A fusion positioning method for a molten iron ladle car and related equipment

Abstract

The application discloses a molten iron tank car fusion positioning method and related equipment, and relates to the field of molten iron tank car positioning. The method comprises the following steps: constructing map information data in a factory area, wherein the map information data comprises signal machine position coordinates, turnout position coordinates, camera position coordinates, dead-end line earth dam position coordinates and level crossing position coordinates; acquiring GNSS original positioning data and DGNSS differential correction information of the molten iron tank car; determining high-precision GNSS differential positioning data based on the GNSS original positioning data and the DGNSS differential correction information; acquiring real-time speed data and running direction data based on a vehicle-mounted wheel speed meter; updating and correcting the position of the molten iron tank car based on the real-time speed data, the running direction data, the high-precision GNSS differential positioning data and the map information data in the factory area, so as to acquire molten iron tank car predicted position information.

Description

Technical Field

[0001] This specification relates to the field of molten iron ladle car positioning, and more specifically, this application relates to a fusion positioning method and related equipment for molten iron ladle cars. Background Technology

[0002] In the transportation of molten iron, the positioning and scheduling of ladle cars is a crucial link in the entire production process. Traditional positioning methods mainly rely on the Global Navigation Satellite System (GNSS) for real-time positioning. However, due to the complex environment of the plant, such as tall buildings and areas with signal obstruction, the accuracy of GNSS positioning is significantly affected, especially in industrial environments with high precision requirements, where GNSS may not be able to provide the required high-precision positioning. Furthermore, the transportation speed and direction of the ladle cars, as well as the complex road structure and intersections within the plant area, also increase the difficulty of positioning. Therefore, how to improve the real-time positioning accuracy of molten iron ladle cars and ensure the safety and efficiency of the transportation process has become a major technological challenge. To at least solve some of the above problems, there is an urgent need to provide a fusion positioning method for molten iron ladle cars. Summary of the Invention

[0003] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. This summary section is not intended to limit the key and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0004] Firstly, this application proposes a fusion positioning method for molten iron ladle cars, comprising:

[0005] Construct map information data within the factory area, including the coordinates of signal lights, turnouts, cameras, dead-end road barriers, and level crossings.

[0006] Obtain the raw GNSS positioning data and DGNSS differential correction information of the molten iron ladle car;

[0007] Based on the above-mentioned raw GNSS positioning data and the above-mentioned DGNSS differential correction information, high-precision GNSS differential positioning data is determined;

[0008] Real-time speed and direction of travel data are obtained based on the vehicle's wheel speed sensor;

[0009] Based on the aforementioned real-time speed data, running direction data, high-precision GNSS differential positioning data, and map information within the plant area, the position of the molten iron ladle car is updated and corrected to obtain the predicted position information of the molten iron ladle car.

[0010] In one feasible implementation, the position of the molten iron ladle car is updated and corrected based on the real-time speed data, the running direction data, the high-precision GNSS differential positioning data, and the map information data within the plant area to obtain predicted position information of the molten iron ladle car, including:

[0011] Based on the above real-time speed data, the above running direction data, and the previous known position, a preliminary predicted position is determined;

[0012] Based on the aforementioned high-precision GNSS differential positioning data and the aforementioned preliminary predicted position, the corrected predicted position is determined using the Kalman filtering method.

[0013] Based on the above-mentioned corrected predicted location and the above-mentioned map information data within the factory area, the above-mentioned predicted location information of the molten iron ladle car is generated using the nearest neighbor matching method and the projection method.

[0014] In one feasible implementation, the above-mentioned generation of the predicted location information of the molten iron ladle car based on the corrected predicted location and the above-mentioned map information data within the factory area, using the nearest neighbor matching method and the projection method, includes:

[0015] Based on the above-mentioned corrected predicted location and the above-mentioned factory area map information data, calculate the Euclidean distance between the above-mentioned corrected predicted location and all tracks in the map;

[0016] The orbital point with the minimum distance is determined based on all Euclidean distances and is the nearest neighbor matching point.

[0017] If the minimum distance to the nearest neighbor matching point is greater than a preset threshold, the projected location of the molten iron ladle car is determined by projecting the nearest neighbor matching point and the corrected predicted location onto the current track segment.

[0018] In one feasible implementation, it further includes:

[0019] The QR code image of the QR code on the molten iron ladle car is obtained by the camera under a long exposure time. The QR code is located on the ladle car and is used for identification.

[0020] Based on the trail length and exposure time of the QR code positioning area, the QR code moving speed is determined;

[0021] Based on the moving speed of the QR code, the real-time speed data obtained by the vehicle-mounted wheel speed meter is corrected to obtain the corrected real-time speed data.

[0022] In one feasible implementation, the angle between the imaging plane of the camera and the plane where the molten iron car and the QR code mark are located is less than a preset angle value.

[0023] In one feasible implementation, it further includes:

[0024] If the number of times the minimum distance to the nearest neighbor matching point exceeds a preset threshold is greater than a preset number, then the operation of adjusting the QR code movement speed will be activated.

