Warning method and device of electronic truck scale, electronic equipment and storage medium

Abstract

The invention discloses an alarm method and device for an electronic truck scale, electronic equipment and a storage medium, and the method comprises the steps: determining whether weighing abnormity exists in response to collected weighing data; when it is determined that weighing abnormity exists, an abnormal signal is generated; and in response to the abnormal signal, generating abnormal warning information based on a preset risk level rule, and pushing the abnormal warning information through a preset pushing mode. Weighing data are collected in real time by adopting a multi-dimensional anomaly monitoring mechanism, the problems of instrument lead seal state anomaly, sensor communication failure, configuration parameter modification and the like are recognized, and warning information is generated based on a preset risk level rule and automatically pushed. The electronic truck scale anti-cheating method can solve the risks that instruments are illegally started, parameters are tampered, data are intercepted and modified and the like due to the fact that traditional instruments and a manual supervision mode are adopted in an existing electronic truck scale anti-cheating method, and the technical effects of improving weighing data reliability and enterprise trade settlement safety and reducing economic loss risks are achieved.

Description

Technical Field

[0001] This disclosure relates to the field of weighing and measurement technology, and in particular to an alarm method, device, electronic equipment and storage medium for an electronic truck scale. Background Technology

[0002] Electronic truck scales, as core equipment in the weighing and measurement field, are widely used in settlement scenarios for bulk commodity trade in infrastructure, coal, metallurgy, and chemical industries. Their accuracy and security directly affect the economic interests and fair transactions of enterprises. Among related technologies, a basic framework for weighing data acquisition and management is constructed through the collaborative operation of sensor networks, weighing instruments, and video monitoring systems.

[0003] However, existing methods for preventing cheating on electronic truck scales rely on traditional instruments and manual supervision. This can lead to risks such as the illegal opening of the instrument casing, tampering with sensor parameters, and interception or modification of weighing data during transmission. These risks can affect the reliability of weighing data and the security of trade settlements for businesses, and even cause serious economic losses. Summary of the Invention

[0004] This disclosure provides an alarm method, device, electronic equipment, and storage medium for electronic truck scales. Its main purpose is to address the risks associated with unauthorized opening of the instrument casing, tampering with sensor parameters, and interception or modification of weighing data during transmission, which could affect the reliability of weighing data, the security of enterprise trade settlements, and even lead to serious economic losses.

[0005] According to a first aspect of this disclosure, an alarm method for an electronic truck scale is provided, comprising:

[0006] In response to the acquisition of weighing data, determine whether there is a weighing abnormality; wherein, the weighing abnormality includes at least one of the following: abnormal instrument seal status, sensor communication failure, and modification of configuration parameters;

[0007] When an abnormal weighing is detected, an abnormal signal is generated;

[0008] In response to the abnormal signal, an abnormal warning message is generated based on a preset risk level rule, and the abnormal warning message is pushed out through a preset push method.

[0009] Optionally, the step of generating an abnormal warning message based on a preset risk level rule in response to the abnormal signal, and pushing the abnormal warning message through a preset push method, further includes:

[0010] The system performs multi-condition queries, statistical analysis, and results export on the abnormal signals, weighing data, and equipment status information. The system then uses a visualization module to display key equipment data, analysis results, and abnormal warning information in chart form.

[0011] Optionally, the abnormality in the weighing process also includes:

[0012] If the instrument fails to return to zero within the first preset time period, a weighing anomaly is determined to exist;

[0013] If the vehicle stops abnormally within the second preset time period, it is determined that there is a weighing abnormality;

[0014] If the instrument reading is not zero before weighing, it indicates that there is a weighing anomaly.

[0015] If the difference between the maximum weight and the steady weight during the weighing process exceeds the first preset threshold, it is determined that there is a weighing anomaly.

[0016] If the instrument reading changes drastically when the scale is empty, it indicates a weighing anomaly.

[0017] Optionally, after generating an abnormal signal upon determining that a weighing anomaly exists, the method further includes:

[0018] The collection of weighing data is prohibited, and the device is not available status information is pushed based on the push method.

