Weighing anti-cheating method and device, electronic equipment and storage medium

Abstract

The invention provides a weighing anti-cheating method and device, electronic equipment and a storage medium, and relates to the technical field of monitoring. Safe communication connection is realized by establishing an encrypted data transmission channel between a weighing instrument and a weighing sensor, and a metal grid protection layer is arranged at a wire outlet of the weighing sensor to arrange a physical protection structure. Abnormal wireless signals in the environment are detected in real time through multi-frequency-band scanning, directional shielding is started to execute signal monitoring and blocking when cheating signals are recognized, and correlation analysis is carried out on weighing data, vehicle characteristic data and personnel behavior data to recognize an abnormal weighing mode. And security audit and authority control for recording system operation logs and distributing system function access rights according to authority levels are implemented, so that the problem that multi-band cheating signals cannot be handled in the prior art can be solved.

Description

Technical Field

[0001] This disclosure relates to the field of monitoring technology, and in particular to a weighing anti-cheating method and device, electronic equipment and storage medium. Background Technology

[0002] The fuel metering system in thermal power plants is the core of power supply chain management, supporting critical operations such as coal procurement, transportation, and storage. Traditional weighing and metering systems cover vehicle identification, dynamic weighing, and data storage, involving key technologies such as mechanical structures, electronic signal conversion, and data communication. Although existing systems have undergone digital upgrades, their protection still relies on physical isolation and basic encryption, making it difficult to cope with new cheating methods such as high-frequency interference devices and quantum encryption cracking.

[0003] Existing anti-cheating methods employ single-band signal shielding and static encryption algorithms, lacking a collaborative mechanism of dynamic cloud encryption and full-band active defense. This results in vulnerabilities such as sensor signal tampering, cheating devices evading monitoring, and data transmission susceptible to man-in-the-middle attacks. Traditional digital sensors also suffer from low longitudinal matching accuracy, insufficient anti-interference capabilities, and signal lag in low-temperature environments. Furthermore, current technologies perform poorly in terms of the accuracy of locating cheating signal sources and anomaly detection response time, impacting both the real-time performance and accuracy of measurement. The difficulty in obtaining evidence also exacerbates the risk of supply chain management spiraling out of control. Summary of the Invention

[0004] This disclosure provides a weighing anti-cheating method, apparatus, electronic device, and storage medium. Its main objective is to at least partially solve one of the technical problems in the related art.

[0005] According to a first aspect of this disclosure, a method for preventing cheating in weighing is provided, comprising: Establish a secure communication connection and transmit weighing information through an encrypted data transmission channel between the weighing instrument and the weighing sensor; A physical protective structure is installed, with a metal mesh protective layer installed at the outlet of the weighing sensor; Perform signal monitoring and blocking, detect abnormal wireless signals in the environment in real time through multi-band scanning, and initiate directional blocking when cheating signals are detected; Correlation analysis is performed on the collected weighing data, vehicle characteristic data, and personnel behavior data to identify abnormal weighing patterns; Implement security audits and access control, record system operation logs, and assign system function access rights according to permission levels.

[0006] Optionally, establishing a secure communication connection includes: Asymmetric encryption algorithms are used to encrypt and protect the transmitted data; It receives calibration instructions through a remote communication channel and automatically adjusts weighing parameters.

[0007] Optionally, the execution signal monitoring and blocking includes: Full-band signal scanning was performed within the 300MHz-6GHz frequency range; The detected signal features are compared with a pre-stored database of cheating signal features in real time. Based on the comparison results, an interference beam of a specific frequency band is transmitted to the direction of the signal source.

[0008] Optionally, the correlation analysis of the collected weighing data, vehicle characteristic data, and personnel behavior data includes: Detecting abrupt changes in weighing data based on time series analysis; Image processing technology is used to identify vehicle outlines and personnel operating behaviors. A spatiotemporal correlation model between weighing data and visual monitoring data was established and cross-validated.

[0009] Optional, also includes: A neural network model trained on historical cheating data is used to assess the probability of anomalies in the weighing process; When the probability of an anomaly exceeds a preset threshold, the alarm device is activated and a complete report containing timestamps, anomaly type, and evidence chain is generated.

[0010] Optional, also includes: Edge computing nodes are deployed at the weighing site to preprocess the collected raw data locally; The device self-test program is triggered based on signal transmission quality indicators, including signal-to-noise ratio and data integrity rate. A dual-storage architecture is used to synchronously back up critical weighing data.

[0011] According to a second aspect of this disclosure, a weighing anti-cheating device is provided, comprising: Establishment unit, used to establish a secure communication connection, and transmit weighing information through an encrypted data transmission channel between the weighing instrument and the weighing sensor; The deployment unit is used to deploy the physical protection structure, and a metal mesh protective layer is set at the outlet of the weighing sensor. The execution unit is used to perform signal monitoring and blocking. It detects abnormal wireless signals in the environment in real time through multi-band scanning and initiates directional shielding when cheating signals are detected. The analysis unit is used to perform correlation analysis on the collected weighing data, vehicle characteristic data, and personnel behavior data to identify abnormal weighing patterns. The recording unit is used to implement security auditing and access control, record system operation logs, and assign system function access rights according to permission levels.

