A radar vehicle control system for surveying underground urban roads

Abstract

The present invention discloses a radar vehicle control system for surveying underground urban roads, specifically related to the technical field of urban road surveying, including a radar vehicle survey control center, a radar vehicle survey data acquisition module, a radar vehicle survey data analysis module, a radar vehicle survey intelligent control module, a radar vehicle survey intelligent control comprehensive analysis module, and a radar vehicle survey intelligent control human-computer interaction module; the radar vehicle survey data acquisition module is used to collect data during the radar vehicle survey process, obtain various control parameters of the radar vehicle survey, and transmit each control parameter to the radar vehicle survey data analysis module; the present invention can automatically adjust the radar transmission and reception parameters according to different characteristics of underground pipelines through intelligent algorithms to meet different pipeline management requirements; the driving control system can automatically adjust the driving speed and path according to real-time information such as road conditions and traffic flow to ensure the safety and efficiency of the survey operation.

Description

Technical Field

[0001] The present invention relates to the technical field of urban road surveying, and particularly to a radar vehicle control system for surveying underground urban roads. Background Art

[0002] In the process of the rapid development of cities, the reasonable layout and precise management of underground pipelines in urban roads have become key elements to ensure the normal operation of cities. There are various types of underground pipelines, including water supply, drainage, gas, electricity, communication, etc., and their distribution is intricate. In recent years, with the progress of technology, radar vehicles using radar detection technology have attracted much attention due to their non-invasive, efficient, and relatively high accuracy.

[0003] Traditional control methods for underground pipeline surveying radar vehicles, on the one hand, rely on manual operation and monitoring. Operators monitor the running state and detection results of the radar vehicle in real time through monitoring equipment, which increases labor costs. On the other hand, the survey parameters of the radar vehicle are statically configured. Before conducting underground pipeline detection, the transmission and reception of radar signals are optimized through sensor configuration and parameter adjustment, which requires professional personnel to make meticulous parameter adjustments and increases the difficulty of use.

[0004] However, the existing radar vehicle control systems for underground pipeline surveying still have a series of problems in practical applications. First, in a complex urban road environment, manual monitoring has lag, and the driving trajectory and speed control of the vehicle are not precise enough. Second, the system has insufficient adaptability to different types, materials, and burial depths of pipelines. Static parameter adjustment often fails to effectively adjust detection parameters, resulting in some pipelines being difficult to be accurately detected. Third, the data processing ability is limited, and a large amount of data generated by radar detection needs to be processed and analyzed quickly and accurately. Therefore, in order to obtain underground pipeline information more accurately, it is very important to improve the intelligent control performance of the radar vehicle during surveying by integrating advanced sensor technology, intelligent algorithms, and automation control technology. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a radar vehicle control system for surveying underground urban roads to solve the problems raised in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solution: A radar vehicle control system for surveying underground urban roads, comprising:

[0007] A radar vehicle survey control center: Through big data technology, it stores and receives the survey data of the radar vehicle, constructs a radar vehicle survey control database, and triggers a control mechanism according to the analysis result of the received survey data of the radar vehicle;

[0008] Radar vehicle survey data acquisition module: It is used to collect data during the survey process of the radar vehicle, obtain various control parameters of the radar vehicle survey, and transmit various control parameters to the radar vehicle survey data analysis module;

[0009] Radar vehicle survey data analysis module: It is used to collect various control parameters obtained by the data acquisition module, obtain various control indicators of the radar vehicle survey, and transmit various control indicators to the radar vehicle survey intelligent control module; The radar vehicle survey data analysis module includes a ground penetration control analysis unit for the radar vehicle, an identification pipeline control analysis unit for the radar vehicle, and a driving control analysis unit for the radar vehicle;

[0010] Radar vehicle survey intelligent control module: It is used to compare various control indicators obtained by the data analysis module with the threshold values respectively, transmit the good comparison results to the radar vehicle survey intelligent control comprehensive analysis module, and the radar vehicle survey control center triggers the control mechanism according to the abnormal comparison results;

[0011] Radar vehicle survey intelligent control comprehensive analysis module: It is used to comprehensively analyze the good comparison results obtained by the intelligent control module, obtain the radar vehicle survey intelligent control comprehensive index, and transmit it to the radar vehicle survey intelligent control human-computer interaction module;

[0012] Radar vehicle survey intelligent control human-computer interaction module: It is used for human-computer interaction of the radar vehicle survey intelligent control comprehensive index of the intelligent control comprehensive analysis module.

