Quantitative loading controller and loading system

Abstract

The invention belongs to the technical field of petrochemical storage and transportation automation, and provides a quantitative loading control instrument and a loading system.The quantitative loading control instrument comprises a safety interlocking all-in-one machine and a distributed batch controller; the safety interlocking all-in-one machine is connected with the distributed batch controller through hard wiring; the safety interlocking all-in-one machine integrates a human body electrostatic discharge alarm, a key manager, an API electrostatic oil spill protector and a voice controller. A dual-communication module and a PLC (Programmable Logic Controller) are arranged in the distributed batch controller; and the safety interlocking all-in-one machine and the distributed batch controller work cooperatively to realize quantitative loading control. The safety and accuracy of loading operation can be effectively improved, and the loading efficiency of petrochemical oil products is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of petrochemical storage and transportation automation, and in particular to a quantitative loading controller and a loading system. Background Art

[0002] During petrochemical storage and transportation, the current quantitative loading scenarios of various oil and chemical products have the following problems: First, safety protection measures are scattered, and devices such as static electricity detection, oil spill protection, and crane pipe return are independent of each other. The safety and reliability of the loading process are difficult to guarantee, and the risk of human error needs to be reduced; second, insufficient efforts have been made to realize the intelligent calculation of loading parameters and improve loading efficiency and accuracy; third, there are still shortcomings in ensuring the authenticity and non-tamperability of data throughout the loading process, and providing a reliable basis for subsequent audits and quality traceability.

[0003] Therefore, it is necessary to provide a quantitative loading controller and a loading system. Summary of the Invention

[0004] The present invention provides a quantitative loading controller and loading system, which can improve the loading safety and loading accuracy during petrochemical storage and transportation. The dual communication module design of the quantitative loading controller ensures the stability and real-time performance of data transmission, and can maintain reliable operation even under complex working conditions. The introduction of the PLC controller enables intelligent management of the loading process and can automatically adjust the loading parameters according to the characteristics of different oil products.

[0005] The present invention provides a quantitative loading controller, comprising: a safety interlock integrated machine and a distributed batch controller; the safety interlock integrated machine is connected to the distributed batch controller via hard wiring; the safety interlock integrated machine integrates a human electrostatic discharge alarm, a key manager, an API electrostatic oil spill protector, and a voice controller; the distributed batch controller has a built-in dual communication module and a PLC controller; the safety interlock integrated machine and the distributed batch controller work in coordination to achieve quantitative loading control.

[0006] Preferably, the human body electrostatic release alarm is used to verify the human body electrostatic release status before loading is started. If the verification fails, the human body electrostatic release status signal is output to the PLC controller, and the loading action is locked based on the PLC controller.

[0007] Preferably, the key manager adopts a sliding self-locking structure for centrally controlling key resetting. When the key manager body is in an unreset state, it outputs a normally closed signal to the PLC controller and locks the loading action based on the PLC controller.

[0008] Preferably, the voice controller stores a plurality of alarm voices for responding to control instructions of the PLC controller and broadcasting the loading status in real time.

[0009] Preferably, the dual communication modules include an Ethernet communication module and an RS485 network communication module.

[0010] Preferably, the API electrostatic oil spill protector is used to monitor the oil level and electrostatic grounding status. If an electrostatic grounding signal and an overflow signal appear, the electrostatic grounding signal and the overflow signal are sent to the PLC controller, and the loading action is locked based on the PLC controller.

[0011] The loading system implements oil loading based on a quantitative loading controller, including: loading preparation module, loading control module and loading settlement module;

[0012] The loading preparation module is used to identify vehicle types, predict loading flow rates, and generate safety monitoring standard data before oil loading based on several loading preparation devices and loading safety monitoring devices configured in the loading area;

[0013] The loading control module is based on a quantitative loading controller, a crane pipe, a control valve, and a flow meter. After the vehicle is connected to the crane pipe, the quantitative loading controller is used to control the loading progress through the control valve according to the set loading flow control strategy, and the loading flow is counted through the flow meter. The loading safety monitoring equipment is used to implement loading safety monitoring.

[0014] The loading settlement module is used to perform loading settlement based on loading flow.

