Out-of-plant ash scale unattended remote monitoring and exception handling method and system

Abstract

The invention discloses an out-of-factory ash scale unattended remote monitoring and exception handling method and system, and relates to the technical field of automatic control, and the method comprises the steps: collecting data; constructing an abnormal index library, and determining abnormal data; triggering an exception handling mechanism based on the exception data; performing exception handling effect evaluation; feedback is added based on the evaluation result. According to the invention, the multi-dimensional anomaly detection model based on the vehicle weighing data and the gray scale station operation data is constructed, and the consistency verification and real-time data calibration of redundant equipment are combined, so that the abnormal condition in the gray scale system can be accurately identified and processed. Through reasonable setting of the abnormity priority and the response mechanism, the high priority abnormity can be ensured to be handled in time, and risks caused by problems such as equipment faults, weighing overspeed or unauthorized operation are avoided. Meanwhile, the system also has dynamic feedback and optimization functions, and can continuously optimize an exception handling flow according to a manual intervention proportion and a response effect evaluation result.

Description

Technical Field

[0001] The present invention relates to the field of automation control technology, and in particular to an unattended remote monitoring and abnormality handling method and system for off-site ash scales. Background Art

[0002] With the continuous advancement of industrial automation and intelligent technology, gray scales (or weight sensors) are increasingly used in industrial production, especially in the fields of mineral resources, coal, steel and other heavy industries. The role of gray scales as measuring equipment is particularly prominent. In the past few decades, traditional manual operation methods have gradually been replaced by automated, unattended systems. At present, the intelligence level of gray scale stations has gradually improved, and innovative means such as Internet of Things technology, sensor technology, and remote monitoring technology have been adopted to ensure production efficiency and safety through data collection, real-time monitoring and fault warning. However, although the unattended remote monitoring technology of gray scales has been applied in some fields, in actual operation, it still faces problems such as complex data processing and untimely response to abnormal situations. Especially in extreme environments, how to ensure the efficient operation of the gray scale system and the ability to deal with complex faults is the bottleneck of current technological development.

[0003] In the prior art, most of the unattended remote monitoring systems of gray scales are based on sensor data collection and anomaly detection, but their data monitoring methods often remain at the stage of single parameter monitoring and lack comprehensive analysis of multi-dimensional data. Most systems rely only on the conventional operating data of the equipment, such as the single output of the weighing sensor, and fail to fully consider environmental factors, redundant relationships between equipment, and error correction of the system itself. In addition, the existing systems often have shortcomings such as long response time and unclear anomaly classification in the process of abnormal handling, and are highly dependent on manual intervention. Especially in the remote monitoring mode, the abnormal identification and response speed directly affect the reliability and efficiency of the system. If the abnormality cannot be handled in a timely and effective manner, it may cause data distortion, which in turn affects production planning and production safety. Therefore, the shortcomings of the prior art are: 1) lack of ability to conduct comprehensive analysis of multi-dimensional data; 2) the abnormal detection and response mechanism is relatively lagging, lacking fast and accurate abnormal classification and priority allocation; 3) in the evaluation of the effect of abnormal handling, the efficiency and effect of manual intervention cannot be scientifically quantified. These shortcomings limit the application of the prior art in complex industrial environments.

[0004] In view of the above problems, the present invention proposes an exception handling method based on unattended remote monitoring of gray scales, which utilizes vehicle weighing data and multi-dimensional operation data of gray scale stations, builds an exception index library and performs multi-dimensional judgment on abnormal data, to ensure accurate identification of abnormalities and rapid response in complex situations. In addition, the present invention introduces priority allocation in the exception handling mechanism, and reasonably configures the response time according to the severity and impact of the abnormal type, thereby improving the system's exception handling efficiency. More importantly, the present invention combines the exception handling effect evaluation and feedback mechanism to further optimize the exception handling process, reduce the reliance on manual intervention, and improve the system's adaptability and intelligence level. Therefore, the present invention has significant innovation on the basis of the existing technology, can significantly improve the exception handling efficiency and reliability of the gray scale unattended system, and provides strong technical support for the intelligent management of related fields. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is that the existing unmanned remote monitoring system of the gray scale lacks multi-dimensional data analysis capabilities and cannot effectively identify and judge abnormal data. The existing technology mostly relies on single weighing data or equipment status monitoring, and fails to comprehensively consider multiple factors such as vehicle weighing data, vehicle model historical data, sensor status, and gray scale station operation data, resulting in low accuracy in abnormal data detection, especially in equipment redundancy and sensor calibration, where misjudgment or missed judgment often occurs. In addition, the existing exception handling mechanism has a delayed response, lacks a clear exception priority classification, and cannot provide timely and effective handling measures for exceptions of different severity.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: an unattended remote monitoring and abnormality handling method for an off-site ash scale, which comprises the following steps:

[0008] Collect data; build an abnormal indicator library and identify abnormal data; trigger the abnormal handling mechanism based on abnormal data; evaluate the effectiveness of abnormal handling; and add feedback based on the evaluation results.