[0013] Preferably, in the radar vehicle survey data analysis module, the ground penetration control analysis unit for the radar vehicle: First, identify the types of underground media in the target pipeline area through the ground penetrating radar equipment, and obtain the underground media type data set UTD, , where n is the number of types of underground media, and ut i is the i-th type of underground media; Then, through sensor technology, obtain the conductivity σ(ut i ) of the i-th type of underground media in the target pipeline area; Secondly, obtain the radar emission frequency f and emission power P through the radar vehicle survey control database; Finally, through big data analysis technology, obtain the ground penetration control index RGCI for the radar vehicle, , where c is the speed of light, μ r (ut i ) is the relative magnetic permeability, γ r (ut i ) is the relative dielectric constant of the underground media, γ0 is the dielectric constant in vacuum, and P loss is the power loss of the radar signal during the process of penetrating the underground media.

[0014] Preferably, the radar vehicle survey data analysis module's radar vehicle identification pipeline control analysis unit includes an underground pipeline identification and radar wave reflection analysis unit and an underground pipeline identification accuracy analysis unit; the radar vehicle driving control analysis unit includes a radar vehicle movement and data acquisition quality analysis unit and a radar vehicle driving stability and adaptive adjustment analysis unit.

[0015] Preferably, in the underground pipeline identification and radar wave reflection analysis unit of the radar vehicle identification pipeline control analysis unit: First, through the ground penetrating radar equipment, identify the types of underground pipeline materials in the target pipeline area, and obtain the underground pipeline material type dataset UPD, , where k is the number of types of underground pipe materials, and up j is the jth type of underground pipeline material; then extract the dielectric constant ε g (up j ) of the identified underground pipeline and the dielectric constant ε d (ut i ) of the underground medium covering the pipeline from the radar vehicle survey control database; secondly, through sensor technology, obtain the conductivity σ g (up j ) of the underground pipeline material and the conductivity σ d (ut i ), where ut i is the ith type of underground medium; finally, through big data analysis technology, obtain the underground pipeline material identification and radar wave reflection coefficient τ, , where γ0 is the dielectric constant in vacuum, ω is the radar wave angular frequency, θ is the radar wave incident angle, and obtain the underground pipeline identification and radar wave reflection matching control index WRCI, , where τ0 is the expected reflection coefficient.

[0016] Preferably, in the underground pipeline identification accuracy analysis unit of the radar vehicle identification pipeline control analysis unit: First, obtain the round-trip time Δt of the radar wave detecting the underground pipeline from the radar vehicle survey control database, and obtain the pipeline depth measurement control coefficient E de , , where c is the speed of light, ε d is the dielectric constant of the underground medium covering the pipeline, and h represents the standard value of the pipeline depth; then, through the ground penetrating radar equipment, identify the pipeline diameter R in the target pipeline area, and compare it with the pipeline diameter data in the radar vehicle survey control database, and count the number of pipes N cor with correct diameter identification and the total number of identified pipes N total , and obtain the pipeline diameter identification accuracy control coefficient A dis , where A dis = N cor / N total, secondly, through the positioning technology, obtain the pipeline position coordinates PCD of the radar detection pipeline, and compare it with the pipeline position coordinates PCD0 in the radar vehicle survey control database to obtain the radar detection pipeline position accuracy control coefficient X p , ; Finally, through the big data analysis technology, obtain the underground pipeline identification accuracy control index PACI, PACI = ε g ×(a1×E de +a2×A dis +a3×X p ), ε g is the dielectric constant of the underground pipeline, and a1, a2 and a3 respectively represent the corresponding weights.

[0017] Preferably, in the radar vehicle driving control analysis unit, the radar vehicle movement and data acquisition quality analysis unit: First, collect the driving speed v of the radar vehicle in real time through the speed sensor; then obtain the data acquisition time interval t and the radar scan width w of the radar detection underground pipeline through the radar vehicle survey control database to obtain the underground pipeline data acquisition efficiency control index DCCI, , A is the pipeline area covered by the radar for each scan.

[0018] Preferably, in the radar vehicle driving control analysis unit, the radar vehicle driving stability and adaptive adjustment analysis unit: First, through the positioning technology, obtain the actual driving route R of the radar vehicle detecting the underground pipeline s , and compare it with the set driving route R to obtain the radar vehicle driving deviation control coefficient DDc, DDc = |R s -R| / R; then within the data acquisition time interval t, collect N radar vehicle driving speed sampling points to obtain the speed standard deviation σ(v) and the average speed μ(v) to obtain the vehicle speed fluctuation control coefficient VVc, VVc = σ(v) / μ(v); secondly, through the timestamp technology, record the response time t from the issuance of the steering control command to the start of the radar vehicle steering each time x ; Finally, through the big data analysis technology, obtain the radar vehicle driving stability and adaptive adjustment control index RAACI, , N adj is the number of times the system adaptively adjusts the actual driving of the radar vehicle according to the set driving path, and b1 and b2 respectively represent the corresponding weights.