[0019] Optionally, the step of generating an abnormal warning message based on a preset risk level rule in response to the abnormal signal, and pushing the abnormal warning message through a preset push method, further includes:

[0020] The risk level is dynamically adjusted based on the type and frequency of the abnormal signal, and the alarm method and push priority are determined based on the risk level.

[0021] According to a second aspect of this disclosure, an alarm device for an electronic truck scale is provided, comprising:

[0022] A determining unit is used to determine whether there is a weighing abnormality in response to the acquisition of weighing data; wherein the weighing abnormality includes at least one of the following: abnormal instrument seal status, sensor communication failure, and modification of configuration parameters;

[0023] The generation unit is used to generate an abnormal signal when an abnormal weighing is determined to exist;

[0024] An alarm unit is used to respond to the abnormal signal, generate abnormal warning information based on preset risk level rules, and push the abnormal warning information through a preset push method.

[0025] Optionally, the alarm unit is further configured to include:

[0026] The system performs multi-condition queries, statistical analysis, and results export on the abnormal signals, weighing data, and equipment status information. The system then uses a visualization module to display key equipment data, analysis results, and abnormal warning information in chart form.

[0027] Optionally, the determining unit is further configured to:

[0028] If the instrument fails to return to zero within the first preset time period, a weighing anomaly is determined to exist;

[0029] If the vehicle stops abnormally within the second preset time period, it is determined that there is a weighing abnormality;

[0030] If the instrument reading is not zero before weighing, it indicates that there is a weighing anomaly.

[0031] If the difference between the maximum weight and the steady weight during the weighing process exceeds the first preset threshold, it is determined that there is a weighing anomaly.

[0032] If the instrument reading changes drastically when the scale is empty, it indicates a weighing anomaly.

[0033] Optionally, the device further includes:

[0034] The push unit is also used to prevent the collection of weighing data after generating an abnormal signal when the generation unit determines that there is a weighing abnormality, and to push the equipment unavailable status information based on the push method.

[0035] Optionally, the alarm unit is further configured to:

[0036] The risk level is dynamically adjusted based on the type and frequency of the abnormal signal, and the alarm method and push priority are determined based on the risk level.

[0037] According to a third aspect of this disclosure, an electronic device is provided, comprising:

[0038] At least one processor; and

[0039] A memory communicatively connected to the at least one processor; wherein,

[0040] 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.

[0041] 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.

[0042] 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.

[0043] The alarm method, device, electronic equipment, and storage medium for electronic truck scales disclosed in this disclosure mainly include: in response to the collection of weighing data, determining whether there is a weighing anomaly; wherein the weighing anomaly includes at least one of: abnormal instrument seal status, sensor communication failure, and modification of configuration parameters; when a weighing anomaly is determined to exist, generating an anomaly signal; in response to the anomaly signal, generating an anomaly warning message based on preset risk level rules, and pushing the anomaly warning message through a preset push method. Compared with related technologies, the embodiments of this application, by adopting a multi-dimensional anomaly monitoring mechanism to collect weighing data in real time and identify problems such as abnormal instrument seal status, sensor communication failure, and modification of configuration parameters, and generating and automatically pushing warning messages based on preset risk level rules, can solve the risks of illegal instrument opening, parameter tampering, and data interception and modification caused by the use of traditional instruments and manual supervision in existing electronic truck scale anti-cheating methods, thereby achieving the technical effect of improving the reliability of weighing data and the security of enterprise trade settlement, and reducing the risk of economic loss.

[0044] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0045] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0046] Figure 1 A flowchart illustrating an alarm method for an electronic truck scale provided in an embodiment of this disclosure;

[0047] Figure 2 A schematic diagram of the structure of an alarm device for an electronic truck scale provided in an embodiment of this disclosure;

[0048] Figure 3 A schematic diagram of the structure of another alarm device for an electronic truck scale provided in an embodiment of this disclosure;

[0049] Figure 4 A schematic block diagram of an example electronic device provided for embodiments of this disclosure. Detailed Implementation

[0050] 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.

