Molten iron straddle carrier fusion positioning system and method

Abstract

The invention discloses a molten iron straddle carrier fusion positioning system and method, and the system comprises a signal collection module which is used for collecting signal data from a set sensor; the data preprocessing module is used for filtering and denoising the signal data acquired by the sensor; 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 straddle carrier; the output module is used for outputting the processed positioning information to a control system; and the storage module is used for storing historical data and supporting data analysis and fault diagnosis. According to the invention, through multi-source data fusion positioning, accurate real-time positioning of the molten iron straddle carrier is realized.

Description

Technical Field

[0001] The present invention relates to positioning technology, and particularly to a molten iron transfer car integrated positioning system and method. Background Art

[0002] In the modern iron and steel production process, the precise positioning of the molten iron transfer car is crucial for improving production efficiency and ensuring personnel safety. Since the molten iron transfer car operates between the steelmaking plant and the ironmaking plant, some areas are within the plant area, covered by metal iron sheets and dense steel structures, and traditional technologies such as GPS or Beidou cannot be used for positioning. Due to the long operating area, the operating trajectory has curved sections, and there are also uphill and downhill sections on some operating paths. Existing positioning technologies usually rely on a single sensor, such as laser ranging or wireless pulse positioning, which is easily interfered by environmental factors, resulting in a decrease in positioning accuracy. In addition, the single sensor may experience signal loss under certain special working conditions, seriously affecting 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 molten iron transfer car integrated positioning system and method in view of the defects in the prior art.

[0004] The technical solution adopted by the present invention to solve its technical problems is as follows: A molten iron transfer car integrated positioning system includes: A signal acquisition module for collecting signal data from the sensors provided; The sensors include: A laser ranging device for measuring the distance data between the transfer car and the fixed position of the laser ranging device, a wireless pulse positioning base station for realizing the position positioning of the transfer car according to the wireless pulse signal, a measuring roller installed on the transfer car for measuring the traveling distance of the transfer car, a travel switch installed on the transfer car for detecting the arrival situation of the transfer car at the set key working positions; a frequency converter for obtaining the running direction and speed of the transfer car; The laser ranging devices are distributed in the straight-line areas of the iron tapping mouth section in the ironmaking plant and the steelmaking ladle section; A data preprocessing module for filtering and denoising the signal data collected by the sensors; A positioning fusion calculation module for performing fusion calculation on multi-source data through matching and calibration algorithms to generate accurate real-time positioning information of the transfer car; An output module for outputting the processed positioning information to the control system or the monitoring and dispatching system; A storage module for storing historical data to support data analysis and fault diagnosis.

[0005] According to the above solution, in the positioning and fusion calculation module, multi-source data is fused and calculated to generate accurate real-time positioning information of the ladle transfer car, which is specifically as follows: Step 1) Set the position of the starting point in the ironmaking area to 0; Step 2) When the laser ranging device collects a signal, calculate the speed V1 of the ladle transfer car through distance differentiation, and calculate the change rate K1 of V1; Step 3) Calculate the speed V2 of the ladle transfer car by differentiating the distance obtained by the measuring roll, and calculate the change rate K2 of V2; Step 4) Calculate the speed V3 of the ladle car at this time by differentiating the position data obtained by wireless pulse positioning, and calculate the change rate K3 of V3; Step 5) Calculate the change rate K4 of the speed V4 obtained by the frequency converter; Step 6) Judge the area where the ladle transfer car is located through the travel switch signal. When the area where the ladle transfer car is located is within the range of the straight line segment of the laser ranging, calculate the pairwise differences of K1, K3, and K4: |K1 - K3|, |K1 - K4|, |K3 - K4|. If all the differences are within the set threshold, then all are correct signals; otherwise, take two signals with deviations within the threshold in one of the groups as correct signals. When the area where the ladle transfer car is located is on the curve segment, calculate the pairwise differences of K2, K3, and K4: |K2 - K3|, |K2 - K4|, |K3 - K4|. If all the differences are within the set threshold, then all are correct signals; otherwise, take two signals with deviations within the threshold in one of the groups as correct signals; Step 7) Perform speed-time integration on the speeds corresponding to the two or three signals determined to be correct according to Step 6) to obtain the current positions S0 of different sensors, which are the positions of the ladle transfer car; Step 8) According to the calculated position data, calculate the slip compensation value B of the measuring roll through the linear function S = AX + B, and correct the measuring roll in real time, where S is the current position, X is the cumulative value of the grating signals of the encoder, and A is the reduction ratio; According to the above solution, the value of the slip compensation value B is as follows: Adopt non-linear regression , where, is the true position value calculated by multi-sensor fusion during the i-th sampling; is the cumulative value of the grating signals of the encoder (number of pulses), which is multiplied by the reduction ratio A (typical value 0.001 m / pulse) to obtain the theoretical displacement; is the slip compensation amount (m) of the previous iteration, and the initial value = 0; is the weight coefficient based on the speed interval division.