[0025] In one feasible implementation, it also includes

[0026] The cumulative statistical feature information of the nearest neighbor matching points whose minimum distance is greater than a preset threshold is counted.

[0027] The first weight information is determined based on the aforementioned cumulative excess statistical characteristic information;

[0028] Determine the statistical characteristics of the longitudinal oscillation of the QR code positioning area in the aforementioned QR code image.

[0029] The second weighting information is determined based on the above-mentioned longitudinal oscillation statistical characteristics.

[0030] The corrected real-time speed data is obtained by weighting the first weight information, the real-time speed data obtained by the vehicle wheel speed meter, the second weight information, and the QR code movement speed.

[0031] Secondly, this application proposes a fusion positioning device for molten iron ladle cars, comprising:

[0032] The construction unit is used to construct map information data within the factory area. The map information data includes the coordinates of signal positions, turnout positions, camera positions, dead-end road barriers, and level crossing positions.

[0033] The first acquisition unit is used to acquire the raw GNSS positioning data and DGNSS differential correction information of the molten iron ladle car;

[0034] The determining unit is used to determine high-precision GNSS differential positioning data based on the above-mentioned raw GNSS positioning data and the above-mentioned DGNSS differential correction information;

[0035] The second acquisition unit is used to acquire real-time speed data and running direction data based on the vehicle-mounted wheel speed meter;

[0036] The third acquisition unit is used to update and correct the position of the molten iron ladle car based on the real-time speed data, the running direction data, the high-precision GNSS differential positioning data, and the map information data within the plant area, so as to obtain the predicted position information of the molten iron ladle car.

[0037] Thirdly, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the fusion positioning method for molten iron ladle cars as described in any of the first aspects above.

[0038] Fourthly, this application also proposes a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the fusion positioning method for molten iron ladle cars according to any one of the first aspects.

[0039] In summary, the fusion positioning method for molten iron ladle cars proposed in this application embodiment integrates raw GNSS positioning data, DGNSS differential correction information, onboard wheel speed meter data, and map information within the factory area, significantly improving positioning accuracy and reliability. Compared with related technologies, this application embodiment not only relies on GNSS and DGNSS but also acquires real-time speed and direction data of the molten iron ladle car through the onboard wheel speed meter, combined with map information within the factory area (including the coordinates of signals, switches, cameras, etc.). By fusing information from multiple data sources, the accuracy limitations of a single GNSS signal source can be effectively compensated, especially when GNSS signals are blocked or inaccurate, allowing for accurate positioning based on auxiliary information such as speed and direction. In this application embodiment, positioning not only relies on historical location data but also acquires and updates vehicle speed and direction data in real time, dynamically correcting the real-time position through algorithms. Compared to the static differential positioning method in existing technologies, this application embodiment ensures accurate tracking and positioning of the molten iron ladle car in complex road conditions through continuous real-time data correction, improving the reliability of the positioning results. This application incorporates the coordinates of key locations within the factory area (such as signals, switches, and cameras) into the positioning process, further enhancing the system's adaptability to specific industrial environments. By adding factory map information, not only are the positioning results optimized, but additional reference information is provided at critical locations, helping the positioning algorithm provide more stable positioning services at key nodes or areas with poor signal coverage. This application also generates predicted location information based on real-time positioning by integrating multiple data sources. The acquisition of predicted location information allows the system to not only accurately determine the current location of the molten iron ladle car but also predict its future travel path and arrival time. This function has significant advantages in improving the efficiency and safety of production scheduling, ensuring the efficiency and continuity of molten iron transportation and reducing scheduling errors and unexpected situations. Overall, this application significantly improves the positioning accuracy and reliability of molten iron ladle cars in complex environments through multi-sensor information fusion and real-time data update technology. Compared to traditional positioning solutions that rely on a single GNSS / DGNSS technology, the embodiments of this application have significant advantages in terms of accuracy, robustness, and adaptability. They are suitable for high-requirement positioning scenarios in molten iron transportation and meet the safety and efficiency requirements in industrial environments. Attached Figure Description

[0040] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0041] Figure 1A flowchart illustrating a fusion positioning method for molten iron ladle cars provided in an embodiment of this application;

[0042] Figure 2 A schematic diagram of a fusion positioning device for a molten iron ladle car provided in an embodiment of this application;

[0043] Figure 3 This is a schematic diagram of a fusion positioning electronic device for a molten iron ladle car, provided as an embodiment of this application. Detailed Implementation

[0044] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0045] Please see Figure 1 This is a flowchart illustrating a fusion positioning method for molten iron ladle cars provided in an embodiment of this application, which may specifically include:

[0046] S110. Construct map information data within the factory area, including the coordinates of signal positions, switch positions, camera positions, dead-end road barriers, and level crossings.

