A vehicle model size measurement method and system for unmanned intelligent loading

Abstract

The present invention relates to the field of vehicle type measurement technology, and in particular to a vehicle type size measurement method and system for unmanned intelligent loading, the system comprising a measurement management platform, an information collection unit, an interference evaluation unit, a transportation efficiency supervision unit, a scanning analysis unit, an optimization analysis unit and a management response unit; the present invention analyzes the working interference and operation performance of a laser radar to understand the operation stability of the laser radar, which helps to improve the size measurement accuracy of the laser radar, and under the premise of the laser radar being stable, by scanning, identifying, matching, supervising and analyzing the vehicle information of the transport vehicle, on the one hand, it helps to manage the scanned transport vehicles, on the other hand, it helps to build a loading model for the unscanned transport vehicles, and helps to command the driver of the vehicle and control the material discharge when loading under the warehouse, and scans, optimizes, dynamically supervises, evaluates and analyzes the transport vehicles after loading, so as to improve the measurement accuracy of the laser radar.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle type measurement, and in particular to a vehicle type size measurement method and system for unmanned intelligent loading. Background Art

[0002] Lidar is a radar system that emits laser beams to detect the position, speed and other characteristic quantities of a target. Its working principle is to emit a detection signal (laser beam) to the target, and then compare the received signal reflected from the target (target echo) with the transmitted signal. After appropriate processing, relevant information about the target can be obtained, such as target distance, direction, altitude, speed, attitude, and even shape parameters, so as to detect, track and identify targets such as aircraft and missiles.

[0003] The dimension measurement of transport vehicles based on laser radar is convenient for rapid loading of transport vehicles under warehouses. However, in the existing technology, it is impossible to supervise the working interference and working performance of laser radar, thereby reducing the working effect and measurement accuracy of laser radar, and at the same time, it is not conducive to stable scanning of transport vehicles, and it is impossible to identify and match the loading model of transport vehicles and verify the construction of dimension measurement model, which is not conducive to the command of transport vehicles, and it is impossible to rationally adjust laser radar, which is not conducive to stable and long-term accurate dimension measurement of laser radar;

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a vehicle size measurement method and system for unmanned intelligent loading to solve the above-mentioned technical defects.

[0006] The object of the present invention can be achieved by the following technical solutions: A vehicle size measurement system for unmanned intelligent loading, comprising a measurement management platform, an information collection unit, an interference assessment unit, a transportation efficiency supervision unit, a scanning analysis unit, an optimization analysis unit and a management response unit;

[0007] The information collection unit is used to collect the working interference data and the operation performance data of the laser radar, and send the working interference data and the operation performance data to the interference assessment unit and the operation efficiency supervision unit respectively;

[0008] After receiving the work interference data, the interference evaluation unit immediately performs interference classification feedback evaluation analysis on the work interference data, performs interference classification from the perspective of work interference, and obtains the operation efficiency obstruction index YZ;

[0009] After receiving the operation performance data, the operation efficiency supervision unit immediately performs operation stability supervision evaluation and analysis on the operation performance data, compares and analyzes the obtained operation condition evaluation coefficients, and obtains a stability signal and an alarm signal;

[0010] The scanning and analysis unit collects vehicle information of the transport vehicle in response to the stable signal, and performs scanning, identification, matching, supervision and analysis on the vehicle information, and sends the matching loading model and the first identification string to the management response unit, and at the same time sends the simulation loading model to the management response unit;

[0011] The optimization analysis unit is used to collect the 3D model of the transport vehicle after loading, and perform scanning optimization dynamic supervision evaluation analysis, compare and analyze the obtained optimization management index, and send the obtained management signal to the management response unit.