[0019] Preferably, the intelligent control module in the radar vehicle survey intelligent control module includes the following steps:

[0020] Step 3.1: Compare the ground penetration control index RGCI of the radar vehicle with the threshold RGCI0 to obtain the performance factor ζ(RGCI) of the ground penetration control of the radar vehicle. ζ(RGCI) = RGCI / RGCI0. If ζ(RGCI) ≥ 1, it indicates that the ground penetration control of the radar vehicle is good; otherwise, it indicates abnormal control and triggers the ground penetration control mechanism of the radar vehicle.

[0021] Step 3.2: First, compare the underground pipeline identification and radar wave reflection matching control index WRCI with the threshold WRCI0 to obtain the performance factor ζ(WRCI) of the radar wave reflection matching control. ζ(WRCI) = WRCI / WRCI0. If ζ(WRCI) ≥ 1, it indicates that the radar wave reflection matching control is good; otherwise, it indicates abnormal control and triggers the radar wave reflection matching control mechanism. Then, compare the underground pipeline identification accuracy control index PACI with the threshold PACI0 to obtain the performance factor ζ(PACI) of the underground pipeline identification accuracy control. ζ(PACI) = PACI / PACI0. If ζ(PACI) ≥ 1, it indicates that the underground pipeline identification accuracy control is good; otherwise, it indicates abnormal control and triggers the underground pipeline radar identification control mechanism.

[0022] Step 3.3: First, compare the underground pipeline data collection efficiency control index DCCI with the threshold DCCI0 to obtain the performance factor ζ(DCCI) of the underground pipeline data collection efficiency control. ζ(DCCI) = DCCI / DCCI0. If ζ(DCCI) ≥ 1, it indicates that the underground pipeline data collection efficiency control is good; otherwise, it indicates abnormal control and triggers the underground pipeline data collection efficiency control mechanism. Then, compare the radar vehicle driving stability and adaptive adjustment control index RAACI with the threshold RAACI0 to obtain the performance factor ζ(RAACI) of the radar vehicle driving stability and adaptive adjustment control. ζ(RAACI) = RAACI / RAACI0. If ζ(RAACI) ≥ 1, it indicates that the radar vehicle driving stability and adaptive adjustment control is good; otherwise, it indicates abnormal control and triggers the radar vehicle driving control mechanism.

[0023] The technical effects and advantages of the present invention:

[0024] 1. Through the intelligent optimization of the ground penetration control parameters, the radar vehicle can more effectively penetrate different media and obtain more accurate underground information. The precise adjustment of the pipeline identification control parameters helps the radar vehicle accurately identify and locate underground pipelines, reducing false alarms and missed detections and improving the survey efficiency. The automatic adjustment of the driving control parameters ensures the stable driving of the radar vehicle in complex urban environments.

[0025] 2. Through intelligent algorithms, the present invention can automatically adjust the radar transmission and reception parameters according to different characteristics of underground pipelines (such as dielectric constant, conductivity, etc.) to meet different pipeline management requirements; the driving control system can automatically adjust the driving speed and path according to real-time information such as road conditions and traffic flow to ensure the safety and efficiency of the survey operation.

[0026] 3. Through the application of intelligent and automated technologies, the present invention simplifies the operation process of the radar vehicle, reduces the skill requirements for operators; through the optimization of control parameters by intelligent algorithms, the need for manual intervention is reduced, and the labor cost is lowered; the efficient survey operation and accurate identification ability help to reduce repeated surveys and misjudgments, further reducing the survey cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the overall process of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0029] Please refer to Figure 1 As shown, the present invention provides a radar vehicle control system for surveying underground urban roads, including a radar vehicle survey control center, a radar vehicle survey data acquisition module, a radar vehicle survey data analysis module, a radar vehicle survey intelligent control module, a radar vehicle survey intelligent control comprehensive analysis module, and a radar vehicle survey intelligent control human-computer interaction module.

[0030] The radar vehicle survey control center is connected to the other modules. The radar vehicle survey data analysis module is respectively connected to the radar vehicle survey data acquisition module and the radar vehicle survey intelligent control module. The radar vehicle survey intelligent control comprehensive analysis module is respectively connected to the radar vehicle survey intelligent control module and the radar vehicle survey intelligent control human-computer interaction module.