[0015] Preferably, the loading preparation module includes a vehicle type recognition unit, an AI loading flow prediction unit, and a safety monitoring standard data generation unit;

[0016] The vehicle model recognition unit is used to identify the vehicle model based on the high-definition camera, lidar scanner, and edge computing terminal in the configured loading preparation equipment, and sends the identified vehicle model code and tank volume data to the PLC controller; the high-definition camera and lidar scanner are configured at the loading platform entrance; the lidar scanner is used to generate a three-dimensional point cloud model of the vehicle; the edge computing terminal is used to run the configured vehicle model recognition AI model; the vehicle model recognition AI model adopts a multimodal fusion architecture; the running configured vehicle model recognition AI model includes: using a convolutional neural network to extract vehicle appearance features, using a PointNet++ network to process point cloud data, based on the feature fusion layer using the attention mechanism to weightedly integrate visual and geometric features, and using the output layer to match the pre-stored vehicle model code database and return the tank volume data;

[0017] The AI ​​loading flow prediction unit is used to input the vehicle model code, as well as the oil density, ambient temperature and historical loading efficiency data obtained from the big database based on the LSTM network model, output the optimal loading curve for the vehicle, and extract the maximum safe loading flow and recommended loading time.

[0018] The safety monitoring standard data generation unit is used to generate corresponding safety monitoring standard data for loading safety monitoring equipment based on the maximum safe loading flow and recommended loading time; the corresponding safety monitoring standard data for loading safety monitoring equipment include: the corresponding standard crane pipe docking angle deviation of the laser displacement sensor, the corresponding standard tank liquid level fluctuation frequency of the millimeter wave radar, the corresponding standard electrostatic accumulated voltage value of the non-contact electrostatic sensor, and the corresponding standard ambient wind speed of the explosion-proof anemometer.

[0019] Preferably, the loading control module further comprises a safety monitoring unit; the safety monitoring unit comprises a safety monitoring disposal subunit and a safety monitoring data storage subunit;

[0020] The safety monitoring and disposal subunit is used to implement safety monitoring and disposal based on the loading safety monitoring equipment, specifically: using a laser displacement sensor to monitor the crane pipe docking angle deviation, and when the crane pipe docking angle deviation is greater than the standard crane pipe docking angle deviation, triggering automatic correction; using a millimeter wave radar to monitor the tank liquid level fluctuation frequency, and if the fluctuation frequency is greater than the standard tank liquid level fluctuation frequency, starting the slow flow protection; using a non-contact electrostatic sensor and an explosion-proof anemometer to measure the static cumulative voltage value, ambient wind speed, and visibility, based on a parameterized triangular membership function, the static cumulative voltage value, ambient wind speed, and visibility are converted into input fuzzy quantity representation, and according to a preset fuzzy rule library, the converted input fuzzy quantity is fuzzy inferenced to obtain an output fuzzy quantity, and the output fuzzy quantity is defuzzified to obtain an environmental operation risk index. If the environmental operation risk index is greater than the set index threshold, the loading operation is terminated;

[0021] The safety monitoring data storage subunit is used to generate blockchain evidence at the end of loading based on blockchain technology, recording safety monitoring data and safety monitoring status timestamps.

[0022] Preferably, the loading control module also includes a loading authentication unit, which is used to set up and implement IC card two-factor authentication; IC card two-factor authentication is: the preset loading flow is encrypted and written into the MI type IC card, and the MI type IC card is used by the driver to swipe the card at the loading site. After the pick-up password is verified, loading is carried out by unlocking the control valve.

[0023] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0024] 1. The collaborative architecture of the safety interlock integrated machine and the distributed batch controller significantly improves the safety and reliability of the loading process and effectively reduces the risk of human error.

[0025] 2. Innovative multimodal fusion vehicle type recognition technology combined with AI loading flow prediction model enables intelligent calculation of loading parameters, significantly improving loading efficiency and accuracy.

[0026] 3. The integrated blockchain evidence storage technology ensures the authenticity and non-tamperability of loading safety monitoring data, providing a reliable basis for subsequent audits and quality traceability.

[0027] 4. The application of fuzzy control algorithm in environmental risk assessment enables the system to dynamically respond to changes in complex working conditions and realizes the intelligent upgrade of safety protection.

[0028] 5. The dual-factor authentication mechanism strengthens the authority management of the loading process, effectively preventing unauthorized loading operations and ensuring the safety of oil transportation.

