Pavement slope collapse sensing and detecting method and system

Abstract

The invention provides a pavement slope collapse sensing and detecting method, which comprises the following steps that an intelligent sensing unit collects pavement slope information in real time, and the intelligent sensing unit is integrated with a sensor network; calculating each parameter characteristic according to the road surface slope information collected by the intelligent sensing unit in real time, and evaluating the road surface slope risk according to each parameter characteristic; the comprehensive collapse risk of the pavement is calculated and judged according to multi-sensor data fusion; and when the road surface slope risk or / and the road surface comprehensive collapse risk exist, an early warning induction scheme is started. The pavement slope collapse sensing and detecting method provided by the invention can realize high-precision and real-time monitoring of a slope geologic structure, and is suitable for slope safety monitoring of high-risk areas such as expressways and mountainous roads.

Description

Technical Field

[0001] The present invention relates to the technical field of road slope collapse perception detection, and in particular to a road slope collapse perception detection method and system. Background Art

[0002] Ground collapse refers to a geological phenomenon in which surface rock and soil collapse due to natural or human factors. Among them, improper handling during the construction process, such as lax control of roadbed filling, improper treatment of soft foundation, loose earth and stone filling of roadbed, inconsistent compaction standards at the junction of filling and excavation, can all cause uneven settlement of the roadbed. In addition, road surface load, construction disturbance and other reasons may also cause road collapse, and slope collapse is particularly serious. Therefore, it is very important to monitor slope collapse by sensing slope data information in real time. Summary of the invention

[0003] The main purpose of the present invention is to overcome the above-mentioned defects in the prior art and propose a road slope collapse perception detection method to achieve high-precision and real-time monitoring of the slope geological structure, which is suitable for slope safety monitoring in high-risk areas such as highways and mountain roads.

[0004] The present invention adopts the following technical solution:

[0005] A road slope collapse sensing and detection method comprises the following steps:

[0006] The intelligent sensing unit collects road slope information in real time, and the intelligent sensing unit integrates a sensor network;

[0007] According to the road slope information collected in real time by the intelligent sensing unit, various parameter characteristics are calculated, and the road slope risk is evaluated according to the various parameter characteristics;

[0008] Determine the comprehensive road collapse risk based on multi-sensor data fusion calculation;

[0009] When there is a risk of road slope and / or comprehensive road collapse, the early warning induction plan is activated.

[0010] Specifically, the sensor network includes a geomagnetic sensor, an acceleration sensor, and a three-axis angular momentum sensor.

[0011] Specifically, according to the road slope information collected in real time by the intelligent sensing unit, various parameter characteristics are calculated, and the road slope risk is evaluated according to the various parameter characteristics, specifically:

[0012] The local magnetic field intensity B(t) of the slope is collected by the geomagnetic sensor, and the geomagnetic change rate is calculated:

[0013]

[0014] If the geomagnetic change rate exceeds the preset geomagnetic change rate threshold T B , then it is judged that there is a displacement risk on the road slope;

[0015] The acceleration a in each direction is collected by the acceleration sensor x ,a y ,a z , a x is the acceleration in the x direction, a y is the acceleration in the y direction, a z is the acceleration in the z direction, then the acceleration value a(t):

[0016]

[0017] Calculate the rate of change of acceleration:

[0018]

[0019] If the acceleration rate exceeds the preset acceleration rate threshold T a , then it is judged that there is a sliding risk on the road slope:

[0020] The angular velocity ω in each direction is collected by the angular momentum sensor x ,ω y ,ω z ,ω x is the angular velocity in the x direction, ω y is the angular velocity in the y direction, ω z is the angular velocity in the z direction; the angular velocity value ω(t):

[0021]

[0022] Calculate the angular velocity change rate ω(t) to determine the slope posture change:

[0023]

[0024] If the angular velocity change rate exceeds the preset angular velocity change threshold T ω , it is determined that there is a risk of tilting of the road slope.

[0025] Specifically, the comprehensive road collapse risk is determined based on multi-sensor data fusion calculation, as follows:

[0026] The posterior probability of collapse is calculated using the Bayesian formula:

[0027]

[0028] When the posterior probability exceeds the set threshold, the system determines that there is a risk of slope collapse and issues a warning signal; where P(C) is the set prior probability of slope collapse, P(B, a, ω) is the overall probability distribution of sensor data, P(B, a, ω|C) is the likelihood function of slope collapse, and P(B), P(a), and P(ω) are the probability density functions of the magnetic field intensity, acceleration, and angular velocity collected by the geomagnetic sensor, acceleration sensor, and angular momentum sensor, respectively.