[0031] Radar vehicle survey control center: Through big data technology, it stores and receives the survey data of the radar vehicle, constructs a radar vehicle survey control database, and triggers a control mechanism according to the analysis result of the received survey data of the radar vehicle.

[0032] It should be specifically noted in this embodiment that the detection data of the radar vehicle includes, but is not limited to, the surveyed route of the set target pipeline area, radar emission parameters, radar reception parameters, driving speed, target pipeline survey data, underground medium survey data of the target pipeline area, etc. The radar vehicle survey control database is connected to the other modules in the system, and is used to receive and store the data of each module, and to update the radar vehicle survey control database in real time.

[0033] Radar vehicle survey data acquisition module: It is used to collect the data during the survey process of the radar vehicle, obtain various control parameters of the radar vehicle survey, and transmit various control parameters to the radar vehicle survey data analysis module;

[0034] It should be specifically noted in this embodiment that the various control parameters include administrator identity authentication information, radar vehicle ground penetration control parameters, radar vehicle pipeline identification control parameters, and radar vehicle driving control parameters. Among them, the radar vehicle ground penetration control parameters include underground medium type data, conductivity of the underground medium, radar emission frequency, and transmission power; the radar vehicle pipeline identification control parameters include underground pipe type data sets, dielectric constants and conductivities of underground pipelines, dielectric constants and conductivities of the underground medium covering the pipelines, radar wave angular frequency, radar wave incident angle, round-trip time of the radar wave detecting the underground pipeline, speed of light, standard value of pipeline depth, number of pipelines with correct diameter identification, total number of identified pipelines, radar detection pipeline position coordinates; the radar vehicle driving control parameters include the driving speed of the radar vehicle, data acquisition time interval, radar scan width, area of the pipeline covered by the radar each time, actual driving route and set driving route of the radar vehicle detecting the underground pipeline, vehicle speed, steering response time, and number of adaptive adjustments.

[0035] Radar vehicle survey data analysis module: It is used to collect various control parameters obtained by the data acquisition module, obtain various control indicators of the radar vehicle survey, and transmit various control indicators to the radar vehicle survey intelligent control module; the radar vehicle survey data analysis module includes a radar vehicle ground penetration control analysis unit, a radar vehicle pipeline identification control analysis unit, and a radar vehicle driving control analysis unit;

[0036] It should be specifically noted in this embodiment that the radar vehicle ground penetration control analysis unit is used to analyze the radar vehicle ground penetration control parameters, the radar vehicle pipeline identification control analysis unit is used to analyze the radar vehicle pipeline identification control parameters, and the radar vehicle driving control analysis unit is used to analyze the radar vehicle driving control parameters.

[0037] The radar vehicle ground penetration control analysis unit: First, identify the types of underground media in the target pipeline area through the ground penetrating radar equipment, and obtain the underground medium type data set UTD, , n is the number of types of underground media, uti is the i-th type of underground medium, and the underground medium includes rocks, soils, concretes, etc.; then, through sensor technology, the conductivity σ(ut i ) of the i-th type of underground medium in the target pipeline area is obtained; secondly, the radar emission frequency f and the emission power P are obtained through the radar vehicle survey control database; finally, through big data analysis technology, the radar vehicle ground penetration control index RGCI is obtained, , c is the speed of light, μ r (ut i ) is the relative magnetic permeability, γ r (ut i ) is the relative dielectric constant of the underground medium, γ0 is the dielectric constant in vacuum, and P loss is the power loss of the radar signal during the process of penetrating the underground medium;

[0038] It should be specifically noted in this embodiment that media with higher conductivity (such as wet soil) will absorb more radar signals, thus reducing the penetration depth. On the contrary, media with lower conductivity (such as dry soil or rock) absorb less radar signals and have a relatively deeper penetration depth; the higher the radar emission frequency, the weaker the penetration ability may be. For underground surveys of urban roads, a frequency that can provide both sufficient penetration depth and high resolution usually needs to be selected.

[0039] The radar vehicle pipeline recognition control analysis unit includes an underground pipeline recognition and radar wave reflection analysis unit and an underground pipeline recognition accuracy analysis unit, and its analysis includes the following steps:

[0040] Step 1.1: Underground pipeline recognition and radar wave reflection analysis unit: First, through a ground penetrating radar device, identify the types of underground pipeline materials in the target pipeline area, and obtain the underground pipe material type dataset UPD, , k is the number of types of underground pipe materials, and up j is the j-th type of underground pipeline material; then, extract the dielectric constant ε g (up j ) of the identified underground pipeline and the dielectric constant ε d (ut i ) of the underground medium covering the pipeline from the radar vehicle survey control database; secondly, through sensor technology, obtain the conductivity σ g (up j ) of the underground pipeline material and the conductivity σ d (ut i ) of the underground medium covering the pipeline, where ut i is the i-th type of underground medium; finally, through big data analysis technology, obtain the underground pipeline material recognition and radar wave reflection coefficient τ, , where γ0 is the permittivity in vacuum, ω is the angular frequency of the radar wave, θ is the incident angle of the radar wave, and the underground pipeline recognition and radar wave reflection matching control index WRCI is obtained. , where τ0 is the expected reflection coefficient;