[0029] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0030] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0032] Figure 1 This is a schematic diagram of the structure of a quantitative loading controller;

[0033] Figure 2 It is the structural diagram of the loading system;

[0034] Figure 3 Schematic diagram of the module's structure for loading into a vehicle. DETAILED DESCRIPTION

[0035] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0036] The present invention provides a quantitative loading controller, such as Figure 1 As shown, it includes: a safety interlock integrated machine and a distributed batch controller; the safety interlock integrated machine is connected to the distributed batch controller through hard wiring; the safety interlock integrated machine integrates a human electrostatic release alarm, a key manager, an API electrostatic oil spill protector and a voice controller; the distributed batch controller has a built-in dual communication module and a PLC controller; the safety interlock integrated machine and the distributed batch controller work together to realize quantitative loading control.

[0037] The working principle of the above technical solution is as follows: To implement a quantitative loading controller, the present invention proposes an integrated safety interlock unit and a distributed batch controller. The integrated safety interlock unit monitors various safety parameters during the loading process in real time and immediately triggers protection mechanisms when an anomaly is detected. A human static discharge alarm ensures that operators are free of static electricity before contacting the equipment. A key manager strictly controls equipment operating permissions. An API static oil spill protector automatically shuts down the loading process upon detecting an oil leak. The distributed batch controller uses dual communication modules to achieve real-time data transmission, and a PLC controller precisely controls the loading flow and total volume, ensuring a safe and efficient loading process.

[0038] The beneficial effects of the above technical solution are as follows: the solution provided in this embodiment can significantly improve the safety and accuracy of loading operations; the coordinated work of the safety interlocking integrated machine and the batch controller realizes multiple protections, effectively preventing the risk of fire and explosion caused by static electricity; the distributed architecture design enhances the reliability and scalability of the system, and the dual communication modules ensure the stability of data transmission; the precise metering function of the PLC controller avoids overloading or underloading, greatly improving loading efficiency.

[0039] In one embodiment, the human body electrostatic release alarm is used to verify the human body electrostatic release status before loading is started. If the verification fails, the human body electrostatic release status signal is output to the PLC controller, and the loading action is locked based on the PLC controller.

[0040] The working principle of the above technical solution is as follows: the human body electrostatic discharge alarm monitors the electrostatic potential of the operator's body surface in real time through the sensing electrode. When the electrostatic voltage is detected to exceed the safety threshold, an audible and visual alarm prompt will be issued; the system adopts a double verification mechanism, first confirming that the operator has correctly grasped the release device through contact detection, and then performing non-contact electrostatic detection to ensure complete discharge; the alarm signal is directly connected to the DI module of the PLC controller through a hard line, and the real-time status data is transmitted to the central control system through the PROFIBUS bus. After the PLC controller receives the abnormal signal, it will immediately cut off the loading control loop and display the specific alarm information on the set HMI interface until the operator completes the standardized electrostatic discharge procedure and passes the system review.

[0041] The beneficial effects of the above technical solution are as follows: the solution provided in this embodiment can realize closed-loop management of electrostatic protection in the entire loading process, and ensure operational safety through a multi-level interlocking mechanism; the dual detection technology effectively avoids the misjudgment problem that may exist in the traditional single detection method, and improves the protection reliability; the signal transmission method of hard wire and bus parallel guarantees the real-time nature of key signals and realizes comprehensive monitoring of the system status; the humanized design of the HMI interface makes fault location and processing more convenient, and significantly shortens the emergency response time under abnormal circumstances.

[0042] In one embodiment, the key manager adopts a sliding self-locking structure for centrally controlling key resetting. When the key manager body is in an unreset state, it outputs a normally closed signal to the PLC controller and locks the loading action based on the PLC controller.

[0043] The working principle of the above technical solution is as follows: the key manager ensures that all keys must be reset in sequence through a mechanical interlocking device to release the locked state. When it is detected that any key is not returned to its position, the mechanical self-locking mechanism will remain locked and trigger an electrical signal; after the PLC controller receives the key abnormality signal, it will immediately interrupt the loading process and activate the alarm device, and at the same time display the specific number of the unreset key on the operation panel; when the mechanical lock fails, secondary protection is provided by the electromagnetic lock to ensure that the loading operation cannot be carried out under the abnormal key management state.

[0044] The beneficial effects of the above technical solution are: by adopting the solution provided by this embodiment, the key reset status is monitored in real time by the photoelectric sensor, and the data is synchronously updated to the central database, providing traceable records for security management.

