the training process, and the parameter combination with the highest determination coefficient is selected as the error compensation coefficient. The trained model parameters are stored in non-volatile memory, and a version control mechanism is established to support dynamic update and rollback of model parameters.

[0039] During the real-time weighing stage, the data acquisition module synchronously obtains the current environmental parameters and weight signals, and performs the same filtering and standardization operations as historical data through the preprocessing unit. The calibration engine calls the stored error compensation coefficient, substitutes the temperature, humidity and vibration intensity parameters into the linear regression equation, and calculates the weight correction value under the current environment. The correction operation adopts the feedforward compensation mode, and the deviation adjustment is completed before the weight signal enters the dynamic control module. The compensation value is set to update at a frequency based on the rate of environmental change. When the temperature fluctuates by more than 2°C / minute or the vibration intensity changes by more than 0.5g, the compensation coefficient is recalculated. When the calibrated weight data is output, a confidence label is attached to reflect the applicability level of the compensation model under the current environmental conditions, which is used as a reference for the dynamic control module when making decisions.

[0040] Specifically, in the weighing device of the present invention, the dynamic control module is further used to: predict the workload of the weighing device through a load prediction model according to the calibrated weight data output by the data processing module; When the workload exceeds a preset threshold, an alarm instruction is generated and a standby request is sent to a control center through the communication module; Receive the spare weighing unit identification assigned by the control center, and establish a communication link with the spare weighing unit based on the identification.

[0041] The dynamic control module receives the calibrated weight data output by the data processing module, constructs a time series data set and performs a sliding window analysis. The sliding window covers the continuous sampling points in the most recent 30 minutes, calculates the mean, variance and change gradient of the weight data within the window, and inputs them into the load prediction model. The load prediction model uses the exponential smoothing algorithm to perform weighted calculations on the trend component, predicts the workload value within the next 5 minutes, and compares and grades the prediction result with a preset threshold. When the predicted value exceeds 80% of the threshold, a primary warning is triggered, and when it exceeds 95%, it is upgraded to a high-level alarm.

[0042] The alarm instruction generation unit encapsulates the alarm code, timestamp and current load parameters according to the warning level, and generates a message format that conforms to the Modbus protocol. After receiving the alarm message, the communication module enables the high-priority transmission queue, splits the message into multiple data blocks and attaches a cyclic redundancy check code, and sends it to the control center through the main relay node of the dynamic ad hoc network. After parsing the message, the control center retrieves the spare weighing unit resource pool, and allocates the optimal spare unit according to the geographical proximity principle and the load balancing strategy, and generates an identification response packet containing the IP address and access key.

[0043] After receiving the identification response packet, the dynamic control module calls the security protocol stack to establish a point-to-point communication with the spare weighing unit. The communication link adopts a two-factor authentication mechanism, and combines the device hardware fingerprint and the dynamic token to complete the identity verification. After the link is established, the working parameters of the main weighing unit are synchronized, including the current sampling frequency, filtering threshold and error compensation coefficient, and the parameter consistency is maintained through the incremental synchronization algorithm. After the spare unit is started, it enters the hot standby state and receives the status heartbeat packet of the main unit in real time. When the load of the main unit continuously exceeds the threshold, the spare unit takes over part of the weighing tasks, and transfers the load amount exceeding the threshold to the spare device in proportion through the traffic distribution algorithm.

[0044] Specifically, for the weighing device described in the present invention, the access of the spare weighing unit includes: sending a spare request to the control center through the communication module; Receiving the spare unit identifier allocated by the control center, and establishing a communication link with the spare weighing unit based on the identifier.

[0045] When the communication module detects that the load of the main weighing unit exceeds the limit, it automatically triggers the spare request generation process. The request message encapsulates the main unit device identifier, real-time load data and geographical location coordinates, and is sent to the control center through the high-priority channel of the dynamic ad hoc network topology. After parsing the message, the control center retrieves the spare unit resource pool, and based on the load balancing algorithm and the topology distance evaluation model, filters out the spare unit in the idle state and with the closest physical location, and generates an identification response packet containing the device MAC address, communication port and temporary session key.