[0041] Step 1.2: Underground pipeline recognition accuracy analysis unit: First, obtain the round-trip time Δt of the radar wave detecting the underground pipeline through the radar vehicle survey control database, and obtain the pipeline depth measurement control coefficient E. de , , where c is the speed of light, ε d is the permittivity of the underground medium covering the pipeline, h represents the standard value of the pipeline depth; then, through the ground penetrating radar equipment, identify the pipeline diameter R in the target pipeline area, and compare it with the pipeline diameter data in the radar vehicle survey control database, and count the number N of pipelines with correct diameter identification. cor and the total number N of identified pipelines total , and obtain the pipeline diameter identification accuracy control coefficient A. dis , A dis =N cor / N total . Secondly, through the positioning technology, obtain the pipeline position coordinates PCD of the radar detecting the pipeline. The pipeline position coordinates can be three-dimensional coordinates, parabola vertex coordinates, etc., and compare them with the pipeline position coordinates PCD0 in the radar vehicle survey control database to obtain the radar detecting pipeline position accuracy control coefficient X. p , ; Finally, through the big data analysis technology, obtain the underground pipeline recognition accuracy control index PACI, PACI = ε g ×(a1×E de +a2×A dis +a3×X p ), where ε g is the permittivity of the underground pipeline, and a1, a2, and a3 respectively represent the corresponding weights. For example, a1 = 0.3, a2 = 0.4, and a3 = 0.3;

[0042] It should be specifically noted in this embodiment that the radar vehicle control system can dynamically adjust the emission angle of the radar wave to optimize the detection effect of the underground pipeline; by adjusting the incident angle, the system can reduce the interference of ground clutter, improve the signal-to-noise ratio, and thus perform high-resolution imaging on the radar reflection signal.

[0043] The radar vehicle driving control analysis unit includes a radar vehicle movement and data acquisition quality analysis unit and a radar vehicle driving stability and adaptive adjustment analysis unit, and its analysis includes the following steps:

[0044] Step 2.1: Radar vehicle movement and data acquisition quality analysis unit: First, the driving speed v of the radar vehicle is collected in real time through a speed sensor; then, the data acquisition time interval t and the radar scan width w for detecting underground pipelines by the radar vehicle are obtained from the radar vehicle survey control database to obtain the underground pipeline data acquisition efficiency control index DCCI. , where A is the pipeline area covered by the radar in each scan.

[0045] Step 2.2: Radar vehicle driving stability and adaptive adjustment analysis unit: First, through positioning technology, the actual driving route R of the radar vehicle for detecting underground pipelines is obtained. s , and it is compared with the set driving route R to obtain the radar vehicle driving deviation control coefficient DDc, DDc = |R s - R| / R; then, within the data acquisition time interval t, N radar vehicle driving speed sampling points are collected to obtain the speed standard deviation σ(v) and the average speed μ(v) to obtain the vehicle speed fluctuation control coefficient VVc, VVc = σ(v) / μ(v); secondly, through timestamp technology, the response time t from issuing a steering control command to the radar vehicle starting to turn is recorded each time. x ; finally, through big data analysis technology, the radar vehicle driving stability and adaptive adjustment control index RAACI is obtained. , N adj is the number of times the system adaptively adjusts the actual driving of the radar vehicle according to the set driving path, and b1 and b2 respectively represent the corresponding weights.

[0046] It should be specifically noted in this embodiment that the control system can monitor and correct the driving deviation in real time to ensure that the radar vehicle can accurately drive along the preset path; when surveying underground in urban roads, the radar vehicle needs to maintain a relatively constant speed to avoid data errors caused by speed changes; in the urban road environment, the radar vehicle may need to frequently adjust its driving direction to avoid obstacles or drive according to a predetermined route; the control system can dynamically adjust vehicle parameters (such as speed, steering angle, etc.) according to real-time road condition information to ensure the efficient and safe progress of the survey operation.