[0006] According to the above solution, the weight coefficient is set according to the speed of the hot metal transfer car: , A higher weight is given at low speed to suppress inertial errors. The length of the sliding window n = 20, corresponding to a 2-second time window (sampling frequency 10 Hz).

[0007] According to the above solution, in the positioning fusion calculation module, in step 8), the calculated position data is used as the observation data, and the improved adaptive Kalman filtering algorithm is used to calculate the accurate position data. Its state equation and observation equation are defined as: The state equation is as follows:

[0008] Where, is the system state vector, including the position (m) and the speed (m / s); is the transition matrix, is the sampling time interval (s); is the control input matrix, corresponding to the uniformly accelerated motion model; is the acceleration output by the frequency converter (m / s²); is the process noise, covariance matrix , where , = , ; The observation equation is as follows:

[0009] Where, is the observation vector of laser ranging, pulse ranging and measuring roll ranging; is the observation matrix, mapping the state vector to the position observations of laser ranging, pulse ranging and measuring roll; is the observation noise, covariance dynamically adjusted.

[0010] According to the above solution, the observation noise covariance matrix adopts the following dynamic adjustment strategy: ), Where, is the calibration error of the laser rangefinder under standard working conditions; is the calibration error of wireless pulse positioning in an interference-free environment; is the calibration error of the encoder without slipping; is the temperature influence coefficient, reflecting the performance attenuation of the laser rangefinder at high temperatures; β is the signal-to-noise ratio adjustment coefficient; is the acceleration influence coefficient; T is the ambient temperature, SNR is the wireless pulse signal-to-noise ratio, is the instantaneous acceleration of the ladle car, and α, β, γ are the environmental adaptation coefficients.

[0011] The present invention also provides a method for fusing the positioning of a hot metal ladle car, and the method includes the following steps: 1) Collect signal data by multiple sensors deployed inside the ironmaking and steelmaking areas; the sensors include: A laser rangefinder for measuring the distance data between the ladle car and the fixed position of the laser ranging device; A wireless pulse positioning base station for realizing the position positioning of the ladle car according to the wireless pulse signal; A measuring roller installed on the ladle car for measuring the traveling distance of the ladle car; A travel switch installed on the ladle car for detecting the arrival of the ladle car at the set key workstations; A frequency converter for obtaining the running direction and speed of the ladle car; The laser rangefinders are distributed in a straight line area between the tapping area of the ironmaking area and the ladle area of the steelmaking area; 2) Filter and denoise the signal data collected by the sensors; 3) Perform fusion calculation on the multi-source data to generate accurate real-time positioning information of the hot metal ladle car; Specifically as follows: Step 3.1) Set the position of the starting point in the ironmaking area to 0; the hot metal ladle car starts working from the starting point; Step 3.2) When the laser rangefinder collects a signal, calculate the speed V1 of the hot metal ladle car by distance differentiation, and calculate the change rate K1 of V1; Step 3.3) Calculate the speed V2 of the hot metal ladle car by distance differentiation of the distance obtained by the measuring roller, and calculate the change rate K2 of V2; Step 3.4) Calculate the speed V3 of the hot metal ladle car at this time by differentiating the position data obtained by wireless pulse positioning, and calculate the change rate K3 of V3; Step 3.5) Calculate the change rate K4 of the speed V4 obtained by the frequency converter; Step 3.6) Determine the area where the gantry crane is located through the travel switch signal. When the area where the gantry crane is located is within the range of the straight line segment of the laser rangefinder, calculate the pairwise differences for K1, K3, and K4: |K1 - K3|, |K1 - K4|, |K3 - K4|. If all differences are within the set threshold, all are correct signals; otherwise, take two signals with a deviation within the threshold in one of the groups as the correct signals. When the area where the gantry crane is located is in the curve segment, calculate the pairwise differences for K2, K3, and K4: |K2 - K3|, |K2 - K4|, |K3 - K4|. If all differences are within the set threshold, all are correct signals; otherwise, take two signals with a deviation within the threshold in one of the groups as the correct signals; Step 3.7) According to Step 3.6), perform speed-time integration on the speeds corresponding to the two or three signals determined to be correct, and obtain the current positions S0 of different sensors, which are the positions of the gantry crane; Step 3.8) According to the calculated position data, calculate the slip compensation value B of the measuring roll through the linear function S = AX + B, and correct the measuring roll in real time, where S is the current position, X is the cumulative value of the grating signals of the encoder, and A is the reduction ratio.