[0046] After receiving the identification response packet, the dynamic control module calls the secure handshake protocol to establish an encrypted communication link with the standby weighing unit. In the link establishment phase, a two-way authentication mechanism is adopted. The master unit sends a digital certificate containing a temporary session key, and after the standby unit verifies the validity of the certificate, it returns an encrypted confirmation message. After authentication, the master unit transmits the current working parameter set to the standby unit through the incremental synchronization protocol, including the dynamic filtering threshold, calibration coefficient, and sampling frequency configuration. The difference comparison algorithm is used during the synchronization process to only transmit the changed data to reduce bandwidth occupancy.

[0047] During the communication link maintenance phase, the master and standby units periodically exchange heartbeat data packets, which include the current load status, link quality indicators, and error counters. The standby unit continuously receives the real-time weight data stream from the master unit in the hot standby state and pre-loads the error compensation model and environmental parameters. When the load of the master unit continuously exceeds the threshold, the control center issues a switching instruction, and the standby unit immediately takes over a specified proportion of the weighing tasks and switches part of the data stream to its own communication module through the traffic redirection mechanism. The master and standby units use the time synchronization protocol to align the operation timing to avoid data processing conflicts caused by clock drift. The incomplete weighing tasks during the switching process maintain data integrity through the transaction rollback mechanism.

[0048] Specifically, for the weighing device described in the present invention, the feedback module is further used for: Comparing the adjusted weighing parameters with the target parameters to generate a deviation value; If the deviation value exceeds the allowable range, trigger the data processing module to re-calibrate the weight data.

[0049] The feedback module obtains the adjusted weighing parameters output by the dynamic control module in real time, including the current sampling frequency and filtering threshold, and performs a mapping comparison with the target parameters pre-stored in the non-volatile memory. The parameter comparison is executed by a difference calculation unit. The difference calculation unit selects the absolute deviation or relative deviation algorithm according to the parameter type. For example, the sampling frequency deviation calculates the absolute difference in hertz, and the filtering threshold deviation calculates the relative difference in percentage form. The calculation result is stored in the deviation log after adding a timestamp and is also input to the threshold comparator for real-time determination.

[0050] The threshold comparator has a multi-level determination logic. When the deviation value first exceeds 70% of the allowable range, a primary warning signal is generated and recorded in the event queue; if the deviation value continuously exceeds the allowable range for two minutes, an interrupt signal is triggered to forcibly start the re-calibration process. The interrupt signal is transmitted to the data processing module through the hardware priority line, pausing the current data stream processing task, clearing the calibration parameter buffer area, and activating the fast sampling mode of the environmental parameter acquisition unit.

[0051] After the data processing module responds to the interruption, it calls the data acquisition module to synchronously acquire the temperature, humidity, and vibration intensity parameters, and obtains the latest environmental data at a frequency ten times that of the conventional rate. After the acquisition is completed, the data processing module inputs the environmental parameters into the linear regression model, recalculates the error compensation coefficient, and uses the sliding window verification method to test the effectiveness of the new coefficient. If the verification passes, the calibration parameters are updated and the weighing process is restored; if the verification fails, the environmental parameter acquisition and model training are iteratively executed until a compensation coefficient that meets the accuracy requirements is generated. The calibration result is written back to the dynamic control module through the feedback module, and the target parameter reference value is synchronously updated to form a closed-loop adaptive adjustment mechanism.

[0052] Specifically, for the weighing device described in the present invention, the device further includes: An encryption module, which is used to encrypt the weight data using an asymmetric encryption algorithm before the communication module transmits the data; A decryption module, which is used to decrypt the encrypted data at the receiving end and verify the data integrity.

