Unattended intelligent supervision method, device and equipment for vehicle weighbridge and medium

Abstract

The invention discloses a vehicle weighbridge unattended intelligent supervision method, device and equipment and a medium, and the method comprises the steps: recognizing a vehicle when the vehicle is detected to approach a weighing region, so as to obtain a vehicle weighing authority; if the vehicle identification is passed, the limitation on the vehicle entering the weighing area is relieved, so that the vehicle is allowed to enter the weighing area; weighing the vehicle, and performing local verification to obtain a local verification result; performing cloud verification on the vehicle according to the vehicle-mounted RFID tag to obtain a cloud verification result; determining whether the vehicle has an abnormal condition according to the local verification result and the cloud verification result; and generating an exception handling mechanism according to the existing exceptions. According to the invention, through verification of the vehicle weighing authority, dependence on manual operation is reduced, identification efficiency and accuracy are improved, local verification and cloud verification are combined, the possibility that data are intervened by human is avoided, and thus fairness and transparency of supervision are fully ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle weighing, and more specifically to an unattended intelligent supervision method, device, equipment and medium for a vehicle weighbridge. Background Art

[0002] With the acceleration of urbanization, garbage disposal has become an important task in urban management. Traditional garbage transportation management systems usually rely on manual operations to weigh vehicles and record transportation information. This method has problems such as low efficiency, prone to errors and potential safety hazards.

[0003] In the existing technology, the typical waste transport vehicle weighing management method includes manual identification and weighing of vehicles, and manual verification of transportation permits and loading information. However, this method not only increases labor costs, but also may lead to data errors due to human errors. Although there are unattended vehicle weighing systems on the market, the accuracy and security of the data cannot be guaranteed, and there is a high possibility of human intervention, which cannot fully ensure the fairness and transparency of supervision. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide an unattended intelligent supervision method, device, equipment and medium for vehicle weighbridges, aiming to enhance the supervision capability of unattended vehicle weighbridges to fully ensure the fairness and transparency of supervision.

[0005] To achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides an unattended intelligent supervision method for a vehicle weighbridge, which is applied to a vehicle transporting a garbage can, wherein the vehicle is provided with an on-board RFID tag, and the information stored in the on-board RFID tag includes the vehicle's own information and the vehicle's transportation information, and the garbage can is provided with a garbage can RFID tag, and the information stored in the garbage can RFID tag includes the garbage can's own information and the garbage can's shipping information; the method comprises: When a vehicle is detected approaching the weighing area, the vehicle is identified to obtain vehicle weighing authority; If the vehicle identification passes, the restriction on the vehicle entering the weighing area is lifted, so that the vehicle can enter the weighing area; Weigh the vehicle and perform local verification to obtain local verification results; Perform cloud verification on the vehicle based on the on-board RFID tag to obtain cloud verification results; Determine whether the vehicle has any abnormality based on the local verification results and the cloud verification results; Generate an exception handling mechanism based on the existing abnormal conditions.

[0006] Furthermore, when a vehicle is detected approaching a weighing area, the vehicle is identified to obtain vehicle weighing authority, including: Obtain the license plate image of the vehicle and use OCR technology to recognize the license plate to obtain the license plate recognition result; Reading vehicle-mounted RFID tags to extract vehicle ID and the vehicle’s transportation permit; The vehicle ID and the vehicle's transportation permit are checked and verified to obtain a verification result.

[0007] Further, the vehicle is weighed and locally verified to obtain a local verification result, including: Obtain the actual total weight of the vehicle weighed by the weighing scale in the weighing area; Calculate the actual total weight of all garbage bins based on the actual total weight of the vehicle and the vehicle's own weight; According to the transportation information in the vehicle-mounted RFID tag, the total theoretical weight of all the garbage bins that can be carried by the vehicle is obtained; Compare the actual total weight of all trash bins with the sum of the theoretical weights stored in all trash bins; If the actual total weight of all trash cans exceeds the sum of the theoretical weights stored in all trash cans, the vehicle is deemed to be overloaded.