[0051] The alarm method, apparatus, electronic device, and storage medium of an electronic truck scale according to embodiments of the present disclosure are described below with reference to the accompanying drawings.

[0052] Figure 1 This is a flowchart illustrating an alarm method for an electronic truck scale provided in an embodiment of this disclosure.

[0053] like Figure 1 As shown, the method includes the following steps:

[0054] Step 101: In response to the acquisition of weighing data, determine whether there is a weighing abnormality; wherein, the weighing abnormality includes at least one of the following: abnormal instrument seal status, sensor communication failure, and modified configuration parameters;

[0055] In the overall anti-cheating solution for truck scales, the front-end data acquisition system simultaneously activates a weighing anomaly detection mechanism during the data collection process to respond to the data acquisition activity and determine if any weighing anomalies exist. Specifically, the detection of abnormal instrument seal status is achieved through a dual protection mechanism of physical and electronic seals employed by the DS12 IoT anti-cheating instrument (customized): the physical seal forms a physical blockage of the instrument casing, while the electronic seal monitors the opening and closing status of the casing in real time through a built-in electronic detection module. Once the casing is detected to be opened (i.e., the seal is damaged or removed), the system immediately determines that the instrument seal status is abnormal. For sensor communication fault detection, the system continuously monitors the communication status between the sensor and the instrument, including the sensor's impedance matching and the stability of the data transmission link. When the sensor exhibits impedance matching abnormalities, communication signal interruptions, or data transmission errors, it is determined to be a sensor communication fault. Regarding the detection of modified configuration parameters, the DS12 IoT anti-cheating instrument monitors its key configuration parameters (such as calibration parameters, angle difference coefficients, etc.) in real time. Through the built-in parameter change detection module, it records the original and current states of the parameters. Once an unauthorized modification of the parameters is detected, it will immediately trigger an anomaly judgment and upload the modification information to the platform, thereby effectively identifying the anomaly of modified configuration parameters.

[0056] Step 102: When an abnormal weighing is determined to exist, an abnormal signal is generated;

[0057] When a weighing anomaly is detected (such as abnormal instrument seal status, sensor communication failure, or modified configuration parameters), the front-end data acquisition system immediately activates the anomaly signal generation mechanism. Specifically, the DS12 IoT anti-cheating instrument, as the core acquisition device, generates a corresponding anomaly signal through its built-in anomaly signal generation module after detecting the above-mentioned anomaly. This signal combines the anomaly type (such as damaged seal, interrupted sensor communication, or unauthorized parameter changes), the occurrence time, and device identification (such as instrument model, version number, and project site information). This signal contains specific anomaly characteristic information to ensure accurate problem localization in subsequent processing. Simultaneously, the anti-cheating client software maintains real-time communication with the instrument via Ethernet or serial port. Upon receiving the anomaly detection results from the instrument, it performs supplementary verification and formatting of the anomaly signal to ensure its integrity and standardization. For example, it adds weighing scenario information and current weighing environment data, thus forming a complete anomaly signal. This lays the foundation for subsequently uploading the signal to the information monitoring platform on the platform side, enabling rapid transmission and response to anomaly information.

[0058] Step 103: In response to the abnormal signal, generate abnormal warning information based on preset risk level rules, and push the abnormal warning information through a preset push method.

[0059] Upon receiving an anomaly signal from the front-end data acquisition system, the online anti-cheating monitoring platform for truck scales on the platform side will immediately respond to the signal and initiate the anomaly warning information generation and push process. The preset risk level rules are based on the severity of the anomaly type and the potential economic loss risk. For example, sensor communication failures may lead to inaccurate weighing data, directly affecting trade settlements, and are classified as high-risk; abnormal instrument seal status (opened casing) may involve human tampering with the equipment, also belonging to the high-risk level; and modifications to configuration parameters, if they do not immediately affect the current weighing data, may be classified as medium-risk. The platform will match the preset risk level rules with information such as the anomaly type (e.g., abnormal instrument seal status, sensor communication failure, modified configuration parameters), equipment identification (e.g., instrument model, project site information), and occurrence time contained in the anomaly signal, determine the corresponding risk level, and generate an anomaly warning message containing the anomaly type, risk level, occurrence time, involved equipment information, and site location. Meanwhile, the preset push methods are information transmission channels pre-configured by the platform for different risk levels or different management personnel. These include pop-up reminders on the visual monitoring interface of the information supervision platform, sending real-time messages to relevant management personnel via WeChat mini-program, and pushing warning content via SMS, ensuring that relevant personnel can be informed of abnormal situations in a timely manner so as to quickly carry out follow-up processing and reduce the economic losses that enterprises may suffer due to abnormal weighing.