[0029] Specifically, when there is a road slope risk or / and a comprehensive road collapse risk, the early warning induction plan is activated, specifically:

[0030] Send warnings to corresponding intelligent sensing units;

[0031] The intelligent sensing unit within 500m will display red to warn you.

[0032] The intelligent sensing unit displays yellow for warning within 500-1000m.

[0033] The embodiment of the present invention also provides a road slope collapse perception detection method, comprising:

[0034] The intelligent sensing unit deployed along the slope route senses the slope data information in real time and sends it to the edge server;

[0035] This edge server receives the slope information sent by the perception node layer, performs real-time calculation of slope collapse risk based on multi-source data, determines the risk results, and uploads the risk results to the central control system;

[0036] The central control system receives the risk results sent by the edge server layer, and when there is a risk, it issues early warning guidance plan instructions to the corresponding upper edge server;

[0037] The upper edge server receives the early warning induction plan instructions issued by the central control system layer, sends them to the corresponding intelligent perception unit for execution.

[0038] The embodiment of the present invention also provides a road slope collapse perception detection system, including: a perception node layer, an edge server, and a central control system, wherein the edge server includes a local edge server and an upper edge server;

[0039] Perception node layer: includes intelligent perception units deployed along the slope route, which perceive slope data information in real time and send it to the local edge server; and receive and execute the early warning induction program instructions sent by the upper edge server;

[0040] Edge server: including an edge server set up on the roadside, which receives slope information sent by the perception node layer, performs real-time calculation of slope collapse risk based on multi-source data, determines the risk result, and uploads the risk result to the central control system. The upper edge server receives the early warning induction plan instruction issued by the central control system layer and sends it to the corresponding intelligent perception unit;

[0041] Central control system layer: receives risk results sent by the edge server layer, and when there is a risk, issues early warning guidance plan instructions to the corresponding upper edge server.

[0042] Another aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of a road slope collapse perception detection method when executing the computer program.

[0043] Yet another aspect of the present invention provides a computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, a method for sensing and detecting road slope collapse is implemented.

[0044] It can be seen from the above description of the present invention that, compared with the prior art, the present invention has the following beneficial effects:

[0045] (1) The present invention proposes a road slope collapse sensing detection method, a road slope collapse sensing detection method, comprising the following steps: an intelligent sensing unit collects road slope information in real time, the intelligent sensing unit integrates a sensor network; based on the road slope information collected in real time by the intelligent sensing unit, various parameter characteristics are calculated, and the road slope risk is evaluated based on the various parameter characteristics; the comprehensive road collapse risk is determined based on multi-sensor data fusion calculation; when there is a road slope risk or / and a comprehensive road collapse risk, an early warning induction scheme is activated. The present invention proposes a road slope collapse sensing detection method, which can achieve high-precision and real-time monitoring of the geological structure of the slope, and is suitable for slope safety monitoring in high-risk areas such as highways and mountain roads.

[0046] (2) The multi-sensor fusion technology adopted in the present invention improves the accuracy of slope collapse perception. Through the intelligent analysis algorithm, it can effectively reduce false alarms and missed alarms, greatly improving traffic safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A flow chart of a road slope collapse sensing and detection method provided by an embodiment of the present invention;

[0048] Figure 2 A flow chart of another road slope collapse sensing and detection method provided by an embodiment of the present invention;

[0049] Figure 3 A schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention;

[0050] Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The present invention is further described below through specific implementation modes.

[0052] The present invention proposes a road slope collapse perception and detection method, which can achieve effective monitoring of vehicle queues at toll stations and flexible lane configuration through precise perception, real-time analysis, and dynamic induction; while reducing congestion and improving traffic efficiency, it reduces reliance on manual operations and provides a reliable solution for the intelligent management of toll stations.

[0053] like Figure 1 The present invention provides a road slope collapse sensing and detection method; specifically comprising the following steps:

[0054] A road slope collapse sensing and detection method comprises the following steps:

[0055] S101: An intelligent sensing unit collects road slope information in real time, wherein the intelligent sensing unit integrates a sensor network;

[0056] Specifically, the sensor network includes a geomagnetic sensor, an acceleration sensor, and a three-axis angular momentum sensor.