[0008] Further, the weighing of the vehicle and performing local verification to obtain a local verification result also includes: Scan the RFID tags of each trash can loaded on the vehicle one by one to obtain the ID of each trash can; Verify that each bin's ID is in the vehicle's list of permitted transports.

[0009] Furthermore, the cloud verification of the vehicle according to the vehicle-mounted RFID tag to obtain a cloud verification result includes: The weighbridge in the weighing area uploads the actual total weight of the vehicle to the cloud; Query the vehicle's load plan from the cloud data based on the vehicle's RFID tag; Determine whether the actual total weight of the vehicle is consistent with the vehicle's load plan.

[0010] Furthermore, generating an exception handling mechanism according to the existing abnormal situation includes: Categorize anomalies based on their severity and impact; Automatic labeling based on anomaly classification.

[0011] Furthermore, the abnormality classification according to the severity and impact scope of the abnormality includes: Determine the urgency of the abnormal situation based on its severity and scope; Develop a response mechanism based on the urgency of the abnormal situation.

[0012] In a second aspect, the present invention further provides an unattended intelligent monitoring device for a vehicle weighbridge, which is applied to a vehicle transporting a garbage can, wherein the vehicle is provided with an on-board RFID tag, and the information stored in the on-board RFID tag includes the vehicle's own information and the vehicle's transportation information, and the garbage can is provided with a garbage can RFID tag, and the information stored in the garbage can RFID tag includes the garbage can's own information and the garbage can's shipping information; the device comprises: A vehicle identification unit is used to identify the vehicle when it is detected approaching the weighing area to obtain vehicle weighing authority; A vehicle release unit is used to release the restriction on the vehicle entering the weighing area if the vehicle passes the identification, so as to release the vehicle to enter the weighing area; A local verification unit, used for weighing the vehicle and performing local verification to obtain a local verification result; A cloud verification unit, used to perform cloud verification on the vehicle according to the vehicle-mounted RFID tag to obtain a cloud verification result; An abnormality determination unit, used to determine whether the vehicle has an abnormality according to the local verification result and the cloud verification result; The processing mechanism generating unit is used to generate an exception processing mechanism according to the existing exception situation.

[0013] In a third aspect, the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the unattended intelligent supervision method for a vehicle weighing scale as described above is implemented.

[0014] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the unattended intelligent supervision method for a vehicle weighing scale as described above.

[0015] Compared with the prior art, the present invention has the following beneficial effects: an unattended intelligent supervision method for vehicle weighbridges, applied to vehicles transporting garbage cans, wherein the vehicle is provided with an on-board RFID tag, and the information stored in the on-board RFID tag includes the vehicle's own information and the vehicle's transportation information, and the garbage can is provided with a garbage can RFID tag, and the information stored in the garbage can RFID tag includes the garbage can's own information and the garbage can's shipping information; the method comprises: when a vehicle is detected approaching a weighing area, the vehicle is identified to obtain vehicle weighing authority; if the vehicle identification passes, the restriction on the vehicle entering the weighing area is lifted to release the vehicle to enter the weighing area; the vehicle is weighed and locally verified to obtain a local verification result; the vehicle is cloud-verified according to the on-board RFID tag to obtain a cloud-verification result; whether the vehicle has an abnormality is determined according to the local verification result and the cloud-verification result; and an abnormality handling mechanism is generated according to the existing abnormality. The present invention reduces the reliance on manual operation and improves the recognition efficiency and accuracy by verifying the vehicle's weighing authority. At the same time, the possibility of human intervention in the data is avoided by combining local verification and cloud-verification, thereby fully ensuring the fairness and transparency of supervision.

[0016] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the following preferred embodiments are specifically cited and described in detail as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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 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 any creative work.