[0045] In one embodiment, the voice controller stores multiple alarm voices for responding to control instructions from the PLC controller to broadcast the loading status in real time.

[0046] The working principle of the above technical solution is as follows: the voice controller has a built-in voice library of up to 200 languages, which can automatically match different levels of alarm content according to preset conditions; when the PLC controller detects an abnormality in the loading process, it will trigger the corresponding voice broadcast module through a digital signal, and at the same time enhance the warning effect through the sound and light alarm. At the same time, the voice controller supports custom voice recording function, and users can update the voice content library according to actual needs.

[0047] The beneficial effects of the above technical solution are as follows: by adopting the solution provided by this embodiment, the voice alarm system can realize multi-language, multi-level accurate prompts, significantly improving the operator's efficiency in identifying abnormal conditions; at the same time, it supports remote voice content updates, which is convenient for timely adjustment of alarm strategies according to changes in on-site working conditions, while reducing subsequent maintenance costs; the sound and light linkage alarm mechanism further ensures that warning information can be effectively transmitted even in noisy environments, greatly improving the safety and reliability of loading operations.

[0048] In one embodiment, the dual communication module includes an Ethernet communication module and an RS485 network communication module.

[0049] The working principle of the above technical solution is as follows: the Ethernet communication module is responsible for high-speed data interaction with the host computer system to realize remote configuration and real-time monitoring of loading parameters; the RS485 network communication module is used to connect on-site instrument equipment to build a stable and reliable distributed data acquisition network.

[0050] The beneficial effects of the above technical solution are: using the solution provided by this embodiment, the two communication modules achieve data intercommunication through the internal protocol converter. When the main communication link fails, the system will automatically switch to the backup communication channel to ensure the continuity of data transmission.

[0051] In one embodiment, the API electrostatic oil spill protector is used to monitor the oil level and electrostatic grounding status. If an electrostatic grounding signal and an overflow signal appear, the electrostatic grounding signal and the overflow signal are sent to the PLC controller, and the loading action is locked based on the PLC controller.

[0052] The working principle of the above technical solution is: through real-time monitoring of the accumulation of static electricity during the loading process, the protection mechanism is immediately triggered when an abnormal potential difference is detected. The use of high-precision ultrasonic probes can accurately identify changes in liquid level height and generate an overflow warning signal when the preset safety threshold is reached; after the PLC controller receives the dual alarm signal, it will cut off the power supply of the loading pump and activate the emergency brake valve within 0.1 seconds, and at the same time send an accident code to the central control room through the bus system.

[0053] The beneficial effect of the above technical solution is: by adopting the solution provided in this embodiment, this multi-protection design effectively avoids explosion accidents caused by static sparks and environmental pollution problems caused by oil spills.

[0054] The loading system implements oil loading based on a quantitative loading controller, such as Figure 2 As shown, it includes: loading preparation module, loading control module and loading settlement module;

[0055] The loading preparation module is used to identify vehicle types, predict loading flow rates, and generate safety monitoring standard data before oil loading based on several loading preparation devices and loading safety monitoring devices configured in the loading area;

[0056] The loading control module is based on a quantitative loading controller, a crane pipe, a control valve, and a flow meter. After the vehicle is connected to the crane pipe, the quantitative loading controller is used to control the loading progress through the control valve according to the set loading flow control strategy, and the loading flow is counted through the flow meter. The loading safety monitoring equipment is used to implement loading safety monitoring.

[0057] The loading settlement module is used to perform loading settlement based on the loading flow.

[0058] The working principle of the above technical solution is: in order to realize the loading system, the present invention implements oil loading through a quantitative loading controller, specifically proposes a loading preparation module, which is based on several loading preparation equipment and loading safety monitoring equipment configured in the loading area to perform vehicle model identification, loading flow prediction and safety monitoring standard data generation before oil loading; proposes a loading control module, which is based on a quantitative loading controller, a crane pipe, a control valve and a flow meter. After the vehicle is connected to the crane pipe, the quantitative loading controller is used to control the loading progress through the control valve according to the set loading flow control strategy, and the loading flow is counted through the flow meter, and the loading safety monitoring equipment is used to implement loading safety monitoring; proposes a loading settlement module, which performs loading settlement according to the loading flow.