[0009] As a preferred solution of the unattended remote monitoring and exception handling method of the off-site ash scale described in the present invention, the data includes the weighing data of the vehicle and the operation data of the ash scale station.

[0010] After the data is collected, the data is preprocessed.

[0011] As a preferred solution of the unattended remote monitoring and exception handling method of the off-site gray scale described in the present invention, the construction of the abnormality database includes setting a normal value range based on the vehicle weighing data and the operation data of the gray scale station, and defining the abnormal threshold.

[0012] As a preferred solution of the unattended remote monitoring and abnormality handling method of the off-site gray scale described in the present invention, the determination of abnormal data includes constructing a vehicle abnormality model, extracting the vehicle's weighing data for multi-dimensional judgment, wherein if the vehicle's single weighing data exceeds the set normal value range, it is deemed to be abnormal.

[0013] The current weighing data is compared with the historical weighing data of the corresponding vehicle model. If the current weighing data deviates from the historical normal range and exceeds the set threshold, it is considered abnormal.

[0014] If the vehicle is weighed non-single times within the restricted time, it is determined whether the vehicle has the authority to perform non-single weighing. If the vehicle does not have the authority, it is considered abnormal.

[0015] The vehicle's speed and weighing data are substituted into the vehicle abnormality model. If the vehicle's speed does not meet the set range during the weighing process, it is considered abnormal.

[0016] The determination of abnormal data also includes extracting the operating data of the gray scale station for multi-dimensional judgment, wherein the weighing sensor is calibrated before weighing is started and compared with the normal value range; if it is not within the normal value range, it is determined to be abnormal.

[0017] When there is redundancy in the weighing sensor, data consistency verification is performed. When the primary and standby weighing sensors are not within the normal value range, it is considered abnormal.

[0018] As a preferred solution of the unattended remote monitoring and exception handling method of the off-site gray scale described in the present invention, the triggering exception handling mechanism includes classifying the exceptions and assigning priorities according to the type of abnormal data detected.

[0019] The assigning of priorities includes classifying the exceptions into high priority exceptions, medium priority exceptions, and low priority exceptions.

[0020] The high priority exception includes that if the weighing sensor cannot perform normal data collection or analysis, the emergency processing mechanism needs to be activated immediately.

[0021] When a redundant device is inconsistent, indicating a hardware failure or system abnormality, an alarm will be immediately issued and manual inspection will be required.

[0022] If the vehicle exceeds the speed limit during weighing, or performs non-single weighing without the corresponding authority, the operator will be immediately warned and manual intervention will be performed.

[0023] If the current weighing data deviates from the normal value range and exceeds the set threshold, the weighing data will be reviewed immediately.

[0024] The medium priority exception includes: if the vehicle's weighing data deviates from the normal range by 2%, indicating that there is a sensor error, manual inspection or system calibration is performed.

[0025] Before weighing starts, if the calibration result of the weighing sensor does not conform to the normal value range and the deviation is within 2%, the abnormal detection and alarm module will automatically alarm and require manual calibration or adjustment.

[0026] The low priority exception includes that if the exception disappears within the limited time, the exception handling mechanism will not be triggered.

[0027] As a preferred scheme of the unattended remote monitoring and exception handling method of the off-site gray scale described in the present invention, the exception handling effect evaluation includes setting the maximum allowable response time according to the exception priority. If the exception is not restored to normal within the corresponding maximum allowable response time, it indicates that the exception handling effect is poor.

[0028] If the anomaly returns to normal within the corresponding maximum allowable response time, the manual intervention ratio of each anomaly is extracted. If the manual intervention ratio is below the limit ratio, it means that the anomaly handling effect is excellent.

[0029] As a preferred solution of the unattended remote monitoring and exception handling method of the off-site ash scale described in the present invention, the adding of feedback based on the evaluation results includes, if the manual intervention ratio is above the limit ratio, feeding back the specific manual intervention ratio, and analyzing the reason why the manual intervention ratio is above the limit ratio.