[0018] Figure 1 A flow chart of an unattended intelligent supervision method for a vehicle weighbridge provided by a specific embodiment of the present invention; Figure 2 A schematic block diagram of an unattended intelligent monitoring device for a vehicle weighbridge provided in a specific embodiment of the present invention; Figure 3 A schematic block diagram of a computer device provided for a specific embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0021] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0022] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0023] like Figure 1 As shown, an embodiment of the present invention provides an unattended intelligent supervision method for a vehicle weighing scale, which is applied to a vehicle transporting a garbage bin. The vehicle is provided with an on-board RFID tag, and the information stored in the on-board RFID tag includes the vehicle's own information and the vehicle's transportation information. The garbage bin is provided with a garbage bin RFID tag, and the information stored in the garbage bin RFID tag includes the garbage bin's own information and the garbage bin shipping information. The method includes the following steps: S10-S60.

[0024] S10. When a vehicle is detected approaching a weighing area, the vehicle is identified to obtain vehicle weighing authority.

[0025] Multiple ultrasonic sensors or infrared sensors are installed around the weighing area to detect the approach of vehicles. These sensors are arranged in a grid to ensure that no matter which direction the vehicle approaches from, it can be sensed in time.

[0026] In one embodiment, step S10 specifically includes the following steps: S101 - S103 .

[0027] S101, obtaining a license plate image of a vehicle, and using OCR technology to recognize the license plate to obtain a license plate recognition result.

[0028] In this embodiment, a high-definition camera is installed at the entrance of the weighing area. When the ultrasonic sensor detects the approach of a vehicle, the high-definition camera automatically starts and captures the front image of the vehicle, especially the license plate. The acquired license plate image is processed using optical character recognition (OCR) technology to extract the license plate number information. The OCR software uses a deep learning algorithm and can process license plate images under different lighting and angles.

[0029] S102: Read the vehicle-mounted RFID tag to extract the vehicle ID and the vehicle's transportation permit.

[0030] In this embodiment, an RFID reading and writing device is set at the entrance of the weighing area. When a vehicle approaches, the RFID device starts and reads the information of the vehicle-mounted RFID tag installed on the vehicle.

[0031] S103: Verify the vehicle ID and the transportation permit of the vehicle to obtain a verification result.

[0032] In this embodiment, the license plate number obtained by OCR recognition is compared with the vehicle ID stored in the RFID tag. At the same time, the transportation permit of the vehicle is obtained from the RFID tag and compared with the pre-stored information in the database.

[0033] For steps S101-S103, if both the license plate recognition and the vehicle transportation permit are passed, it is determined that the vehicle meets the relevant requirements and is allowed to enter the weighing area.

[0034] By accurately identifying the vehicle's identity information when it approaches the weighing area, only vehicles with weighing authority can enter the weighing area. This automated identification method improves the efficiency and safety of the entire process, reduces the need for manual intervention, and also reduces the risk of errors caused by human operation.

[0035] S20. If the vehicle identification passes, the restriction on the vehicle entering the weighing area is lifted to allow the vehicle to enter the weighing area.

[0036] In this embodiment, an automatic barrier is provided at the entrance of the weighing area. After the vehicle passes the identification, a release signal is sent to the barrier, so that the barrier is automatically lifted to allow the vehicle to pass. Through the control of the automatic barrier, effective management of vehicles entering the weighing area is achieved, ensuring that authorized vehicles can enter smoothly while maintaining the safety of the area.

[0037] In addition, a signal light (such as a traffic light) is installed next to the barrier. When the vehicle passes through and the barrier is lifted, the signal light changes from red to green, indicating that the driver can safely enter the weighing area. The use of signal lights provides drivers with clear visual instructions, reduces the possibility of misoperation, and improves traffic efficiency.

[0038] S30: Weigh the vehicle and perform local verification to obtain a local verification result.

[0039] In one embodiment, step S30 specifically includes the following steps: S301 - S305 .

[0040] S301, obtaining the actual total weight of the vehicle weighed by the scale in the weighing area.

[0041] In this embodiment, after the vehicle enters the weighing area, the sensor system of the weighbridge starts to measure the actual total weight of the vehicle. The weighbridge is composed of multiple high-precision pressure sensors to collect the vehicle's weight data in real time.

[0042] S302: Calculate the actual total weight of all garbage bins according to the actual total weight of the vehicle and the vehicle's own weight.

