Fusion positioning method of molten iron tank car and related equipment

Abstract

The invention 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: building map information data in a plant area, the map information data comprising a signal machine position coordinate, a turnout position coordinate, a camera position coordinate, an end line soil block position coordinate and a level crossing position coordinate; gNSS original positioning data and DGNSS differential correction information of the molten iron tank car are obtained; 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; and 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 to obtain predicted position information of the molten iron tank car.

Description

Technical Field

[0001] The present specification relates to the field of molten iron tanker positioning, and more specifically, the present application relates to a fusion positioning method and related equipment for a molten iron tanker. Background Art

[0002] During the transportation of molten iron, the positioning and scheduling of molten iron tank trucks is a key 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 factory environment, such as tall buildings, signal blocking areas, etc., the GNSS positioning accuracy is significantly affected. Especially in industrial environments with high precision requirements, GNSS may not be able to provide high-precision positioning that meets the requirements. In addition, the transportation speed and running direction of the molten iron tank truck, as well as the complex road structure and intersections in the factory, also increase the difficulty of positioning. Therefore, how to improve the real-time positioning accuracy of the molten iron tank truck and ensure the safety and efficiency of the transportation process has become a major challenge in current technology. In order to at least solve some of the above problems, it is urgent to provide a fusion positioning method for molten iron tank trucks. Summary of the invention

[0003] A series of simplified concepts are introduced in the Summary of the Invention section, which will be further described in detail in the Detailed Description of the Invention section. The Summary of the Invention section of this application does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0004] In a first aspect, the present application proposes a fusion positioning method for a molten iron tank car, comprising:

[0005] Constructing map information data within the plant area, wherein the map information data includes the position coordinates of the signal machine, the position coordinates of the turnout, the position coordinates of the camera, the position coordinates of the dead-end line earth retaining, and the position coordinates of the level crossing;

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

[0007] Determine high-precision GNSS differential positioning data based on the above-mentioned GNSS original positioning data and the above-mentioned DGNSS differential correction information;

[0008] Obtain real-time speed data and running direction data based on the on-board wheel speedometer;

[0009] The position of the molten iron tank 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 factory area to obtain the predicted position information of the molten iron tank car.

[0010] In a feasible implementation manner, the above-mentioned updating and correction of the position of the above-mentioned molten iron tanker 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 to obtain the predicted position information of the molten iron tanker includes:

[0011] Determine a preliminary predicted position based on the real-time speed data, the running direction data and the last known position;

[0012] Determine a corrected predicted position based on the Kalman filter method according to the high-precision GNSS differential positioning data and the preliminary predicted position;

[0013] The above-mentioned molten iron tank car predicted position information is generated based on the above-mentioned corrected predicted position and the above-mentioned map information data within the factory area based on the nearest neighbor matching method and the projection method.

[0014] In a feasible implementation manner, the above-mentioned method of generating the above-mentioned predicted position information of the molten iron tank car based on the above-mentioned corrected predicted position and the above-mentioned map information data within the plant area based on the nearest neighbor matching method and the projection method includes:

[0015] Based on the above-mentioned corrected predicted position and the above-mentioned plant map information data, calculating the Euclidean distance between the above-mentioned corrected predicted position and all tracks in the map;

[0016] Based on all Euclidean distances, the track point with the minimum distance is determined as the nearest neighbor matching point;

[0017] When the minimum distance corresponding to the nearest neighbor matching point is greater than a preset threshold, projection is performed on the current track segment based on the nearest neighbor matching point and the corrected predicted position to determine the predicted position information of the molten iron tank car.

[0018] In a feasible implementation, it also includes:

[0019] Based on the camera, a two-dimensional code image of the two-dimensional code mark on the above-mentioned molten iron tank car is obtained under a long exposure time, wherein the two-dimensional code mark is located on the tank car for identity recognition;

[0020] Determine the moving speed of the QR code based on the smear length and exposure time of the QR code positioning area;

[0021] Based on the moving speed of the two-dimensional code, a wheel speed correction operation is performed on the real-time speed data obtained by the vehicle-mounted wheel speed meter to obtain corrected real-time speed data.

[0022] In a feasible implementation manner, the angle between the imaging plane of the camera and the plane where the molten iron tank car and the two-dimensional code logo are located is less than a preset angle value.

[0023] In a feasible implementation, it also includes:

[0024] When the number of times that the minimum distance corresponding to the nearest neighbor matching point is greater than the preset threshold is greater than the preset number of times, the operation of correcting the wheel speed by using the moving speed of the two-dimensional code is started.

