A molten iron overpassing car fusion positioning system and method

Abstract

The application discloses a molten iron over-crossing vehicle fusion positioning system and method, and relates to the technical field of positioning systems. The system comprises a signal acquisition module, a data preprocessing module, a positioning fusion calculation module, an output module and a storage module. The signal acquisition module is used for collecting signal data from the set sensors. The data preprocessing module is used for filtering and denoising the signal data collected by the sensors. The positioning fusion calculation module is used for performing fusion calculation on the multi-source data through a matching and calibration algorithm to generate accurate real-time positioning information of the over-crossing vehicle. The output module is used for outputting the processed positioning information to a control system. The storage module is used for storing historical data to support data analysis and fault diagnosis. The application realizes accurate real-time positioning of the molten iron over-crossing vehicle through multi-source data fusion positioning.

Description

Technical Field

[0001] This invention relates to positioning technology, and more particularly to a fusion positioning system and method for molten iron crossing cars. Background Technology

[0002] In modern steel production, precise positioning of molten iron transfer cars is crucial for improving production efficiency and ensuring personnel safety. Because these cars operate between steel mills and ironworks, and some areas within the plant premises are covered by sheet metal and dense steel structures, traditional GPS or BeiDou technologies are ineffective for positioning. Due to the long operating area, curved tracks, and inclines / declines along the route, existing positioning technologies, typically relying on single sensors such as laser ranging or wireless pulse positioning, are susceptible to environmental interference, leading to decreased positioning accuracy. Furthermore, single sensors may experience signal loss under certain special operating conditions, severely impacting the continuity and safety of the production process. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a fusion positioning system and method for molten iron crossing cars, addressing the deficiencies in the prior art.

[0004] The technical solution adopted by this invention to solve its technical problem is: a fusion positioning system for molten iron crossing cars, comprising: The signal acquisition module is used to acquire signal data from the sensors that are set up. The sensor includes: The laser ranging device is used to measure the distance between the overpass vehicle and the fixed position of the laser ranging device; the wireless pulse positioning base station is used to locate the overpass vehicle based on the wireless pulse signal; the measuring roller installed on the overpass vehicle is used to measure the travel distance of the overpass vehicle; the limit switch installed on the overpass vehicle is used to detect the arrival of the overpass vehicle at the set key work position; and the frequency converter is used to obtain the running direction and speed of the overpass vehicle. The laser ranging devices are distributed in the straight-line area between the iron tapping port of ironmaking and the steelmaking ladle. The data preprocessing module is used to filter and denoise the signal data acquired by the sensor; The positioning fusion computing module is used to perform fusion computing on multi-source data through matching and calibration algorithms to generate accurate real-time positioning information for vehicles crossing the road. The output module is used to output the processed positioning information to the control system or monitoring and dispatching system. The storage module is used to store historical data and supports data analysis and fault diagnosis.

[0005] According to the above scheme, the positioning fusion calculation module performs fusion calculation on multi-source data to generate accurate real-time positioning information for vehicles crossing the line, as detailed below: Step 1) Set the starting point of the ironmaking zone to 0; Step 2) When the laser rangefinder collects the signal, calculate the speed V1 of the molten iron passing through the span car by distance differentiation, and calculate the rate of change K1 of V1; Step 3) By differentiating the distance obtained from the measuring roller, calculate the speed V2 of the molten iron passing through the span car, and calculate the rate of change K2 of V2; Step 4) By differentiating the position data obtained from wireless pulse positioning, calculate the velocity V3 of the molten iron car at this time, and calculate the rate of change K3 of V3; Step 5) Calculate the rate of change K4 of the speed V4 obtained by the frequency converter; Step 6) Determine the location of the overpass vehicle using the limit switch signal. When the overpass vehicle's location is within the laser ranging straight section, calculate the pairwise differences between K1, K3, and K4: |K1-K3|, |K1-K4|, |K3-K4|. If all differences are within the set threshold, all are considered correct signals; otherwise, select the two signals with deviations within the threshold as the correct signals. When the overpass vehicle's location is within the curve section, calculate the pairwise differences between K2, K3, and K4: |K2-K3|, |K2-K4|, |K3-K4|. If all differences are within the set threshold, all are considered correct signals; otherwise, select the two signals with deviations within the threshold as the correct signals. Step 7) Based on Step 6), select the speeds corresponding to the two or three correctly judged signals and perform speed-time integration to obtain the current position S0 of different sensors, which is the position of the vehicle crossing. Step 8) Based on the calculated position data, calculate the slippage compensation value B of the measuring roller using the linear function S=AX+B, and correct the measuring roller in real time, where S is the current position, X is the cumulative value of the encoder grating signal, and A is the reduction ratio; According to the above scheme, the value of the slippage compensation value B is as follows: Using nonlinear regression ,in, This is the true position value calculated through multi-sensor fusion during the i-th sampling. The theoretical displacement is obtained by multiplying the cumulative value of the encoder grating signal (number of pulses) by the reduction ratio A (typical value 0.001 m / pulse); The initial value is the slippage compensation amount (in meters) from the previous iteration. =0; These are the weighting coefficients based on the speed range division.