[0053] Before data encapsulation, the encryption module calls the elliptic curve encryption algorithm to generate a public-private key pair. The public key is preset in the hardware security module of the weighing device, and the private key is injected into the control center key library through the secure boot process. The dynamic key generation unit combines the device serial number and the current timestamp to generate a seed value, uses the HMAC-SHA256 algorithm to derive a one-time session key, and binds the seed value to the weight data to generate an encrypted data packet. The key version identifier and the initialization vector are attached to the data packet header, and the weight data is encrypted using the block encryption mode. The encrypted data blocks are associated with the original data stream through serial number marking.

[0054] When the encrypted data packet is transmitted, it adopts a hierarchical encapsulation structure. The outer layer encapsulates the transmission frame format that conforms to the industrial communication protocol, and the inner layer encapsulates the encrypted payload and the digital signature. The digital signature is generated by encrypting the data packet hash value with the private key, and the hash algorithm selects the SHA-3 series functions with strong collision resistance. Before sending, the communication module detects the channel state, dynamically selects the fragmentation size of the encrypted data blocks, enables forward error correction coding in a high-interference environment, and embeds redundant check codes into the data fragmentation gaps to improve the packet loss resistance ability.

[0055] After the decryption module receives the encrypted data packet at the control center, it first parses the key version identifier, matches the corresponding private key in the key library, and verifies the digital signature. After the signature verification passes, it extracts the session key seed value, restores the session key through the same HMAC algorithm, and performs the reverse decryption operation on the encrypted data blocks in combination with the initialization vector. The decrypted weight data is input into the integrity verification unit, and the cyclic redundancy check code is used to compare the decrypted data with the original hash value. The data packets that fail the verification trigger the retransmission request mechanism.

[0056] The decryption module maintains a key lifecycle management table, performs automatic abandonment operations on expired keys, and notifies the weighing device to update the key seed through the reverse heartbeat protocol. When continuous verification failures are detected during the decryption process, a key rotation process is initiated, a key update instruction is sent to the weighing device, and the elliptic curve parameters are renegotiated to generate a new key pair. The decrypted weight data is stored in association with the integrity verification result, and the verification status flag is embedded in the header of the data record for subsequent data processing modules to execute differential processing strategies.

[0057] Specifically, for the weighing device described in the present invention, the encryption module is further configured to: Generate a dynamic key based on the device identifier of the weighing device, and transmit the dynamic key bound to the weight data; In the decryption module, decryption is completed by verifying the matching of the dynamic key and the device identifier.

[0058] The encryption module generates a dynamic key based on the device identifier of the weighing device. The device identifier is extracted through a hardware security module and includes a hash combination of the device serial number and the MAC address. The dynamic key generation unit inputs the device identifier and the timestamp provided by the real-time clock source into a key derivation function, and iteratively calculates a 256-bit session key using the HMAC-SHA256 algorithm. After the key is generated, it is bound to the weight data through an association identifier, which is formed by splicing the prefix of the device identifier and the suffix of the timestamp and is embedded in the header metadata field of the encrypted data packet.

[0059] The encrypted data packet is encapsulated in a hierarchical structure. The outer layer is the transport protocol header, which contains the target control center address and the hop count limit; the middle layer is the encrypted payload, where the weight data is divided into fixed-length data blocks, and each data block is encrypted in chunks using the dynamic key; the inner layer is the binding information layer, which stores the association identifier and the key version number. The encryption module performs data fragmentation optimization before transmission, dynamically adjusts the fragmentation size according to the current network latency, and enables data block interleaving arrangement in a high-latency environment to reduce the impact of transmission interruption on decryption integrity.

[0060] After receiving the encrypted data packet, the decryption module parses the association identifier to extract the device identifier and timestamp components, and verifies the legality of the identifier by querying the device registry of the control center. After successful verification, the decryption module calls the same key derivation function to recalculate the dynamic key based on the device identifier and timestamp, and compares the consistency with the key version number in the data packet. After successful key matching, the weight data is restored in a block decryption mode, and the decryption result of each data block is verified through a hash chain. The hash value of the previous block is used as the initial vector of the next block to form a data integrity verification chain.