[0012] According to the above scheme, the value of the slip compensation value B is as follows: Using non-linear regression , where is the true position value calculated by multi-sensor fusion during the i-th sampling; is the cumulative value of the encoder grating signals (number of pulses), which is multiplied by the reduction ratio A (typical value 0.001 m / pulse) to obtain the theoretical displacement; is the slip compensation amount (m) of the previous iteration, and the initial value = 0; is the weight coefficient based on the speed interval division.

[0013] According to the above scheme, the weight coefficient is set according to the speed of the hot metal gantry crane: , Higher weights are given at low speeds to suppress inertial errors. The sliding window length n = 20, corresponding to a 2-second time window (sampling frequency 10 Hz).

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

[0015] Where, is the system state vector, including the position in meters and the speed in m / s; is the transition matrix, is the sampling time interval in seconds; is the control input matrix, corresponding to the uniformly accelerated motion model; is the acceleration output by the frequency converter in m / s²; is the process noise, covariance matrix where , = , ; The observation equation is as follows:

[0016] where is the observation vector of laser ranging, pulse ranging, and measuring roll ranging; is the observation matrix, mapping the state vector to the position observations of laser ranging, pulse ranging, and measuring roll; is the observation noise, with dynamically adjusted covariance.

[0017] According to the above scheme, the covariance matrix of the observation noise adopts the following dynamic adjustment strategy: ), where is the calibration error of the laser rangefinder under standard working conditions; is the calibration error of wireless pulse positioning in an interference-free environment; is the calibration error of the encoder when there is no slippage; is the temperature influence coefficient, reflecting the performance attenuation of the laser rangefinder at high temperatures; β is the signal-to-noise ratio adjustment coefficient; is the acceleration influence coefficient; T is the ambient temperature, SNR is the wireless pulse signal-to-noise ratio, is the instantaneous acceleration of the overcrossing vehicle, and α, β, γ are the environmental adaptation coefficients.

[0018] The beneficial effects produced by the present invention are: The present invention realizes the precise real-time positioning of the hot metal transfer car by deploying a variety of sensors in the ironmaking and steelmaking areas for multi-source data fusion positioning.

[0019] The present invention can adapt to harsh conditions such as high temperature and electromagnetic interference, realizes the precise real-time positioning of the hot metal transfer car, and provides reliable data support for automatic control. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention will be further described below in conjunction with the drawings and embodiments. In the drawings: Figure 1 is a schematic structural diagram of an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0021] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] As Figure 1 shown, a fusion positioning system for a hot metal transfer car includes: A signal acquisition module for acquiring signal data from sensors provided. The sensors include: A laser ranging device for measuring the distance data between the transfer car and the fixed position of the laser ranging device, a wireless pulse positioning base station for realizing the position positioning of the transfer car according to the wireless pulse signal, a measuring roller installed on the transfer car for measuring the traveling distance of the transfer car, a travel switch installed on the transfer car for detecting the arrival of the transfer car at a set key work station; a frequency converter for obtaining the running direction and speed of the transfer car; The laser ranging devices are distributed in a straight line area of the tapping area of the ironmaking and the ladle area of the steelmaking. A data preprocessing module for filtering and denoising the signal data collected by the sensors. A positioning fusion calculation module for performing fusion calculation on multi-source data through matching and calibration algorithms to generate precise real-time positioning information of the transfer car. An output module for outputting the processed positioning information to a control system or a monitoring and dispatching system. A storage module for storing historical data to support data analysis and fault diagnosis.