[0047] For example, detailed map data is constructed by collecting information from multiple key coordinate points within the factory area. This map data includes, but is not limited to: signal coordinates, turnout coordinates, camera coordinates, dead-end road barriers coordinates, and level crossing coordinates. Precise measurement and recording of these coordinate points ensures the completeness and accuracy of the geographic information data within the factory area, providing a reliable reference for subsequent positioning.

[0048] S120. Obtain the raw GNSS positioning data and DGNSS differential correction information of the molten iron ladle car;

[0049] For example, real-time raw GNSS positioning data is acquired from the GNSS (Global Navigation Satellite System) receiver of the molten iron ladle car, while DGNSS (Differential Global Navigation Satellite System) correction information is received simultaneously to enhance positioning accuracy. Raw GNSS positioning data is often affected by environmental factors, and the DGNSS correction information can effectively reduce these errors.

[0050] S130. Based on the above-mentioned raw GNSS positioning data and the above-mentioned DGNSS differential correction information, determine the high-precision GNSS differential positioning data;

[0051] For example, based on the raw GNSS positioning data obtained in the previous steps and the DGNSS differential correction information, high-precision GNSS differential positioning data is calculated and generated. By correcting and adjusting the raw data, the system can obtain more accurate positioning results, ensuring that the current position of the molten iron ladle car is accurate.

[0052] S140. Acquire real-time speed data and running direction data based on the vehicle-mounted wheel speed meter;

[0053] For example, real-time speed data is obtained from the onboard wheel speedometer of the molten iron ladle car, and combined with the ladle car's direction of travel information to further enrich the dynamic data required for positioning. Accurate acquisition of this data is crucial for ensuring positioning accuracy and predicting the future position of the molten iron ladle car.

[0054] S150. Based on the above-mentioned real-time speed data, the above-mentioned running direction data, the above-mentioned high-precision GNSS differential positioning data and the map information data within the plant area, the position of the above-mentioned molten iron ladle car is updated and corrected to obtain the predicted position information of the molten iron ladle car.

[0055] For example, by integrating multiple data sources, including real-time speed data, direction of travel data, high-precision GNSS differential positioning data, and map information within the plant area, a specific algorithm is used to update and correct the current position of the molten iron ladle car, ultimately obtaining its predicted position information. This predicted position information can more accurately reflect the positional changes of the molten iron ladle car over a future period, helping to improve the stability and reliability of positioning.

[0056] In summary, this application proposes a method for locating molten iron ladle cars by fusing raw GNSS positioning data, DGNSS differential correction information, onboard wheel speedometer data, and map information within the factory area, significantly improving positioning accuracy and reliability. Compared with related technologies, this application not only relies on GNSS and DGNSS, but also acquires real-time speed and direction data of the molten iron ladle car through an onboard wheel speedometer, combined with map information within the factory area (including the coordinates of signals, switches, cameras, etc.). By fusing information from multiple data sources, the accuracy limitations of a single GNSS signal source can be effectively compensated, especially when GNSS signals are blocked or inaccurate, allowing for accurate positioning based on auxiliary information such as speed and direction. In this application, positioning not only relies on historical location data, but also acquires and updates vehicle speed and direction data in real time, dynamically correcting the real-time position using algorithms. Compared to the static differential positioning methods in existing technologies, this application ensures accurate tracking and positioning of molten iron ladle cars in complex road conditions through continuous real-time data correction, improving the reliability of the positioning results. This application incorporates the coordinates of key locations within the factory area (such as signals, switches, and cameras) into the positioning process, further enhancing the system's adaptability to specific industrial environments. By adding factory map information, not only are the positioning results optimized, but additional reference information is provided at critical locations, helping the positioning algorithm provide more stable positioning services at key nodes or areas with poor signal coverage. This application also generates predicted location information based on real-time positioning by integrating multiple data sources. The acquisition of predicted location information allows the system to not only accurately determine the current location of the molten iron ladle car but also predict its future travel path and arrival time. This function has significant advantages in improving the efficiency and safety of production scheduling, ensuring the efficiency and continuity of molten iron transportation and reducing scheduling errors and unexpected situations. Overall, this application significantly improves the positioning accuracy and reliability of molten iron ladle cars in complex environments through multi-sensor information fusion and real-time data update technology. Compared to traditional positioning solutions that rely on a single GNSS / DGNSS technology, the embodiments of this application have significant advantages in terms of accuracy, robustness, and adaptability. They are suitable for high-requirement positioning scenarios in molten iron transportation and meet the safety and efficiency requirements in industrial environments.

[0057] In some examples, the position of the molten iron ladle car is updated and corrected based on the aforementioned real-time speed data, the aforementioned direction of travel data, the aforementioned high-precision GNSS differential positioning data, and the factory area map information data to obtain the predicted position information of the molten iron ladle car, including:

[0058] Based on the above real-time speed data, the above running direction data, and the previous known position, a preliminary predicted position is determined;

[0059] Based on the aforementioned high-precision GNSS differential positioning data and the aforementioned preliminary predicted position, the corrected predicted position is determined using the Kalman filtering method.