[0061] When decryption fails, the decryption module retrieves the historical key library according to the timestamp in the associated identifier and attempts to decrypt the data again with the keys within the adjacent time window. If the consecutive decryption failures exceed the threshold, a key synchronization request is sent to the encryption module to trigger a new round of dynamic key generation and transmission. The key lifecycle management unit monitors the validity period of the timestamp, and the expired dynamic keys automatically become invalid and are moved to the archive library to prevent replay attacks. The decrypted weight data is appended with a decryption status flag, including the key matching result and the integrity verification level, for the subsequent data processing module to perform credibility grading processing.

[0062] In a second aspect, the present invention provides a control system for a weighing device, which is applied to the weighing device. The system includes: a control center, connected to the communication module of the weighing device, for receiving the encrypted weight data, decrypting it through the decryption module, verifying the data integrity, and then storing it in the local database, and issuing control instructions generated by the dynamic control module to the weighing device; A cloud platform, connected to the control center, for extracting historical weight data from the local database, performing trend analysis and anomaly detection on the weight data based on a preset statistical model, and generating a visualization report including load prediction results; A user terminal, connected to the cloud platform, for receiving the visualization report in real time, and issuing adjustment instructions to the dynamic control module through the control center according to the load prediction result to update the sampling frequency or filtering threshold of the weighing device.

[0063] The control center establishes a connection with the communication module of the weighing device through the industrial Ethernet protocol and receives the weight data stream processed by the asymmetric encryption algorithm. The decryption module calls the private key stored in the key management unit to perform hierarchical decryption on the data packet. First, it verifies the binding relationship between the digital signature and the device identifier, and then restores the original weight data through the session key. The decrypted data is input into the integrity verification unit, and the cyclic redundancy check algorithm is used to verify the continuity of the data block sequence. The data passing the verification is sorted by timestamp and written into the time series database. The database adopts a sharded storage architecture and divides the data partition according to the device identifier. The adjustment instructions generated by the dynamic control module are encapsulated into control messages, and the priority identifier and the target device code are embedded in the message header, and are sent to the weighing device in parallel through the redundant communication link.

[0064] The cloud platform extracts historical weight data from the time-series database of the control center through a database connection pool and performs data preprocessing operations, including missing value filling and noise filtering. The preprocessed dataset is input into the statistical model engine. The trend analysis module uses the autoregressive integrated moving average algorithm to identify the periodic fluctuation law of the load, and the anomaly detection module locates the data points deviating from the normal distribution based on the density clustering algorithm. The analysis results are input into the visualization rendering engine, which integrates the load prediction curve, anomaly event markers, and environmental parameter correlation maps into an interactive report. The report data is pushed to the message bus through the message queue middleware, supporting dual-channel transmission of HTTP long polling and WebSocket.

[0065] After the user terminal loads the visualization report, it displays the dynamic load distribution heat map and the device status panel in the graphical interface. The operator sets the sampling frequency range and the filtering threshold gradient through the parameter configuration interface. After the configuration instructions are authenticated, they are encapsulated into a JSON format instruction packet and transmitted to the control center instruction proxy service through the HTTPS protocol. After parsing the instruction packet, the control center verifies the operation permissions and forwards the legal instructions to the parameter update interface of the dynamic control module. The dynamic control module uses an interrupt response mechanism to receive the instructions, pauses the current control thread, and loads the new parameters into the register group. It drives the sensors and actuators through the hardware abstraction layer to synchronously adjust the sampling frequency and the filtering circuit parameters. After the adjustment is completed, it returns a status confirmation code to the user terminal, forming a closed-loop control link.