[0023] In the positioning fusion calculation module, the multi-source data is fused and calculated to generate precise real-time positioning information of the transfer car, specifically as follows: Step 1) Set the position of the starting point of the ironmaking area to 0; Step 2) When the laser ranging device collects a signal, calculate the speed V1 of the hot metal transfer car through distance differentiation, and calculate the change rate K1 of V1; Step 3) Calculate the speed V2 of the hot metal transfer car by differentiating the distance obtained by the measuring roller, and calculate the change rate K2 of V2; Step 4) Differentiate the position data obtained by wireless pulse positioning to calculate the speed V3 of the hot metal ladle car at this time, and calculate the change rate K3 of V3; Step 5) Calculate the change rate K4 of the speed V4 obtained by the frequency converter; Step 6) Determine the area where the transfer car is located through the travel switch signal. When the area where the transfer car is located is within the range of the straight line segment of the laser ranging, compare the deviations between K1, K3, and K4 pairwise, and take the signal with the deviation within the set threshold as the correct signal; when the area where the transfer car is located is in the curve segment, compare the deviations of K2, K3, and K4, and take the signal with the deviation within a certain threshold as the correct signal; Step 7) Perform speed-time integration on the speeds corresponding to the two or three signals determined to be correct according to Step 6) to obtain the current positions S0 of different sensors, which is the position of the transfer car; Step 8) Calculate the slip compensation value B of the measuring roller according to 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 grating signal of the encoder, and A is the reduction ratio; In Step 8), the slip compensation value B is taken as follows: Using non-linear regression , where, is the true position value calculated by multi-sensor fusion at the i-th sampling; is the cumulative value of the encoder grating signal (number of pulses), which is multiplied by the reduction ratio A (typical value 0.001 m / pulse) to obtain the theoretical displacement; is the slip compensation amount (m) of the previous iteration, with an initial value = 0; is the weight coefficient based on the speed interval division.

[0024] The weight coefficient is set according to the speed of the hot metal transfer car: , Higher weights are given at low speeds to suppress inertial errors. The sliding window length n = 20, corresponding to a 2-second time window (sampling frequency 10 Hz).

[0025] In the positioning fusion calculation module, in Step 8), using the calculated position data as the observation data, an improved adaptive Kalman filtering algorithm is used to calculate the accurate position data, and its state equation and observation equation are defined as: The state equation is as follows:

[0026] Where, is the system state vector, including the position (m) and the velocity (m / s); is the transition matrix, is the sampling time interval (s); is the control input matrix, corresponding to the uniform acceleration motion model; is the acceleration output by the frequency converter (m / s²); is the process noise, with the covariance matrix where , = , ; The observation equation is as follows:

[0027] Where, is the observation vector of laser ranging, pulse ranging, and measuring roll ranging; is the observation matrix, which maps the state vector to the position observations of laser ranging, pulse ranging, and measuring roll; is the observation noise, with dynamically adjusted covariance.

[0028] The observation noise covariance matrix adopts the following dynamic adjustment strategy: ), Where, = 0.01m, which is the calibration error of the laser rangefinder under standard working 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 slipping.

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

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

[0033] / m is the acceleration influence coefficient, which is the linear relationship parameter between the encoder slip rate and the acceleration.

[0034] T is 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 the wireless pulse signal (unit: dB), which is obtained by calculation at the receiving end of the base station; is the instantaneous acceleration of the ladle car (unit: m / s²), which is jointly estimated by the acceleration output by the frequency converter and the encoder differentiation; where T is the ambient temperature, SNR is the wireless pulse signal-to-noise ratio, is the instantaneous acceleration of the ladle car, and α, β, γ are the environmental adaptation coefficients.