[0025] In a feasible implementation, it also includes

[0026] Count the cumulative exceeding statistical feature information of the minimum distance corresponding to the nearest neighbor matching points that is greater than a preset threshold;

[0027] Determine first weight information based on the above-mentioned cumulative excess statistical feature information;

[0028] Determine the longitudinal swing statistical characteristic information of the two-dimensional code positioning area in the two-dimensional code image

[0029] Determine second weight information based on the longitudinal swing statistical feature information;

[0030] A weighted average is performed based on the first weight information, the real-time speed data obtained by the vehicle-mounted wheel speedometer, the second weight information and the moving speed of the two-dimensional code to obtain the corrected real-time speed data.

[0031] In a second aspect, the present application proposes a fusion positioning device for a molten iron tank car, comprising:

[0032] A construction unit, used to construct map information data within the factory area, wherein the map information data includes the position coordinates of the signal machine, the position coordinates of the turnout, the position coordinates of the camera, the position coordinates of the dead-end line earth barrier, and the position coordinates of the level crossing;

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

[0034] A determination unit, configured to determine high-precision GNSS differential positioning data based on the above-mentioned GNSS original positioning data and the above-mentioned DGNSS differential correction information;

[0035] A second acquisition unit, used for acquiring real-time speed data and running direction data based on the vehicle-mounted wheel speedometer;

[0036] The third acquisition unit is used to update and correct the position of the molten iron tanker 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 factory to obtain the predicted position information of the molten iron tanker.

[0037] In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of the fusion positioning method of a molten iron tank car as described in any one of the first aspects when executing the computer program stored in the memory.

[0038] In a fourth aspect, the present application further proposes a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the fusion positioning method of the molten iron tank car of any one of the first aspects is implemented.

[0039] In summary, the fusion positioning method of the molten iron tank car proposed in the embodiment of the present application proposes a molten iron tank car positioning method that integrates GNSS original positioning data, DGNSS differential correction information, on-board wheel speedometer data and map information in the factory area, which significantly improves the positioning accuracy and reliability. Compared with the relevant technology, the embodiment of the present application not only relies on GNSS and DGNSS, but also obtains the real-time speed and running direction data of the molten iron tank car through the on-board wheel speedometer, and combines the map information in the factory area (including the position coordinates of signal machines, switches, cameras, etc.). Through the information fusion of multiple data sources, it is possible to effectively make up for the lack of accuracy caused by the single signal source of GNSS, especially when the GNSS signal is blocked or misaligned, it is still possible to rely on auxiliary information such as speed and direction for accurate positioning. In the embodiment of the present application, positioning not only relies on historical position data, but also obtains and updates the speed and direction data in real time, and dynamically corrects the real-time position through an algorithm. Compared with the static differential positioning method in the prior art, the embodiment of the present application ensures the accurate tracking and positioning of the molten iron tank car in complex road conditions through the continuous correction of real-time data, and improves the reliability of the positioning result. The embodiment of the present application incorporates the coordinate information of important locations in the factory (such as signal machines, switches, cameras, etc.) into the positioning process, further enhancing the adaptability of the system in a specific industrial environment. By adding the factory map information, not only can the positioning results be optimized, but also additional reference information can be provided at key locations to help the positioning algorithm provide more stable positioning services at key nodes or areas with poor signals. The embodiment of the present application generates predicted position information on the basis of real-time positioning by integrating multiple data sources. The acquisition of predicted position information enables the system to not only accurately grasp the current position of the molten iron tank car, but also to estimate its future travel path and arrival time. This function has significant advantages in improving the efficiency and safety of production scheduling, can ensure the efficiency and continuity of the molten iron transportation process, and reduce the occurrence of scheduling errors and emergencies. In general, the embodiment of the present application greatly improves the positioning accuracy and reliability of the molten iron tank car in a complex environment through multi-sensor information fusion and real-time data update technology. Compared with traditional positioning solutions that rely on a single GNSS / DGNSS technology, the embodiments of the present application have significant advantages in accuracy, robustness and adaptability. They are suitable for high-demand positioning scenarios during molten iron transportation and meet the safety and efficiency requirements in industrial environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0041] Figure 1A schematic diagram of the process of a fusion positioning method for a molten iron tank car provided in an embodiment of the present application;

[0042] Figure 2 A schematic diagram of the structure of a fusion positioning device for a molten iron tank car provided in an embodiment of the present application;

[0043] Figure 3 A schematic diagram of the structure of a fusion positioning electronic device for a molten iron tank truck provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0045] See also Figure 1 , which is a schematic diagram of the process of a fusion positioning method for a molten iron tank car provided in an embodiment of the present application, and may specifically include:

[0046] S110, constructing map information data within the factory area, wherein the map information data includes the position coordinates of the signal machine, the position coordinates of the turnout, the position coordinates of the camera, the position coordinates of the dead-end line earth barrier, and the position coordinates of the level crossing;

[0047] For example, detailed map information data is constructed by collecting information of multiple important coordinate points in the factory area. The above map information data includes but is not limited to: signal position coordinates, turnout position coordinates, camera position coordinates, dead-end line earth retaining position coordinates, and level crossing position coordinates. By accurately measuring and recording these coordinate points, the geographic information data in the factory area is ensured to be complete and accurate, providing a reliable reference for subsequent positioning.