[0066] Please refer to Figure 1 and Figure 2, the present invention provides a weighing device. In the control scenario of metallurgical equipment, the specific implementation of the weighing device is as follows: The weighing device obtains the weight signal of the object to be measured in real time through the high-precision sensor in the data acquisition module and converts the analog signal into a digital signal. The sensor adopts an anti-interference design and is suitable for high-temperature, high-humidity and vibration environments. The communication module transmits the digital signal to the control center based on the wireless communication protocol. During the transmission process, real-time data and non-real-time data are distinguished according to the preset priority strategy, and real-time data is preferentially transmitted to ensure timeliness. The construction of the dynamic self-organizing network topology detects the communication signal strength and position information of adjacent weighing devices periodically, calculates the link stability score using the weighted algorithm, selects the node with the highest score as the relay node, and establishes redundant communication links to improve transmission reliability. After receiving the digital signal from the control center, the data processing module parses it into weight data, combines environmental interference parameters such as temperature, humidity and vibration intensity, inputs them into the linear regression model to generate the error compensation coefficient, and calibrates the real-time data to eliminate the influence of environmental factors on the weighing accuracy. The dynamic control module predicts the working load based on the calibrated weight data. When the load exceeds the preset threshold, it generates an alarm instruction and sends a standby request to the control center through the communication module, triggering the access of the standby weighing unit. After the standby unit is accessed, the control center assigns a unique identifier and establishes an encrypted communication link to synchronize the current weighing parameters for seamless switching. The feedback module monitors the adjusted sampling frequency or filtering threshold in real time, compares it with the target parameter. If the deviation exceeds the allowable range, it triggers the data processing module to recalibrate the data, forming a closed-loop control. The encryption module encrypts the weight data using the asymmetric encryption algorithm before data transmission, generates a dynamic key and binds it to the device identifier for transmission; the decryption module decrypts it after verifying the matching of the key and the identifier at the receiving end, and verifies the data integrity through the hash algorithm. The cloud platform of the control system extracts historical data from the local database, uses the statistical model for trend analysis and anomaly detection, generates a visual report containing the load prediction results, and the user terminal remotely adjusts the parameters according to the report. The dynamic control module updates the sampling frequency or filtering threshold in real time. The above implementation solves the problems of insufficient accuracy, equipment wear and low efficiency caused by environmental interference, unstable data transmission and structural defects of traditional weighing devices through modular design and intelligent control.

[0067] The specific implementation of the weighing device system of the present invention in metallurgical equipment involves the collaborative work of multiple modules. The data acquisition module obtains the weight signal of the material in the weighing hopper in real time through high-precision sensors. The surface of the sensors is coated with ceramic to resist high-temperature corrosion, and the internal circuit design has the ability to resist electromagnetic interference, suitable for complex working conditions in the metallurgical environment. After the weight signal is converted into a digital signal by the analog-to-digital conversion circuit, it is transmitted to the communication module. The communication module constructs a dynamic self-organizing network topology based on the IEEE 802.15.4 protocol. After the data acquisition module completes the signal conversion, it triggers the networking process of adjacent weighing devices. It periodically detects the signal strength and three-dimensional coordinates of surrounding nodes, calculates the link stability score through a weighted algorithm, with the signal strength accounting for 60% and the position stability accounting for 40%, and selects the node with the highest score as the relay node to form redundant communication links to cope with the shielding effect of metal structures in the metallurgical plant on wireless signals. The digital signal is transmitted to the control center through multi-node synchronization. During the transmission process, the data priority is dynamically divided according to the real-time network load. Real-time data includes alarm instructions and key control parameters, and non-real-time data includes historical records and log information. Real-time data is preferentially transmitted to ensure the timeliness of control instructions.