[0006] According to the above scheme, the weighting coefficient is set based on the speed of the molten iron passing through the span car: , At low speeds, higher weights are assigned to suppress inertial errors. The sliding window length n=20, corresponding to a 2-second time window (sampling frequency 10Hz).

[0007] According to the above scheme, in the positioning fusion calculation module, step 8) uses the calculated position data as observation data and employs an improved adaptive Kalman filter algorithm to calculate accurate position data. Its state equation and observation equation are defined as follows: The state equations are as follows:

[0008] in, It is the system state vector, containing position. (meters) and speed (meters per second); Let be the transition matrix. The sampling time interval (seconds); The control input matrix corresponds to a uniformly accelerated motion model; The acceleration (m / s²) output by the frequency converter; For process noise, the covariance matrix ,in , = , ; The observation equation is as follows:

[0009] in, For laser ranging, pulse ranging, and measuring roller ranging; The observation matrix maps the state vector to the position observations of laser ranging, pulse ranging, and measuring rollers; To observe noise, the covariance is dynamically adjusted.

[0010] According to the above scheme, the observation noise covariance matrix The dynamic adjustment strategy is as follows: ), in, This refers to the calibration error of the laser rangefinder under standard operating conditions. The calibration error of wireless pulse positioning in an interference-free environment; This represents the encoder's calibration error when there is no slippage. This is the temperature effect coefficient, reflecting the performance degradation of the laser rangefinder at high temperatures; β is the signal-to-noise ratio adjustment coefficient; This is the acceleration influence coefficient; T represents the ambient temperature, and SNR represents the wireless pulse signal-to-noise ratio. Let α, β, and γ be the instantaneous acceleration of the vehicle crossing the bridge, and α, β, and γ be the environmental adaptation coefficients.

[0011] This invention also provides a method for fusion positioning of molten iron crossing cars, the method comprising the following steps: 1) Multiple sensors deployed within the ironmaking and steelmaking areas collect signal data; the sensors include: A laser rangefinder used to measure the distance between a vehicle crossing a bridge and a fixed position of a laser rangefinder. A wireless pulse positioning base station used for locating the position of a vehicle crossing a bridge based on wireless pulse signals; Measuring rollers installed on the crossing vehicle to measure the travel distance of the crossing vehicle; Limit switches installed on the transfer vehicle to detect when the transfer vehicle arrives at a set critical station; A frequency converter used to obtain the running direction and speed of a vehicle crossing the road; The laser ranging devices are distributed in the straight-line area between the iron tapping port of ironmaking and the steelmaking ladle. 2) Filter and denoise the signal data acquired by the sensor; 3) Perform fusion calculations on multi-source data to generate accurate real-time positioning information of the molten iron crossing car; Specifically as follows: Step 3.1) Set the starting point of the ironmaking area to 0; the molten iron transfer car starts operation from the starting point; Step 3.2) When the laser rangefinder collects the signal, calculate the speed V1 of the molten iron passing through the span car by distance differentiation, and calculate the rate of change K1 of V1; Step 3.3) By differentiating the distance obtained from the measuring roller, calculate the speed V2 of the molten iron passing through the span car, and calculate the rate of change K2 of V2; Step 3.4) By differentiating the position data obtained from wireless pulse positioning, calculate the velocity V3 of the molten iron car at this time, and calculate the rate of change K3 of V3; Step 3.5) Calculate the rate of change K4 of the speed V4 obtained by the frequency converter; Step 3.6) Determine the area where the vehicle is crossing using the limit switch signal. When the area is within the straight section of the laser ranging, calculate the pairwise differences between K1, K3, and K4: |K1-K3|, |K1-K4|, |K3-K4|. If all differences are within the set threshold, all are considered correct signals; otherwise, select the two signals with deviations within the threshold as the correct signals. When the area is within the curved section, calculate the pairwise differences between K2, K3, and K4: |K2-K3|, |K2-K4|, |K3-K4|. If all differences are within the set threshold, all are considered correct signals; otherwise, select the two signals with deviations within the threshold as the correct signals. Step 3.7) Based on step 3.6), select the speeds corresponding to the two or three correctly judged signals and perform speed-time integration to obtain the current position S0 of different sensors, which is the position of the vehicle crossing. Step 3.8) Based on the calculated position data, calculate the slippage compensation value B of the measuring roller using the linear function S=AX+B, and correct the measuring roller in real time, where S is the current position, X is the cumulative value of the encoder grating signal, and A is the reduction ratio.