[0048] S120, obtaining GNSS original positioning data and DGNSS differential correction information of the molten iron tank car;

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

[0050] S130, determining high-precision GNSS differential positioning data based on the above-mentioned GNSS original positioning data and the above-mentioned DGNSS differential correction information;

[0051] Exemplarily, based on the GNSS original positioning data and DGNSS differential correction information obtained in the previous step, high-precision GNSS differential positioning data is calculated and generated. By correcting and adjusting the original data, the system can obtain more accurate positioning results to ensure that the current position of the molten iron tank car is accurate.

[0052] S140, acquiring real-time speed data and running direction data based on the vehicle-mounted wheel speedometer;

[0053] For example, real-time speed data is obtained from the wheel speedometer of the molten iron tank car, and combined with the running direction information of the molten iron tank car, the dynamic data required for positioning is further enriched. The accurate acquisition of these data is crucial to ensure positioning accuracy and predict the future position of the molten iron tank car.

[0054] S150. 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, the position of the molten iron tank car is updated and corrected to obtain the predicted position information of the molten iron tank car.

[0055] For example, the above multiple data sources, including real-time speed data, running direction data, high-precision GNSS differential positioning data, and plant map information data, are integrated to update and correct the current position of the molten iron tank car through a specific algorithm, and finally obtain the predicted position information of the molten iron tank car. This predicted position information can more accurately reflect the position change of the molten iron tank car in the future, helping to improve the stability and reliability of positioning.

[0056] In summary, the embodiment of the present application proposes a method for locating a molten iron tank car that integrates GNSS original positioning data, DGNSS differential correction information, on-board wheel speedometer data, and map information within the factory area, which significantly improves positioning accuracy and reliability. Compared with the relevant technology, the embodiment of the present application not only relies on GNSS and DGNSS, but also obtains the real-time speed and running direction data of the molten iron tank car through the on-board wheel speedometer, and combines the map information within the factory area (including the position coordinates of signal machines, switches, cameras, etc.). Through the information fusion of multiple data sources, it is possible to effectively make up for the lack of accuracy caused by a single GNSS signal source, especially when the GNSS signal is blocked or inaccurate, it is still possible to rely on auxiliary information such as speed and direction for accurate positioning. In the embodiment of the present application, positioning not only relies on historical position data, but also obtains and updates vehicle speed and direction data in real time, and dynamically corrects the real-time position through an algorithm. Compared with the static differential positioning method in the prior art, the embodiment of the present application ensures the accurate tracking and positioning of the molten iron tank car in complex road conditions through the continuous correction of real-time data, and improves the reliability of the positioning result. The embodiment of the present application incorporates the coordinate information of important locations in the factory (such as signal machines, switches, cameras, etc.) into the positioning process, further enhancing the adaptability of the system in a specific industrial environment. By adding the factory map information, not only can the positioning results be optimized, but also additional reference information can be provided at key locations to help the positioning algorithm provide more stable positioning services at key nodes or areas with poor signals. The embodiment of the present application generates predicted position information on the basis of real-time positioning by integrating multiple data sources. The acquisition of predicted position information enables the system to not only accurately grasp the current position of the molten iron tank car, but also to estimate its future travel path and arrival time. This function has significant advantages in improving the efficiency and safety of production scheduling, can ensure the efficiency and continuity of the molten iron transportation process, and reduce the occurrence of scheduling errors and emergencies. In general, the embodiment of the present application greatly improves the positioning accuracy and reliability of the molten iron tank car in a complex environment through multi-sensor information fusion and real-time data update technology. Compared with traditional positioning solutions that rely on a single GNSS / DGNSS technology, the embodiments of the present application have significant advantages in accuracy, robustness and adaptability. They are suitable for high-demand positioning scenarios during molten iron transportation and meet the safety and efficiency requirements in industrial environments.