[0012] Optionally, the creation unit is also used for: Asymmetric encryption algorithms are used to encrypt and protect the transmitted data; It receives calibration instructions through a remote communication channel and automatically adjusts weighing parameters.

[0013] Optionally, the execution unit is also used for: Full-band signal scanning was performed within the 300MHz-6GHz frequency range; The detected signal features are compared with a pre-stored database of cheating signal features in real time. Based on the comparison results, an interference beam of a specific frequency band is transmitted to the direction of the signal source.

[0014] Optionally, the analysis unit is also used for: Detecting abrupt changes in weighing data based on time series analysis; Image processing technology is used to identify vehicle outlines and personnel operating behaviors. A spatiotemporal correlation model between weighing data and visual monitoring data was established and cross-validated.

[0015] Optional, also includes: A neural network model trained on historical cheating data is used to assess the probability of anomalies in the weighing process; When the probability of an anomaly exceeds a preset threshold, the alarm device is activated and a complete report containing timestamps, anomaly type, and evidence chain is generated.

[0016] Optional, also includes: Edge computing nodes are deployed at the weighing site to preprocess the collected raw data locally; The device self-test program is triggered based on signal transmission quality indicators, including signal-to-noise ratio and data integrity rate. A dual-storage architecture is used to synchronously back up critical weighing data.

[0017] According to a third aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0018] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0019] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0020] The weighing anti-cheating method, device, electronic equipment, and storage medium disclosed herein achieve secure communication by establishing an encrypted data transmission channel between the weighing instrument and the weighing sensor; by setting a metal mesh protective layer at the weighing sensor output port to establish a physical protection structure; by using multi-band scanning to detect abnormal wireless signals in the environment in real time and activating directional shielding to perform signal monitoring and blocking when cheating signals are detected; by performing correlation analysis on weighing data, vehicle characteristic data, and personnel behavior data to identify abnormal weighing patterns; and by implementing security auditing and access control by recording system operation logs and allocating system function access rights according to permission levels. Therefore, This technology can solve the problems in existing technologies, such as the lack of encrypted data transmission mechanisms leading to vulnerability of weighing information transmission to attacks and tampering, the lack of physical protection at sensor outlets making them susceptible to the installation of cheating devices, the inability of single-band protection to cope with multi-band cheating signals, the difficulty of identifying abnormal weighing behavior with single data detection, and the risk to system operation due to the lack of standardized security audits and access control. It achieves the technical effects of ensuring the security of weighing information transmission, preventing malicious modification of sensors, effectively intercepting multi-band cheating signals, accurately identifying abnormal weighing patterns, standardizing system operation permissions and retaining evidence of cheating and operation traceability, thereby comprehensively improving the anti-cheating capabilities and operational reliability of the fuel metering system in thermal power plants.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0022] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein: Figure 1 A flowchart illustrating a weighing anti-cheating method provided in this embodiment of the present disclosure; Figure 2 This is a schematic diagram of the structure of a weighing anti-cheating device provided in an embodiment of the present disclosure; Figure 3 A schematic block diagram of an example electronic device provided for embodiments of this disclosure. Detailed Implementation

[0023] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0024] The following description, with reference to the accompanying drawings, describes a weighing anti-cheating method and apparatus, electronic device, and storage medium according to embodiments of the present disclosure.

[0025] Figure 1 This is a flowchart illustrating a weighing anti-cheating method provided in an embodiment of the present disclosure.

[0026] like Figure 1 As shown, the method includes the following steps: Step 101: Establish a secure communication connection and transmit weighing information through an encrypted data transmission channel between the weighing instrument and the weighing sensor.

[0027] In the embodiments of this disclosure, to address the technical problem of data transmission between the weighing instrument and the weighing sensor being easily intercepted and tampered with in traditional fuel truck weighing systems for thermal power plants, leading to distorted weighing information, this step establishes a secure communication connection between the weighing instrument and the weighing sensor. The core of this connection is the establishment of a dedicated encrypted data transmission channel. This channel is used to transmit various types of weighing information generated during the weighing process (including but not limited to real-time weight signals and sensor operating status data). This encrypted data transmission channel must possess data encryption and decryption capabilities to ensure that the weighing information is not illegally obtained or tampered with by unauthorized entities throughout the entire process from the sensor to the instrument, thereby guaranteeing data security during transmission. As one implementation method, cloud encryption technology can be used to construct this encrypted channel. Weighing instruments with cloud encryption capabilities and matching encrypted weighing sensors are selected, so that the weighing information transmitted between the two is encrypted before being sent. After receiving the data, the instrument restores it through a corresponding decryption mechanism, achieving secure transmission.

[0028] This step establishes an encrypted data transmission channel to achieve secure communication, effectively solving the technical defects of traditional unencrypted transmission scenarios where weighing data is easily tampered with and stolen. It ensures the integrity and security of weighing information from the sensor to the instrument, providing key transmission layer support for the accuracy of subsequent weighing and measurement results, and avoiding economic losses to enterprises due to data distortion in the transmission process.