[0036] 1) Multiple sensors deployed inside the ironmaking and steelmaking areas collect signal data; the sensors include: A laser ranging device for measuring the distance data between the ladle car and the fixed position of the laser ranging device; A wireless pulse positioning base station for realizing the position positioning of the ladle car according to the wireless pulse signal; A measuring roller installed on the ladle car for measuring the traveling distance of the ladle car; A travel switch installed on the ladle car for detecting the arrival situation of the ladle car at the set key working positions; A frequency converter for obtaining the running direction and speed of the ladle car; The laser ranging devices are distributed in the straight-line area of the tap hole section of ironmaking and the ladle section of steelmaking; 2) Filter and denoise the signal data collected by the sensors; 3) Perform fusion calculation on the multi-source data to generate accurate real-time positioning information of the hot metal ladle car; Specifically as follows: Step 3.1) Set the position of the starting point in the ironmaking area to 0; the hot metal ladle car starts working from the starting point; Step 3.2) When the laser ranging device collects a signal, calculate the speed V1 of the hot metal ladle car through distance differentiation, and calculate the change rate K1 of V1; Step 3.3) Calculate the speed V2 of the hot metal ladle car by performing distance differentiation on the distance obtained by the measuring roller, and calculate the change rate K2 of V2; Step 3.4) Differentiate the position data obtained by wireless pulse positioning to calculate the speed V3 of the hot metal car at this time, and calculate the change rate K3 of V3; Step 3.5) Calculate the change rate K4 of the speed V4 obtained by the frequency converter; Step 3.6) Determine the area where the transfer car is located through the travel switch signal. When the area where the transfer car is located is within the range of the straight line segment of the laser ranging, compare the deviations between K1, K3, and K4 pairwise, and take the signal with the deviation within the set threshold as the correct signal; when the area where the transfer car is located is in the curve segment, compare the deviations of K2, K3, and K4, and take the signal with the deviation within a certain threshold as the correct signal; Step 3.7) According to the two or three signals determined to be correct selected in Step 3.6), perform speed-time integration on the corresponding speed means respectively to obtain the current position S0 of the sensor, which is the position of the transfer car; Step 3.8) According to the calculated position data, calculate the slip compensation value B of the measuring roll through the linear function S = AX + B, and correct the measuring roll in real time, where S is the current position, X is the cumulative value of the grating signals of the encoder, and A is the reduction ratio.

[0037] The value of the slip compensation value B is as follows: Use non-linear regression , where, is the true position value calculated by multi-sensor fusion at the i-th sampling; is the cumulative value of the encoder grating signals (number of pulses), which is multiplied by the reduction ratio A (typical value 0.001 m / pulse) to obtain the theoretical displacement; is the slip compensation amount of the previous iteration (m), and the initial value = 0; is the weight coefficient based on the speed interval division, and is set as follows: , Higher weights are given at low speeds to suppress inertial errors. The sliding window length n = 20, corresponding to a 2-second time window (sampling frequency 10 Hz).

[0038] Step 3.8) Use the calculated position data as the observation data, and adopt an improved adaptive Kalman filtering algorithm to calculate the accurate position data. Its state equation and observation equation are defined as: The state equation is as follows:

[0039] where, is the system state vector, including the position (m) and the speed (m / s); is the transition matrix, is the sampling time interval (seconds); is the control input matrix, corresponding to the uniformly accelerated motion model; is the acceleration output by the frequency converter (m / s²); is the process noise, covariance matrix , where , = , ; The observation equation is as follows:

[0040] where, is the observation vector of laser ranging, pulse ranging and measuring roll ranging; is the observation matrix, mapping the state vector to the position observations of laser ranging, pulse ranging and measuring roll; is the observation noise, covariance dynamically adjusted.