[0012] According to the above scheme, the value of the slippage compensation value B is as follows: Using nonlinear regression ,in, This is the true position value calculated through multi-sensor fusion during the i-th sampling. The theoretical displacement is obtained by multiplying the cumulative value of the encoder grating signal (number of pulses) by the reduction ratio A (typical value 0.001 m / pulse); The initial value is the slippage compensation amount (in meters) from the previous iteration. =0; These are the weighting coefficients based on the speed range division.

[0013] According to the above scheme, the weighting coefficient is set based on the speed of the molten iron passing through the span car: , At low speeds, higher weights are assigned to suppress inertial errors. The sliding window length n=20, corresponding to a 2-second time window (sampling frequency 10Hz).

[0014] According to the above scheme, in step 3.8), the calculated position data is used as observation data, and an improved adaptive Kalman filter algorithm is used to calculate accurate position data. The state equation and observation equation are defined as follows: The state equations are as follows:

[0015] in, It is the system state vector, containing position. (meters) and speed (meters per second); Let be the transition matrix. The sampling time interval (seconds); The control input matrix corresponds to a uniformly accelerated motion model; The acceleration (m / s²) output by the frequency converter; For process noise, the covariance matrix ,in , = , ; The observation equation is as follows:

[0016] in, For laser ranging, pulse ranging, and measuring roller ranging; The observation matrix maps the state vector to the position observations of laser ranging, pulse ranging, and measuring rollers; To observe noise, the covariance is dynamically adjusted.

[0017] According to the above scheme, the observation noise covariance matrix The dynamic adjustment strategy is as follows: ), in, This refers to the calibration error of the laser rangefinder under standard operating conditions. The calibration error of wireless pulse positioning in an interference-free environment; This represents the encoder's calibration error when there is no slippage. This is the temperature effect coefficient, reflecting the performance degradation of the laser rangefinder at high temperatures; β is the signal-to-noise ratio adjustment coefficient; This is the acceleration influence coefficient; T represents the ambient temperature, and SNR represents the wireless pulse signal-to-noise ratio. Let α, β, and γ be the instantaneous acceleration of the vehicle crossing the bridge, and α, β, and γ be the environmental adaptation coefficients.

[0018] The beneficial effects of this invention are: This invention achieves accurate real-time positioning of molten iron passing cars by deploying multiple sensors in the ironmaking and steelmaking areas and performing multi-source data fusion positioning.