[0060] Based on the above-mentioned corrected predicted location and the above-mentioned map information data within the factory area, the above-mentioned predicted location information of the molten iron ladle car is generated using the nearest neighbor matching method and the projection method.

[0061] For example, by using the real-time speed and direction data of the molten iron ladle car, combined with its previously known position, the preliminary predicted position of the ladle car is calculated using kinematic equations. The core of this step lies in using speed and direction information to deduce the short-term displacement of the molten iron ladle car, thereby obtaining a preliminary position prediction.

[0062] Next, based on the aforementioned high-precision GNSS differential positioning data and the initial predicted position, a corrected predicted position is determined using the Kalman filter method. The Kalman filter method is used to correct the initial predicted position. First, the high-precision GNSS differential positioning data obtained through GNSS differential correction is compared with the initial predicted position. The Kalman filter method comprehensively considers the confidence levels of the GNSS positioning data and the initial predicted position, and performs noise processing on the positioning data to eliminate random errors, ultimately obtaining the corrected predicted position. This process effectively smooths the positioning data, improving the stability and accuracy of positioning.

[0063] Finally, based on the revised predicted location and the aforementioned map information within the plant area, the predicted location information for the molten iron ladle car is generated using the nearest neighbor matching method and the projection method. Combined with the map information within the plant area, the revised predicted location is further optimized. Specifically, the system uses the nearest neighbor matching method to match the revised predicted location with known tracks, switches, or other key coordinate points on the plant area map to ensure that the location of the molten iron ladle car conforms to the actual track layout. Simultaneously, the system also uses the projection method to project the revised predicted location onto the map coordinate system, ensuring spatial consistency of the location data. Through the combination of these two methods, the predicted location information for the molten iron ladle car is finally generated.

[0064] The embodiments of this application, through the aforementioned multi-step data fusion and location correction, can significantly improve the accuracy and robustness of molten iron ladle car positioning. Especially in complex plant environments, it ensures accurate real-time location updates and predictions for molten iron ladle cars, meeting the high requirements of production scheduling and safety management.

[0065] In some examples, the predicted location information of the molten iron ladle car is generated based on the corrected predicted location and the map information data within the factory area using the nearest neighbor matching method and the projection method, including:

[0066] Based on the above-mentioned corrected predicted location and the above-mentioned factory area map information data, calculate the Euclidean distance between the above-mentioned corrected predicted location and all tracks in the map;

[0067] The orbital point with the minimum distance is determined based on all Euclidean distances and is the nearest neighbor matching point.

[0068] If the minimum distance to the nearest neighbor matching point is greater than a preset threshold, the projected location of the molten iron ladle car is determined by projecting the nearest neighbor matching point and the corrected predicted location onto the current track segment.

[0069] For example, based on map information data within the factory area (including track coordinates) and the corrected predicted location obtained through Kalman filtering, the Euclidean distance between the corrected predicted location and all tracks on the map is calculated. Euclidean distance is a standard spatial distance calculation method used to measure the straight-line distance between the corrected predicted location and each track. By calculating the distance from each track point to the corrected predicted location, the system can determine the track most likely to be near the molten iron ladle car in the current environment.

[0070] Next, based on all Euclidean distances, the track point with the smallest distance is determined as the nearest neighbor matching point. The previously calculated Euclidean distances are compared, and the track point with the smallest distance is selected as the nearest neighbor matching point. The nearest neighbor matching point is the coordinate point on the track closest to the current position of the molten iron ladle car; this point accurately reflects the actual position of the ladle car on the track. Through this step, the system can effectively match the corrected predicted position with the track in the plant area map.

[0071] Finally, if the minimum distance between the nearest neighbor matching point and the corrected predicted position is greater than a preset threshold, the system projects the nearest neighbor matching point and the corrected predicted position onto the current track segment to determine the predicted position of the ladle car. The minimum distance between the nearest neighbor matching point and the corrected predicted position is then further checked. If this minimum distance is greater than the preset threshold, it means the distance between the corrected predicted position and the track is too large, and further adjustments to the positioning result may be needed. Therefore, the system projects the corrected predicted position onto the track based on the nearest neighbor matching point and the corrected predicted position. Through this projection operation, the system projects the corrected predicted position onto the track to generate predicted position information for the ladle car that better matches the actual track position. This process ensures that the positioning result matches the actual track, thereby improving positioning accuracy.

[0072] In summary, this application's embodiments generate predicted location information for molten iron ladle cars by combining Euclidean distance calculation, nearest neighbor matching, and projection methods. This method can ensure high-precision positioning of molten iron ladle cars in complex factory environments, especially under conditions of weak or interfered GNSS signals, it can still accurately locate and adjust the car using map information and other sensor data.

[0073] In some examples, it also includes:

[0074] The QR code image of the QR code on the molten iron ladle car is obtained by the camera under a long exposure time. The QR code is located on the ladle car and is used for identification.

[0075] Based on the trail length and exposure time of the QR code positioning area, the QR code moving speed is determined;

[0076] Based on the moving speed of the QR code, the real-time speed data obtained by the vehicle-mounted wheel speed meter is corrected to obtain the corrected real-time speed data.