[0029] Step 102: Install a physical protection structure by setting a metal mesh protective layer at the outlet of the weighing sensor.

[0030] In the embodiments of this disclosure, to address the technical problem in traditional coal-fired power plant fuel truck scale weighing systems where the weighing sensor outlet lacks effective physical protection, making it vulnerable to unauthorized installation of cheating devices (such as signal jammers or data tampering devices) that could disrupt normal sensor operation, this step involves installing a dedicated physical protection structure at the weighing sensor outlet. The core of this structure is a metal mesh protective layer. This metal mesh protective layer must possess sufficient structural strength and protective density to ensure the normal outgoing of sensor cables (such as signal wires and power lines) to maintain sensor functionality, while also physically preventing unauthorized personnel from tampering with the outlet and internal wiring or installing external cheating devices, thus forming a physical barrier for critical sensor components. As one implementation method, the metal mesh protective layer can be made of corrosion-resistant metal, with its mesh size and installation method adapted to the structural specifications of the sensor outlet. For example, it can be a steel mesh of approximately 6 cm, tightly fitted to the sensor outlet through a fixed structure to achieve reliable protection.

[0031] This step, by setting a metal mesh protective layer at the sensor outlet, directly blocks the path for unscrupulous personnel to install cheating devices through the outlet at a physical level, significantly increasing the difficulty of cheating operations. It effectively compensates for the shortcomings of traditional systems that rely solely on electronic protection while lacking strong physical protection. At the same time, this protective structure does not affect the normal cable output and working performance of the sensor, providing physical protection for the stable operation of the weighing sensor and further improving the anti-cheating system of the truck scale weighing system.

[0032] Step 103: Perform signal monitoring and blocking. Detect abnormal wireless signals in the environment in real time through multi-band scanning, and initiate directional blocking when cheating signals are detected.

[0033] In the embodiments of this disclosure, to address the technical problems in traditional fuel truck scale weighing systems for thermal power plants, where signal protection is often limited to single-band monitoring, making it difficult to cover diverse and high-frequency cheating wireless signals, and where conventional shielding methods easily interfere with the communication of surrounding normal equipment, this step performs signal monitoring and blocking through a collaborative approach of "multi-band scanning detection + directional shielding": first, multi-band scanning covers the frequency bands of various cheating wireless signals that may exist in the environment, enabling real-time capture and identification of abnormal wireless signals; then, directional shielding is initiated for confirmed cheating signals, ensuring that only the transmission path of the cheating signal is blocked, avoiding impact on the communication functions of the weighing system and surrounding normal equipment. The multi-band scanning needs to be real-time to quickly respond to sudden cheating signals, and the directional shielding needs to accurately locate the source or transmission link of the cheating signal to ensure the targeted nature of the shielding effect. As one implementation method, a monitoring device equipped with multiple high-gain antennas can be used to achieve full-band scanning. After identifying a cheating signal, its frequency band and transmission direction are locked, and the interaction between the cheating signal and the weighing equipment is cut off through a directional signal blocking module, while simultaneously retaining relevant records of the cheating signal.

[0034] This step solves the problem of missed detection of new cheating signals by traditional single-band monitoring through multi-band scanning. Combined with directional shielding, it avoids interference with normal equipment caused by full-band shielding. It not only achieves comprehensive capture and accurate blocking of cheating signals, but also ensures the normal operation of the weighing system and surrounding equipment. It significantly improves the coverage and accuracy of anti-cheating at the signal level, and further reduces the risk of weighing data distortion caused by wireless cheating signals.

[0035] Step 104: Perform correlation analysis on the collected weighing data, vehicle characteristic data, and personnel behavior data to identify abnormal weighing patterns.

[0036] In the embodiments of this disclosure, to address the technical problem that traditional fuel truck weighbridge systems in thermal power plants often only analyze single weighing data without correlating vehicle characteristics and personnel behavior data, making it difficult to identify hidden abnormal weighing patterns such as "mismatch between vehicle information and weighing data" and "unauthorized personnel operation accompanied by abnormal fluctuations in weighing data," this step integrates the collected weighing data (including real-time weight values, weight change trends, etc.), vehicle characteristic data (including vehicle identification information, vehicle model parameters, etc.), and personnel behavior data (including operator identity, operation time sequence, operation permission association, etc.) to conduct correlation analysis on the three types of data. This correlation analysis focuses on verifying the logical consistency between the data, such as verifying the matching degree between the reasonable load range corresponding to the vehicle model and the actual weighing data, and the conformity between the personnel operation process specifications and the timing of weighing data changes, thereby determining whether there are abnormal patterns that deviate from the normal weighing rules. As one implementation method, the three types of data can be compared in multiple dimensions through preset data analysis rules. If the weighing data of a vehicle exceeds the theoretical load threshold of its model, and the operator recorded simultaneously is an unauthorized person, it is determined to be an abnormal weighing pattern and a flag is triggered.