[0043] The vehicle's own weight information can be obtained by reading the on-board RFID tag information. The actual total weight of the trash can is calculated as follows: actual total weight of the trash can = actual total weight of the vehicle - vehicle's own weight.

[0044] S303: Obtain the total theoretical weight of all trash cans that can be carried by the vehicle according to the transportation information in the vehicle-mounted RFID tag.

[0045] By reading the on-board RFID tag, the transportation information of the vehicle is extracted, including the pre-stored theoretical weight sum of all the garbage bins that the vehicle can carry. This data is input in advance and stored in the database.

[0046] Automatically acquiring theoretical data through RFID tags simplifies the information acquisition process and eliminates human input errors.

[0047] S304: Compare the actual total weight of all trash bins with the sum of the theoretical weights stored in all trash bins.

[0048] Specifically, automated weight verification is implemented to ensure the compliance of vehicle transportation.

[0049] S305: If the actual total weight of all trash cans exceeds the sum of the pre-stored theoretical weights of all trash cans, the vehicle is determined to be overloaded.

[0050] In this implementation, if the actual total weight of the trash can exceeds the theoretical total weight, the overload information is automatically recorded and an alarm mechanism (such as an audible and visual alarm or SMS notification) is triggered. At the same time, the information is uploaded to the management backend for subsequent processing.

[0051] Through timely overload detection and alarm, transportation safety is guaranteed, and real-time data support is provided to management departments, which helps to carry out effective supervision and decision-making.

[0052] For steps S301-S305, a series of automated weighing and calculation processes are used to ensure the compliance of vehicles during transportation. The high-precision measurement of the weighbridge combined with RFID technology simplifies the process of information acquisition and processing, improving efficiency and accuracy. The real-time overload determination and alarm mechanism significantly enhances the safety supervision capability of the transportation system and reduces the potential risks caused by vehicle overload.

[0053] In one embodiment, step S30 further specifically includes the following steps: S306 - S307 .

[0054] S306, scanning each RFID tag of the trash can loaded on the vehicle one by one to obtain the ID of each trash can.

[0055] In this embodiment, each trash can loaded on the vehicle is scanned one by one by an RFID reader / writer in the weighing area or by a handheld mobile RFID scanner by a staff member. An RFID tag is pre-pasted on each trash can, and the tag contains a unique trash can ID.

[0056] Using RFID technology to quickly scan trash cans in batches can effectively improve recognition efficiency and reduce errors and time costs of manual recording.

[0057] S307: Verify whether the ID of each trash can is in the transportation permission list of the vehicle.

[0058] In this embodiment, each garbage bin ID obtained is compared with the ID in the vehicle transportation permission list. The transportation permission list is generated by the transportation management system before the vehicle starts transportation and stored in the central database.

[0059] The matching algorithm can adopt hash or array search to ensure fast identification. The system will display the matching results in real time on the monitoring terminal and mark the unauthorized trash can (if any).

[0060] Through an automated ID verification process, we ensure that the trash cans transported by vehicles are within the authorized range and avoid being replaced, thereby ensuring the legality and compliance of transportation and preventing unauthorized trash cans from entering the sanitation treatment process.

[0061] For steps S306-S307, the identity of each trash can loaded on the vehicle can be efficiently and accurately identified and verified, ensuring that only authorized trash cans are transported. This automated scanning and verification method not only improves operational efficiency, but also enhances the security and compliance of transportation management.

[0062] S40: Perform cloud verification on the vehicle according to the vehicle-mounted RFID tag to obtain a cloud verification result.

[0063] In one embodiment, step S40 specifically includes the following steps: S401 - S403 .

[0064] S401. The weighing scale in the weighing area uploads the actual total weight of the vehicle to the cloud.

[0065] In this embodiment, after the vehicle enters the weighing area, the weighbridge system measures the actual total weight of the vehicle. This weight data is uploaded to the cloud database through the control module of the weighing system. To ensure the real-time and accuracy of data transmission, a wireless network (such as Wi-Fi or 4G / 5G) can be used to achieve data transmission.