[0019] This invention can adapt to harsh conditions such as high temperature and electromagnetic interference, and achieves accurate real-time positioning of molten iron passing cars, providing reliable data support for automated control. Attached Figure Description

[0020] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a schematic diagram of the structure of an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0022] like Figure 1 As shown, a fusion positioning system for molten iron crossing cars includes: The signal acquisition module is used to acquire signal data from the sensors that are set up. The sensor includes: The laser ranging device is used to measure the distance between the overpass vehicle and the fixed position of the laser ranging device; the wireless pulse positioning base station is used to locate the overpass vehicle based on the wireless pulse signal; the measuring roller installed on the overpass vehicle is used to measure the travel distance of the overpass vehicle; the limit switch installed on the overpass vehicle is used to detect the arrival of the overpass vehicle at the set key work position; and the frequency converter is used to obtain the running direction and speed of the overpass vehicle. The laser rangefinders are distributed in the straight-line area between the iron tapping outlet of the ironmaking plant and the steelmaking ladle. The data preprocessing module is used to filter and denoise the signal data acquired by the sensor; The positioning fusion computing module is used to perform fusion computing on multi-source data through matching and calibration algorithms to generate accurate real-time positioning information for vehicles crossing the road. The output module is used to output the processed positioning information to the control system or monitoring and dispatching system. The storage module is used to store historical data and supports data analysis and fault diagnosis.

[0023] The positioning fusion computing module performs fusion computing on multi-source data to generate accurate real-time positioning information for vehicles crossing the same location, as detailed below: Step 1) Set the starting point of the ironmaking zone to 0; Step 2) When the laser rangefinder collects the signal, calculate the speed V1 of the molten iron passing through the span car by distance differentiation, and calculate the rate of change K1 of V1; Step 3) By differentiating the distance obtained from the measuring roller, calculate the speed V2 of the molten iron passing through the span car, and calculate the rate of change K2 of V2; Step 4) By differentiating the position data obtained from wireless pulse positioning, calculate the velocity V3 of the molten iron car at this time, and calculate the rate of change K3 of V3; Step 5) Calculate the rate of change K4 of the speed V4 obtained by the frequency converter; Step 6) Determine the area where the vehicle is located by using the limit switch signal. When the area where the vehicle is located is within the straight section of the laser ranging, compare the deviations between each pair of K1, K3, and K4, and take the signal with the deviation at the set threshold as the correct signal. When the area where the vehicle is located is in the curved section, compare the deviations of K2, K3, and K4, and take the signal with the deviation at a certain threshold as the correct signal. Step 7) Based on Step 6), select the speeds corresponding to the two or three correctly judged signals and perform speed-time integration to obtain the current position S0 of different sensors, which is the position of the vehicle crossing. Step 8) Calculate the slippage compensation value B of the measuring roller based on the calculated position data, and correct the measuring roller in real time, where S is the current position, X is the cumulative value of the encoder grating signal, and A is the reduction ratio; In step 8), the slippage compensation value B is set as follows: Using nonlinear regression ,in, This is the true position value calculated through multi-sensor fusion during the i-th sampling. The theoretical displacement is obtained by multiplying the cumulative value of the encoder grating signal (number of pulses) by the reduction ratio A (typical value 0.001 m / pulse); The initial value is the slippage compensation amount (in meters) from the previous iteration. =0; These are the weighting coefficients based on the speed range division.

[0024] The weighting coefficient is set according to the speed of the molten iron passing through the span car: , At low speeds, higher weights are assigned to suppress inertial errors. The sliding window length n=20, corresponding to a 2-second time window (sampling frequency 10Hz).

[0025] In the positioning fusion calculation module, step 8) uses the calculated position data as observation data and employs an improved adaptive Kalman filter algorithm to calculate accurate position data. Its state equation and observation equation are defined as follows: The state equations are as follows:

[0026] in, It is the system state vector, containing position. (meters) and speed (meters per second); Let be the transition matrix. The sampling time interval (seconds); The control input matrix corresponds to a uniformly accelerated motion model; The acceleration (m / s²) output by the frequency converter; For process noise, the covariance matrix ,in , = , ; The observation equation is as follows:

[0027] in, For laser ranging, pulse ranging, and measuring roller ranging; The observation matrix maps the state vector to the position observations of laser ranging, pulse ranging, and measuring rollers; To observe noise, the covariance is dynamically adjusted.

[0028] Observation noise covariance matrix The dynamic adjustment strategy is as follows: ), in, =0.01m, which is the calibration error of the laser rangefinder under standard operating conditions.

[0029] =0.05m, which is the calibration error of wireless pulse positioning in an interference-free environment.

[0030] =0.02m, which is the calibration error of the encoder when there is no slippage.

[0031] , is the temperature influence coefficient, obtained from laser ranging samples, reflecting the performance degradation of the laser rangefinder at high temperatures.