[0047] Radar vehicle survey intelligent control module: It is used to compare each control index obtained by the data analysis module with the threshold respectively, and transmit the good comparison results to the radar vehicle survey intelligent control comprehensive analysis module. The radar vehicle survey control center triggers the control mechanism according to the abnormal comparison results. The intelligent control module includes the following steps:

[0048] Step 3.1: Compare the ground penetration control index RGCI of the radar vehicle with the threshold RGCI0 to obtain the performance factor ζ(RGCI) of the ground penetration control of the radar vehicle. ζ(RGCI) = RGCI / RGCI0. If ζ(RGCI) ≥ 1, it indicates that the ground penetration control of the radar vehicle is good; otherwise, it indicates abnormal control, triggering the ground penetration control mechanism of the radar vehicle, such as dynamically adjusting the radar transmission power.

[0049] Step 3.2: First, compare the underground pipeline identification and radar wave reflection matching control index WRCI with the threshold WRCI0 to obtain the performance factor ζ(WRCI) of the radar wave reflection matching control. ζ(WRCI) = WRCI / WRCI0. If ζ(WRCI) ≥ 1, it indicates that the radar wave reflection matching control is good; otherwise, it indicates abnormal control, triggering the radar wave reflection matching control mechanism, such as automatically calibrating the radar wave transmission parameters to ensure the optimal reflection coefficient matching degree. Then, compare the underground pipeline identification accuracy control index PACI with the threshold PACI0 to obtain the performance factor ζ(PACI) of the underground pipeline identification accuracy control. ζ(PACI) = PACI / PACI0. If ζ(PACI) ≥ 1, it indicates that the underground pipeline identification accuracy control is good; otherwise, it indicates abnormal control, triggering the underground pipeline radar identification control mechanism, such as optimizing the imaging technology and algorithm to perform high-resolution imaging on the radar reflection signal.

[0050] Step 3.3: First, compare the underground pipeline data acquisition efficiency control index DCCI with the threshold DCCI0 to obtain the performance factor ζ(DCCI) of the underground pipeline data acquisition efficiency control. ζ(DCCI) = DCCI / DCCI0. If ζ(DCCI) ≥ 1, it indicates that the underground pipeline data acquisition efficiency control is good; otherwise, it indicates abnormal control, triggering the underground pipeline data acquisition efficiency control mechanism, such as adjusting the moving speed of the radar vehicle. Then, compare the radar vehicle driving stability and adaptive adjustment control index RAACI with the threshold RAACI0 to obtain the performance factor ζ(RAACI) of the radar vehicle driving stability and adaptive adjustment control. ζ(RAACI) = RAACI / RAACI0. If ζ(RAACI) ≥ 1, it indicates that the radar vehicle driving stability and adaptive adjustment control is good; otherwise, it indicates abnormal control, triggering the radar vehicle driving control mechanism, such as automatically adjusting the vehicle driving trajectory and speed.

[0051] Radar vehicle survey intelligent control comprehensive analysis module: It is used to comprehensively analyze the good comparison results obtained by the intelligent control module to obtain the radar vehicle survey intelligent control comprehensive index RVCI, and transmit it to the radar vehicle survey intelligent control human-computer interaction module. The comprehensive analysis model is: RVCI = c1×ζ(RGCI)+c2×[ζ(WRCI)+ζ(PACI)]+c3×[ζ(DCCI)+ζ(RAACI)], where c1, c2, and c3 respectively represent the corresponding weights, ζ(RGCI) is the performance factor of the radar vehicle's ground penetration control, ζ(WRCI) is the performance factor of the radar wave reflection matching control, ζ(PACI) is the performance factor of the underground pipeline identification accuracy control, ζ(DCCI) is the performance factor of the underground pipeline data acquisition efficiency control, and ζ(RAACI) is the performance factor of the radar vehicle's driving stability and adaptive adjustment control;

[0052] Radar vehicle survey intelligent control human-computer interaction module: It is used to perform human-computer interaction on the radar vehicle survey intelligent control comprehensive index of the intelligent control comprehensive analysis module. According to the radar vehicle survey intelligent control comprehensive index, judge whether it is within the set allowable range. If so, it means that the radar vehicle survey intelligent control is good; otherwise, it prompts the administrator to take measures in time, such as optimizing the radar wave transmission and reception parameters according to the dielectric constant of the pipeline material; intelligently adjusting the vehicle speed to reduce the volatility and ensure the consistency and continuity of radar data acquisition.

[0053] Secondly: In the attached drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments of the present disclosure are involved. For other structures, reference can be made to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0054] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention.

[0055] Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall

[0056] be included within the protection scope of the present invention.