[0066] It should be noted that the weighbridge system communicates with the cloud server through a secure API interface to ensure that the data is not tampered with or lost during transmission. The data format uses standardized JSON or XML format to facilitate cloud parsing and processing.

[0067] By uploading weighing data in real time, it is ensured that the cloud can quickly obtain on-site weighing information, providing a data basis for subsequent verification steps.

[0068] S402: Query the vehicle's load plan from cloud data based on the vehicle's RFID tag.

[0069] In this embodiment, after receiving the actual total weight, the cloud server queries the cloud database for the vehicle's scheduled load plan based on the vehicle ID contained in the vehicle's RFID tag. The load plan includes the vehicle's scheduled total weight in the current transportation task.

[0070] The vehicle ID in the RFID tag is transmitted to the cloud through encryption, and the cloud server quickly obtains the corresponding load plan data through indexing or fast search algorithms (such as hash tables).

[0071] Utilizing load plans stored in the cloud ensures that the system has access to the latest, audited transport plan data, reducing the complexity of local storage and management.

[0072] S403: Determine whether the actual total weight of the vehicle is consistent with the vehicle's load plan.

[0073] In this embodiment, the actual total weight uploaded is compared with the load plan obtained from the cloud, and the deviation in the execution of the transportation plan is identified by comparing the planned weight with the actual weight. If the deviation exceeds the set tolerance range, it is considered abnormal.

[0074] It should be noted that, during local verification, it is already clear whether the vehicle is overweight, but even if it is not overweight, there may be other potential abnormal problems. For example, the abnormal problem may be that the weighing data or RFID data is wrong or delayed in the process of uploading to the cloud, or the same vehicle is weighed and the data is uploaded multiple times in a short period of time, which may reflect system operation or hardware failure, or the vehicle's RFID tag data is abnormal, such as incorrect format, incomplete information or inconsistent with database records, etc.

[0075] By combining local and cloud authentication, we can complement each other's strengths and provide a more reliable and comprehensive solution. For example, local authentication can be used for real-time monitoring and operational control to ensure that on-site problems are solved in a timely manner; at the same time, cloud authentication can be used for high-level analysis and management to optimize overall operational efficiency and strategy formulation. This two-tier authentication system can improve the robustness and flexibility of the system and enhance the security and intelligence of operations.

[0076] S50: Determine whether the vehicle is in an abnormal condition based on the local verification result and the cloud verification result.

[0077] In this embodiment, if the local verification determines that the vehicle is overweight, or the cloud verification identifies other abnormalities, the vehicle is marked as being in an abnormal state.

[0078] By combining the results of multiple layers of verification, we can comprehensively judge the abnormal conditions of the vehicle and ensure the safety and compliance of the transportation process.

[0079] S60: Generate an exception handling mechanism according to the existing abnormal situation.

[0080] In one embodiment, step S60 specifically includes the following steps: S601 - S602 .

[0081] S601. Classify the anomalies according to the severity and impact scope of the anomalies.

[0082] In one embodiment, step S601 specifically includes the following steps: S6011 - S6012 .

[0083] S6011. Determine the urgency of the abnormal situation based on the severity and impact scope of the abnormal situation.

[0084] In this embodiment, the severity of the abnormality is evaluated, such as the percentage of overweight or the degree of equipment failure. Each abnormality type is provided with a specific severity scoring standard. For example, for an overweight abnormality, an overweight within 10% may be rated as slight, while an overweight of more than 50% may be rated as severe.

[0085] Analyze the potential impact of the anomaly on business operations, including the affected physical areas (such as distribution stations, transportation routes) and possible business links.

[0086] Based on the severity and scope of impact, the urgency of abnormal conditions is divided into several levels, such as urgent, important, ordinary, etc. The urgency is comprehensively scored through a rule engine or machine learning algorithm.

[0087] S6012. Develop a response mechanism based on the urgency of the abnormal situation.

[0088] In this embodiment, standard response plans for different emergency levels, for example, for emergency overweight anomalies, may include steps such as immediately notifying the security department and rescheduling the transportation plan.