[0032] β=2.0 is the signal-to-noise ratio adjustment coefficient. When the wireless signal SNR<10dB, the upper limit of the error amplification coefficient is 3.0.

[0033] / m is the acceleration influence coefficient, a parameter representing the linear relationship between encoder slippage rate and acceleration.

[0034] T represents the ambient temperature (unit: °C), which is collected in real time by the vehicle-mounted temperature sensor.

[0035] SNR is the signal-to-noise ratio of a wireless pulse signal (unit: dB), which is calculated by the base station receiver. The instantaneous acceleration of the vehicle crossing (unit: m / s²) is estimated by combining the acceleration output from the frequency converter and the encoder differential. Where T is the ambient temperature and SNR is the wireless pulse signal-to-noise ratio. Let α, β, and γ be the instantaneous acceleration of the vehicle crossing the bridge, and α, β, and γ be the environmental adaptation coefficients.

[0036] 1) Multiple sensors deployed within the ironmaking and steelmaking areas collect signal data; the sensors include: A laser rangefinder used to measure the distance between a vehicle crossing a bridge and a fixed position of a laser rangefinder. A wireless pulse positioning base station used for locating the position of a vehicle crossing a bridge based on wireless pulse signals; Measuring rollers installed on the crossing vehicle to measure the travel distance of the crossing vehicle; Limit switches installed on the transfer vehicle to detect when the transfer vehicle arrives at a set critical station; A frequency converter used to obtain the running direction and speed of a vehicle crossing the road; The laser ranging devices are distributed in the straight-line area between the iron tapping port of ironmaking and the steelmaking ladle. 2) Filter and denoise the signal data acquired by the sensor; 3) Perform fusion calculations on multi-source data to generate accurate real-time positioning information of the molten iron crossing car; Specifically as follows: Step 3.1) Set the starting point of the ironmaking area to 0; the molten iron transfer car starts operation from the starting point; Step 3.2) When the laser rangefinder collects the signal, calculate the speed V1 of the molten iron passing through the span car by distance differentiation, and calculate the rate of change K1 of V1; Step 3.3) By differentiating the distance obtained from the measuring roller, calculate the speed V2 of the molten iron passing through the span car, and calculate the rate of change K2 of V2; Step 3.4) By differentiating the position data obtained from wireless pulse positioning, calculate the velocity V3 of the molten iron car at this time, and calculate the rate of change K3 of V3; Step 3.5) Calculate the rate of change K4 of the speed V4 obtained by the frequency converter; Step 3.6) Determine the area where the vehicle is located by using the limit switch signal. When the area where the vehicle is located is within the straight section of the laser ranging, compare the deviations between each pair of K1, K3, and K4, and take the signal with the deviation at the set threshold as the correct signal. When the area where the vehicle is located is in the curved section, compare the deviations of K2, K3, and K4, and take the signal with the deviation at a certain threshold as the correct signal. Step 3.7) Based on step 3.6), select the average speed of the two or three correctly judged signals and perform speed-time integration to obtain the current position S0 of the sensor, which is the position of the vehicle crossing. Step 3.8) Based on the calculated position data, calculate the slippage compensation value B of the measuring roller using the linear function S=AX+B, and correct the measuring roller in real time, where S is the current position, X is the cumulative value of the encoder grating signal, and A is the reduction ratio.

[0037] The values ​​for the slippage compensation B are as follows: Using nonlinear regression ,in, This is the true position value calculated through multi-sensor fusion during the i-th sampling. The theoretical displacement is obtained by multiplying the cumulative value of the encoder grating signal (number of pulses) by the reduction ratio A (typical value 0.001 m / pulse); The initial value is the slippage compensation amount (in meters) from the previous iteration. =0; The weighting coefficients based on the speed range division are set as follows: , At low speeds, higher weights are assigned to suppress inertial errors. The sliding window length n=20, corresponding to a 2-second time window (sampling frequency 10Hz).