[0012] Preferably, the interference partition feedback evaluation and analysis process of the interference evaluation unit is as follows:

[0013] Set the monitoring period and set the monitoring period as the time threshold to obtain the working interference data of the laser radar within the time threshold. The working interference data includes the periodic interference value and the continuous interference value. The periodic interference value and the continuous interference value are compared and analyzed with the preset periodic interference value threshold and the preset continuous interference value threshold stored in the interference assessment unit:

[0014] If the periodic interference value is less than the preset periodic interference value threshold, and the continuous interference value is less than the preset continuous interference value threshold, it is determined to be a first-level interference;

[0015] If the periodic interference value is greater than or equal to the preset periodic interference value threshold, or the continuous interference value is greater than or equal to the preset continuous interference value threshold, it is determined to be a second-level interference, and the first-level interference and the second-level interference are set as the efficiency obstruction index YZ, YZ=1, 2.

[0016] Preferably, the periodic interference value indicates the maximum duration of time that the value corresponding to the environmental data in the working environment of the laser radar exceeds the preset threshold, and the environmental data include the average environmental dust concentration and the environmental electromagnetic interference value; the continuous interference value indicates the product of the proportion of delayed maintenance times in the total historical maintenance times of the laser radar and the historical fault frequency after data normalization.

[0017] Preferably, the operation stability supervision evaluation and analysis process of the operation efficiency supervision unit is as follows:

[0018] The time threshold is divided into i sub-time periods, where i is a natural number greater than zero. The operation performance data of the lidar in each sub-time period is obtained. The operation performance data includes the operation efficiency evaluation value and the performance characteristic value. The operation efficiency evaluation value and the performance characteristic value are labeled YP and BT respectively. The operation efficiency evaluation value YP, the performance characteristic value BT and the operation efficiency obstacle index YZ are substituted into the formula The operating condition evaluation coefficient is obtained, where a1, a2 and a3 are respectively the preset proportional factor coefficients of the transport efficiency evaluation value YP, the performance characteristic value BT and the transport efficiency obstacle index YZ, a1, a2 and a3 are all greater than zero, a4 is the preset fault tolerance factor coefficient, and the value is 1.882, S is the operating condition evaluation coefficient, and the operating condition evaluation coefficient S is compared and analyzed with the preset operating condition evaluation coefficient threshold value stored in the transport efficiency supervision unit:

[0019] If the ratio between the operating condition evaluation coefficient and the preset operating condition evaluation coefficient threshold is less than 1, a stable signal is generated;

[0020] If the ratio between the operating condition evaluation coefficient and the preset operating condition evaluation coefficient threshold is greater than or equal to 1, an alarm signal is generated.

[0021] Preferably, the operational efficiency evaluation value represents the ratio between the average scanning speed of the laser radar in a sub-time period and the total energy consumption; the performance characteristic value represents the duration corresponding to the preset threshold value when the product obtained by multiplying the actual scanning frequency of the laser radar in the sub-time period by the effective measurement distance after data normalization is less than the preset threshold value.

[0022] Preferably, the scanning, identifying, matching, and monitoring analysis process of the scanning and analyzing unit is as follows:

[0023] S1: Acquire vehicle information of a transport vehicle within a time threshold, the vehicle information including a front license plate continuous frame image and a rear license plate continuous frame image, extract text characters from the front license plate continuous frame image and the rear license plate continuous frame image, and set a character string composed of the extracted text characters as a first recognition character string;

[0024] S2: Compare and analyze the first recognition string with the preset recognition string list stored in the scanning and analysis unit:

[0025] If the first recognition string belongs to the preset recognition string list, an extraction signal is generated, and a matching loading model corresponding to the extraction signal is retrieved;

[0026] If the first recognition string does not belong to the preset recognition string list, a supplementary recording signal is generated. When the supplementary recording signal is generated: the 3D model of the transport vehicle corresponding to the supplementary recording signal is obtained through the laser radar, and the 3D model of the transport vehicle is divided into the front area and the car body area, and then the length, width, height and railing height of the car body area are obtained, and the simulated loading model of the transport vehicle is obtained based on the length, width, height and railing height of the car body area, and the simulated loading model of the transport vehicle is bound to the first recognition string corresponding to the simulated loading model.