[0089] Assign specific tasks in the response plan to the corresponding responsible persons, notify relevant personnel via email, SMS or internal system, and track the implementation of response measures in real time to ensure that each task is completed on time.

[0090] By automatically formulating and executing response mechanisms, it is possible to respond quickly and effectively to abnormal events of various levels of urgency, reduce operational risks, improve processing efficiency, and improve overall management levels.

[0091] S602: Automatically mark according to abnormal classification.

[0092] In this embodiment, once the anomaly is classified, it enters the automatic marking stage. According to the classification result of the anomaly, the anomaly handling system marks each abnormal event.

[0093] The marking method can be to visually mark the anomaly using color coding (such as red for severe, yellow for moderate, and green for mild). The marking information includes the anomaly type, severity, impact scope, and recommended handling measures.

[0094] The present invention reduces the reliance on manual operations and improves recognition efficiency and accuracy by verifying vehicle weighing authority. At the same time, it combines local verification and cloud verification to avoid the possibility of human intervention in data, thereby fully ensuring the fairness and transparency of supervision.

[0095] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0096] The embodiment of the present invention also provides an unattended intelligent monitoring device for a vehicle scale, which is used to perform the steps in any embodiment of the unattended intelligent monitoring method for a vehicle scale. Figure 2 , Figure 2A schematic block diagram of an unattended intelligent monitoring device 100 for a vehicle weighbridge provided in an embodiment of the present application is shown. The unattended intelligent monitoring device 100 for a vehicle weighbridge is applied to a vehicle transporting a garbage can. The vehicle is provided with an on-board RFID tag. The information stored in the on-board RFID tag includes the vehicle's own information and the vehicle's transportation information. The garbage can is provided with a garbage can RFID tag. The information stored in the garbage can RFID tag includes the garbage can's own information and the garbage can's shipping information. The device includes: The vehicle identification unit 110 is used to identify the vehicle when it is detected that the vehicle is approaching the weighing area to obtain the vehicle weighing authority; the vehicle release unit 120 is used to lift the restriction on the vehicle entering the weighing area if the vehicle identification is passed, so as to release the vehicle to enter the weighing area; the local verification unit 130 is used to weigh the vehicle and perform local verification to obtain a local verification result; the cloud verification unit 140 is used to perform cloud verification on the vehicle based on the on-board RFID tag to obtain a cloud verification result; the abnormality determination unit 150 is used to determine whether there is an abnormality in the vehicle based on the local verification result and the cloud verification result; the processing mechanism generation unit 160 is used to generate an abnormality processing mechanism based on the existing abnormality.

[0097] In one embodiment, the vehicle identification unit 110 is specifically used to: obtain a license plate image of the vehicle, and use OCR technology to identify the license plate to obtain a license plate recognition result; read the on-board RFID tag to extract the vehicle ID and the vehicle's transportation permit; and verify the vehicle ID and the vehicle's transportation permit to obtain a verification result.

[0098] In one embodiment, the local verification unit 130 is specifically used to: obtain the actual total weight of the vehicle weighed by the scale in the weighing area; calculate the actual total weight of all garbage bins based on the actual total weight of the vehicle and the vehicle's own weight; obtain the total pre-stored theoretical weight of all garbage bins that can be carried on the vehicle based on the transportation information in the on-board RFID tag; compare the actual total weight of all garbage bins with the total pre-stored theoretical weight of all garbage bins; if the actual total weight of all garbage bins exceeds the total pre-stored theoretical weight of all garbage bins, the vehicle is determined to be overloaded.

[0099] In one embodiment, the local verification unit 130 is further specifically used to: scan the RFID tag of each trash can loaded on the vehicle one by one to obtain the ID of each trash can; and verify whether the ID of each trash can is in the transportation permission list of the vehicle.

[0100] In one embodiment, the cloud verification unit 140 is specifically used for: the scale in the weighing area uploads the actual total weight of the vehicle to the cloud; queries the vehicle's load plan from the cloud data based on the on-board RFID tag; and determines whether the actual total weight of the vehicle is consistent with the vehicle's load plan.