[0057] In some examples, the position of the molten iron tanker 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 the predicted position information of the molten iron tanker, including:

[0058] Determine a preliminary predicted position based on the real-time speed data, the running direction data and the last known position;

[0059] Determine a corrected predicted position based on the Kalman filter method according to the high-precision GNSS differential positioning data and the preliminary predicted position;

[0060] The above-mentioned molten iron tank car predicted position information is generated based on the above-mentioned corrected predicted position and the above-mentioned map information data within the factory area based on the nearest neighbor matching method and the projection method.

[0061] For example, the real-time speed data and running direction data of the molten iron tank car are used in combination with the last known position to calculate the preliminary predicted position of the molten iron tank car through the kinematic equation. The core of this step is to use the speed and direction information to infer the displacement of the molten iron tank car in a short period of time and obtain the preliminary position prediction of the tank car.

[0062] Next, the corrected predicted position is determined based on the Kalman filter method according to the above-mentioned high-precision GNSS differential positioning data and the above-mentioned preliminary predicted position, and the preliminary predicted position is corrected using the Kalman filter method. First, the high-precision GNSS differential positioning data obtained by GNSS differential correction is compared with the preliminary predicted position. The Kalman filter method will comprehensively consider the confidence of the GNSS positioning data and the preliminary predicted position, and perform noise processing on the positioning data to eliminate random errors in the data, and finally obtain the corrected predicted position. This process can effectively smooth the positioning data and improve the stability and accuracy of positioning.

[0063] Finally, the predicted position information of the molten iron tank car is generated based on the nearest neighbor matching method and the projection method according to the above-mentioned corrected predicted position and the above-mentioned map information data within the plant area, and the corrected predicted position is further optimized in combination with the map information within the plant area. Specifically, the system matches the corrected predicted position with the known tracks, switches or other key coordinate points on the plant map through the nearest neighbor matching method to ensure that the position of the molten iron tank car conforms to the actual track layout. At the same time, the system also projects the corrected predicted position into the map coordinate system based on the projection method to ensure the spatial consistency of the position data. Through the combination of these two methods, the predicted position information of the molten iron tank car is finally generated.

[0064] The embodiment of the present application can significantly improve the accuracy and robustness of the positioning of the molten iron tank car through the above-mentioned multi-step data fusion and position correction, especially in a complex factory environment, to ensure that the real-time position update and prediction of the molten iron tank car are accurate and meet the high requirements of production scheduling and safety management.

[0065] In some examples, the method of generating the predicted position information of the molten iron tanker based on the corrected predicted position and the map information data in the plant area based on the nearest neighbor matching method and the projection method includes:

[0066] Based on the above-mentioned corrected predicted position and the above-mentioned plant map information data, calculating the Euclidean distance between the above-mentioned corrected predicted position and all tracks in the map;

[0067] Based on all Euclidean distances, the track point with the minimum distance is determined as the nearest neighbor matching point;

[0068] When the minimum distance corresponding to the nearest neighbor matching point is greater than a preset threshold, projection is performed on the current track segment based on the nearest neighbor matching point and the corrected predicted position to determine the predicted position information of the molten iron tank car.

[0069] Exemplarily, based on the map information data in the plant (including the coordinate point information of the track) and the corrected predicted position obtained by the Kalman filter method, the Euclidean distance between the corrected predicted position and all the tracks in the map is calculated. The Euclidean distance is a standard spatial distance calculation method used to measure the straight-line distance between the corrected predicted position and each track. By calculating the distance from each track point to the corrected predicted position, the system can determine the vicinity of the track where the molten iron ladle is most likely to be located in the current environment.

[0070] Next, based on all the Euclidean distances, the track point with the minimum distance is determined as the nearest neighbor matching point, and the previously calculated Euclidean distances are compared to determine the track point with the minimum distance 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 tank car, which can well reflect the actual position of the tank car on the track. Through this step, the system can effectively match the corrected predicted position with the track in the plant map.

[0071] Finally, when the minimum distance corresponding to the nearest neighbor matching point is greater than the preset threshold, the nearest neighbor matching point and the corrected predicted position are projected on the current track segment to determine the predicted position information of the molten iron tank car, and the minimum distance between the nearest neighbor matching point and the corrected predicted position is further checked. If the minimum distance is greater than the preset threshold, it means that the distance between the corrected predicted position and the track is too large, and the positioning result may need to be further adjusted. Therefore, the system projects along the current track segment 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 of the molten iron tank car that is more consistent with the actual track position. This process ensures that the positioning result can match the actual track, thereby improving the positioning accuracy.

[0072] In summary, the embodiment of the present application generates predicted position information of the molten iron tanker by combining Euclidean distance calculation, nearest neighbor matching and projection method. This method can ensure high-precision positioning of the molten iron tanker in a complex factory environment, especially when the GNSS signal is weak or interfered, and can still accurately locate and adjust through map information and other sensor data.