[0057] The installation method of the intelligent sensing unit is flexible. It can be installed at the slope monitoring point or deployed on the road surface or near the guardrail to ensure the best monitoring effect.

[0058] In the embodiment of the present invention, the intelligent sensing unit can be installed on the slopes, guardrails or other suitable supporting positions along the highway through the basic fixed structure to achieve monitoring of key slope areas.

[0059] Each intelligent sensing unit has an independent power supply system, equipped with solar panels and low-power power modules, which can achieve long-term stable operation without relying on external power supply. Solar power supply combined with internal battery energy storage allows the device to maintain normal operation in bad weather and at night, adapting to various environmental conditions.

[0060] S102: Calculating various parameter characteristics according to the road slope information collected in real time by the intelligent sensing unit, and evaluating the road slope risk according to the various parameter characteristics;

[0061] Specifically, according to the road slope information collected in real time by the intelligent sensing unit, various parameter characteristics are calculated, and the road slope risk is evaluated according to the various parameter characteristics, specifically:

[0062] The local magnetic field intensity B(t) of the slope is collected by the geomagnetic sensor, and the geomagnetic change rate is calculated:

[0063]

[0064] If the geomagnetic change rate exceeds the preset geomagnetic change rate threshold T B , then it is judged that there is a displacement risk on the road slope;

[0065] The acceleration a in each direction is collected by the acceleration sensor x ,a y ,a z , a x is the acceleration in the x direction, a y is the acceleration in the y direction, a z is the acceleration in the z direction, then the acceleration value a(t):

[0066]

[0067] Calculate the rate of change of acceleration:

[0068]

[0069] If the acceleration rate exceeds the preset acceleration rate threshold T a , then it is judged that there is a sliding risk on the road slope:

[0070] The angular velocity ω in each direction is collected by the angular momentum sensor x ,ω y ,ω z ,ω x is the angular velocity in the x direction, ω y is the angular velocity in the y direction, ω z is the angular velocity in the z direction; the angular velocity value ω(t):

[0071]

[0072] Calculate the angular velocity change rate ω(t) to determine the slope posture change:

[0073]

[0074] If the angular velocity change rate exceeds the preset angular velocity change threshold T ω , it is determined that there is a risk of tilting of the road slope.

[0075] When there is any one of the risks of displacement, sliding and tilting, it means there is a risk of road slope collapse.

[0076] S103: Determine the comprehensive road collapse risk based on multi-sensor data fusion calculation;

[0077] When any of the three risks does not exist, that is, the parameters of displacement risk, sliding risk and tilt risk do not reach the corresponding threshold value, but considering the comprehensive effect of the three situations, the present invention adopts multi-sensor fusion technology to improve the accuracy of slope collapse perception.

[0078] Specifically, the comprehensive road collapse risk is determined based on multi-sensor data fusion calculation, as follows:

[0079] The posterior probability of collapse is calculated using the Bayesian formula:

[0080]

[0081] When the posterior probability exceeds the set threshold, the system determines that there is a risk of slope collapse and issues a warning signal; where P(C) is the set prior probability of slope collapse, P(B, a, ω) is the overall probability distribution of sensor data, P(B, a, ω|C) is the likelihood function of slope collapse, and P(B), P(a), and P(ω) are the probability density functions of the magnetic field intensity, acceleration, and angular velocity collected by the geomagnetic sensor, acceleration sensor, and angular momentum sensor, respectively.

[0082] The real-time prior probability of the present invention is set to 0.01, and the annual average occurrence rate of local historical collapse is taken as the prior probability.

[0083] P(B,a,ω|C) is the product of the estimated values ​​of each sensor based on its historical data, that is, P(B,a,ω|C)=P(B|C)P(a|C)P(ω|C).

[0084] P(B,a,ω) is the joint distribution of the data. The overall probability distribution can be expressed as the product of their respective marginal probabilities, P(B,a,ω)=P(B)P(a)P(ω). Here, the three states P(B)P(a)P(ω) are considered independent, and the probability density function is obtained through the historical data obtained by the corresponding sensors.

[0085] S104: When there is a road slope risk and / or a comprehensive road collapse risk, start the early warning induction plan.

[0086] Specifically, when there is a road slope risk or / and a comprehensive road collapse risk, the early warning induction plan is activated, specifically:

[0087] Send warnings to corresponding intelligent sensing units;

[0088] The intelligent sensing unit within 500m will display red to warn you.