[0101] In one implementation, the processing mechanism generation unit 160 is specifically used to: classify the anomalies according to the severity and impact range of the anomalies; and automatically mark the anomalies according to the anomaly classification.

[0102] In one implementation, the processing mechanism generation unit 160 is further specifically used to: determine the urgency of the abnormal situation according to the severity and impact scope of the abnormal situation; and formulate a response mechanism according to the urgency of the abnormal situation.

[0103] It should be noted that technicians in the relevant field can clearly understand that the specific implementation process of the above-mentioned vehicle scale unattended intelligent supervision device 100 and each unit can refer to the corresponding description in the aforementioned method embodiment, and for the convenience and conciseness of the description, it will not be repeated here.

[0104] The above-mentioned unattended intelligent monitoring device for vehicle weighbridges can be implemented in the form of a computer program, which can be used in Figure 3 Runs on the computer device shown.

[0105] See also Figure 3 , Figure 3 700 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 700 may be a server, wherein the server may be an independent server or a server cluster composed of multiple servers.

[0106] like Figure 3 As shown, the computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the unattended intelligent supervision method for vehicle weighbridges described above are implemented.

[0107] The computer device 700 may be a terminal or a server. The computer device 700 includes a processor 720 , a memory, and a network interface 750 connected via a system bus 710 , wherein the memory may include a non-volatile storage medium 730 and an internal memory 740 .

[0108] The non-volatile storage medium 730 can store an operating system 731 and a computer program 732. When the computer program 732 is executed, the processor 720 can execute the unattended intelligent supervision method for vehicle weighbridges.

[0109] The processor 720 is used to provide computing and control capabilities and support the operation of the entire computer device 700.

[0110] The internal memory 740 provides an environment for the operation of the computer program 732 in the non-volatile storage medium 730. When the computer program 732 is executed by the processor 720, the processor 720 can execute the unattended intelligent supervision method for vehicle weighbridges.

[0111] The network interface 750 is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will appreciate that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the present application scheme, and does not constitute a limitation on the computer device 700 to which the present application scheme is applied. The specific computer device 700 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements. The processor 720 is used to run the program code stored in the memory to implement the unattended intelligent supervision method for vehicle weighbridges.

[0112] Those skilled in the art will understand that Figure 3 The embodiments of the computer device shown in the figure do not constitute a limitation on the specific composition of the computer device. In other embodiments, the computer device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. For example, in some embodiments, the computer device may only include a memory and a processor. In such embodiments, the structure and function of the memory and the processor are the same as those of the embodiment of the present invention. Figure 3 The embodiments shown are consistent and will not be described again here.

[0113] It should be understood that in the embodiment of the present application, the processor 720 may be a central processing unit (CPU), and the processor 720 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0114] In another embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium may be a non-volatile computer-readable storage medium. The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the unattended intelligent supervision method for a vehicle weighbridge disclosed in an embodiment of the present invention is implemented.

[0115] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the above-described equipment, devices and units can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. Those of ordinary skill in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0116] In the several embodiments provided by the present invention, it should be understood that the disclosed equipment, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. Units with the same function may also be combined into one unit. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices or units, or may be electrical, mechanical or other forms of connection.

[0117] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0118] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0119] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or 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, server, or 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), disk or optical disk and other media that can store program codes.

[0120] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. An unattended intelligent supervision method for a vehicle weighbridge is applied to a vehicle transporting a garbage can, wherein the vehicle is provided with an on-board RFID tag, and the information stored in the on-board RFID tag includes the vehicle's own information and the vehicle's transportation information; the garbage can is provided with a garbage can RFID tag, and the information stored in the garbage can RFID tag includes the garbage can's own information and the garbage can's shipping information; characterized in that: The method comprises: When a vehicle is detected approaching the weighing area, the vehicle is identified to obtain vehicle weighing authority; If the vehicle identification passes, the restriction on the vehicle entering the weighing area is lifted, so that the vehicle can enter the weighing area; Weigh the vehicle and perform local verification to obtain local verification results; Perform cloud verification on the vehicle based on the on-board RFID tag to obtain cloud verification results; Determine whether the vehicle has any abnormality based on the local verification results and the cloud verification results; Generate an exception handling mechanism based on the existing abnormal conditions.