[0030] Another object of the present invention is to provide an unattended remote monitoring and exception handling system for an off-site gray scale, which can effectively identify and handle abnormal situations in gray scale weighing data and equipment status through multi-dimensional data collection and analysis, exception priority classification and dynamic feedback mechanism, thereby solving the problems of insufficient exception detection accuracy, delayed processing and lack of real-time response capability in existing systems.

[0031] To solve the above technical problems, the present invention provides the following technical solutions: an unmanned remote monitoring and exception handling system for off-site ash scales, comprising: a data acquisition module, a data preprocessing module, an exception detection and alarm module, a priority allocation module and an exception handling mechanism module.

[0032] The data acquisition module is responsible for collecting vehicle weighing data and gray scale station operation data.

[0033] The data preprocessing module cleans, filters and standardizes the collected data.

[0034] The abnormality detection and alarm module monitors abnormal conditions during the entire weighing process.

[0035] The priority assignment module classifies the anomalies and assigns priorities according to the detected anomaly data types.

[0036] The exception handling mechanism module starts corresponding handling mechanisms for exceptions of different priorities.

[0037] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned unattended remote monitoring and exception handling method of an off-site gray scale are implemented.

[0038] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for unattended remote monitoring and exception handling of an off-site gray scale.

[0039] Beneficial effects of the present invention: The present invention can accurately identify and handle abnormal situations in the gray scale system by constructing a multi-dimensional anomaly detection model based on vehicle weighing data and gray scale station operation data, combined with redundant equipment consistency verification and real-time data calibration. By reasonably setting the exception priority and response mechanism, the present invention ensures that high-priority exceptions can be handled in a timely manner, avoiding the risks caused by equipment failure, weighing overspeed or unauthorized operation. At the same time, the system also has dynamic feedback and optimization functions, and can continuously optimize the exception handling process according to the proportion of manual intervention and response effect evaluation results. Compared with the prior art, the present invention not only improves the intelligence level of unattended remote monitoring of gray scales, but also significantly improves the accuracy, response speed and system reliability of exception handling, effectively ensuring the long-term stable operation of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0041] Figure 1 This is an overall flow chart of the unattended remote monitoring and exception handling method of an off-site ash scale provided in the first embodiment of the present invention.

[0042] Figure 2This is an overall framework diagram of the unattended remote monitoring and exception handling system for off-site ash scales provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0044] Example 1, reference Figure 1 , is an embodiment of the present invention, providing an unattended remote monitoring and exception handling method for an off-site ash scale, characterized in that:

[0045] S1: Collect data.

[0046] The data includes vehicle weighing data and gray scale station operation data.

[0047] After collecting the data, the data is preprocessed.

[0048] S2: Build an abnormal indicator library to determine abnormal data;

[0049] Constructing the abnormality database includes setting a normal value range based on the vehicle weighing data and the operation data of the gray scale station, and defining the abnormal threshold.

[0050] Determining abnormal data includes constructing a vehicle abnormality model and extracting the vehicle's weighing data for multi-dimensional judgment. If a single weighing data of the vehicle exceeds a set normal value range, it is considered abnormal.

[0051] The current weighing data is compared with the historical weighing data of the corresponding vehicle model. If the current weighing data deviates from the historical normal range and exceeds the set threshold, it is considered abnormal.

[0052] If the vehicle is weighed non-single times within the restricted time, it is determined whether the vehicle has the authority to perform non-single weighing. If the vehicle does not have the authority, it is considered abnormal.

[0053] The vehicle's speed and weighing data are substituted into the vehicle abnormality model. If the vehicle's speed does not meet the set range during the weighing process, it is considered abnormal.

[0054] Determining abnormal data also includes extracting the operating data of the ash scale station for multi-dimensional judgment, wherein the weighing sensor is calibrated before weighing is started and compared with the normal value range; if it is not within the normal value range, it is considered abnormal.

[0055] When there is redundancy in the weighing sensor, data consistency verification is performed. When the primary and standby weighing sensors are not within the normal value range, it is considered abnormal.

[0056] Furthermore, by setting the normal value range and defining the abnormal threshold, this step digitizes the operating status of vehicles and equipment and can quickly identify abnormal data. The constructed vehicle anomaly model can not only detect anomalies through vehicle weighing data, but also consider factors such as vehicle speed and non-single weighing to comprehensively determine whether the data is abnormal. Sensor calibration and verification of redundant equipment at the gray scale station further enhance the reliability of the system. This method effectively improves the accuracy and comprehensiveness of anomaly detection, can promptly detect potential equipment failures or operational errors, and ensure the accuracy of weighing data.