[0041] According to the above scheme, the observation noise covariance matrix adopts the following dynamic adjustment strategy: ), where, is the calibration error of the laser rangefinder under standard working conditions; is the calibration error of wireless pulse positioning in an interference-free environment; is the calibration error of the encoder without slipping; is the temperature influence coefficient, reflecting the performance attenuation of the laser rangefinder at high temperatures; β is the signal-to-noise ratio adjustment coefficient; is the acceleration influence coefficient; T is the ambient temperature, SNR is the wireless pulse signal-to-noise ratio, is the instantaneous acceleration of the overcrossing vehicle, and α, β, γ are environmental adaptation coefficients.

[0042] It should be understood that those of ordinary skill in the art can make improvements or transformations according to the above description, and all such improvements and transformations shall fall within the protection scope of the appended claims of the present invention.

Claims

1. A fusion positioning system for a molten iron straddle car, characterized in that: include: A signal acquisition module, used for collecting signal data from a set sensor; The sensor comprises: A laser distance measuring device for measuring the distance data between the straddle vehicle and the fixed position of the laser distance measuring device, a wireless pulse positioning base station for realizing the position positioning of the straddle vehicle according to the wireless pulse signal, a measuring roller installed on the straddle vehicle for measuring the travel distance of the straddle vehicle, a travel switch installed on the straddle vehicle for detecting the arrival of the straddle vehicle at the set key position; a frequency converter for obtaining the running direction and speed of the straddle vehicle; The laser distance measuring devices are distributed in the straight line area between the tap hole section of iron making and the hanging pot section of steel making; A data preprocessing module is used to filter and denoise the signal data collected by the sensor; The positioning fusion calculation module is used to perform fusion calculations on multi-source data through matching and calibration algorithms to generate accurate real-time positioning information of the cross-car; An output module, used for outputting the processed positioning information to the control system; Storage module, used to store historical data and support data analysis and fault diagnosis.

2. The molten iron crossing car fusion positioning system according to claim 1 is characterized in that: In the positioning fusion calculation module, multi-source data is fused and calculated to generate accurate real-time positioning information of the straddle vehicle, as follows: Step 1) Set the position of the starting point of the ironmaking area to 0; Step 2) When the laser distance measuring device collects the signal, the speed V1 of the molten iron passing the straddle car is calculated by differential distance, and the rate of change K1 of V1 is calculated; Step 3) by performing distance differentiation on the distance obtained by the measuring roller, the speed V2 of the molten iron passing through the straddle car is calculated, and the rate of change K2 of V2 is calculated; Step 4) by differentiating the position data obtained by wireless pulse positioning, calculate the speed 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 acquired by the frequency converter; Step 6) Determine the area where the cross-car is located by using the travel switch signal. When the area where the cross-car is located is within the laser ranging straight line range, compare the deviations between K1, K3, and K4, and take the signal with the deviation within the set threshold as the correct signal; when the area where the cross-car is located is in the curved section, compare the deviations of K2, K3, and K4, and take the signal with the deviation within a certain threshold as the correct signal; Step 7) According to step 6), the speeds corresponding to the two or three signals that are correctly determined are respectively integrated with respect to the speed time to obtain the current position S0 of different sensors, which is the position of the straddle vehicle; Step 8) Calculate the slip 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 accumulated value of the encoder's grating signal, and A is the reduction ratio.

3. The molten iron crossing car fusion positioning system according to claim 2 is characterized in that: The slip compensation value B is as follows: Using nonlinear regression ,in, is the true position value calculated by multi-sensor fusion at the i-th sampling; is the accumulated value of the encoder grating signal, and A is the reduction ratio A (typical value 0.001 m / pulse), which is multiplied to obtain the theoretical displacement; is the slip compensation amount of the i-1th iteration; Initial Value =0; is the weight coefficient based on speed interval division.

4. The molten iron crossing car fusion positioning system according to claim 3 is characterized in that: The weight coefficient is set according to the speed of the molten iron crossing car: 。 5. The molten iron crossing car fusion positioning system according to claim 1 is characterized in that: In the positioning fusion calculation module, step 8) uses an improved adaptive Kalman filter algorithm based on the calculated position data, and its state equation and observation equation are defined as: The state equation is as follows: in, is the system state vector, containing the position and speed ; is the transfer matrix, is the sampling time interval; is the control input matrix, corresponding to the uniform acceleration motion model; is the acceleration output by the inverter; is the process noise, the covariance matrix ,in , = , ; The observation equation is as follows: in, Observation vector for laser ranging, pulse ranging and measuring roller ranging; is the observation matrix, which maps the state vector to the position observations of laser ranging, pulse ranging and measuring roller; To account for observation noise, the covariance is adjusted dynamically.