[0068] The control center receives the encrypted weight data. The encryption module uses the elliptic curve asymmetric encryption algorithm to encrypt the data, generates a dynamic key bound to the device identifier of the weighing device, and the validity period of the key is 30 minutes. The decryption module verifies the legitimacy of the dynamic key through device identifier matching at the receiving end and decrypts it using the AES-256 algorithm. After decryption, the data integrity is verified through CRC32 check. The decrypted data is stored in the local time series database and simultaneously forwarded to the data processing module. The data processing module calculates the error compensation coefficient through a multiple linear regression model based on the temperature, humidity, and vibration intensity parameters in the historical data. The temperature influence coefficient is 0.15% / °C, the humidity influence coefficient is 0.08% / %RH, and the vibration intensity influence coefficient is 0.05% / m / s², and dynamically corrects the real-time weight data to eliminate the measurement deviation caused by environmental interference. The calibrated data is input into the dynamic control module. The dynamic control module analyzes the working load trend in the last 10 minutes through the sliding window algorithm. When the load exceeds 90% of the preset threshold, it generates a level-three alarm instruction and sends a standby request to the control center through the communication module. After the control center responds to the request, it assigns a unique MAC address identifier to the standby weighing unit and establishes an encrypted communication link based on the TLS 1.3 protocol, synchronizes the current sampling frequency (default 500Hz) and filtering threshold (default 50Hz) parameters, and realizes seamless switching between the primary and standby units.

[0069] The feedback module monitors the adjustment parameters output by the dynamic control module in real time, calculates the difference between the actual sampling frequency and the target value. If the deviation exceeds ±5 Hz, it triggers the data processing module to re-collect the environmental parameters and update the compensation coefficient of the linear regression model. The encryption module generates a new dynamic key before each data transmission. The key and the weighing device serial number generate a digital signature through the HMAC-SHA256 algorithm. The transmission data packet contains encrypted weight data, the dynamic key, and the digital signature. The decryption module extracts the key at the control center after verifying the signature validity through the serial number, decrypts the data, and stores it in the database. The cloud platform of the control system extracts the historical data of the past 24 hours from the database, uses the exponential smoothing method for trend prediction, generates a visualization report containing the load curve for the next 2 hours, and pushes the report data to the user terminal through the WebSocket protocol. The operator views the real-time load distribution through the terminal interface and issues commands to adjust the sampling frequency or the filtering threshold. The dynamic control module responds to the command and updates the parameters within 200 ms. Through the above closed-loop control mechanism, the weighing device reduces the stress concentration on the weighing hopper liner caused by material impact, and the wear rate of the liner is reduced to 40% of the traditional design. At the same time, it optimizes the opening and closing logic of the gate to keep the material flow rate stable within ±3% of the set value, effectively preventing the belt conveyor from running off track and foreign objects from getting stuck.

[0070] Example 1; The implementation method of the weighing device in the metallurgical weighing scenario is as follows: The data acquisition module uses vibration-resistant sensors with a ceramic coating on the surface to collect the weight signals of the materials in the weighing hopper in real time. After the sensor signals are converted from analog to digital, the communication module constructs a dynamic self-organizing network topology based on a wireless protocol, periodically detects the signal strength and position information of adjacent nodes, and selects the node with the optimal link stability as the relay node to form redundant communication links. When transmitting digital signals, the priority is divided according to the network load, and real-time data is preferentially transmitted to the control center. After receiving the signals, the data processing module dynamically generates an error compensation coefficient through a linear regression model in combination with environmental temperature, humidity, and vibration intensity parameters to calibrate the weight data. The dynamic control module predicts the load trend based on the calibrated data. When the load approaches the threshold, it triggers the access of the standby weighing unit, and the standby unit synchronizes the working parameters through an encrypted communication link. The feedback module monitors the parameter deviation after adjustment in real time. When it exceeds the allowable range, it triggers data recalibration to form a closed-loop control to optimize the stress distribution of the weighing hopper liner and reduce local wear.

[0071] Example 2; In the dust control scenario, the weighing device gate actuator and the communication module work together: the dynamic self-organizing network preferentially transmits the gate control instruction, and the data processing module precisely adjusts the opening and closing angle of the gate according to the calibrated weight data, controls the material flow rate and the falling height of the material, and reduces the dust generated by the free-fall collision. The encryption module uses an asymmetric encryption algorithm to generate a dynamic key, binds it to the device identifier and then transmits the data, and the control center decrypts and verifies the data integrity. The cloud platform trains a statistical model based on historical data, predicts the load fluctuation and generates a visual report, and the user terminal remotely adjusts the gate parameters. An electromagnetic iron removal device is integrated at the bottom of the gate to adsorb mixed metal foreign objects in real time. The weighing hopper lining is designed with a layered structure, and the surface wear-resistant material is combined with the bottom toughness layer, and the stress is optimized with closed-loop control to extend the service life of the lining.