[0037] This step, through correlation analysis of three types of data, breaks through the limitations of traditional single-data-dimensional analysis, effectively identifying hidden abnormal weighing behaviors that are difficult to detect by relying solely on weighing data, thus improving the comprehensiveness and accuracy of anomaly identification. At the same time, the correlation of multiple data dimensions provides a complete data chain to support the tracing of the causes of anomalies, facilitating the rapid location of the root cause of anomalies (such as falsification of vehicle information or personnel violations), providing a basis for the formulation of subsequent control measures, and further ensuring the fairness of weighing and measurement.

[0038] Step 105: Implement security auditing and access control, record system operation logs, and assign system function access rights according to permission levels.

[0039] In the embodiments of this disclosure, to address the technical problems in traditional coal-fired power plant fuel truck weighing systems where the lack of hierarchical access control leads to unauthorized personnel operating critical functions (such as data modification and parameter configuration), and the lack of complete operation records makes it difficult to trace the root cause of abnormal operations, this step achieves system control through a dual mechanism of "security audit + access control": On the one hand, all operations during system operation (including weighing data query, modification, equipment parameter adjustment, user login and logout, etc.) are comprehensively recorded, forming a system operation log containing the operation subject, operation time, operation content, and operation result, ensuring that every operation is traceable; on the other hand, according to preset access level rules, corresponding function access rights are assigned to system users with different roles, allowing users to operate system functions within their authorized scope only, preventing unauthorized operations. As one implementation method, a three-level access system can be set based on user responsibilities: "ordinary operator - administrator - auditor." Ordinary operators can only view weighing data and perform basic operations, administrators can configure parameters, and auditors can view operation logs. Simultaneously, the operation logs are automatically stored and cannot be tampered with, meeting traceability requirements.

[0040] This step avoids the risk of unauthorized operation from the source by allocating permission levels, ensuring the standardization of system function usage; combined with complete operation log recording, it enables full traceability of system operations, making it easy to quickly locate the responsible party and the cause of the problem when anomalies occur. The two work together to improve the security and controllability of system management, and provide management-level protection for the authenticity of weighing data and the stability of system operation.

[0041] The weighing anti-cheating method disclosed herein establishes an encrypted data transmission channel between the weighing instrument and the weighing sensor to achieve secure communication; installs a metal mesh protective layer at the weighing sensor outlet to provide physical protection; detects abnormal wireless signals in the environment in real time through multi-band scanning and activates directional shielding to monitor and block signals when cheating signals are detected; performs correlation analysis on weighing data, vehicle characteristic data, and personnel behavior data to identify abnormal weighing patterns; and implements security auditing and access control by recording system operation logs and allocating system function access rights according to permission levels. Therefore, it can solve the problems of existing technologies. The lack of encrypted data transmission mechanisms during the operation led to vulnerabilities in the transmission of weighing information, making it susceptible to attacks and tampering. The sensor output ports lacked physical protection, making them vulnerable to the installation of cheating devices. Single-band protection was insufficient to handle multi-band cheating signals. Single data detection struggled to identify abnormal weighing behavior. Furthermore, the absence of standardized security audits and access controls resulted in operational risks. The solution aims to ensure the security of weighing information transmission, prevent malicious modification of sensors, effectively intercept multi-band cheating signals, accurately identify abnormal weighing patterns, standardize system operation permissions, and retain evidence of cheating and operational traceability. Ultimately, this will comprehensively improve the anti-cheating capabilities and operational reliability of the fuel metering system in thermal power plants.

[0042] As a specific implementation of this disclosure, based on the basic scheme, the establishment of a secure communication connection is further defined as follows: using an asymmetric encryption algorithm to encrypt and protect the transmitted data; receiving calibration instructions through a remote communication channel and automatically adjusting the weighing parameters.

[0043] Specifically, in the implementation of establishing a secure communication connection between the weighing instrument and the weighing sensor, asymmetric encryption algorithms are used to achieve layered protection for the encryption protection of transmitted data: the weighing instrument and the weighing sensor pre-generate their own exclusive asymmetric key pairs (including public and private keys), and complete the bidirectional exchange of public keys and identity verification through the initial secure interaction process to ensure the legitimacy of both parties; when the sensor transmits weighing information (such as real-time weight signals and sensor operating data) to the instrument, the instrument's public key is first used to encrypt the original data, generating an irreversible encrypted data packet. After the data packet is transmitted to the instrument, the instrument decrypts it using its own private key and restores the original data, eliminating the risk of data being illegally intercepted and cracked during transmission. Meanwhile, the secure communication connection supports remote parameter calibration. Specifically, a remote interactive link is built through an industrial-grade wireless communication channel (such as a 4G / 5G industrial module) or a wired industrial Ethernet. When the remote management terminal needs to adjust the weighing parameters, it generates a calibration command containing the calibration target value (such as the zero-point correction value and the range coefficient). The command is sent to the weighing instrument through the remote channel after being asymmetrically encrypted. After receiving the command, the instrument first verifies the legality of the digital signature of the command. After confirming that there is no error, it automatically triggers the built-in parameter adjustment module, updates the weighing calculation parameters according to the command requirements, and encrypts the adjustment result and feeds it back to the remote terminal, forming a calibration closed loop.