[0057] ​

Claims

1. A radar vehicle control system for surveying underground urban roads, characterized in that: Including: Radar vehicle survey control center: By means of big data technology, it stores and receives the survey data of the radar vehicle, constructs a radar vehicle survey control database, and triggers a control mechanism according to the analysis result of the received survey data of the radar vehicle; Radar vehicle survey data acquisition module: Used to collect the data during the survey process of the radar vehicle, obtain various control parameters of the radar vehicle survey, and transmit various control parameters to the radar vehicle survey data analysis module; Radar vehicle survey data analysis module: Used to collect various control parameters obtained by the data acquisition module, obtain various control indicators of the radar vehicle survey, and transmit various control indicators to the radar vehicle survey intelligent control module; The radar vehicle survey data analysis module includes a ground penetration control analysis unit for the radar vehicle, a pipeline identification control analysis unit for the radar vehicle, and a driving control analysis unit for the radar vehicle; In the radar vehicle survey data analysis module, the ground penetration control analysis unit of the radar vehicle: First, identify the types of underground media in the target pipeline area through the ground penetrating radar equipment, and obtain the underground media type data set UTD, UTD = [ut1, ut2,..., ut i ,..., ut n , where n is the number of types of underground media, and ut i is the i-th type of underground media; then, through sensor technology, obtain the conductivity σ(ut i ) of the i-th type of underground media in the target pipeline area; secondly, obtain the radar emission frequency f and emission power P through the radar vehicle survey control database; finally, through big data analysis technology, obtain the radar vehicle ground penetration control index RGCI, c is the speed of light, μ r (ut i ) is the relative magnetic permeability, γ r (ut i ) is the relative permittivity of the underground medium, γ0 is the permittivity in vacuum, P loss is the power loss of the radar signal during the penetration of the underground medium; Radar vehicle survey intelligent control module: Used to compare various control indicators obtained by the data analysis module with thresholds respectively, transmit good comparison results to the radar vehicle survey intelligent control comprehensive analysis module, and the radar vehicle survey control center triggers a control mechanism according to abnormal comparison results; Radar vehicle survey intelligent control comprehensive analysis module: Used to comprehensively analyze the good comparison results obtained by the intelligent control module, obtain the radar vehicle survey intelligent control comprehensive index, and transmit it to the radar vehicle survey intelligent control human-computer interaction module; Radar vehicle survey intelligent control human-computer interaction module: Used to perform human-computer interaction on the radar vehicle survey intelligent control comprehensive index of the intelligent control comprehensive analysis module.

2. The radar vehicle control system for surveying the underground of urban roads according to claim 1, characterized in that: The pipeline identification control analysis unit for the radar vehicle in the radar vehicle survey data analysis module includes an underground pipeline identification and radar wave reflection analysis unit and an underground pipeline identification accuracy analysis unit; The driving control analysis unit for the radar vehicle includes a radar vehicle movement and data acquisition quality analysis unit and a radar vehicle driving stability and adaptive adjustment analysis unit.

3. The radar vehicle control system for surveying underground of urban roads according to claim 2, wherein: The underground pipeline identification and radar wave reflection analysis unit in the radar vehicle identification pipeline control analysis unit: First, through the ground penetrating radar equipment, identify the types of underground pipeline materials in the target pipeline area, and obtain the underground pipeline material type dataset UPD, UPD = |[up1, up2, …, up j , …, up k , where k is the number of types of underground pipe materials, and up j is the jth type of underground pipeline material; then extract the dielectric constant ε g (up j ) of the identified underground pipeline and the dielectric constant ε d (ut i ) of the underground medium covering the pipeline from the radar vehicle survey control database; secondly, through sensor technology, obtain the conductivity σ g (up j ) of the underground pipeline material and the conductivity σ d (ut i ) of the underground medium covering the pipeline, where ut i is the ith type of underground medium; finally, through big data analysis technology, obtain the underground pipeline material identification and radar wave reflection coefficient τ, γ0 is the permittivity in vacuum, ω is the angular frequency of the radar wave, θ is the incident angle of the radar wave, and the underground pipeline identification and radar wave reflection matching control index WRCI is obtained. τ0 is the expected reflection coefficient.