[0060] In some embodiments, the step of generating an abnormal warning message based on a preset risk level rule in response to the abnormal signal, and pushing the abnormal warning message through a preset push method, further includes:

[0061] The system performs multi-condition queries, statistical analysis, and results export on the abnormal signals, weighing data, and equipment status information. The system then uses a visualization module to display key equipment data, analysis results, and abnormal warning information in chart form.

[0062] In response to the abnormal signal, the platform generates anomaly warning information based on preset risk level rules and pushes it through a preset method. Simultaneously, the online anti-cheating monitoring platform for truck scales also performs multi-condition queries, statistical analysis, and result export on the abnormal signal, weighing data, and equipment status information. The platform then uses a visualization module to display key equipment data, analysis results, and anomaly warning information in chart form. Specifically, the multi-condition query supports precise retrieval based on various combinations of conditions, such as anomaly type (e.g., abnormal instrument seal status, sensor communication failure), time range (e.g., the last 24 hours, the last 7 days), equipment identification (e.g., a specific DS12 IoT anti-cheating instrument number), site information (e.g., a construction project site), and risk level, facilitating quick location of required information by management personnel. The statistical analysis summarizes and deeply analyzes the queried data, such as calculating the frequency of anomalies at each site within a certain time period, the proportion of different anomaly types, and the distribution of high-risk anomalies, forming structured analysis results. The result export function supports exporting query results and statistical analysis data in common formats such as Excel and PDF for archiving and subsequent offline analysis. The visualization module uses a dashboard and other formats to present key equipment data (such as the number of online devices, sensor health status, real-time weighing data, etc.), statistical analysis results (such as pie charts of anomaly type percentages, line graphs of site anomaly trends, etc.), and anomaly warning information (such as real-time high-risk anomaly lists, anomaly geographical distribution maps, etc.) in intuitive chart formats, including bar charts, line graphs, pie charts, map markers, etc. This allows managers to have a clear understanding of the overall operating status and anomalies of the truck scale equipment, improving regulatory efficiency and the timeliness of decision-making.

[0063] In some embodiments, the weighing process abnormality also includes:

[0064] If the instrument fails to return to zero within the first preset time period, a weighing anomaly is determined to exist;

[0065] If the vehicle stops abnormally within the second preset time period, it is determined that there is a weighing abnormality;

[0066] If the instrument reading is not zero before weighing, it indicates that there is a weighing anomaly.

[0067] If the difference between the maximum weight and the steady weight during the weighing process exceeds the first preset threshold, it is determined that there is a weighing anomaly.

[0068] If the instrument reading changes drastically when the scale is empty, it indicates a weighing anomaly.

[0069] When a weighing anomaly is detected, the judgment of the anomaly in the weighing process is based on the real-time monitoring and analysis of the front-end data information acquisition system and the DS12 IoT anti-cheating instrument (customized). Specifically, if the instrument does not return to zero within a first preset time period, a weighing anomaly is determined. The first preset time period can be preset according to the actual weighing scenario (such as the weighing efficiency requirements of different industries), for example, 5 minutes. The DS12 IoT anti-cheating instrument continuously monitors its own zeroing status. If it still does not return to zero after this time period, it will immediately record and trigger the anomaly judgment.

[0070] If a vehicle's stay on the scale is abnormal within the second preset time period, a weighing abnormality is determined: The second preset time period is set for the reasonable stay time of a vehicle on the scale (such as 2 minutes required for a normal weighing process). The anti-cheating client software combines the vehicle location information collected by video surveillance and the weighing data of the instrument. If it detects that the vehicle stays on the scale for more than this time period and there is no normal weighing operation (such as failure to complete the weighing record), it is determined to be an abnormal stay.