[0044] The application of asymmetric encryption algorithms avoids the risk of key leakage caused by key sharing in traditional symmetric encryption, and greatly improves the security level of weighing data transmission. The design of receiving calibration instructions and automatically adjusting parameters through remote communication channels eliminates the need for on-site operation by staff, which reduces the error and time cost of manual calibration, and enables centralized parameter management of multiple truck scales, further adapting to the control needs of large-scale fuel metering scenarios in thermal power plants.

[0045] As a specific implementation of this disclosure, based on the basic scheme, the execution signal monitoring and blocking are further defined as follows: performing full-band signal scanning in the 300MHz-6GHz frequency band; comparing the detected signal features with a pre-stored cheating signal feature library in real time; and transmitting a specific frequency band interference beam to the signal source location based on the comparison result.

[0046] Specifically, in the specific operation of signal monitoring and blocking, the monitoring device equipped with a multi-channel radio frequency receiver is first used to carry out a full-band continuous scan in the 300MHz-6GHz frequency range. The monitoring device captures key parameters such as frequency, bandwidth, and modulation method of various wireless signals in the environment through a preset high-frequency sampling rate (such as completing no less than 10 full-band traversal samplings per second). This ensures coverage of the operating frequency bands commonly used by current mainstream cheating devices (such as wireless remote control jammers and data tampering transmitters) and avoids missing cheating signals due to incomplete frequency band coverage. Meanwhile, the monitoring device has a built-in pre-stored cheating signal feature library. This library contains a large number of signal feature templates of known cheating devices (such as the signal modulation format and fixed frequency jump rules of specific brand remote control cheating devices), and supports periodic updates of feature data through a secure network to adapt to new cheating devices. During the scanning process, the monitoring device compares the signal features collected in real time with the templates in the feature library parameter by parameter, sets a similarity threshold (such as similarity ≥ 90%), and determines that the signal is a cheating signal when the similarity between a certain signal feature and any template in the feature library reaches or exceeds the threshold. Subsequently, the monitoring device activates the directional blocking function: through the device's built-in multi-element antenna array, it calculates the azimuth and distance of the cheating signal source based on the phase difference and intensity difference when the signal is received, and then adjusts the beam direction of the antenna array to emit an interference beam with a frequency band matching the cheating signal and controllable power in the direction of the signal source. This interference beam only forms effective interference on the transmission path of the cheating signal, ensuring that it does not affect the operation of the truck scale system and other normal wireless devices in the surrounding area (such as industrial communication modules and monitoring wireless transmission equipment).

[0047] The 300MHz-6GHz full-band scanning covers the operating frequency bands of most current cheating devices, effectively solving the coverage blind spot problem of traditional narrow-band monitoring; the real-time comparison mechanism with the pre-stored cheating signal feature library greatly improves the accuracy of cheating signal identification and reduces false and false positives; and the directional interference beam design based on the signal source location avoids the impact of blind interference across the entire frequency band on surrounding normal devices, ensuring the stability of the overall system communication environment while accurately blocking cheating signals.

[0048] As a specific implementation of this disclosure, based on the basic scheme, the analysis of the correlation between the collected weighing data, vehicle feature data, and personnel behavior data is further defined, including: detecting abrupt changes in the weighing data based on time series analysis; identifying vehicle outlines and personnel operation behaviors through image processing technology; and establishing a spatiotemporal correlation model between the weighing data and visual monitoring data for cross-validation.

[0049] Specifically, in the process of correlation analysis of the collected weighing data, vehicle characteristic data, and personnel behavior data, the first step is to use time series analysis to detect abrupt changes in the weighing data: the real-time data acquisition module of the weighing system acquires continuous weighing data (including instantaneous weight values, weight change rates, etc.) at a preset sampling frequency (e.g., 10 times per second). The sliding window time series analysis algorithm is used to process the data sequence, and a reasonable threshold for abrupt change is set (e.g., the weight difference between adjacent sampling points exceeds 20% of the normal load fluctuation range, or the weight change rate within a single window exceeds a preset threshold). When the algorithm detects that a data point meets the abrupt change condition, the moment is marked as an abnormal abrupt change point in the weighing data, and key information such as the abrupt change time and the weight difference before and after the abrupt change is recorded. Secondly, image processing technology is used to identify vehicle outlines and personnel operating behaviors: high-definition cameras deployed in the weighbridge area collect visual data, and Canny edge detection and outline extraction algorithms are used to process vehicle images, extracting outline feature parameters such as the length, width, and height of the vehicle, and performing preliminary matching with vehicle size data in the vehicle registration information; at the same time, deep learning-based human skeleton extraction and action recognition algorithms are used to identify the limb movements of personnel in the images in real time (such as whether they touch the weighing sensor, whether they are close to the instrument control panel, whether they are carrying suspicious equipment), and mark behaviors that do not conform to normal operating procedures. Finally, a spatiotemporal correlation model between weighing data and visual monitoring data was established for cross-validation: using the system timestamp as a benchmark, the time nodes corresponding to the abnormal change points in the weighing data were correlated and matched with the vehicle location information (such as whether the vehicle has fully entered the weighing area, whether there are wheel crossing / offline situations) and personnel operation behavior markers in the same time dimension of the visual monitoring data, and a four-dimensional spatiotemporal correlation model of "time-weight change-vehicle status-personnel action" was constructed. If the model detects a change in weighing data and there are visual abnormalities such as the vehicle not being fully on the scale or personnel abnormally touching the equipment, it is determined to be an abnormal weighing mode; if there is only a data change without visual abnormalities, the data acquisition module is further checked for hardware failure, thus achieving multi-dimensional cross-validation.