2. The unattended intelligent supervision method for vehicle weighbridges according to claim 1 is characterized in that: When a vehicle is detected approaching a weighing area, the vehicle is identified to obtain vehicle weighing authority, including: Obtain the license plate image of the vehicle and use OCR technology to recognize the license plate to obtain the license plate recognition result; Reading vehicle-mounted RFID tags to extract vehicle ID and the vehicle’s transportation permit; The vehicle ID and the vehicle's transportation permit are checked and verified to obtain a verification result.

3. The unattended intelligent supervision method for vehicle weighbridges according to claim 1 is characterized in that: The vehicle is weighed and locally verified to obtain a local verification result, including: Obtain the actual total weight of the vehicle weighed by the weighing scale in the weighing area; Calculate the actual total weight of all garbage bins based on the actual total weight of the vehicle and the vehicle's own weight; According to the transportation information in the vehicle-mounted RFID tag, the total theoretical weight of all the garbage bins that can be carried by the vehicle is obtained; Compare the actual total weight of all trash bins with the sum of the theoretical weights stored in all trash bins; If the actual total weight of all trash cans exceeds the sum of the theoretical weights stored in all trash cans, the vehicle is deemed to be overloaded.

4. The unattended intelligent supervision method for vehicle weighbridges according to claim 1 is characterized in that: The weighing of the vehicle and performing local verification to obtain a local verification result also includes: Scan the RFID tags of each trash can loaded on the vehicle one by one to obtain the ID of each trash can; Verify that each bin's ID is in the vehicle's list of permitted transports.

5. The unattended intelligent supervision method for vehicle weighbridges according to claim 3 is characterized in that: The cloud verification of the vehicle according to the vehicle-mounted RFID tag to obtain a cloud verification result includes: The weighbridge in the weighing area uploads the actual total weight of the vehicle to the cloud; Query the vehicle's load plan from the cloud data based on the vehicle's RFID tag; Determine whether the actual total weight of the vehicle is consistent with the vehicle's load plan.

6. The unattended intelligent supervision method for vehicle weighbridges according to claim 1 is characterized in that: The generating of an exception handling mechanism according to the existing abnormal situation includes: Categorize anomalies based on their severity and impact; Automatic labeling based on anomaly classification.

7. The unattended intelligent supervision method for vehicle weighbridges according to claim 6 is characterized in that: The anomaly classification is performed based on the severity and impact of the anomaly, including: Determine the urgency of the abnormal situation based on its severity and scope; Develop a response mechanism based on the urgency of the abnormal situation.

8. An unattended intelligent monitoring device for vehicle weighbridges, applied to vehicles transporting garbage cans, wherein the vehicles are provided with on-board RFID tags, the information stored in the on-board RFID tags includes the vehicle's own information and vehicle transportation information, and the garbage cans are provided with garbage can RFID tags, the information stored in the garbage can RFID tags includes the garbage can's own information and garbage can shipping information; characterized in that: The device comprises: A vehicle identification unit is used to identify the vehicle when it is detected approaching the weighing area to obtain vehicle weighing authority; A vehicle release unit is used to release the restriction on the vehicle entering the weighing area if the vehicle passes the identification, so as to release the vehicle to enter the weighing area; A local verification unit, used for weighing the vehicle and performing local verification to obtain a local verification result; A cloud verification unit, used to perform cloud verification on the vehicle according to the vehicle-mounted RFID tag to obtain a cloud verification result; An abnormality determination unit, used to determine whether the vehicle has an abnormality according to the local verification result and the cloud verification result; The processing mechanism generating unit is used to generate an exception processing mechanism according to the existing exception situation.

9. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the unattended intelligent supervision method for vehicle weighbridges as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, which includes program instructions. When the program instructions are executed by the processor, the processor executes the unattended intelligent supervision method for vehicle weighing scales as described in any one of claims 1 to 7.

Images