[0073] In some examples, this also includes:

[0074] Based on the camera, a two-dimensional code image of the two-dimensional code mark on the above-mentioned molten iron tank car is obtained under a long exposure time, wherein the two-dimensional code mark is located on the tank car for identity recognition;

[0075] Determine the moving speed of the QR code based on the smear length and exposure time of the QR code positioning area;

[0076] Based on the moving speed of the two-dimensional code, a wheel speed correction operation is performed on the real-time speed data obtained by the vehicle-mounted wheel speed meter to obtain corrected real-time speed data.

[0077] For example, a QR code logo of a hot metal ladle is photographed in long exposure mode by a camera arranged in the factory. The QR code logo is installed on the hot metal ladle as an identification mark of the ladle. In the QR code image photographed in long exposure mode, the QR code will produce a smear effect due to the movement of the ladle. The length of this smear is related to the moving speed of the ladle. By photographing this QR code logo image, the system can not only identify the identity information of the hot metal ladle, but also use the smear information for speed analysis.

[0078] The moving speed of the QR code is calculated using the smear length of the QR code positioning area and the exposure time of the camera. Specifically, the smear length reflects the distance the QR code moves during the long exposure process, while the exposure time is the time when the smear is generated. With these two parameters, the system can use a formula to calculate the moving speed of the QR code. This speed directly corresponds to the moving speed of the molten iron ladle car, providing an additional speed measurement method, especially when the on-board wheel speedometer data may be inaccurate, providing a reliable reference.

[0079] The moving speed of the QR code calculated by the QR code smear length is compared with the real-time speed data obtained by the on-board wheel speedometer. When there is an error or inconsistency, the system will correct the real-time speed data of the on-board wheel speedometer to ensure that the final speed data is more accurate. This correction operation can effectively compensate for the error of the wheel speedometer data in certain circumstances, such as inaccurate wheel speed measurement due to equipment failure or environmental interference. The corrected real-time speed data will be used for subsequent positioning and prediction operations to further improve the accuracy of the positioning of the molten iron tank car.

[0080] In summary, the embodiment of the present application obtains the QR code image of the molten iron tank truck through long exposure technology, and calculates the moving speed of the QR code based on the smear length and exposure time, and then corrects the real-time speed data obtained by the on-board wheel speedometer to ensure the accuracy of the tank truck speed information. Through the fusion and correction of multiple sensor data, the embodiment of the present application effectively improves the real-time positioning accuracy of the molten iron tank truck, and provides technical support for high-precision molten iron transportation scheduling in complex environments.

[0081] In some examples, the angle between the imaging plane of the camera and the plane where the molten iron tank car and the QR code logo are located is less than a preset angle value.

[0082] For example, in order to ensure that the camera can accurately capture the QR code on the molten iron tank car and reduce the recognition error caused by angle deviation, the embodiment of the present application stipulates that the angle between the imaging plane of the camera and the plane where the QR code on the molten iron tank car is located should be less than a preset angle value. The preset angle value is set according to the actual environment and imaging requirements, and is usually less than a certain threshold (such as 5 degrees or 10 degrees) to ensure that the camera can shoot directly at the QR code or in a nearly parallel position.

[0083] By limiting the angle to be less than the preset angle value, the distortion of the QR code image and the smear can be minimized. Since the QR code logo is installed on the molten iron ladle car, if the camera's imaging angle deviates too much, it may cause obvious geometric deformation or smear elongation of the QR code image, thereby affecting the recognition effect of the QR code and the accuracy of the smear length. Therefore, by maintaining a small angle between the camera and the plane where the QR code is located, the QR code image can be ensured to be clear and accurate, which is conducive to the subsequent speed calculation and wheel speed correction operations.

[0084] In summary, the embodiment of the present application ensures the accuracy and imaging quality of the QR code image by setting the angle between the imaging plane of the camera and the plane where the QR code logo of the molten iron tanker is located to be less than a preset angle value. This design not only improves the accuracy of the identification of the molten iron tanker, but also ensures the accurate measurement of the length of the QR code smear, thereby providing a reliable basis for subsequent speed data correction operations. This optimized design further improves the accuracy and robustness of the embodiment of the present application in the positioning of the molten iron tanker, and is suitable for complex industrial environments.

[0085] In some examples, this also includes:

[0086] When the number of times that the minimum distance corresponding to the nearest neighbor matching point is greater than the preset threshold is greater than the preset number of times, the operation of correcting the wheel speed by using the moving speed of the two-dimensional code is started.