[0071] If the instrument data is not zero before weighing, an abnormal weighing is identified: The DS12 IoT anti-cheating instrument will automatically detect the current data status before the vehicle starts weighing. If the detected data is not zero (i.e. there is a non-zero base value), it will be judged as abnormal. This is to avoid the non-zero base value affecting the accuracy of this weighing and to prevent people from cheating by setting a preset base value.

[0072] If the difference between the maximum weight and the stable weight during the weighing process exceeds the first preset threshold, a weighing anomaly is determined. The first preset threshold is set according to the weighing accuracy requirements and material characteristics (such as the maximum allowable deviation value for a certain type of material). The system collects weight change data in real time during the weighing process, records the maximum weight and the final stable weight, and calculates the difference between the two. If the difference exceeds the threshold, it is determined to be an anomaly, which may involve cheating behaviors such as adding or removing loads during the weighing process.

[0073] When the instrument reading changes erratically while the scale is empty, it indicates a weighing anomaly. When the scale is empty, the instrument should maintain a stable reading (usually zero or a fixed small error value). The system continuously monitors changes in the reading. If it detects irregular changes or fluctuations beyond the normal error range, it is considered an anomaly, which may involve sensor interference or human tampering.

[0074] The determination of all the above-mentioned abnormalities in the weighing process is performed in real time by the front-end data information acquisition system (including the DS12 IoT anti-cheating instrument and anti-cheating client software). Once a situation that meets the above conditions is detected, it is determined that there is a weighing abnormality.

[0075] In some embodiments, after generating an anomaly signal upon determining that a weighing anomaly exists, the method further includes:

[0076] The collection of weighing data is prohibited, and the device is not available status information is pushed based on the push method.

[0077] Upon detecting a weighing anomaly and generating an anomaly signal, the front-end data acquisition system and related equipment will immediately execute an operation to prohibit the collection of weighing data. This is to prevent inaccurate or cheating-affected weighing data from entering the system and causing economic losses to the enterprise. Specifically, after detecting a weighing anomaly (such as sensor communication failure, modified configuration parameters, etc.) and generating an anomaly signal, the DS12 IoT anti-cheating instrument (customized) will trigger its built-in protection mechanism, stopping the 232 serial port output, thereby cutting off the transmission of weighing data to subsequent systems (such as unattended systems and anti-cheating client software), prohibiting the collection of weighing data from the source. At the same time, upon receiving the anomaly signal, the unattended system will immediately cease operation and stop responding to any weighing operation commands to prevent inaccurate data from being collected and recorded under abnormal conditions.

[0078] While prohibiting the collection of weighing data, the system will push equipment unavailability information based on a preset push method. This equipment unavailability information includes the anomaly type, equipment identifier (such as instrument model, project site information), and the start time of unavailability. It is uploaded in real time to the online anti-cheating monitoring platform for truck scales on the platform side via anti-cheating client software. The platform then pushes the information to relevant management personnel (such as site maintenance personnel and enterprise supervisors) according to preset push methods (such as WeChat mini-program messages, SMS notifications, and pop-ups on the platform's visual monitoring interface). This allows management personnel to be aware of the equipment's inability to work normally due to an anomaly, thereby arranging maintenance or handling, avoiding disruption to the normal weighing process due to equipment unavailability, and also preventing the unauthorized use of equipment in an abnormal state.

[0079] In some embodiments, the step of generating an abnormal warning message based on a preset risk level rule in response to the abnormal signal, and pushing the abnormal warning message through a preset push method, further includes:

[0080] The risk level is dynamically adjusted based on the type and frequency of the abnormal signal, and the alarm method and push priority are determined based on the risk level.