[0077] For example, cameras deployed within the factory area capture images of QR code markings on molten iron ladle cars using long-exposure mode. These QR code markings, mounted on the ladle cars, serve as identification markers. In the long-exposure image, the QR code produces a motion blur effect due to the movement of the ladle cars. The length of this motion blur is related to the speed of the ladle cars. By capturing this QR code image, the system can not only identify the identity information of the molten iron ladle cars but also perform speed analysis using the motion blur information.

[0078] The system calculates the QR code's moving speed by using the length of its trailing image within the QR code's positioning area and the camera's exposure time. Specifically, the trailing image length reflects the distance the QR code travels during a long exposure, while the exposure time is the time it takes for the trailing image to be generated. Using these two parameters, the system can calculate the QR code's moving speed using a formula. This speed directly corresponds to the moving speed of the molten iron ladle car, providing an additional method for speed measurement, especially when onboard wheel speedometer data may be inaccurate, offering a reliable reference.

[0079] The system compares the QR code's moving speed, calculated from its trail length, with the real-time speed data obtained from the vehicle's wheel speed meter. If errors or inconsistencies exist, the system corrects the real-time speed data from the wheel speed meter to ensure greater accuracy. This correction effectively compensates for errors in the wheel speed meter data under specific conditions, such as inaccurate wheel speed measurements due to equipment malfunction or environmental interference. The corrected real-time speed data will be used for subsequent positioning and prediction operations, further improving the positioning accuracy of the molten iron ladle car.

[0080] In summary, this embodiment of the application acquires the QR code image of the molten iron ladle car using long exposure technology, and calculates the moving speed of the QR code based on the trail length and exposure time, thereby correcting the real-time speed data obtained by the onboard wheel speed meter and ensuring the accuracy of the ladle car speed information. Through the fusion and correction of data from multiple sensors, this embodiment of the application effectively improves the real-time positioning accuracy of the molten iron ladle car, providing technical support for high-precision molten iron transportation scheduling in complex environments.

[0081] In some examples, the angle between the imaging plane of the aforementioned camera and the plane containing the aforementioned molten iron tanker and the aforementioned QR code label is less than a preset angle value.

[0082] For example, in order to ensure that the camera can accurately capture the QR code markings on the molten iron ladle car and reduce recognition errors caused by angular deviations, this application embodiment specifies that the angle between the imaging plane of the camera and the plane where the QR code markings on the molten iron ladle car are located should be less than a preset angle value. This preset angle value is set according to the actual environment and imaging requirements, and is usually less than a certain threshold (e.g., 5 degrees or 10 degrees) to ensure that the camera can capture images directly facing the QR code markings or at a near-parallel position.

[0083] By limiting the included angle to a preset value, distortion and ghosting in the QR code image can be minimized. Since the QR code is mounted on the molten iron ladle car, if the camera's imaging angle deviates too much, it may cause significant geometric distortion or ghosting in the QR code image, affecting the recognition accuracy and the precision of the ghosting length. Therefore, maintaining a small angle between the camera and the plane containing the QR code ensures a clear and accurate image, which is beneficial for subsequent speed calculations and wheel speed correction.

[0084] In summary, this embodiment ensures the accuracy and image quality of the QR code image by setting the angle between the camera's imaging plane and the plane containing the QR code on the molten iron ladle car to be less than a preset angle value. This design not only improves the accuracy of molten iron ladle car identification but also ensures the precise measurement of the QR code trail length, thus providing a reliable basis for subsequent speed data correction operations. This optimized design further enhances the accuracy and robustness of this embodiment in molten iron ladle car positioning, making it suitable for complex industrial environments.

[0085] In some examples, it also includes:

[0086] If the number of times the minimum distance to the nearest neighbor matching point exceeds a preset threshold is greater than a preset number, then the operation of adjusting the QR code movement speed will be activated.

[0087] For example, the system first determines the corrected predicted position of the molten iron ladle car and the track position on the factory map using the nearest neighbor matching method, and calculates the Euclidean distance between the nearest neighbor matching point and the corrected predicted position. If this minimum distance is greater than a preset threshold (i.e., the deviation between the corrected predicted position and the actual track position exceeds the allowable range), the system records the number of times this error occurs. If the number of errors accumulates to more than a preset number, the system considers that there is a problem with the current positioning accuracy, which may be due to inaccurate speed data obtained by the onboard wheel speed sensor.

[0088] To address this issue, if the number of errors exceeds a preset limit, the system will initiate a wheel speed correction operation based on the QR code's movement speed. Specifically, the system acquires an image of the QR code on the molten iron ladle car using a camera with a long exposure time, and calculates the QR code's movement speed using the length of the QR code's trail and the exposure time. Then, the system compares this calculated QR code movement speed with the real-time speed data obtained from the vehicle's onboard wheel speed sensor. If there is a significant difference, the system will correct the wheel speed sensor's real-time speed data to obtain the corrected real-time speed data.