[0072] Embodiment 3; For the operation risks of the belt conveyor, the dynamic control module continuously collects the belt tension data, and immediately triggers the standby unit to work together when detecting the offload risk, synchronizes the parameters and distributes the load through the secure communication link. The feedback module links with the foreign object detection system, and after identifying the foreign object, controls the gate to adjust the material landing position, deviating from the preset falling area to isolate the dangerous object. The cloud platform analyzes the historical fault data through the machine learning model, warns the deviation trend in advance, and the user terminal issues an instruction to optimize the material distribution. A pneumatic damping device is set at the interface between the weighing hopper and the belt conveyor to dynamically absorb the impact energy of the material. The encryption module periodically updates the dynamic key and verifies the data integrity through the hash algorithm. The above solution realizes the load balance control of the belt conveyor and reduces the risks of foreign object jamming and deviation.

[0073] Through modular design and intelligent control technology, the present invention proposes a systematic solution for the structural defects of traditional weighing devices. The data acquisition module adopts an anti-vibration sensor and a ceramic coating protection design to obtain the material weight signal in real time. Combined with the closed-loop adjustment of the sampling frequency and the filtering threshold by the dynamic control module, the sensor signal drift caused by the material impact is reduced. The feedback module triggers the dynamic update of the error compensation model by comparing the deviation between the adjustment parameter and the target value in real time, optimizes the stress distribution of the weighing hopper lining, equalizes the impact load borne by the lining, and thus reduces the local wear and deformation. At the same time, the dynamic control module adjusts the opening and closing angle and speed of the gate based on the load prediction model, so that the material flow rate is stabilized within the set threshold range, avoiding the crushing of sintered ore caused by free-fall collision, and the return ore rate is reduced compared with the traditional device.

[0074] To solve the problem of dust emission, the communication module constructs a dynamic self-organizing network topology to achieve multi-node redundant transmission, and gives priority to transmitting real-time control instructions to the gate actuator, reducing the space for dust generation by accurately controlling the material unloading height and the trajectory of the material. The data processing module combines temperature, humidity and vibration intensity parameters to calibrate the weight data in real time, eliminate the impact of environmental interference on the weighing accuracy, accurately match the gate action with the material flow, and avoid dust emission caused by excessive accumulation or no-load operation. The encryption module uses an asymmetric encryption algorithm to ensure data transmission security, prevent abnormal vibration of the equipment caused by tampering with control instructions, and further reduce the risk of secondary dust diffusion.

[0075] In response to the risks of belt conveyor operation, the dynamic control module analyzes the real-time load trend through a sliding window algorithm. When the risk of overloading is detected, the backup weighing unit is immediately triggered to connect and synchronize the working parameters, and the material distribution is balanced through the collaborative operation of the two units. The feedback module monitors the belt tension data in real time. When foreign objects are detected to be stuck or the tendency of deviation is detected, the gate actuator is linked to adjust the material landing position and activate the electromagnetic iron removal device to form a foreign object interception barrier. The cloud platform trains anomaly detection models based on historical data, and warns of potential faults in advance through visual reports. Operators can remotely adjust the weighing strategy to reduce the load fluctuation range of the belt conveyor to the safety threshold, thereby improving the overall service life of the system.