4. A radar vehicle control system for surveying underground urban roads according to claim 2, characterized in that: Underground pipeline recognition accuracy analysis unit in the radar vehicle recognition pipeline control analysis unit: First, obtain the round-trip time Δt of the radar wave detecting the underground pipeline from the radar vehicle survey control database, and obtain the pipeline depth measurement control coefficient E de , where c is the speed of light, and ε d is the dielectric constant of the underground medium covering the pipeline, and h represents the standard value of the pipeline depth; then, through the ground penetrating radar equipment, identify the pipeline diameter R in the target pipeline area, and compare it with the pipeline diameter data in the radar vehicle survey control database, and count the number of pipelines N with correct diameter identification cor and the total number of identified pipelines N total , and obtain the pipeline diameter identification accuracy control coefficient A dis , A dis =N cor / N total . Secondly, through the positioning technology, obtain the pipeline position coordinate PCD detected by the radar, and compare it with the pipeline position coordinate PCD0 in the radar vehicle survey control database to obtain the radar detection pipeline position accuracy control coefficient X p , Finally, through the big data analysis technology, obtain the underground pipeline recognition accuracy control index PACI, PACI = ε g ×(a1×E de +a2×A dis +a3×X p ), where ε g is the dielectric constant of the underground pipeline, and a1, a2, and a3 respectively represent the corresponding weights.

5. The radar vehicle control system for surveying underground urban roads according to claim 2, characterized in that: In the radar vehicle movement and data acquisition quality analysis unit of the radar vehicle driving control analysis unit: First, the driving speed v of the radar vehicle is collected in real time through a speed sensor; then, the data acquisition time interval t and the radar scan width w of the radar detecting underground pipelines are obtained from the radar vehicle survey control database, and the underground pipeline data acquisition efficiency control index DCCI is obtained. A is the area of the pipeline covered by the radar in each scan.

6. The radar vehicle control system for surveying the underground of urban roads according to claim 2, characterized in that: In the radar vehicle driving control analysis unit, the radar vehicle driving stability and adaptive adjustment analysis unit: First, through the positioning technology, obtain the actual driving route R of the radar vehicle for detecting underground pipelines s , and compare it with the set driving route R to obtain the radar vehicle driving deviation control coefficient DDc, DDc = |R s - R| / R; Then, within the data acquisition time interval t, collect N radar vehicle driving speed sampling points to obtain the speed standard deviation σ(v) and the average speed μ(v), and obtain the vehicle speed fluctuation control coefficient VVc, VVc = σ(v) / μ(v); Secondly, through the timestamp technology, record the response time t x from the issuance of the steering control instruction to the start of the radar vehicle's steering each time; Finally, through the big data analysis technology, obtain the radar vehicle driving stability and adaptive adjustment control index RAACI N adj is the number of times the system adaptively adjusts the actual driving of the radar vehicle according to the set driving path, and b1 and b2 respectively represent the corresponding weights.

7. A radar vehicle control system for surveying underground urban roads according to claim 1, characterized in that: The intelligent control module in the radar vehicle survey intelligent control module implements the following steps: Step 3.1: Compare the ground penetration control index RGCI of the radar vehicle with the threshold RGCI0 to obtain the performance factor ζ(RGCI) of the ground penetration control of the radar vehicle, ζ(RGCI)=RGCI / RGCI0. If ζ(RGCI)≥1, it means that the ground penetration control of the radar vehicle is good; otherwise, it means that the control is abnormal and triggers the ground penetration control mechanism of the radar vehicle; Step 3.2: First, compare the underground pipeline identification and radar wave reflection matching control index WRCI with the threshold WRCI0 to obtain the performance factor ζ(WRCI) of the radar wave reflection matching control, ζ(WRCI) = WRCI / WRCI0. If ζ(WRCI) ≥ 1, it indicates that the radar wave reflection matching control is good; otherwise, it indicates abnormal control and triggers the radar wave reflection matching control mechanism. Then, compare the underground pipeline identification accuracy control index PACI with the threshold PACI0 to obtain the performance factor ζ(PACI) of the underground pipeline identification accuracy control, ζ(PACI) = PACI / PACI0. If ζ(PACI) ≥ 1, it indicates that the underground pipeline identification accuracy control is good; otherwise, it indicates abnormal control and triggers the underground pipeline radar identification control mechanism. Step 3.3: First, compare the underground pipeline data acquisition efficiency control index DCCI with the threshold DCCI0 to obtain the performance factor ζ(DCCI) of the underground pipeline data acquisition efficiency control, ζ (DCCI) = DCCI / DCCI0. If ζ(DCCI) ≥ 1, it indicates that the underground pipeline data acquisition efficiency control is good; otherwise, it indicates abnormal control and triggers the underground pipeline data acquisition efficiency control mechanism. Then, compare the radar vehicle driving stability and adaptive adjustment control index RAACI with the threshold RAACI0 to obtain the performance factor ζ(RAACI) of the radar vehicle driving stability and adaptive adjustment control, ζ (RAACI) = RAACI / RAACI0. If ζ(RAACI) ≥ 1, it indicates that the radar vehicle driving stability and adaptive adjustment control is good; otherwise, it indicates abnormal control and triggers the radar vehicle driving control mechanism.

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