[0089] The main purpose of this process is to ensure that, even if the data from the vehicle's wheel speedometer is inaccurate, the system can still correct the speed using the QR code's movement speed through dual verification (nearest neighbor matching point error detection and QR code speed calculation), thus guaranteeing the accuracy and reliability of the final positioning result. The introduction of QR code movement speed provides an additional source of reference data for the positioning process, effectively avoiding positioning deviations caused by errors from a single data source.

[0090] In summary, this embodiment of the application automatically activates a wheel speed correction operation for the QR code movement speed when the minimum distance to the nearest neighbor matching point exceeds a preset threshold more than a preset number of times, ensuring the reliability and accuracy of the positioning data. This method can effectively improve the positioning accuracy of molten iron ladle cars, especially when the on-board sensor data is abnormal, it can automatically correct through the fusion of multiple data sources, making it suitable for complex industrial environments and positioning tasks with high precision requirements.

[0091] In some examples, it also includes

[0092] The cumulative statistical feature information of the nearest neighbor matching points whose minimum distance is greater than a preset threshold is counted.

[0093] The first weight information is determined based on the aforementioned cumulative excess statistical characteristic information;

[0094] Determine the statistical characteristics of the longitudinal oscillation of the QR code positioning area in the aforementioned QR code image.

[0095] The second weighting information is determined based on the above-mentioned longitudinal oscillation statistical characteristics.

[0096] The corrected real-time speed data is obtained by weighting the first weight information, the real-time speed data obtained by the vehicle wheel speed meter, the second weight information, and the QR code movement speed.

[0097] For example, the minimum distance between the nearest neighbor matching point and the corrected predicted location is monitored. If this minimum distance is greater than a preset threshold, the system records each instance of exceeding the preset threshold and accumulates these instances. These accumulated exceedances constitute the statistical characteristics of the nearest neighbor matching error, used to assess the frequency and severity of the positioning error.

[0098] Based on the cumulative number of nearest neighbor matching errors and feature information, the system assigns a weight to the error, called the first weight information. The higher the frequency of the error, the smaller the value of the first weight information, indicating that the real-time speed data obtained by the vehicle wheel speed sensor may have a large deviation, requiring more reliance on other data sources for correction.

[0099] By analyzing the QR code images captured by the camera, the longitudinal oscillation characteristics of the QR code positioning area are calculated. The longitudinal oscillation of the QR code positioning area reflects the up-and-down fluctuations in the QR code position caused by uneven tracks, vehicle vibrations, and other factors during the movement of the molten iron ladle car. The amplitude of these fluctuations is closely related to the operating state of the ladle car, and the system statistically analyzes these fluctuations to form statistical feature information of the longitudinal oscillation in the QR code image.

[0100] Based on the vertical swaying of the QR code image, the system assigns a weight to this feature, called the second weight information. More pronounced vertical swaying means the reliability of the QR code's movement speed data may be affected; therefore, the value of the second weight information is adjusted accordingly, decreasing with greater swaying. This ensures that the impact of the QR code's speed on the final result is reduced even with significant swaying.

[0101] The system combines various weighted information and multi-dimensional data sources to perform a weighted average process. Specifically, it combines the first weighted information with real-time speed data obtained from the vehicle's wheel speed sensor, and the second weighted information with the QR code's movement speed, calculating the corrected real-time speed data using a weighted average formula. This weighting ensures that when data quality is high, more reliance is placed on the vehicle's wheel speed sensor or the QR code's movement speed, while when data quality is poor (e.g., frequent errors or severe QR code oscillations), more reliance is placed on other reliable data sources to balance the overall accuracy of the speed data.

[0102] In summary, this embodiment of the application determines different weight information by combining the cumulative statistical characteristics of nearest neighbor matching errors and the longitudinal sway characteristics of the QR code image, and obtains corrected real-time speed data using a weighted average method from multiple data sources. This method greatly improves the accuracy and robustness of the molten iron ladle car speed data, especially in complex environments or when sensor data is uncertain, ensuring that the system always obtains high-precision speed information.

[0103] like Figure 2 As shown, this application proposes a fusion positioning device for molten iron ladle cars, comprising:

[0104] Construction unit 21 is used to construct map information data within the factory area, wherein the map information data includes the coordinates of signal positions, switch positions, camera positions, dead-end earthworks, and level crossings.

[0105] The first acquisition unit 22 is used to acquire the raw GNSS positioning data and DGNSS differential correction information of the molten iron ladle car;

[0106] Determining unit 23 is used to determine high-precision GNSS differential positioning data based on the above-mentioned raw GNSS positioning data and the above-mentioned DGNSS differential correction information;

[0107] The second acquisition unit 24 is used to acquire real-time speed data and running direction data based on the vehicle-mounted wheel speed meter;

[0108] The third acquisition unit 25 is used to update and correct the position of the molten iron ladle car based on the real-time speed data, the running direction data, the high-precision GNSS differential positioning data and the map information data in the factory area, so as to obtain the predicted position information of the molten iron ladle car.