[0027] Preferably, the scanning optimization dynamic supervision evaluation analysis process of the optimization analysis unit is as follows:

[0028] T1: Obtain the 3D model of the transport vehicle after loading within the time threshold, and set the 3D model of the transport vehicle after loading as the actual loading model, obtain the difference value between the actual loading model and the simulated loading model, and set the difference value between the actual loading model and the simulated loading model as the optimization demand coefficient;

[0029] T2: The total number of transport vehicles within the time threshold is obtained, and then the number of transport vehicles corresponding to the optimized demand coefficient greater than the preset optimized demand coefficient threshold in the total number of transport vehicles is obtained, and the number of transport vehicles corresponding to the optimized demand coefficient greater than the preset optimized demand coefficient threshold in the total number of transport vehicles is set as the demand value, and the ratio between the demand value and the total number of transport vehicles is set as the optimization management index, and the optimization management index is compared and analyzed with the preset optimization management index threshold entered and stored in the optimization analysis unit:

[0030] If the ratio between the optimization management index and the preset optimization management index threshold is less than 1, no signal is generated;

[0031] If the ratio between the optimization management index and the preset optimization management index threshold is greater than or equal to 1, a management signal is generated.

[0032] A vehicle size measurement method for unmanned intelligent loading includes the following steps:

[0033] Step 1: Collect the working interference data and operating performance data of the lidar and substitute them into steps 2 and 3;

[0034] Step 2: Perform interference classification feedback evaluation analysis on the work interference data through information feedback, so as to analyze from the interference perspective and obtain the operation efficiency obstacle index YZ, which is substituted into step 3;

[0035] Step 3: Conduct operation stability supervision and evaluation analysis on the operation performance data through information feedback, so as to analyze from the perspective of work performance and perform discrimination processing on the obtained operation condition evaluation coefficient. If a stable signal is obtained, proceed to step 4; if an alarm signal is obtained, feedback management is performed;

[0036] Step 4: Under the premise of laser radar being stable, collect the vehicle information of the transport vehicle, scan and identify the matching supervision analysis of the vehicle information, so as to identify the matching loading model of the transport vehicle, and trigger the laser radar to measure the size of the transport vehicle to build a simulation loading model;

[0037] Step 5: Scan and optimize the 3D model of the transport vehicle after loading in a progressive manner, conduct dynamic supervision evaluation and analysis, compare and analyze the obtained optimization management index, and feedback management if a management signal is obtained.

[0038] The beneficial effects of the present invention are as follows:

[0039] (1) The present invention analyzes the working interference and operating performance of the laser radar to understand the operating stability of the laser radar, thereby helping to improve the scanning accuracy and scanning rate of the laser radar, and at the same time helping to improve the dimensional measurement accuracy of the laser radar, thereby helping to improve the loading management efficiency of the transport vehicle. In addition, the analysis combined with the working interference of the laser radar helps to improve the comprehensiveness of the analysis data;

[0040] (2) Under the premise of laser radar stability, the present invention scans, identifies, matches, supervises and analyzes the vehicle information of the transport vehicles. On the one hand, it helps to manage the scanned transport vehicles, and on the other hand, it helps to build a loading model for the unscanned transport vehicles. It helps to direct the driver of the vehicle and control the material discharge when loading under the warehouse. At the same time, it scans and optimizes the transport vehicles after loading, and performs dynamic supervision, evaluation and analysis to verify the measurement accuracy and loading effect of the laser radar, and then rationally adjusts the laser radar to improve the measurement accuracy of the laser radar, which helps to improve the loading management effect of the transport vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The present invention will be further described below in conjunction with the accompanying drawings;

[0042] Figure 1 It is a flowchart of the system of the present invention;

[0043] Figure 2 It is a reference schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] Embodiment 1