[0087] Exemplarily, the system first determines the corrected predicted position of the molten iron tank car and the track position in the plant map by the nearest neighbor matching method, and calculates the Euclidean distance between the nearest neighbor matching point and the corrected predicted position. If the minimum distance is greater than the preset threshold (i.e., the deviation between the corrected predicted position and the actual track position exceeds the allowable range), the system will record the number of times this error occurs. If the number of errors accumulates to more than the preset number, the system believes that there is a problem with the current positioning accuracy, which may be caused by inaccurate speed data obtained by the on-board wheel speedometer.

[0088] To solve this problem, when the number of errors is greater than the preset number, the system will start the QR code moving speed to correct the wheel speed. Specifically, the system uses the camera to obtain the image of the QR code logo on the molten iron tank car under a long exposure time, and uses the smear length and exposure time of the QR code to calculate the moving speed of the QR code. Then, the system compares the calculated QR code moving speed with the real-time speed data obtained by the on-board wheel speedometer. If there is a significant difference between the two, the system will correct the real-time speed data of the wheel speedometer to obtain the corrected real-time speed data.

[0089] The main purpose of this process is to ensure that, through double verification (nearest neighbor matching point error detection and QR code speed calculation), when the on-board wheel speedometer data is inaccurate, the system can still make speed corrections through the QR code moving speed, ensuring that the final positioning result is accurate and reliable. The introduction of the QR code moving speed provides an additional reference data source for the positioning process, effectively avoiding positioning deviations caused by errors in a single data source.

[0090] In summary, the embodiment of the present application ensures the reliability and accuracy of the positioning data by automatically starting the QR code moving speed to perform wheel speed correction operation when the minimum distance of the nearest neighbor matching point exceeds the preset threshold for more than the preset number of times. This method can effectively improve the positioning accuracy of the molten iron tank car, especially when the on-board sensor data is abnormal, and can automatically correct through the fusion of multiple data sources, which is suitable for complex industrial environments and high-precision positioning tasks.

[0091] In some examples, this also includes

[0092] Count the cumulative exceeding statistical feature information of the minimum distance corresponding to the nearest neighbor matching points that is greater than a preset threshold;

[0093] Determine first weight information based on the above-mentioned cumulative excess statistical feature information;

[0094] Determine the longitudinal swing statistical characteristic information of the two-dimensional code positioning area in the two-dimensional code image

[0095] Determine second weight information based on the longitudinal swing statistical feature information;

[0096] A weighted average is performed based on the first weight information, the real-time speed data obtained by the vehicle-mounted wheel speedometer, the second weight information and the moving speed of the two-dimensional code to obtain the corrected real-time speed data.

[0097] Exemplarily, the minimum distance between the nearest neighbor matching point and the corrected predicted position is monitored. If the minimum distance is greater than a preset threshold, the system will record each time the preset threshold is exceeded and accumulate statistics on the number of times these exceedances occur. These accumulated exceedance data constitute the statistical characteristic information of the nearest neighbor matching error, which is used to evaluate the frequency and severity of the positioning error.

[0098] According to the cumulative number of times the nearest neighbor matching error exceeds the characteristic information, the system will assign a weight to the error, which is 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 on-board wheel speedometer may have a large deviation and needs to rely more on other data sources for correction.

[0099] By analyzing the QR code image captured by the camera, the longitudinal swing characteristics of the QR code positioning area are calculated. The longitudinal swing of the QR code positioning area can reflect the up and down fluctuations of the QR code position caused by uneven tracks, body shaking, etc. during the driving of the molten iron tank car. The amplitude of these fluctuations is closely related to the operating status of the tank car. The system will count these fluctuations to form the longitudinal swing statistical characteristic information in the QR code image.

[0100] According to the longitudinal swing in the QR code image, the system will assign a weight to the feature, which is called the second weight information. The more violent the longitudinal swing, the more likely the reliability of the QR code moving speed data will be affected, so the value of the second weight information will be adjusted accordingly. The more violent the swing, the smaller the value of the second weight information. This ensures that in the case of large swing, the impact of the QR code speed on the final result is reduced.

[0101] Combining various weight information and multi-dimensional data sources, weighted average processing is performed. Specifically, the system combines the first weight information with the real-time speed data obtained by the on-board wheel speedometer, and combines the second weight information with the moving speed of the QR code, and calculates the corrected real-time speed data through the weighted average formula. The setting of weights ensures that when the data quality is high, more reliance is placed on the on-board wheel speedometer or the moving speed of the QR code, and when the data quality is poor (for example, frequent errors or violent swings of the QR code), more reliance is placed on other reliable data sources to balance the accuracy of the overall speed data.