[0109] In some examples, the position of the molten iron ladle car is updated and corrected based on the aforementioned real-time speed data, the aforementioned direction of travel data, the aforementioned high-precision GNSS differential positioning data, and the factory area map information data to obtain the predicted position information of the molten iron ladle car, including:

[0110] Based on the above real-time speed data, the above running direction data, and the previous known position, a preliminary predicted position is determined;

[0111] Based on the aforementioned high-precision GNSS differential positioning data and the aforementioned preliminary predicted position, the corrected predicted position is determined using the Kalman filtering method.

[0112] Based on the above-mentioned corrected predicted location and the above-mentioned map information data within the factory area, the above-mentioned predicted location information of the molten iron ladle car is generated using the nearest neighbor matching method and the projection method.

[0113] In some examples, the predicted location information of the molten iron ladle car is generated based on the corrected predicted location and the map information data within the factory area using the nearest neighbor matching method and the projection method, including:

[0114] Based on the above-mentioned corrected predicted location and the above-mentioned factory area map information data, calculate the Euclidean distance between the above-mentioned corrected predicted location and all tracks in the map;

[0115] The orbital point with the minimum distance is determined based on all Euclidean distances and is the nearest neighbor matching point.

[0116] If the minimum distance to the nearest neighbor matching point is greater than a preset threshold, the projected location of the molten iron ladle car is determined by projecting the nearest neighbor matching point and the corrected predicted location onto the current track segment.

[0117] In some examples, it also includes:

[0118] The QR code image of the QR code on the molten iron ladle car is obtained by the camera under a long exposure time. The QR code is located on the ladle car and is used for identification.

[0119] Based on the trail length and exposure time of the QR code positioning area, the QR code moving speed is determined;

[0120] Based on the moving speed of the QR code, the real-time speed data obtained by the vehicle-mounted wheel speed meter is corrected to obtain the corrected real-time speed data.

[0121] In some examples, the angle between the imaging plane of the aforementioned camera and the plane containing the aforementioned molten iron tanker and the aforementioned QR code label is less than a preset angle value.

[0122] In some examples, it also includes:

[0123] If the number of times the minimum distance to the nearest neighbor matching point exceeds a preset threshold is greater than a preset number, then the operation of adjusting the QR code movement speed will be activated.

[0124] In some examples, it also includes

[0125] The cumulative statistical feature information of the nearest neighbor matching points whose minimum distance is greater than a preset threshold is counted.

[0126] The first weight information is determined based on the aforementioned cumulative excess statistical characteristic information;

[0127] Determine the statistical characteristics of the longitudinal oscillation of the QR code positioning area in the aforementioned QR code image.

[0128] The second weighting information is determined based on the above-mentioned longitudinal oscillation statistical characteristics.

[0129] The corrected real-time speed data is obtained by weighting the first weight information, the real-time speed data obtained by the vehicle wheel speed meter, the second weight information, and the QR code movement speed.

[0130] like Figure 3As shown, this application embodiment also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, it implements any of the steps of the above-mentioned method for fusion positioning of molten iron ladle cars.

[0131] Since the electronic device described in this embodiment is the device used to implement the fusion positioning device for a molten iron ladle car in the embodiments of this application, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in the embodiments of this application. Therefore, how the electronic device implements the method in the embodiments of this application will not be described in detail here. Any device used by those skilled in the art to implement the method in the embodiments of this application falls within the scope of protection of this application.

[0132] In practical implementation, when the computer program 311 is executed by the processor, it can achieve the following: Figure 1 Any of the corresponding implementation methods in the embodiments.

[0133] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0134] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0135] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0136] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0137] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0138] This application also provides a computer program product, which includes computer software instructions. When the computer software instructions are run on a processing device, the processing device executes the fusion positioning process of the molten iron ladle car in the corresponding embodiment.

[0139] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0140] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0141] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0142] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0143] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0144] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0145] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A fusion positioning method for molten iron ladle cars, characterized in that, include: Construct map information data within the factory area, wherein the map information data includes the coordinates of signal positions, switch positions, camera positions, dead-end road barriers, and level crossing positions; Obtain the raw GNSS positioning data and DGNSS differential correction information of the molten iron ladle car; High-precision GNSS differential positioning data is determined based on the raw GNSS positioning data and the DGNSS differential correction information; Real-time speed and direction of travel data are obtained based on the vehicle's wheel speed sensor; The position of the molten iron ladle car is updated and corrected based on the real-time speed data, the running direction data, the high-precision GNSS differential positioning data, and the map information data within the plant area, so as to obtain the predicted position information of the molten iron ladle car. The process of updating and correcting the position of the molten iron ladle car based on the real-time speed data, the running direction data, the high-precision GNSS differential positioning data, and the map information data within the plant area to obtain predicted position information for the molten iron ladle car includes: The preliminary predicted position is determined based on the real-time speed data, the running direction data, and the previous known position; The corrected predicted position is determined based on the high-precision GNSS differential positioning data and the preliminary predicted position using the Kalman filter method. Based on the corrected predicted location and the map information data within the factory area, the predicted location information of the molten iron ladle car is generated using the nearest neighbor matching method and the projection method. Based on the corrected predicted location and the factory area map information data, calculate the Euclidean distance between the corrected predicted location and all tracks in the map; The orbital point with the minimum distance is determined based on all Euclidean distances and is the nearest neighbor matching point. The cumulative statistical feature information of the nearest neighbor matching points whose minimum distance is greater than a preset threshold is statistically analyzed. The first weight information is determined based on the cumulative excess statistical feature information; The camera captures a QR code image of the QR code on the molten iron ladle car under a long exposure time. The QR code is located on the molten iron ladle car and is used for identification. The QR code image is used to correct the speed of the molten iron ladle car. Determine the statistical feature information of the vertical swing of the QR code positioning area in the QR code image. The second weighting information is determined based on the longitudinal oscillation statistical characteristics. The corrected real-time speed data is obtained by weighting the first weight information, the real-time speed data obtained by the vehicle wheel speed meter, the second weight information, and the moving speed of the QR code.