[0057] S3: Triggering the exception handling mechanism based on the abnormal data.

[0058] Triggering the exception handling mechanism includes classifying and assigning priorities to exceptions based on the type of abnormal data detected;

[0059] Assigning priorities includes classifying exceptions into high priority exceptions, medium priority exceptions, and low priority exceptions;

[0060] High-priority exceptions include: if the weighing sensor cannot collect or analyze data normally, the emergency response mechanism must be activated immediately;

[0061] When a redundant device is inconsistent, indicating a hardware failure or system abnormality, an alarm will be immediately issued and manual inspection will be required;

[0062] If the vehicle exceeds the speed limit during weighing, or performs non-single weighing without the corresponding authority, the operator will be immediately warned and manual intervention will be performed.

[0063] If the current weighing data deviates from the normal value range and exceeds the set threshold, the weighing data will be reviewed immediately.

[0064] Medium priority exceptions include: if the vehicle's weighing data deviates from the normal range by 2%, indicating a sensor error, manual inspection or system calibration is required.

[0065] Before weighing starts, if the calibration result of the weighing sensor does not conform to the normal value range and the deviation is within 2%, the abnormal detection and alarm module will automatically alarm and require manual calibration or adjustment.

[0066] Low priority exceptions include: if the exception disappears within the limited time, the exception handling mechanism will not be triggered.

[0067] Furthermore, the activation of the exception handling mechanism can be classified according to the type of abnormal data, ensuring that high-priority exceptions (such as load cell failure or vehicle speeding) are responded to immediately, reducing the impact on the production process. Through automated classification and priority allocation, the system can effectively optimize resource allocation and ensure that the most urgent issues are handled first. The beneficial effect of this step is reflected in the significant improvement of the system's response speed and processing efficiency to abnormal events through automated response and reasonable priority management.

[0068] S4: Evaluate the effectiveness of exception handling.

[0069] The evaluation of the exception handling effect includes setting the maximum allowable response time according to the exception priority. If the exception is not restored to normal within the corresponding maximum allowable response time, it means that the exception handling effect is poor.

[0070] If the anomaly returns to normal within the corresponding maximum allowable response time, the manual intervention ratio of each anomaly is extracted. If the manual intervention ratio is below the limit ratio, it means that the anomaly handling effect is excellent.

[0071] Furthermore, the evaluation of the effect of exception handling provides a basis for system optimization. By analyzing the time of exception recovery and the proportion of manual intervention, the system can identify deficiencies in the processing flow and make adjustments based on the evaluation results. Especially in the case of high-priority exceptions, real-time evaluation can ensure the timeliness and accuracy of processing. This step helps to continuously optimize the system, reduce manual intervention, and improve automated processing capabilities.

[0072] S5: Add feedback based on the evaluation results.

[0073] Adding feedback based on the evaluation results includes, if the manual intervention ratio is above the limit ratio, feeding back the specific manual intervention ratio and analyzing the reasons why the manual intervention ratio is above the limit ratio.

[0074] Example 2, reference Figure 2 , which is an embodiment of the present invention, provides a system for unattended remote monitoring and exception handling of off-site gray scales, characterized in that it includes a data acquisition module 100, a data preprocessing module 200, an anomaly detection and alarm module 300, a priority allocation module 400 and an exception handling mechanism module 500.

[0075] The data collection module 100 is responsible for collecting vehicle weighing data and gray scale station operation data.

[0076] The data preprocessing module 200 cleans, filters and standardizes the collected data.

[0077] The abnormality detection and alarm module 300 monitors abnormal conditions during the entire weighing process.

[0078] The priority assignment module 400 classifies and assigns priorities to the anomalies according to the type of detected anomaly data.

[0079] The exception handling mechanism module 500 starts corresponding handling mechanisms for exceptions of different priorities.

[0080] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0081] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0082] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0083] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0084] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An unattended remote monitoring and abnormality handling method for an off-site ash scale, characterized in that: include: Collect data; Build an abnormal indicator library to identify abnormal data; Triggering exception handling mechanisms based on abnormal data; Evaluate the effectiveness of exception handling; Add feedback based on the assessment results.

2. The method for unattended remote monitoring and abnormality handling of an off-site ash scale according to claim 1, characterized in that: The data includes vehicle weighing data and gray scale station operation data; After the data is collected, the data is preprocessed.