[0081] During the process of responding to abnormal signal generation and pushing abnormal warning information, the online anti-cheating monitoring platform for truck scales on the platform side will also dynamically adjust the risk level according to the type and frequency of the abnormal signal, and determine the corresponding alarm method and push priority based on the adjusted risk level. Specifically, the type of abnormal signal itself corresponds to the initial risk level. For example, types that directly affect weighing accuracy or involve equipment tampering, such as sensor communication failure or abnormal instrument seal status (outer shell opened), have a high initial risk level; types that may affect data accuracy, such as non-zero instrument data before weighing or fluctuating instrument readings in an empty weighbridge, have a medium initial risk level; and minor abnormalities, such as the instrument briefly not returning to zero, have a low initial risk level. Based on this, the platform will count the frequency of the same type of anomaly within a preset time window (such as 1 hour or 24 hours): if an anomaly of high initial risk level occurs 2 or more times within 1 hour, it indicates that the equipment may be continuously interfered with or maliciously tampered with, and the risk level will be dynamically upgraded to "emergency"; if an anomaly of medium initial risk level (such as the instrument data not being zero before weighing) occurs 3 or more times within 24 hours, it may indicate repeated cheating attempts, and the risk level will be upgraded to high; if an anomaly of low initial risk level (such as a brief period of non-zeroing) does not occur again for a long time, the risk level may remain or be downgraded to low.

[0082] Based on dynamically adjusted risk levels, the platform determines the corresponding alarm methods and push priorities: For "urgent" risk level anomalies, alarms will be triggered using a combination of methods (e.g., platform visual monitoring interface pop-ups + WeChat mini-program real-time messages + SMS notifications), with the highest push priority, ensuring relevant management personnel (such as site managers and technical maintenance personnel) receive the alerts immediately; for high-risk level anomalies, alarms will be triggered via WeChat mini-program real-time messages + platform pop-ups, with a second-highest push priority; for medium-risk level anomalies, alarms will primarily be triggered via platform pop-ups + anomaly log records, with a medium push priority; and for low-risk level anomalies, they will only be recorded in the platform's anomaly query module, with a low push priority. Through this dynamic adjustment mechanism, the platform can more accurately match the actual risk level of anomalies, optimize alarm resource allocation, ensure that critical anomalies receive the most timely response, and avoid low-risk anomalies excessively consuming management resources.

[0083] Corresponding to the alarm method for electronic truck scales described above, this invention also proposes an alarm device for electronic truck scales. Since the device embodiments of this invention correspond to the method embodiments described above, details not disclosed in the device embodiments can be referred to in the method embodiments described above, and will not be repeated here.

[0084] Figure 2 This is a schematic diagram of the structure of an alarm device for an electronic truck scale provided in an embodiment of this disclosure, as shown below. Figure 2 As shown, it includes:

[0085] The determining unit 21 is used to determine whether there is a weighing abnormality in response to the acquisition of weighing data; wherein the weighing abnormality includes at least one of the following: abnormal instrument seal status, sensor communication failure, and modification of configuration parameters;

[0086] The generation unit 22 is used to generate an abnormal signal when it is determined that there is a weighing abnormality;

[0087] Alarm unit 23 is used to respond to the abnormal signal, generate abnormal warning information based on preset risk level rules, and push the abnormal warning information through a preset push method.

[0088] Furthermore, in one possible implementation of this disclosure, the alarm unit 23 is further configured to include:

[0089] The system performs multi-condition queries, statistical analysis, and results export on the abnormal signals, weighing data, and equipment status information. The system then uses a visualization module to display key equipment data, analysis results, and abnormal warning information in chart form.

[0090] Furthermore, in one possible implementation of this disclosure, the determining unit 21 is further configured to:

[0091] If the instrument fails to return to zero within the first preset time period, a weighing anomaly is determined to exist;

[0092] If the vehicle stops abnormally within the second preset time period, it is determined that there is a weighing abnormality;

[0093] If the instrument reading is not zero before weighing, it indicates that there is a weighing anomaly.

[0094] If the difference between the maximum weight and the steady weight during the weighing process exceeds the first preset threshold, it is determined that there is a weighing anomaly.

[0095] If the instrument reading changes drastically when the scale is empty, it indicates a weighing anomaly.