[0046] See also Figure 1 to Figure 2 As shown, the present invention is a vehicle size measurement system for unmanned intelligent loading, including a measurement management platform, an information collection unit, an interference assessment unit, a transportation efficiency supervision unit, a scanning analysis unit, an optimization analysis unit and a management response unit. The measurement management platform is connected to the information collection unit in a one-way communication, the information collection unit is connected to the interference assessment unit and the transportation efficiency supervision unit in a one-way communication, the interference assessment unit is connected to the transportation efficiency supervision unit in a one-way communication, the transportation efficiency supervision unit is connected to the scanning analysis unit, the optimization analysis unit and the management response unit in a one-way communication, and the scanning analysis unit and the optimization analysis unit are connected to the management response unit in a one-way communication;

[0047] When the measurement management platform generates a supervision instruction, it sends the supervision instruction to the information collection unit. After receiving the supervision instruction, the information collection unit immediately collects the working interference data and operation performance data of the laser radar, and sends the working interference data and operation performance data to the interference assessment unit and the operation efficiency supervision unit respectively. After receiving the working interference data, the interference assessment unit immediately performs interference division feedback evaluation and analysis on the working interference data to understand the working interference risk of the laser radar, so as to provide data support for subsequent analysis. The specific interference division feedback evaluation and analysis process is as follows:

[0048] Set the monitoring period and set the monitoring period as the time threshold to obtain the working interference data of the laser radar within the time threshold. The working interference data includes the periodic interference value and the continuous interference value. The periodic interference value and the continuous interference value are compared and analyzed with the preset periodic interference value threshold and the preset continuous interference value threshold stored in the interference assessment unit:

[0049] If the periodic interference value is less than the preset periodic interference value threshold, and the continuous interference value is less than the preset continuous interference value threshold, it is determined to be a first-level interference;

[0050] If the periodic interference value is greater than or equal to the preset periodic interference value threshold, or the continuous interference value is greater than or equal to the preset continuous interference value threshold, it is determined to be a second-level interference, wherein the operation interference degrees corresponding to the first-level interference and the second-level interference are increased in sequence, and the first-level interference and the second-level interference are set as the operation efficiency obstruction index YZ, YZ=1, 2, that is, when the operation efficiency obstruction index YZ=1, it indicates the first-level interference, and when the operation efficiency obstruction index YZ=2, it indicates the second-level interference, and the operation efficiency obstruction index YZ is sent to the operation efficiency supervision unit;

[0051] In the embodiment of the present invention, the periodic interference value indicates the maximum duration corresponding to the value of the environmental data in the working environment of the laser radar exceeding the preset threshold value, and the environmental data includes the average value of the environmental dust concentration, the environmental electromagnetic interference value, etc. It should be noted that the risk of interference to the laser radar operation is understood from the perspective of environmental interference;

[0052] In the embodiment of the present invention, the continuous interference value represents the product value obtained by multiplying the ratio of the number of delayed maintenance times in the total number of historical maintenance times of the laser radar by the historical fault frequency after data normalization. It should be noted that the potential damage interference of the laser radar is analyzed from the perspective of historical management to provide data support for subsequent analysis;

[0053] After receiving the operation performance data, the operation efficiency supervision unit immediately conducts operation stability supervision evaluation and analysis on the operation performance data to understand the operation stability of the lidar, which helps to improve the scanning accuracy and scanning rate of the lidar. The specific operation stability supervision evaluation and analysis process is as follows:

[0054] The time threshold is divided into i sub-time periods, where i is a natural number greater than zero. The operation performance data of the lidar in each sub-time period is obtained. The operation performance data includes the operation efficiency evaluation value and the performance characteristic value. The operation efficiency evaluation value and the performance characteristic value are labeled YP and BT respectively. The operation efficiency evaluation value YP, the performance characteristic value BT and the operation efficiency obstacle index YZ are substituted into the formula The operating condition evaluation coefficient is obtained, where a1, a2 and a3 are respectively the preset proportional factor coefficients of the operation efficiency evaluation value YP, the performance characteristic value BT and the operation efficiency obstacle index YZ. The proportional factor coefficient is used to correct the deviation of various parameters in the formula calculation process, so as to make the calculation result more accurate. a1, a2 and a3 are all greater than zero. a4 is the preset fault tolerance factor coefficient, which is 1.882. S is the operating condition evaluation coefficient. The operating condition evaluation coefficient S is compared with the preset operating condition evaluation coefficient threshold value stored in the operation efficiency supervision unit for analysis:

[0055] If the ratio between the operating condition evaluation coefficient and the preset operating condition evaluation coefficient threshold is less than 1, a stabilization signal is generated and sent to the scanning analysis unit and the optimization analysis unit;

[0056] If the ratio between the operating condition evaluation coefficient and the preset operating condition evaluation coefficient threshold is greater than or equal to 1, an alarm signal is generated, and the stable signal and the alarm signal are sent to the management response unit. After receiving the stable signal and the alarm signal, the management response unit immediately performs the preset early warning operation corresponding to the stable signal and the alarm signal, so as to intuitively understand the operating status of the laser radar according to the information feedback, so as to reasonably manage the laser radar, and at the same time help reduce the impact of the laser radar on the scanning efficiency of the transport vehicle;

[0057] In the embodiment of the present invention, the operational efficiency evaluation value represents the ratio between the average scanning speed of the laser radar in the sub-time period and the total energy consumption. It should be noted that the analysis is performed from the perspective of scanning speed and energy consumption to achieve the effect of low-consumption and high-efficiency scanning, and at the same time, it is helpful to supervise and evaluate the working performance of the laser radar;

[0058] In an embodiment of the present invention, the performance characteristic value represents the time duration during which the product of the actual scanning frequency of the laser radar in a sub-time period and the effective measurement distance after data normalization is less than a preset threshold. It should be noted that the analysis is performed from the perspective of the actual working performance of the laser radar in order to understand the fault performance risk of the laser radar.

[0059] Embodiment 2

[0060] When a stable signal is generated, the scanning and analysis unit responds to the stable signal to collect the vehicle information of the transport vehicle, and scans, identifies, matches, supervises and analyzes the vehicle information, which helps to direct the driver and control the material discharge when loading under the warehouse, and also helps to verify and correct the stored information to ensure the accuracy of the loading model. The specific scanning, identifying, matching, supervision and analysis process is as follows:

[0061] The vehicle information of the transport vehicle within the time threshold is obtained, the vehicle information includes the front license plate continuous frame image and the rear license plate continuous frame image, and the text characters are extracted from the front license plate continuous frame image and the rear license plate continuous frame image, and the character string composed of the extracted text characters is set as the first recognition character string, and the first recognition character string is compared and analyzed with the preset recognition character string list recorded and stored in the scanning and analysis unit:

[0062] If the first recognition string belongs to the preset recognition string list, an extraction signal is generated, a matching loading model corresponding to the extraction signal is retrieved, and the matching loading model and the first recognition string are sent to the management response unit. After receiving the matching loading model and the first recognition string, the management response unit immediately displays the matching loading model and the transport vehicle license plate corresponding to the matching loading model, which is helpful for rapid loading and management of the modeled transport vehicles.

[0063] If the first recognition character string does not belong to the preset recognition character string list, generating a supplementary recording signal;

[0064] When the supplementary recording signal is generated: the 3D model of the transport vehicle corresponding to the supplementary recording signal is obtained through the laser radar, and the 3D model of the transport vehicle is divided into the front area and the carriage area, and then the length, width, height and railing height of the carriage area are obtained, and the simulated loading model of the transport vehicle is obtained based on the length, width, height and railing height of the carriage area, and the simulated loading model and the first identification string corresponding to the simulated loading model are bound, and the simulated loading model is sent to the management response unit. After receiving the simulated loading model, the management response unit immediately displays the simulated loading model and the transport vehicle license plate corresponding to the simulated loading model, which is helpful for the driver to command the vehicle and control the material discharge when loading under the warehouse;

[0065] The optimization analysis unit is used to collect the 3D model of the transport vehicle after loading, and perform scanning optimization dynamic supervision evaluation analysis to understand the optimization requirements of the laser radar, so as to adjust the laser radar to improve the working accuracy of the laser radar, and at the same time help improve the loading management effect of the transport vehicle. The specific scanning optimization dynamic supervision evaluation analysis process is as follows:

[0066] A 3D model of the transport vehicle after loading within a time threshold is obtained, and the 3D model of the transport vehicle after loading is set as the actual loading model, a difference value between the actual loading model and the simulated loading model is obtained, and the difference value between the actual loading model and the simulated loading model is set as the optimization demand coefficient;

[0067] The total number of transport vehicles within the time threshold is obtained, and then the number of transport vehicles corresponding to the optimized demand coefficient greater than the preset optimized demand coefficient threshold in the total number of transport vehicles is obtained, and the number of transport vehicles corresponding to the optimized demand coefficient greater than the preset optimized demand coefficient threshold in the total number of transport vehicles is set as the demand value, and the ratio between the demand value and the total number of transport vehicles is set as the optimization management index, and the optimization management index is compared and analyzed with the preset optimization management index threshold entered and stored in the optimization analysis unit:

[0068] If the ratio between the optimization management index and the preset optimization management index threshold is less than 1, no signal is generated;

[0069] If the ratio between the optimization management index and the preset optimization management index threshold is greater than or equal to 1, a management signal is generated and sent to the management response unit. After receiving the management signal, the management response unit immediately displays the preset warning operation corresponding to the management signal, so as to optimize the performance of the laser radar according to the information feedback, so as to improve the measurement effect and measurement accuracy of the laser radar, and help the driver to command the vehicle and control the material discharge when loading under the warehouse.

[0070] Embodiment 3

[0071] A vehicle size measurement method for unmanned intelligent loading includes the following steps:

[0072] Step 1: Collect the working interference data and operating performance data of the lidar and substitute them into steps 2 and 3;

[0073] Step 2: Perform interference classification feedback evaluation analysis on the work interference data through information feedback, so as to analyze from the interference perspective and obtain the operation efficiency obstacle index YZ, which is substituted into step 3;

[0074] Step 3: Conduct operation stability supervision and evaluation analysis on the operation performance data through information feedback, so as to analyze from the perspective of work performance and perform discrimination processing on the obtained operation condition evaluation coefficient. If a stable signal is obtained, proceed to step 4; if an alarm signal is obtained, feedback management is performed;

[0075] Step 4: Under the premise of laser radar being stable, collect the vehicle information of the transport vehicle, scan and identify the matching supervision analysis of the vehicle information, so as to identify the matching loading model of the transport vehicle, and trigger the laser radar to measure the size of the transport vehicle to build a simulation loading model;

[0076] Step 5: Scan and optimize the 3D model of the transport vehicle after loading in a progressive manner to conduct dynamic supervision evaluation and analysis, compare and analyze the obtained optimization management index, and feedback management if a management signal is obtained;

[0077] In summary, the present invention analyzes the working interference and operating performance of the laser radar to understand the operating stability of the laser radar, which helps to improve the scanning accuracy and scanning rate of the laser radar, and helps to improve the dimensional measurement accuracy of the laser radar, which helps to improve the loading management efficiency of the transport vehicle. The analysis combined with the working interference of the laser radar helps to improve the comprehensiveness of the analysis data. Under the premise of laser radar stability, by scanning, identifying, matching, and supervising and analyzing the vehicle information of the transport vehicle, it helps to manage the scanned transport vehicles on the one hand, and build a loading model for the unscanned transport vehicles on the other hand, which helps to direct the driver of the vehicle and control the material discharge when loading under the warehouse. At the same time, the transport vehicles after loading are scanned, optimized, dynamically supervised, evaluated and analyzed to verify the measurement accuracy and loading effect of the laser radar, and then rationally adjust the laser radar to improve the measurement accuracy of the laser radar, and help to improve the loading management effect of the transport vehicle.