Claims

1. A weighing device, characterized in that: include: A data acquisition module is used to obtain the weight signal of the object to be measured in real time through a sensor and convert the weight signal into a digital signal; A communication module, connected to the data acquisition module, and used to transmit the digital signal to a control center based on a preset wireless communication protocol; a data processing module connected to the control center, configured to receive the digital signal from the control center, parse the digital signal into weight data, and calibrate the weight data according to a linear regression model based on environmental interference parameters, wherein the environmental interference parameters include temperature, humidity, and vibration intensity; A dynamic control module, connected to the data processing module, for generating a control instruction according to the calibrated weight data, and adjusting a sampling frequency or a filtering threshold of the weighing device based on the control instruction; The feedback module is connected to the dynamic control module and is used to feed back the adjusted sampling frequency or filtering threshold to the data acquisition module to adjust the signal acquisition process of the sensor to form a closed-loop control.

2. The weighing device according to claim 1, characterized in that: The communication module is also used for: After the data acquisition module completes the weight signal conversion, triggering the construction of a dynamic ad hoc network topology with adjacent weighing devices; Based on the dynamic ad hoc network topology, the digital signal is transmitted to the control center through data synchronization between multiple nodes; During the transmission process, the weight data is dynamically divided into real-time data and non-real-time data according to the network load, and the real-time data is preferentially transmitted to the control center based on a preset priority strategy.

3. The weighing device according to claim 2, characterized in that: The construction of the dynamic ad hoc network topology includes: The communication module periodically detects the communication signal strength and position information of adjacent weighing devices; Calculating a node connection weight based on the signal strength and location information, wherein the signal strength and location information generate a link stability score through a weighted algorithm; The node with the highest link stability score is selected as the relay node to establish a redundant communication link.

4. The weighing device according to claim 1, characterized in that: The error compensation model is generated in the following way: Collect environmental interference parameters in historical weighing data through the data acquisition module, wherein the environmental interference parameters include temperature, humidity and vibration intensity; Inputting the environmental interference parameter and the weight signal into a linear regression model for correlation analysis to generate an error compensation coefficient; The real-time weighing data is corrected based on the error compensation coefficient.

5. The weighing device according to claim 1, characterized in that: The dynamic control module is also used to: predict the workload of the weighing device through a load prediction model according to the calibrated weight data output by the data processing module; When the workload exceeds a preset threshold, an alarm instruction is generated and a standby request is sent to a control center through the communication module; Receive the spare weighing unit identification assigned by the control center, and establish a communication link with the spare weighing unit based on the identification.

6. The weighing device according to claim 5, characterized in that: The access of the standby weighing unit includes: sending a standby request to a control center through the communication module; Receive the spare unit identification assigned by the control center, and establish a communication link with the spare weighing unit based on the identification.

7. The weighing device according to claim 1, characterized in that: The feedback module is also used for: Compare the adjusted weighing parameters with the target parameters to generate deviation values; If the deviation value exceeds the allowable range, the data processing module is triggered to recalibrate the weight data.

8. The weighing device according to claim 1, characterized in that: The device also includes: An encryption module, used for encrypting the weight data using an asymmetric encryption algorithm before the communication module transmits the data; The decryption module is used to decrypt the encrypted data at the receiving end and verify the data integrity.

9. The weighing device according to claim 8, characterized in that: The encryption module is also used for: Generate a dynamic key according to the device identification of the weighing device, and bind the dynamic key with the weight data for transmission; In the decryption module, decryption is completed by verifying the matching of the dynamic key and the device identification.

10. A control system for a weighing device, applied to the weighing device according to any one of claims 1 to 9, characterized in that: The system comprises: a control center connected to the communication module of the weighing device, used to receive the encrypted weight data, decrypt and verify the data integrity through the decryption module, store it in a local database, and issue it to the weighing device based on the control instructions generated by the dynamic control module; A cloud platform connected to the control center, used to extract historical weight data from the local database, perform trend analysis and anomaly detection on the weight data based on a preset statistical model, and generate a visual report containing load forecast results; The user terminal is connected to the cloud platform and is used to receive the visual report in real time, and send adjustment instructions to the dynamic control module through the control center according to the load forecast result to update the sampling frequency or filtering threshold of the weighing device.

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