[0102] In summary, the embodiment of the present application determines different weight information by combining the cumulative statistical characteristics of the nearest neighbor matching error and the longitudinal swing characteristics of the two-dimensional code image, and obtains the corrected real-time speed data by weighted average of multiple data sources. This method greatly improves the accuracy and robustness of the speed data of the molten iron tank car, especially in complex environments or when there is uncertainty in sensor data, and can ensure that the system always obtains high-precision speed information.

[0103] like Figure 2 As shown, the present application proposes a fusion positioning device for a molten iron tank car, comprising:

[0104] A construction unit 21 is used to construct map information data within the factory area, wherein the map information data includes the position coordinates of the signal machine, the position coordinates of the turnout, the position coordinates of the camera, the position coordinates of the dead-end line earth barrier, and the position coordinates of the level crossing;

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

[0106] A determination unit 23, configured to determine high-precision GNSS differential positioning data based on the above-mentioned GNSS original positioning data and the above-mentioned DGNSS differential correction information;

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

[0108] The third acquisition unit 25 is used to update and correct the position of the molten iron tanker 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 to obtain the predicted position information of the molten iron tanker.

[0109] In some examples, the position of the molten iron tanker 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 the predicted position information of the molten iron tanker, including:

[0110] Determine a preliminary predicted position based on the real-time speed data, the running direction data and the last known position;

[0111] Determine a corrected predicted position based on the Kalman filter method according to the high-precision GNSS differential positioning data and the preliminary predicted position;

[0112] The above-mentioned molten iron tank car predicted position information is generated based on the above-mentioned corrected predicted position and the above-mentioned map information data within the factory area based on the nearest neighbor matching method and the projection method.

[0113] In some examples, the method of generating the predicted position information of the molten iron tanker based on the corrected predicted position and the map information data in the plant area based on the nearest neighbor matching method and the projection method includes:

[0114] Based on the above-mentioned corrected predicted position and the above-mentioned plant map information data, calculating the Euclidean distance between the above-mentioned corrected predicted position and all tracks in the map;

[0115] Based on all Euclidean distances, the track point with the minimum distance is determined as the nearest neighbor matching point;

[0116] When the minimum distance corresponding to the nearest neighbor matching point is greater than a preset threshold, projection is performed on the current track segment based on the nearest neighbor matching point and the corrected predicted position to determine the predicted position information of the molten iron tank car.

[0117] In some examples, this also includes:

[0118] Based on the camera, a two-dimensional code image of the two-dimensional code mark on the above-mentioned molten iron tank car is obtained under a long exposure time, wherein the two-dimensional code mark is located on the tank car for identity recognition;

[0119] Determine the moving speed of the QR code based on the smear length and exposure time of the QR code positioning area;

[0120] Based on the moving speed of the two-dimensional code, a wheel speed correction operation is performed on the real-time speed data obtained by the vehicle-mounted wheel speed meter to obtain corrected real-time speed data.

[0121] In some examples, the angle between the imaging plane of the camera and the plane where the molten iron tank car and the QR code logo are located is less than a preset angle value.

[0122] In some examples, this also includes:

[0123] When the number of times that the minimum distance corresponding to the nearest neighbor matching point is greater than the preset threshold is greater than the preset number of times, the operation of correcting the wheel speed by using the moving speed of the two-dimensional code is started.

[0124] In some examples, this also includes

[0125] Count the cumulative exceeding statistical feature information of the minimum distance corresponding to the nearest neighbor matching points that is greater than a preset threshold;

[0126] Determine first weight information based on the above-mentioned cumulative excess statistical feature information;

[0127] Determine the longitudinal swing statistical characteristic information of the two-dimensional code positioning area in the two-dimensional code image

[0128] Determine second weight information based on the longitudinal swing statistical feature information;

[0129] A weighted average is performed based on the first weight information, the real-time speed data obtained by the vehicle-mounted wheel speedometer, the second weight information and the moving speed of the two-dimensional code to obtain the corrected real-time speed data.

[0130] like Figure 3As shown, an embodiment of the present application 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, and when the processor 320 executes the computer program 311, the steps of any method of fusion positioning of the above-mentioned molten iron tank car are implemented.

[0131] Since the electronic device introduced in this embodiment is a device used to implement a fusion positioning device of a molten iron tank car in the embodiment of the present application, based on the method introduced in the embodiment of the present application, the technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by the technical personnel in this field to implement the method in the embodiment of the present application is adopted, it belongs to the scope of protection of this application.