[0059] The beneficial effects of the above technical solution are: by adopting the solution provided in this embodiment, the solution realizes the full-process automated management of the loading process, significantly improving the safety and efficiency of oil loading and unloading operations; through intelligent vehicle model recognition and loading flow prediction functions, it can accurately match the loading needs of different vehicles to avoid overloading or underloading; at the same time, the real-time safety monitoring system can promptly detect and deal with potential risks to ensure the safety of the operation process.

[0060] In one embodiment, Figure 3 As shown, the loading preparation module includes a vehicle type recognition unit, an AI loading flow prediction unit, and a safety monitoring standard data generation unit;

[0061] The vehicle model recognition unit is used to identify the vehicle model based on the high-definition camera, lidar scanner, and edge computing terminal in the configured loading preparation equipment, and sends the identified vehicle model code and tank volume data to the PLC controller; the high-definition camera and lidar scanner are configured at the loading platform entrance; the lidar scanner is used to generate a three-dimensional point cloud model of the vehicle; the edge computing terminal is used to run the configured vehicle model recognition AI model; the vehicle model recognition AI model adopts a multimodal fusion architecture; the running configured vehicle model recognition AI model includes: using a convolutional neural network to extract vehicle appearance features, using a PointNet++ network to process point cloud data, based on the feature fusion layer using the attention mechanism to weightedly integrate visual and geometric features, and using the output layer to match the pre-stored vehicle model code database and return the tank volume data;

[0062] The AI ​​loading flow prediction unit is used to input the vehicle model code, as well as the oil density, ambient temperature and historical loading efficiency data obtained from the big database based on the LSTM network model, output the optimal loading curve for the vehicle, and extract the maximum safe loading flow and recommended loading time.

[0063] The safety monitoring standard data generation unit is used to generate corresponding safety monitoring standard data for loading safety monitoring equipment based on the maximum safe loading flow and recommended loading time; the corresponding safety monitoring standard data for loading safety monitoring equipment include: the corresponding standard crane pipe docking angle deviation of the laser displacement sensor, the corresponding standard tank liquid level fluctuation frequency of the millimeter wave radar, the corresponding standard electrostatic accumulated voltage value of the non-contact electrostatic sensor, and the corresponding standard ambient wind speed of the explosion-proof anemometer.

[0064] The working principle of the above technical solution is as follows: the loading preparation module realizes intelligent pre-loading inspection through the collaborative work of multiple sensors and deep learning technology; the vehicle model recognition unit uses multiple sensors and complex AI models to conduct comprehensive scanning and modeling of vehicles, ensuring that the loading system can accurately match the loading needs of different vehicle models; the AI ​​loading flow prediction unit makes full use of big data and advanced network models to conduct a comprehensive analysis of various influencing factors and dynamically optimize the loading plan, which not only improves loading efficiency but also provides a solid guarantee for operational safety; the safety monitoring standard data generation unit converts the prediction results into executable safety thresholds, providing an accurate monitoring benchmark for the subsequent loading process.

[0065] The beneficial effects of the above technical solution are: by adopting the solution provided in this embodiment, the entire process from vehicle identification to safety standard generation is automated, which significantly improves the intelligence level and safety factor of dangerous goods loading, and lays a solid foundation for the smooth progress of the entire loading process.

[0066] In one embodiment, the loading control module further includes a safety monitoring unit; the safety monitoring unit includes a safety monitoring disposal subunit and a safety monitoring data storage subunit;

[0067] The safety monitoring and disposal subunit is used to implement safety monitoring and disposal based on the loading safety monitoring equipment, specifically: using a laser displacement sensor to monitor the crane pipe docking angle deviation, and when the crane pipe docking angle deviation is greater than the standard crane pipe docking angle deviation, triggering automatic correction; using a millimeter wave radar to monitor the tank liquid level fluctuation frequency, and if the fluctuation frequency is greater than the standard tank liquid level fluctuation frequency, starting the slow flow protection; using a non-contact electrostatic sensor and an explosion-proof anemometer to measure the static cumulative voltage value, ambient wind speed, and visibility, based on a parameterized triangular membership function, the static cumulative voltage value, ambient wind speed, and visibility are converted into input fuzzy quantity representation, and according to a preset fuzzy rule library, the converted input fuzzy quantity is fuzzy inferenced to obtain an output fuzzy quantity, and the output fuzzy quantity is defuzzified to obtain an environmental operation risk index. If the environmental operation risk index is greater than the set index threshold, the loading operation is terminated;

[0068] The safety monitoring data storage subunit is used to generate blockchain evidence at the end of loading based on blockchain technology, recording safety monitoring data and safety monitoring status timestamps.