[0078] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0079] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and the inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims

1. A vehicle size measurement system for unmanned intelligent loading, characterized in that: It includes measurement management platform, information collection unit, interference assessment unit, operation efficiency supervision unit, scanning analysis unit, optimization analysis unit and management response unit; The information collection unit is used to collect the working interference data and the operation performance data of the laser radar, and send the working interference data and the operation performance data to the interference assessment unit and the operation efficiency supervision unit respectively; After receiving the work interference data, the interference evaluation unit immediately performs interference classification feedback evaluation analysis on the work interference data, performs interference classification from the perspective of work interference, and obtains the operation efficiency obstruction index YZ; After receiving the operation performance data, the operation efficiency supervision unit immediately performs operation stability supervision evaluation and analysis on the operation performance data, compares and analyzes the obtained operation condition evaluation coefficients, and obtains a stability signal and an alarm signal; The scanning and analysis unit collects vehicle information of the transport vehicle in response to the stable signal, and performs scanning, identification, matching, supervision and analysis on the vehicle information, and sends the matching loading model and the first identification string to the management response unit, and at the same time sends the simulation loading model to the management response unit; The optimization analysis unit is used to collect the 3D model of the transport vehicle after loading, and perform scanning optimization dynamic supervision evaluation analysis, compare and analyze the obtained optimization management index, and send the obtained management signal to the management response unit; The scanning, identifying, matching, supervision and analysis process of the scanning and analysis unit is as follows: S1: Acquire vehicle information of a transport vehicle within a time threshold, the vehicle information including a front license plate continuous frame image and a rear license plate continuous frame image, extract text characters from the front license plate continuous frame image and the rear license plate continuous frame image, and set a character string composed of the extracted text characters as a first recognition character string; S2: Compare and analyze the first recognition string with the preset recognition string list stored in the scanning and analysis unit: If the first recognition string belongs to the preset recognition string list, an extraction signal is generated, and a matching loading model corresponding to the extraction signal is retrieved; If the first recognition string does not belong to the preset recognition string list, a supplementary recording signal is generated. When the supplementary recording signal is generated: a 3D model of the transport vehicle corresponding to the supplementary recording signal is obtained through a laser radar, and the 3D model of the transport vehicle is divided into a front area and a carriage area, and then the length, width, height and height of the carriage area are obtained, and a simulated loading model of the transport vehicle is obtained based on the length, width, height and height of the carriage area, and at the same time, the simulated loading model and the first recognition string corresponding to the simulated loading model are bound; The scanning optimization dynamic supervision evaluation analysis process of the optimization analysis unit is as follows: T1: Obtain the 3D model of the transport vehicle after loading within the time threshold, and set the 3D model of the transport vehicle after loading as the actual loading model, obtain the difference value between the actual loading model and the simulated loading model, and set the difference value between the actual loading model and the simulated loading model as the optimization demand coefficient; T2: The total number of transport vehicles within the time threshold is obtained, and then the number of transport vehicles corresponding to the optimized demand coefficient greater than the preset optimized demand coefficient threshold in the total number of transport vehicles is obtained, and the number of transport vehicles corresponding to the optimized demand coefficient greater than the preset optimized demand coefficient threshold in the total number of transport vehicles is set as the demand value, and the ratio between the demand value and the total number of transport vehicles is set as the optimization management index, and the optimization management index is compared and analyzed with the preset optimization management index threshold entered and stored in the optimization analysis unit: If the ratio between the optimization management index and the preset optimization management index threshold is less than 1, no signal is generated; If the ratio between the optimization management index and the preset optimization management index threshold is greater than or equal to 1, a management signal is generated.

2. The vehicle size measurement system for unmanned intelligent loading according to claim 1 is characterized in that: The interference partition feedback evaluation and analysis process of the interference evaluation unit is as follows: Set the monitoring period and set the monitoring period as the time threshold to obtain the working interference data of the laser radar within the time threshold. The working interference data includes the periodic interference value and the continuous interference value. The periodic interference value and the continuous interference value are compared and analyzed with the preset periodic interference value threshold and the preset continuous interference value threshold stored in the interference assessment unit: If the periodic interference value is less than the preset periodic interference value threshold, and the continuous interference value is less than the preset continuous interference value threshold, it is determined to be a first-level interference; If the periodic interference value is greater than or equal to the preset periodic interference value threshold, or the continuous interference value is greater than or equal to the preset continuous interference value threshold, it is determined to be a second-level interference, and the first-level interference and the second-level interference are set as the efficiency obstruction index YZ, YZ=1, 2.