6. The molten iron crossing car fusion positioning system according to claim 1 is characterized in that: The observation noise covariance matrix The dynamic adjustment strategy is as follows: ), in, is the calibration error of the laser rangefinder under standard working conditions; Calibration error of wireless pulse positioning in interference-free environment; is the calibration error of the encoder when there is no slip; is the temperature influence coefficient, which reflects the performance attenuation of the laser rangefinder at high temperature; β is the signal-to-noise ratio adjustment coefficient; is the acceleration influence coefficient; T is the ambient temperature, SNR is the wireless pulse signal-to-noise ratio, is the instantaneous acceleration of the vehicle passing through, and α, β, γ are the environmental adaptation coefficients.

7. A method for fusion positioning of a molten iron straddle car, characterized in that: The method comprises the following steps: 1) Multiple sensors deployed in the ironmaking and steelmaking areas collect signal data; the sensors include: A laser distance measuring device for measuring distance data between the straddle carrier and a fixed position of the laser distance measuring device; A wireless pulse positioning base station for realizing the position positioning of a cross-car according to a wireless pulse signal; A measuring roller mounted on the straddle carrier for measuring the travel distance of the straddle carrier; A travel switch installed on the straddle carrier to detect the arrival of the straddle carrier at a set key workstation; A frequency converter for obtaining the running direction and speed of the straddle carrier; The laser distance measuring devices are distributed in the straight line area between the tap hole section of iron making and the hanging pot section of steel making; 2) Filter and denoise the signal data collected by the sensor; 3) Fusion calculation of multi-source data to generate accurate real-time positioning information of the molten iron crossing car; The details are as follows: Step 3.1) Set the starting point of the ironmaking area to 0; the molten iron cross-car starts operating from the starting point; Step 3.2) When the laser distance measuring device collects the signal, the speed V1 of the molten iron passing the straddle car is calculated by differential distance, and the rate of change K1 of V1 is calculated; Step 3.3) Calculate the speed V2 of the molten iron passing the straddle car by performing distance differentiation on the distance obtained by the measuring roller, and calculate the rate of change K2 of V2; Step 3.4) Calculate the speed V3 of the molten iron car at this time by differentiating the position data obtained by wireless pulse positioning, and calculate the rate of change K3 of V3; Step 3.5) Calculate the rate of change K4 of the speed V4 acquired by the frequency converter; Step 3.6) Determine the area where the cross-car is located by the travel switch signal. When the area where the cross-car is located is within the laser ranging straight line range, compare the deviations between K1, K3, and K4, and take the signal with the deviation within the set threshold as the correct signal; when the area where the cross-car is located is in the curve section, compare the deviations of K2, K3, and K4, and take the signal with the deviation within a certain threshold as the correct signal; Step 3.7) According to step 3.6), select the speed corresponding to the correct signal, perform speed-time integration, and obtain the current position S0, which is the position of the overpass vehicle; Step 3.8) Calculate the slip 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 accumulated value of the encoder's grating signal, and A is the reduction ratio.

8. The method for fusion positioning of molten iron straddle cars according to claim 7, characterized in that: The slip compensation value B is as follows: Using nonlinear regression ,in, is the true position value calculated by multi-sensor fusion at the i-th sampling; is the accumulated value of the encoder grating signal, and A is the reduction ratio A (typical value 0.001 m / pulse), which is multiplied to obtain the theoretical displacement; is the slip compensation amount of the i-1th iteration; Initial Value =0; is the weight coefficient based on speed interval division.

9. An electronic device, characterized in that: include: one or more processors; as well as a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the method according to any one of claims 1 to 8.

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 method according to any one of claims 1 to 8 is implemented.

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