[0069] The working principle of the above technical solution is as follows: all-round safety monitoring of the loading process is achieved through multi-sensor fusion technology; laser displacement sensors collect crane pipe position data in real time, millimeter-wave radars dynamically monitor the liquid level status in the tank, and non-contact sensor networks continuously obtain environmental parameters; the system uses fuzzy logic algorithms to intelligently evaluate complex working conditions, and when an abnormal situation is detected, it automatically executes a hierarchical safety response mechanism; all monitoring data is encrypted and stored using blockchain distributed ledger technology to ensure that the data cannot be tampered with and is traceable, providing complete and reliable operation records for subsequent analysis.

[0070] The beneficial effects of the above technical solution are: the solution provided in this embodiment can significantly improve the safety and reliability of loading operations; the collaborative work of multi-source sensors realizes full-dimensional monitoring of the loading process, effectively preventing the monitoring blind spot problem that may exist in traditional single sensors; the intelligent fuzzy evaluation system can accurately identify potential risks in complex environments, and has higher adaptability and accuracy than traditional threshold judgment methods; the application of blockchain technology not only ensures data security, but also establishes a complete responsibility traceability chain, providing technical support for safety management.

[0071] In one embodiment, the loading control module also includes a loading authentication unit, which is used to set up and implement IC card two-factor authentication; IC card two-factor authentication is: the preset loading flow is encrypted and written into the MI type IC card, and the MI type IC card is used by the driver to swipe the card at the loading site. After the pick-up password is verified, loading is carried out by unlocking the control valve.

[0072] The working principle of the above technical solution is as follows: the loading authentication unit uses an asymmetric encryption algorithm to encrypt the loading flow data to ensure the security of the data transmission process; when the driver swipes the card at the loading terminal, the system will verify the authenticity and validity of the IC card through a dedicated card reader, and at the same time require the input of a dynamic pickup password for secondary identity verification; after the authentication is passed, the system automatically generates a unique loading operation token, which is time-sensitive and single-time valid to prevent replay attacks; the unlocking command of the control valve is transmitted through an industrial-grade encryption channel, and a final security check is performed before execution to ensure that the loading operation fully complies with the preset safety process; the entire process uses hardware-level security chips to protect key data to prevent the risk of data leakage.

[0073] The beneficial effects of the above technical solution are: the solution provided in this embodiment can achieve full-process security control of loading operations, forming a closed-loop management from identity authentication to operation execution; the two-factor authentication mechanism greatly increases the difficulty of illegal operations and effectively prevents security risks such as identity fraud; dynamic token technology ensures the uniqueness and non-replicability of each loading operation, eliminating the risk of operation credentials being stolen.

[0074] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A quantitative loading controller, characterized in that: include: Safety interlock integrated machine and distributed batch controller; The safety interlock integrated machine is connected to the distributed batch controller through hard wiring; the safety interlock integrated machine integrates a human electrostatic discharge alarm, a key manager, an API electrostatic oil spill protector and a voice controller; the distributed batch controller has a built-in dual communication module and a PLC controller; the safety interlock integrated machine and the distributed batch controller work together to achieve quantitative loading control.

2. A quantitative loading controller according to claim 1, characterized in that: The human body static electricity release alarm is used to verify the human body static electricity release status before loading starts. If the verification fails, the human body static electricity release status signal is output to the PLC controller, and the loading action is locked based on the PLC controller.

3. A quantitative loading controller according to claim 1, characterized in that: The key manager adopts a sliding self-locking structure for centralized control of key resetting. When the key manager body is in the unreset state, it outputs a normally closed signal to the PLC controller and locks the loading action based on the PLC controller.

4. A quantitative loading controller according to claim 1, characterized in that: The voice controller stores multiple alarm voices, which are used to respond to the control instructions of the PLC controller and report the loading status in real time.

5. The quantitative loading controller according to claim 1, characterized in that: The dual communication modules include an Ethernet communication module and an RS485 network communication module.

6. A quantitative loading controller according to claim 1, characterized in that: The API electrostatic oil spill protector is used to monitor the oil level and electrostatic grounding status. If an electrostatic grounding signal or overflow signal appears, the electrostatic grounding signal or overflow signal will be sent to the PLC controller, and the loading action will be locked based on the PLC controller.