[0038] Step 3.8) Using the calculated position data as observation data, the improved adaptive Kalman filter algorithm is used to calculate the accurate position data. Its state equation and observation equation are defined as follows: The state equations are as follows:

[0039] in, It is the system state vector, containing position. (meters) and speed (meters per second); Let be the transition matrix. The sampling time interval (seconds); The control input matrix corresponds to a uniformly accelerated motion model; The acceleration (m / s²) output by the frequency converter; For process noise, the covariance matrix ,in , = , ; The observation equation is as follows:

[0040] in, For laser ranging, pulse ranging, and measuring roller ranging; The observation matrix maps the state vector to the position observations of laser ranging, pulse ranging, and measuring rollers; To observe noise, the covariance is dynamically adjusted.

[0041] According to the above scheme, the observation noise covariance matrix The dynamic adjustment strategy is as follows: ), in, This refers to the calibration error of the laser rangefinder under standard operating conditions. The calibration error of wireless pulse positioning in an interference-free environment; This represents the encoder's calibration error when there is no slippage. This is the temperature effect coefficient, reflecting the performance degradation of the laser rangefinder at high temperatures; β is the signal-to-noise ratio adjustment coefficient; This is the acceleration influence coefficient; T represents the ambient temperature, and SNR represents the wireless pulse signal-to-noise ratio. Let α, β, and γ be the instantaneous acceleration of the vehicle crossing the bridge, and α, β, and γ be the environmental adaptation coefficients.

[0042] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A fusion positioning system for molten iron crossing cars, characterized in that, include: The signal acquisition module is used to acquire signal data through the set sensors; The sensor includes: The laser ranging device is used to measure the distance between the overpass vehicle and the fixed position of the laser ranging device; the wireless pulse positioning base station is used to locate the overpass vehicle based on the wireless pulse signal; the measuring roller installed on the overpass vehicle is used to measure the travel distance of the overpass vehicle; the limit switch installed on the overpass vehicle is used to detect the arrival of the overpass vehicle at the set key work position; and the frequency converter is used to obtain the running direction and speed of the overpass vehicle. The laser ranging devices are distributed in a straight line area between the iron tapping area of ​​ironmaking and the steelmaking ladle area. The data preprocessing module is used to filter and denoise the signal data acquired by the sensor; The positioning fusion computing module is used to perform fusion computing on multi-source data through matching and calibration algorithms to generate accurate real-time positioning information for vehicles crossing the road. The positioning fusion calculation module performs fusion calculations on multi-source data to generate accurate real-time positioning information for vehicles crossing each other, as detailed below: Step 1) Set the starting point of the ironmaking zone to 0; Step 2) When the laser rangefinder collects the signal, calculate the speed V1 of the molten iron passing through the span car by distance differentiation, and calculate the rate of change K1 of V1; Step 3) By differentiating the distance obtained from the measuring roller, calculate the speed V2 of the molten iron passing through the span car, and calculate the rate of change K2 of V2; Step 4) By differentiating the position data obtained from the wireless pulse positioning, calculate the speed V3 of the molten iron passing through the span car at this time, and calculate the rate of change K3 of V3; Step 5) Calculate the rate of change K4 of the speed V4 obtained by the frequency converter; Step 6) Determine the area where the vehicle is located by using the limit switch signal. When the area where the vehicle is located is within the straight section of the laser ranging, compare the deviations between each pair of K1, K3, and K4, and take the signal with the deviation at the set threshold as the correct signal. When the area where the vehicle is located is in the curved section, compare the deviations of K2, K3, and K4, and take the signal with the deviation at a certain threshold as the correct signal. Step 7) Based on Step 6), select the speeds corresponding to the two or three correctly judged signals and perform speed-time integration to obtain the current position S0 of different sensors, which is the position of the vehicle crossing. Step 8) Based on the calculated position data, calculate the slippage compensation value B of the measuring roller and correct the measuring roller in real time; The slippage compensation value B is taken as follows: Using a nonlinear regression formula, , in, Let be the true value of the position of the molten iron passing through the span car calculated by multi-sensor fusion at the i-th sampling; n is the number of samplings. Here, A represents the cumulative value of the encoder grating signal, and A is the encoder pulse equivalent. Multiplying yields the theoretical displacement; This represents the slippage compensation amount for the (t-1)th iteration; initial value =0; These are weighting coefficients based on the speed range division; For step 8), based on the calculated location data, execute the improved adaptive Kalman filter algorithm. The state equation and observation equation of the algorithm are defined as follows: The state equations are as follows: in, It is the system state vector, containing position. and speed ; Let be the transition matrix. The sampling time interval; The control input matrix corresponds to a uniformly accelerated motion model; The acceleration output by the frequency converter; For process noise, the covariance matrix ,in , = , ; The observation equation is as follows: in, For observation vectors, including laser ranging Pulse ranging Measuring roller distance measurement ; The observation matrix maps the state vector to the position observations of laser ranging, pulse ranging, and measuring rollers; To observe the noise, To observe the noise covariance matrix covariance matrix Adaptive and dynamic adjustment based on real-time sensor operating conditions; The output module is used to output the processed positioning information to the control system. The storage module is used to store historical data and supports data analysis and fault diagnosis.