2. The fusion positioning method for molten iron ladle cars according to claim 1, characterized in that, The step of generating the predicted location information of the molten iron ladle car based on the corrected predicted location and the map information data within the factory area using the nearest neighbor matching method and the projection method includes: Based on the corrected predicted location and the factory area map information data, calculate the Euclidean distance between the corrected predicted location and all tracks in the map; The orbital point with the minimum distance is determined based on all Euclidean distances and is the nearest neighbor matching point. If the minimum distance corresponding to the nearest neighbor matching point is greater than a preset threshold, the predicted position of the molten iron ladle car is determined by projecting the nearest neighbor matching point and the corrected predicted position onto the current track segment.

3. The fusion positioning method for molten iron ladle cars according to claim 2, characterized in that, Also includes: The QR code moving speed is determined based on the trail length and exposure time of the QR code positioning area; Based on the moving speed of the QR code, the real-time speed data obtained by the vehicle wheel speed meter is corrected to obtain the corrected real-time speed data.

4. The fusion positioning method for molten iron ladle cars according to claim 3, characterized in that, The angle between the imaging plane of the camera and the plane containing the molten iron tanker and the QR code is less than a preset angle value.

5. The fusion positioning method for molten iron ladle cars according to claim 3, characterized in that, Also includes: If the number of times the minimum distance corresponding to the nearest neighbor matching point is greater than a preset threshold is greater than a preset number, the operation of adjusting the wheel speed of the QR code movement speed will be activated.

6. A fusion positioning device for molten iron ladle cars, characterized in that, include: The construction unit is used to construct map information data within the factory area, wherein the map information data includes the coordinates of signal positions, switch positions, camera positions, dead-end road barriers, and level crossing positions. The first acquisition unit is used to acquire the raw GNSS positioning data and DGNSS differential correction information of the molten iron ladle car; The determining unit is used to determine high-precision GNSS differential positioning data based on the raw GNSS positioning data and the DGNSS differential correction information; The second acquisition unit is used to acquire real-time speed data and running direction data based on the vehicle-mounted wheel speed meter; The third acquisition unit is used to update and correct the position of the molten iron ladle car based on the real-time speed data, the running direction data, the high-precision GNSS differential positioning data and the map information data in the factory area, so as to obtain the predicted position information of the molten iron ladle car. The third acquisition unit is also used for: The preliminary predicted position is determined based on the real-time speed data, the running direction data, and the previous known position; The corrected predicted position is determined based on the high-precision GNSS differential positioning data and the preliminary predicted position using the Kalman filter method. Based on the corrected predicted location and the map information data within the factory area, the predicted location information of the molten iron ladle car is generated using the nearest neighbor matching method and the projection method. Based on the corrected predicted location and the factory area map information data, calculate the Euclidean distance between the corrected predicted location and all tracks in the map; The orbital point with the minimum distance is determined based on all Euclidean distances and is the nearest neighbor matching point. The cumulative statistical feature information of the nearest neighbor matching points whose minimum distance is greater than a preset threshold is statistically analyzed. The first weight information is determined based on the cumulative excess statistical feature information; The camera captures a QR code image of the QR code on the molten iron ladle car under a long exposure time. The QR code is located on the molten iron ladle car and is used for identification. The QR code image is used to correct the speed of the molten iron ladle car. Determine the statistical feature information of the vertical swing of the QR code positioning area in the QR code image. The second weighting information is determined based on the longitudinal oscillation statistical characteristics. The corrected real-time speed data is obtained by weighting the first weight information, the real-time speed data obtained by the vehicle wheel speed meter, the second weight information, and the moving speed of the QR code.

7. An electronic device, comprising: The memory and processor are characterized in that the processor is used to execute the computer program stored in the memory to implement the steps of the fusion positioning method for molten iron ladle cars as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the fusion positioning method for molten iron ladle cars as described in any one of claims 1-5.

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