[0050] Time series analysis can accurately capture instantaneous abnormal changes in weighing data, providing early warnings at the data level for anomaly identification; image processing technology can intuitively obtain visual evidence of vehicle physical characteristics and personnel operation behavior, making up for the lack of information in a single data dimension; and the spatiotemporal correlation model, through cross-validation of time and space dimensions, effectively distinguishes whether sudden changes in weighing data are caused by cheating or equipment failure, significantly reducing the false judgment rate of anomaly identification and improving the accuracy and reliability of anti-cheating judgment.

[0051] As a specific implementation of this disclosure, based on the basic solution, the embodiments of this disclosure further include: using a neural network model trained with historical cheating data to evaluate the probability of anomalies in the weighing process; when the probability of anomalies exceeds a preset threshold, activating an alarm device and generating a complete report containing a timestamp, anomaly type, and a chain of evidence.

[0052] Specifically, in the anti-cheating process of the basic solution, an anomaly probability assessment and intelligent alarm reporting mechanism are added, and the specific implementation is as follows: First, a neural network model is constructed and trained. Cheating case data (including weighing data sequences during cheating, vehicle feature anomaly records, and feature parameters corresponding to personnel violation operation images) and normal weighing data from the historical weighing process of thermal power plants are collected. After standardizing and preprocessing the two types of data (such as data normalization, missing value imputation, and feature dimension alignment), they are divided into training set and validation set. A Long Short-Term Memory (LSTM) neural network with time series data processing capabilities is selected as the basic model. The input layer receives the preprocessed weighing data, vehicle feature data, and personnel behavior data. The hidden layer learns the correlation patterns and cheating pattern features between data through multiple sets of neurons. The output layer outputs the anomaly probability value in the 0-1 interval. The classification accuracy of the validation set is used as the optimization goal. The model parameters (such as learning rate and number of hidden layer neurons) are iteratively adjusted until the model converges. The trained model is then deployed to the data analysis module of the truck scale anti-cheating system. During real-time weighing, the system processes the collected weighing data, vehicle contour feature data, and personnel operation behavior data according to preprocessing rules, and then inputs them into the deployed neural network model in real time. The model outputs the anomaly probability value of the current weighing process once per second. A preset anomaly probability threshold (e.g., 80%) is set, and the system compares the anomaly probability output by the model with the threshold in real time. When the anomaly probability exceeds the threshold for 3 consecutive seconds, the system's built-in audible and visual alarm device (installed in the weighbridge control room and monitoring center) is immediately triggered. At the same time, the system automatically starts the report generation module: extracts the timestamp of the current weighing process (accurate to milliseconds), determines the anomaly type (e.g., "weighing data mutation anomaly" or "personnel touching sensor anomaly") based on the feature contribution of the model output, and automatically associates the corresponding evidence chain (including screenshots of the weighing data curve during the anomaly period, vehicle monitoring image frames, personnel operation video clips, and system operation logs), generates an exportable electronic report in a preset format, stores it in the system database, and pushes it synchronously to the terminal devices of management personnel.

[0053] LSTM neural networks trained on historical cheating data can fully learn the characteristic patterns of past cheating methods. Compared with traditional fixed rule judgment, they significantly improve the accuracy and adaptability of anomaly identification for complex and concealed cheating behaviors. The audible and visual alarms when the anomaly probability exceeds the threshold can realize real-time response to cheating behaviors. The automatically generated report with a complete chain of evidence eliminates the tedious process of manually collecting evidence, providing direct and comprehensive evidence for subsequent verification and accountability of cheating incidents, and further strengthening the closed-loop management of the anti-cheating process.

[0054] As a specific implementation of this disclosure, based on the basic scheme, the embodiments of this disclosure further include: deploying edge computing nodes at the weighing site to perform local preprocessing on the collected raw data; triggering a device self-test program based on signal transmission quality indicators, the quality indicators including signal-to-noise ratio and data integrity rate; and using a dual storage architecture to synchronously back up key weighing data.