7. A loading system for implementing oil loading based on a quantitative loading controller according to any one of claims 1 to 6, characterized in that: include: Loading preparation module, loading control module and loading settlement module; The loading preparation module is used to identify vehicle types, predict loading flow rates, and generate safety monitoring standard data before oil loading based on several loading preparation devices and loading safety monitoring devices configured in the loading area; The loading control module is based on a quantitative loading controller, a crane pipe, a control valve, and a flow meter. After the vehicle is connected to the crane pipe, the quantitative loading controller is used to control the loading progress through the control valve according to the set loading flow control strategy, and the loading flow is counted through the flow meter. The loading safety monitoring equipment is used to implement loading safety monitoring. The loading settlement module is used to perform loading settlement based on loading flow.

8. The vehicle loading system according to claim 7, characterized in that: The loading preparation module includes a vehicle type recognition unit, an AI loading flow prediction unit, and a safety monitoring standard data generation unit; The vehicle model recognition unit is used to identify the vehicle model based on the high-definition camera, lidar scanner, and edge computing terminal in the configured loading preparation equipment, and send the identified vehicle model code and tank volume data to the PLC controller; High-definition cameras and lidar scanners are deployed at the loading platform entrance; the lidar scanner is used to generate a three-dimensional point cloud model of the vehicle; the edge computing terminal is used to run the configured vehicle type recognition AI model; the vehicle type recognition AI model adopts a multimodal fusion architecture; The running configured vehicle model recognition AI model includes: using a convolutional neural network to extract vehicle appearance features, using the PointNet++ network to process point cloud data, using a feature fusion layer to weightedly integrate visual and geometric features through an attention mechanism, and using an output layer to match a pre-stored vehicle model code database and return tank volume data; The AI ​​loading flow prediction unit is used to input the vehicle model code, as well as oil density, ambient temperature, and historical loading efficiency data obtained from a large database based on an LSTM network model. It outputs the optimal loading curve for the vehicle and extracts the maximum safe loading flow and recommended loading time. The safety monitoring standard data generation unit is used to generate corresponding safety monitoring standard data for loading safety monitoring equipment based on the maximum safe loading flow and recommended loading time; the corresponding safety monitoring standard data for loading safety monitoring equipment include: the corresponding standard crane pipe docking angle deviation of the laser displacement sensor, the corresponding standard tank liquid level fluctuation frequency of the millimeter wave radar, the corresponding standard electrostatic accumulated voltage value of the non-contact electrostatic sensor, and the corresponding standard ambient wind speed of the explosion-proof anemometer.

9. The vehicle loading system according to claim 8, characterized in that: The loading control module also includes a safety monitoring unit; the safety monitoring unit includes a safety monitoring disposal subunit and a safety monitoring data storage subunit; The safety monitoring and disposal subunit is used to implement safety monitoring and disposal based on the loading safety monitoring equipment. Specifically, it uses a laser displacement sensor to monitor the angle deviation of the crane pipe docking. When the angle deviation of the crane pipe docking angle exceeds the standard crane pipe docking angle deviation, it triggers automatic correction. It uses a millimeter wave radar to monitor the fluctuation frequency of the tank liquid level. If the fluctuation frequency exceeds the standard tank liquid level fluctuation frequency, it activates slow flow protection. The static electricity cumulative voltage value, ambient wind speed, and visibility measured using a non-contact static electricity sensor and an explosion-proof anemometer are converted into input fuzzy quantities based on a parameterized triangular membership function. Fuzzy reasoning is then performed on the converted input fuzzy quantities according to a preset fuzzy rule base to obtain an output fuzzy quantity. The output fuzzy quantity is defuzzified to obtain an environmental operation risk index. If the environmental operation risk index is greater than the set index threshold, the loading operation is terminated. The safety monitoring data storage subunit is used to generate blockchain evidence at the end of loading based on blockchain technology, recording safety monitoring data and safety monitoring status timestamps.

10. The vehicle loading system according to claim 7, characterized in that: The loading control module also includes a loading authentication unit, which is used to set up and implement IC card two-factor authentication; IC card two-factor authentication is: the preset loading flow is encrypted and written into the MI type IC card, and the MI type IC card is used by the driver to swipe the card at the loading site. After the pick-up password is verified, loading is carried out by unlocking the control valve.

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