3. The vehicle size measurement system for unmanned intelligent loading according to claim 2 is characterized in that: The periodic interference value indicates the maximum duration of time that the value corresponding to the environmental data in the working environment of the laser radar exceeds the preset threshold, and the environmental data includes the average environmental dust concentration and the environmental electromagnetic interference value; the continuous interference value indicates the product of the proportion of delayed maintenance times in the total historical maintenance times of the laser radar and the historical failure frequency after data normalization.

4. The vehicle size measurement system for unmanned intelligent loading according to claim 2 is characterized in that: The operation stability supervision evaluation and analysis process of the operation efficiency supervision unit is as follows: The time threshold is divided into i sub-time periods, where i is a natural number greater than zero. The operation performance data of the lidar in each sub-time period is obtained. The operation performance data includes the operation efficiency evaluation value and the performance characteristic value. The operation efficiency evaluation value and the performance characteristic value are labeled YP and BT respectively. The operation efficiency evaluation value YP, the performance characteristic value BT and the operation efficiency obstacle index YZ are substituted into the formula The operating condition evaluation coefficient is obtained, where a1, a2 and a3 are respectively the preset proportional factor coefficients of the transport efficiency evaluation value YP, the performance characteristic value BT and the transport efficiency obstacle index YZ, a1, a2 and a3 are all greater than zero, a4 is the preset fault tolerance factor coefficient, and the value is 1.882, S is the operating condition evaluation coefficient, and the operating condition evaluation coefficient S is compared and analyzed with the preset operating condition evaluation coefficient threshold value stored in the transport efficiency supervision unit: If the ratio between the operating condition evaluation coefficient and the preset operating condition evaluation coefficient threshold is less than 1, a stable signal is generated; If the ratio between the operating condition evaluation coefficient and the preset operating condition evaluation coefficient threshold is greater than or equal to 1, an alarm signal is generated.

5. The vehicle size measurement system for unmanned intelligent loading according to claim 4 is characterized in that: The operational efficiency evaluation value represents the ratio between the average scanning speed of the laser radar in the sub-time period and the total energy consumption; the performance characteristic value represents the duration corresponding to the time when the product obtained by multiplying the actual scanning frequency of the laser radar in the sub-time period by the effective measurement distance after data normalization is less than the preset threshold.

6. A vehicle size measurement method for unmanned intelligent loading, the method is applied to a vehicle size measurement system for unmanned intelligent loading as claimed in any one of claims 1 to 5, characterized in that: The following steps are involved: Step 1: Collect the working interference data and operating performance data of the lidar and substitute them into steps 2 and 3; Step 2: Perform interference classification feedback evaluation analysis on the work interference data through information feedback, so as to analyze from the interference perspective and obtain the operation efficiency obstacle index YZ, which is substituted into step 3; Step 3: Conduct operation stability supervision and evaluation analysis on the operation performance data through information feedback, so as to analyze from the perspective of work performance and perform discrimination processing on the obtained operation condition evaluation coefficient. If a stable signal is obtained, proceed to step 4; if an alarm signal is obtained, feedback management is performed; Step 4: Under the premise of laser radar being stable, collect the vehicle information of the transport vehicle, scan and identify the matching supervision analysis of the vehicle information, so as to identify the matching loading model of the transport vehicle, and trigger the laser radar to measure the size of the transport vehicle to build a simulation loading model; Step 5: Scan and optimize the 3D model of the transport vehicle after loading in a progressive manner, conduct dynamic supervision evaluation and analysis, compare and analyze the obtained optimization management index, and feedback management if a management signal is obtained.

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