[0055] Specifically, in the anti-cheating system of the basic solution, edge computing preprocessing, device self-testing, and dual storage backup mechanisms are added. The specific implementation is as follows: First, an industrial-grade edge computing node (such as an edge gateway equipped with an ARM architecture processor) is deployed at the weighing site. This node is directly connected to weighing sensors, high-definition cameras, and other devices via an industrial bus (such as RS485, EtherNet / IP) to perform local preprocessing on the collected raw data (including weighing analog signals and image pixel data): For the raw weighing data, a Kalman filter algorithm is used to filter out noise signals caused by environmental electromagnetic interference and eliminate outliers that exceed the reasonable range; For the raw image data, size compression, format conversion (such as converting RAW format to JPEG format) and preliminary extraction of key features (such as cropping out vehicle and personnel areas) are performed. After preprocessing, only valid data is transmitted to the cloud or backend system to reduce the amount of data transmitted remotely. Secondly, a self-test program is triggered based on signal transmission quality indicators: edge computing nodes monitor the signal-to-noise ratio (SNR) of the weighing data transmission link in real time (calculated by the ratio of effective signal power to noise power, with a preset pass threshold ≥30dB) and data integrity rate (calculated by comparing the number of sent and received data packets to determine the complete reception rate, with a preset pass threshold ≥99.5%). When the SNR is lower than the threshold or the data integrity rate fails to meet the standard for three consecutive sampling periods, the self-test program is automatically triggered—first, the continuity of the sensor terminals is checked, then the sampling accuracy of the instrument data acquisition module is verified, and finally the integrity of the shielding layer of the transmission cable is checked. The self-test results are fed back to the monitoring center in real time. Finally, a dual-storage architecture is used to synchronously back up key weighing data (such as final weighing results, data from abnormal periods, and vehicle and personnel association information): industrial-grade solid-state drives (SSDs) are deployed locally to store real-time data, while key data is synchronized to a remote redundant server (using a distributed storage architecture) via an encrypted communication link. During the backup process, a CRC32 check algorithm is used to ensure data transmission integrity. When the local storage device fails, historical data can be quickly restored from the remote server to ensure no data loss.

[0056] Local preprocessing at edge computing nodes reduces bandwidth consumption and latency in remote data transmission, improving system response speed; device self-checking based on signal-to-noise ratio and data integrity rate can detect hardware failures or transmission problems in advance, avoiding data distortion due to device malfunctions; the dual-storage architecture ensures the security and recoverability of critical weighing data at the physical level, further improving the system's reliability and resilience.

[0057] Corresponding to the aforementioned weighing anti-cheating method, this disclosure also proposes a weighing anti-cheating device. Since the device embodiments of this disclosure correspond to the aforementioned method embodiments, details not disclosed in the device embodiments can be referred to the aforementioned method embodiments, and will not be repeated here.

[0058] Figure 2 This is a schematic diagram of the structure of a weighing anti-cheating device provided in an embodiment of this disclosure, as shown below. Figure 2 As shown, it includes: Establishment unit 21 is used to establish a secure communication connection and transmit weighing information through an encrypted data transmission channel between the weighing instrument and the weighing sensor. The deployment unit 22 is used to deploy a physical protection structure, and a metal mesh protective layer is set at the outlet of the weighing sensor. Execution unit 23 is used to perform signal monitoring and blocking, detect abnormal wireless signals in the environment in real time through multi-band scanning, and initiate directional blocking when cheating signals are detected; Analysis unit 24 is used to perform correlation analysis on the collected weighing data, vehicle characteristic data and personnel behavior data to identify abnormal weighing patterns; Recording unit 25 is used to implement security auditing and access control, record system operation logs, and assign system function access rights according to permission levels.

[0059] The weighing anti-cheating device disclosed herein establishes an encrypted data transmission channel between the weighing instrument and the weighing sensor to achieve secure communication; installs a metal mesh protective layer at the weighing sensor outlet to provide physical protection; detects abnormal wireless signals in the environment in real time through multi-band scanning and activates directional shielding to monitor and block signals when cheating signals are detected; performs correlation analysis on weighing data, vehicle characteristic data, and personnel behavior data to identify abnormal weighing patterns; and implements security auditing and access control by recording system operation logs and allocating system function access rights according to permission levels. Therefore, it can solve the problems of existing technologies. The lack of encrypted data transmission mechanisms during the operation led to vulnerabilities in the transmission of weighing information, making it susceptible to attacks and tampering. The sensor output ports lacked physical protection, making them vulnerable to the installation of cheating devices. Single-band protection was insufficient to handle multi-band cheating signals. Single data detection struggled to identify abnormal weighing behavior. Furthermore, the absence of standardized security audits and access controls resulted in operational risks. The solution aims to ensure the security of weighing information transmission, prevent malicious modification of sensors, effectively intercept multi-band cheating signals, accurately identify abnormal weighing patterns, standardize system operation permissions, and retain evidence of cheating and operational traceability. Ultimately, this will comprehensively improve the anti-cheating capabilities and operational reliability of the fuel metering system in thermal power plants.

[0060] Furthermore, in one possible implementation of this embodiment, the establishing unit 21 is also used for: Asymmetric encryption algorithms are used to encrypt and protect the transmitted data; It receives calibration instructions through a remote communication channel and automatically adjusts weighing parameters.

[0061] Furthermore, in one possible implementation of this embodiment, the execution unit 23 is further configured to: Full-band signal scanning was performed within the 300MHz-6GHz frequency range; The detected signal features are compared with a pre-stored database of cheating signal features in real time. Based on the comparison results, an interference beam of a specific frequency band is transmitted to the direction of the signal source.