[0132] In the specific implementation process, when the computer program 311 is executed by the processor, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0133] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0134] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0135] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0136] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0137] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

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

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

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

[0141] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

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

[0143] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0144] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0145] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A fusion positioning method for a molten iron tank car, characterized in that: include: Constructing map information data within the plant area, wherein the map information data includes the position coordinates of the signal machine, the position coordinates of the turnout, the position coordinates of the camera, the position coordinates of the dead-end line earth retaining, and the position coordinates of the level crossing; Obtain the 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; Obtain real-time speed data and running direction data based on the on-board wheel speedometer; The position of the molten iron tanker 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 to obtain the predicted position information of the molten iron tanker.

2. The fusion positioning method of the molten iron tank car according to claim 1 is characterized in that: The updating and correction of the position of the molten iron tanker 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 the predicted position information of the molten iron tanker includes: Determine a preliminary predicted position based on the real-time speed data, the running direction data and the last known position; Determine a corrected predicted position based on the high-precision GNSS differential positioning data and the preliminary predicted position based on the Kalman filter method; The predicted position information of the molten iron tank car is generated based on the nearest neighbor matching method and the projection method according to the corrected predicted position and the map information data within the plant.

3. The fusion positioning method of the molten iron tank car according to claim 2 is characterized in that: The method of generating the predicted position information of the molten iron tank car based on the corrected predicted position and the map information data within the plant area based on the nearest neighbor matching method and the projection method includes: Based on the corrected predicted position and the plant map information data, calculating the Euclidean distance between the corrected predicted position and all tracks in the map; Based on all Euclidean distances, the track point with the minimum distance is determined as the nearest neighbor matching point; When the minimum distance corresponding to the nearest neighbor matching point is greater than a preset threshold, projection is performed on the current track segment based on the nearest neighbor matching point and the corrected predicted position to determine the predicted position information of the molten iron tank car.

4. The fusion positioning method of the molten iron tank car according to claim 3 is characterized in that: Also includes: Based on the camera, a two-dimensional code image of the two-dimensional code mark on the molten iron tank car is obtained under a long exposure time, wherein the two-dimensional code mark is located on the tank car for identity recognition; Determining the moving speed of the QR code based on the smear length and exposure time of the QR code positioning area; A wheel speed correction operation is performed on the real-time speed data acquired by the vehicle-mounted wheel speedometer based on the moving speed of the two-dimensional code to acquire the corrected real-time speed data.

5. The fusion positioning method of the molten iron tank car according to claim 4, characterized in that: The angle between the imaging plane of the camera and the plane where the molten iron tank car and the two-dimensional code logo are located is less than a preset angle value.

6. The fusion positioning method of the molten iron tank car according to claim 4, characterized in that: Also includes: When the number of times that the minimum distance corresponding to the nearest neighbor matching point is greater than the preset threshold is greater than the preset number of times, the operation of correcting the wheel speed by using the moving speed of the two-dimensional code is started.

7. The fusion positioning method of the molten iron tank car according to claim 6, characterized in that: Also includes Counting the cumulative exceeding statistical feature information of the minimum distance corresponding to the nearest neighbor matching point being greater than a preset threshold; Determine first weight information based on the accumulated exceeding statistical feature information; Determine the longitudinal swing statistical characteristic information of the two-dimensional code positioning area in the two-dimensional code image Determining second weight information based on the longitudinal swing statistical feature information; A weighted average is performed based on the first weight information, the real-time speed data obtained by the vehicle-mounted wheel speedometer, the second weight information and the moving speed of the two-dimensional code to obtain the corrected real-time speed data.

8. A fusion positioning device for a molten iron tank car, characterized in that: include: A construction unit, used to construct map information data within the factory area, wherein the map information data includes the position coordinates of the signal machine, the position coordinates of the turnout, the position coordinates of the camera, the position coordinates of the dead-end line earth barrier, and the position coordinates of the level crossing; A first acquisition unit is used to acquire the GNSS original positioning data and DGNSS differential correction information of the molten iron tank car; A determination unit, configured to determine high-precision GNSS differential positioning data based on the GNSS original positioning data and the DGNSS differential correction information; A second acquisition unit, used for acquiring real-time speed data and running direction data based on the vehicle-mounted wheel speedometer; The third acquisition unit is used to update and correct 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 within the factory area to obtain the predicted position information of the molten iron tank car.

9. An electronic device, comprising: A memory and a processor, characterized in that the processor is used to implement the steps of the fusion positioning method of the molten iron tank car as described in any one of claims 1-7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the fusion positioning method for a molten iron tank car according to any one of claims 1 to 7 are implemented.

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