2. The fusion positioning system for molten iron crossing cars according to claim 1, characterized in that, The weighting coefficient is set according to the speed of the molten iron passing through the span car: ，。 3. The fusion positioning system for molten iron crossing cars according to claim 1, characterized in that, The observation noise covariance matrix The dynamic adjustment strategy is as follows: ), in, This refers to the calibration error of the laser rangefinder under standard operating conditions. The calibration error of wireless pulse positioning in an interference-free environment; This represents the encoder's calibration error when there is no slippage. T represents the ambient temperature, and SNR represents the wireless pulse signal-to-noise ratio. The instantaneous acceleration of the vehicle crossing the bridge is given by α, β, and γ, which are environmental adaptation coefficients. Wherein, α is the temperature influence coefficient, which reflects the performance degradation of the laser rangefinder at high temperatures; β is the signal-to-noise ratio adjustment coefficient; This is the acceleration influence coefficient.

4. A method for fusion positioning of molten iron crossing cars based on the fusion positioning system for molten iron crossing cars according to any one of claims 1 to 3, characterized in that, The method includes the following steps: 1) Multiple sensors deployed within the ironmaking and steelmaking areas collect signal data; the sensors include: A laser rangefinder used to measure the distance between a vehicle crossing a bridge and a fixed position of a laser rangefinder. A wireless pulse positioning base station used for locating the position of a vehicle crossing a bridge based on wireless pulse signals; Measuring rollers installed on the crossing vehicle to measure the travel distance of the crossing vehicle; Limit switches installed on the transfer vehicle to detect when the transfer vehicle arrives at a set critical station; A frequency converter used to obtain the running direction and speed of a vehicle crossing the road; The laser ranging devices are distributed in a straight line area between the iron tapping area of ​​ironmaking and the steelmaking ladle area. 2) Filter and denoise the signal data acquired by the sensor; 3) Perform fusion calculations on multi-source data to generate accurate real-time positioning information of the molten iron crossing car; Specifically as follows: Step 3.1) Set the starting point of the ironmaking area to 0; the molten iron transfer car starts operation from the starting point; Step 3.2) When the laser rangefinder collects the signal, calculate the speed V1 of the molten iron passing through the span car by distance differentiation, and calculate the rate of change K1 of V1; Step 3.3) By differentiating the distance obtained from the measuring roller, calculate the speed V2 of the molten iron passing through the span car, and calculate the rate of change K2 of V2; Step 3.4) By differentiating the position data obtained from the wireless pulse positioning, calculate the speed V3 of the molten iron passing through the span car at this time, and calculate the rate of change K3 of V3; Step 3.5) Calculate the rate of change K4 of the speed V4 obtained by the frequency converter; Step 3.6) Determine the area where the vehicle is located by using the limit switch signal. When the area where the vehicle is located is within the straight section of the laser ranging, compare the deviations between each pair of K1, K3, and K4, and take the signal with the deviation at the set threshold as the correct signal. When the area where the vehicle is located is in the curved section, compare the deviations of K2, K3, and K4, and take the signal with the deviation at a certain threshold as the correct signal. Step 3.7) Based on step 3.6), select the speed corresponding to the correctly determined signal, perform speed-time integration, and obtain the current position S0, which is the position of the vehicle crossing. Step 3.8) Based on the calculated position data, calculate the slippage compensation value B of the measuring roller and correct the measuring roller in real time.

5. An electronic device, characterized in that, include: One or more processors; as well as Storage device for storing one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the method according to claim 4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of claim 4.

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