[0062] Furthermore, in one possible implementation of this embodiment, the analysis unit 24 is also used for: Detecting abrupt changes in weighing data based on time series analysis; Image processing technology is used to identify vehicle outlines and personnel operating behaviors. A spatiotemporal correlation model between weighing data and visual monitoring data was established and cross-validated.

[0063] Furthermore, in one possible implementation of this embodiment, it also includes: A neural network model trained on historical cheating data is used to assess the probability of anomalies in the weighing process; When the probability of an anomaly exceeds a preset threshold, the alarm device is activated and a complete report containing timestamps, anomaly type, and evidence chain is generated.

[0064] Furthermore, in one possible implementation of this embodiment, it also includes: Edge computing nodes are deployed at the weighing site to preprocess the collected raw data locally; The device self-test program is triggered based on signal transmission quality indicators, including signal-to-noise ratio and data integrity rate. A dual-storage architecture is used to synchronously back up critical weighing data.

[0065] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of this embodiment, and the principle is the same, so it is not limited in this embodiment.

[0066] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0067] Figure 3A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0068] like Figure 3 As shown, the electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 302 or a computer program loaded from storage unit 308 into RAM (Random Access Memory) 303. The RAM 303 may also store various programs and data required for the operation of the electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An I / O (Input / Output) interface 305 is also connected to the bus 304.

[0069] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0070] The computing unit 301 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as the weighing anti-cheating method. For example, in some embodiments, the weighing anti-cheating method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the aforementioned weighing anti-cheating method by any other suitable means (e.g., by means of firmware).

[0071] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0072] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0073] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0074] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0075] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0076] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0077] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0078] The various numerical designations such as "first," "second," etc., used in this disclosure are merely for ease of description and are not intended to limit the scope of the embodiments of this disclosure, nor do they indicate a sequential order.

[0079] At least one of the features described in this disclosure can also be described as one or more, and multiple features can be two, three, four or more, and this disclosure does not impose any limitations. In the embodiments of this disclosure, for a technical feature, the technical features in that technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", etc., and there is no sequential order or size order among the technical features described by "first", "second", "third", "A", "B", "C" and "D".

[0080] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0081] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method of preventing fraud in a weighing operation, characterized in that, The method comprises the following steps: Establishing a secure communication connection to transmit weighing information through an encrypted data transmission channel between the weighing instrument and the weighing sensor; Laying a physical protection structure by setting a metal mesh protection layer at the outlet of the weighing sensor; Performing signal monitoring and blocking by real-time detection of abnormal wireless signals in the environment through multi-frequency scanning, and starting directional shielding when identifying cheating signals; Correlation analysis of collected weighing data, vehicle feature data and personnel behavior data to identify abnormal weighing patterns; Implementing security audit and permission control, recording system operation logs and assigning system function access rights according to permission levels.

2. The method of claim 1, wherein, The establishment of a secure communication connection includes: Using an asymmetric encryption algorithm to encrypt and protect the transmission data; Receiving calibration instructions through a remote communication channel and automatically adjusting the weighing parameters.

3. The method of claim 1, wherein, The execution of signal monitoring and blocking includes: Full-band signal scanning within the 300MHz-6GHz frequency range; Real-time comparison of detected signal characteristics with a pre-stored cheating signal characteristic library; Based on the comparison results, emit interference beams of specific frequency bands to the signal source direction.

4. The method of claim 1, wherein, The correlation analysis of collected weighing data, vehicle feature data and personnel behavior data includes: Detecting the mutation characteristics of the weighing data based on time series analysis; Identifying vehicle contours and personnel operation behavior through image processing technology; Establishing a spatio-temporal correlation model of weighing data and visual monitoring data for cross-validation.

5. The method of claim 4, wherein, Further comprising: Using a neural network model trained on historical cheating data to evaluate the probability of abnormality in the weighing process; When the abnormal probability exceeds the preset threshold, start the alarm device and generate a complete report containing the timestamp, abnormal type and evidence chain.

6. The method of claim 1, wherein, Further comprising: Deploying edge computing nodes at the weighing site for local preprocessing of collected raw data; Triggering a device self-check program based on signal transmission quality indicators, including signal-to-noise ratio and data integrity rate; Using a dual storage architecture to synchronize backup of critical weighing data.

7. A tamper-proof device for weighing, characterized in that The method comprises the following steps: Establishing a unit for establishing a secure communication connection to transmit weighing information through an encrypted data transmission channel between the weighing instrument and the weighing sensor; Laying a unit for laying a physical protection structure by setting a metal mesh protection layer at the outlet of the weighing sensor; An execution unit for performing signal monitoring and blocking by real-time detection of abnormal wireless signals in the environment through multi-frequency scanning, and starting directional shielding when identifying cheating signals; An analysis unit for correlation analysis of collected weighing data, vehicle feature data and personnel behavior data to identify abnormal weighing patterns; A recording unit for implementing security audit and permission control, recording system operation logs and assigning system function access rights according to permission levels.

8. An electronic device, comprising: The method comprises the following steps: At least one processor; And A memory connected in communication with the at least one processor; wherein The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

9. A non-transitory computer-readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to make the computer execute the method according to any one of claims 1-6.

10. A computer program product, characterised in that, comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.

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