3. The method for unattended remote monitoring and abnormality handling of an off-site ash scale according to claim 2, characterized in that: The construction of the abnormal database includes setting a normal value range based on the vehicle weighing data and the operation data of the gray scale station, and defining an abnormal threshold.

4. The method for unattended remote monitoring and abnormality handling of an off-site ash scale according to claim 3, characterized in that: Determining abnormal data includes constructing a vehicle abnormality model, extracting vehicle weighing data for multi-dimensional judgment, wherein if a single weighing data of a vehicle exceeds a set normal value range, it is considered abnormal; Compare the current weighing data with the historical weighing data of the corresponding vehicle model. If the current weighing data deviates from the historical normal range and exceeds the set threshold, it is considered abnormal. If the vehicle is weighed non-single times within the time limit, it is determined whether the vehicle has the authority to perform non-single weighing. If not, it is considered abnormal. Substitute the vehicle's speed and weighing data into the vehicle abnormality model. If the vehicle's speed does not meet the set range during the weighing process, it is considered abnormal. Determining abnormal data also includes extracting the operating data of the gray scale station for multi-dimensional judgment, wherein the weighing sensor is calibrated before weighing is started and compared with the normal value range; if it is not within the normal value range, it is determined to be abnormal; When there is redundancy in the weighing sensor, data consistency verification is performed. When the primary and standby weighing sensors are not within the normal value range, it is considered abnormal.

5. The method for unattended remote monitoring and abnormality handling of an off-site ash scale according to claim 4, characterized in that: The triggering of the exception handling mechanism includes classifying the exceptions and assigning priorities according to the type of abnormal data detected; The assigning of priorities includes classifying the exceptions into high priority exceptions, medium priority exceptions and low priority exceptions; The high priority exceptions include: if the weighing sensor cannot collect or analyze data normally, the emergency processing mechanism needs to be activated immediately; When a redundant device is inconsistent, indicating a hardware failure or system abnormality, an alarm will be immediately issued and manual inspection will be required; If the vehicle exceeds the speed limit during weighing, or if it is not a single weighing and does not have the corresponding authority, the operator will be immediately warned and manual intervention will be performed; If the current weighing data deviates from the normal value range and exceeds the set threshold, the weighing data will be reviewed immediately; The medium priority exceptions include: if the vehicle's weighing data deviates from the normal range by 2%, indicating a sensor error, manual inspection or system calibration is performed; Before weighing starts, if the calibration result of the weighing sensor does not conform to the normal value range and the deviation is within 2%, the abnormal detection and alarm module will automatically alarm and require manual calibration or adjustment; The low priority exception includes that if the exception disappears within the limited time, the exception handling mechanism will not be triggered.

6. The method for unattended remote monitoring and abnormality handling of an off-site ash scale according to claim 5, characterized in that: The evaluation of the exception handling effect includes setting the maximum allowable response time according to the exception priority, and if the exception is not restored to normal within the corresponding maximum allowable response time, it means that the exception handling effect is poor; If the anomaly returns to normal within the corresponding maximum allowable response time, the manual intervention ratio of each anomaly is extracted. If the manual intervention ratio is below the limit ratio, it means that the anomaly handling effect is excellent.

7. The method for unattended remote monitoring and abnormality handling of an off-site ash scale according to claim 6, characterized in that: The adding feedback based on the evaluation result includes, if the manual intervention ratio is above the limit ratio, feeding back the specific manual intervention ratio, and analyzing the reason why the manual intervention ratio is above the limit ratio.

8. A system using the unattended remote monitoring and abnormality handling method of an off-site ash scale as claimed in any one of claims 1 to 7, characterized in that: It comprises a data acquisition module (100), a data preprocessing module (200), an anomaly detection and alarm module (300), a priority allocation module (400) and an anomaly handling mechanism module (500); The data collection module (100) is responsible for collecting vehicle weighing data and gray scale station operation data; The data preprocessing module (200) cleans, filters and standardizes the collected data; The abnormality detection and alarm module (300) monitors abnormal conditions during the entire weighing process; The priority allocation module (400) classifies the anomalies and allocates priorities according to the detected abnormal data types; The exception handling mechanism module (500) starts corresponding handling mechanisms for exceptions of different priorities.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for unattended remote monitoring and exception handling of an off-site gray scale according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the method for unattended remote monitoring and exception handling of an off-site gray scale as described in any one of claims 1 to 7 are implemented.

Images