[0096] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 3 As shown, the device further includes:

[0097] The push unit 24 is also used to, after generating an abnormal signal when the generation unit 22 determines that there is a weighing abnormality, prohibit the collection of weighing data and push the equipment unavailable status information based on the push method.

[0098] Furthermore, in one possible implementation of this disclosure, the alarm unit 23 is further configured to:

[0099] The risk level is dynamically adjusted based on the type and frequency of the abnormal signal, and the alarm method and push priority are determined based on the risk level.

[0100] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of the embodiments of this disclosure, and the principle is the same. Therefore, the embodiments of this disclosure are not limited thereto.

[0101] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0102] Figure 4 A 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 assistants, 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.

[0103] like Figure 4 As shown, 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. RAM 303 can also store various programs and data required for the operation of device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via bus 304. I / O (Input / Output) interface 305 is also connected to bus 304.

[0104] Multiple components in 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 monitors, 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 device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0105] 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 alarm method for an electronic truck scale. For example, in some embodiments, the alarm method for an electronic truck scale 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 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 alarm method for the electronic truck scale by any other suitable means (e.g., by means of firmware).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or 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 (e.g., communication networks) of any form or medium. Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0111] 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 ecosystem, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0112] 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.

[0113] 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.

[0114] 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. An alarm method for an electronic truck scale, characterized in that, include: In response to the acquisition of weighing data, determine whether there is a weighing abnormality; wherein, the weighing abnormality includes at least one of the following: abnormal instrument seal status, sensor communication failure, and modification of configuration parameters; When an abnormal weighing is detected, an abnormal signal is generated; In response to the abnormal signal, an abnormal warning message is generated based on a preset risk level rule, and the abnormal warning message is pushed out through a preset push method.

2. The method according to claim 1, characterized in that, The step of generating an abnormal warning message based on a preset risk level rule in response to the abnormal signal, and pushing the abnormal warning message through a preset push method, further includes: The system performs multi-condition queries, statistical analysis, and results export on the abnormal signals, weighing data, and equipment status information. The system then uses a visualization module to display key equipment data, analysis results, and abnormal warning information in chart form.

3. The method according to claim 1, characterized in that, The abnormality in the weighing process also includes: If the instrument fails to return to zero within the first preset time period, a weighing anomaly is determined to exist; If the vehicle stops abnormally within the second preset time period, it is determined that there is a weighing abnormality; If the instrument reading is not zero before weighing, it indicates that there is a weighing anomaly. If the difference between the maximum weight and the steady weight during the weighing process exceeds the first preset threshold, it is determined that there is a weighing anomaly. If the instrument reading changes drastically when the scale is empty, it indicates a weighing anomaly.

4. The method according to claim 1, characterized in that, After generating an abnormality signal upon determining that a weighing anomaly exists, the method further includes: The collection of weighing data is prohibited, and the device is not available status information is pushed based on the push method.

5. The method according to claim 1, characterized in that, The step of generating an abnormal warning message based on a preset risk level rule in response to the abnormal signal, and pushing the abnormal warning message through a preset push method, further includes: The risk level is dynamically adjusted based on the type and frequency of the abnormal signal, and the alarm method and push priority are determined based on the risk level.

6. An alarm device for an electronic truck scale, characterized in that, include: A determining unit is used to determine whether there is a weighing abnormality in response to the acquisition of weighing data; wherein the weighing abnormality includes at least one of the following: abnormal instrument seal status, sensor communication failure, and modification of configuration parameters; The generation unit is used to generate an abnormal signal when an abnormal weighing is determined to exist; An alarm unit is used to respond to the abnormal signal, generate abnormal warning information based on preset risk level rules, and push the abnormal warning information through a preset push method.

7. The apparatus according to claim 6, characterized in that, The alarm unit is further configured to include: The system performs multi-condition queries, statistical analysis, and results export on the abnormal signals, weighing data, and equipment status information. The system then uses a visualization module to display key equipment data, analysis results, and abnormal warning information in chart form.

8. An electronic device, characterized in that, include: At least one processor; as well as 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 of any one of claims 1-5.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-5.

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