[0089] The intelligent sensing unit displays yellow for warning within 500-1000m.

[0090] The embodiment of the present invention also provides a road slope collapse perception detection method, comprising:

[0091] S201: The intelligent sensing unit arranged at the position of the slope along the route senses the slope data information in real time and sends it to the edge server;

[0092] Specifically, the sensor network includes a geomagnetic sensor, an acceleration sensor, and a three-axis angular momentum sensor.

[0093] The installation method of the intelligent sensing unit is flexible. It can be installed at the slope monitoring point or deployed on the road surface or near the guardrail to ensure the best monitoring effect.

[0094] In the embodiment of the present invention, the intelligent sensing unit can be installed on the slopes, guardrails or other suitable supporting positions along the highway through the basic fixed structure to achieve monitoring of key slope areas.

[0095] Each intelligent sensing unit has an independent power supply system, equipped with solar panels and low-power power modules, which can achieve long-term stable operation without relying on external power supply. Solar power supply combined with internal battery energy storage allows the device to maintain normal operation in bad weather and at night, adapting to various environmental conditions.

[0096] S202: The edge server receives the slope information sent by the perception node layer, performs real-time calculation of the slope collapse risk based on multi-source data, determines the risk result, and uploads the risk result to the central control system;

[0097] According to the road slope information collected in real time by the intelligent sensing unit, various parameter characteristics are calculated, and the road slope risk is evaluated according to the various parameter characteristics;

[0098] Specifically, according to the road slope information collected in real time by the intelligent sensing unit, various parameter characteristics are calculated, and the road slope risk is evaluated according to the various parameter characteristics, specifically:

[0099] The local magnetic field intensity B(t) of the slope is collected by the geomagnetic sensor, and the geomagnetic change rate is calculated:

[0100]

[0101] If the geomagnetic change rate exceeds the preset geomagnetic change rate threshold T B , then it is judged that there is a displacement risk on the road slope;

[0102] The acceleration a in each direction is collected by the acceleration sensor x ,a y ,az , a x is the acceleration in the x direction, a y is the acceleration in the y direction, a z is the acceleration in the z direction, then the acceleration value a(t):

[0103]

[0104] Calculate the rate of change of acceleration:

[0105]

[0106] If the acceleration rate exceeds the preset acceleration rate threshold T a , then it is judged that there is a sliding risk on the road slope:

[0107] The angular velocity ω in each direction is collected by the angular momentum sensor x ,ω y ,ω z ,ω x is the angular velocity in the x direction, ω y is the angular velocity in the y direction, ω z is the angular velocity in the z direction; the angular velocity value ω(t):

[0108]

[0109] Calculate the angular velocity change rate ω(t) to determine the slope posture change:

[0110]

[0111] If the angular velocity change rate exceeds the preset angular velocity change threshold T ω , it is determined that there is a risk of tilting of the road slope.

[0112] When there is any one of the risks of displacement, sliding and tilting, it means there is a risk of road slope collapse.

[0113] Determine the comprehensive road collapse risk based on multi-sensor data fusion calculation;

[0114] When any of the three risks does not exist, that is, the parameters of displacement risk, sliding risk and tilt risk do not reach the corresponding threshold value, but considering the comprehensive effect of the three situations, the present invention adopts multi-sensor fusion technology to improve the accuracy of slope collapse perception.

[0115] Specifically, the comprehensive road collapse risk is determined based on multi-sensor data fusion calculation, as follows:

[0116] The posterior probability of collapse is calculated using the Bayesian formula:

[0117]

[0118] When the posterior probability exceeds the set threshold, the system determines that there is a risk of slope collapse and issues a warning signal; where P(C) is the set prior probability of slope collapse, P(B, a, ω) is the overall probability distribution of sensor data, P(B, a, ω|C) is the likelihood function of slope collapse, and P(B), P(a), and P(ω) are the probability density functions of the magnetic field intensity, acceleration, and angular velocity collected by the geomagnetic sensor, acceleration sensor, and angular momentum sensor, respectively.

[0119] The real-time prior probability of the present invention is set to 0.01, and the annual average occurrence rate of local historical collapse is taken as the prior probability.

[0120] P(B,a,ω|C) is the product of the estimated values ​​of each sensor based on its historical data, that is, P(B,a,ω|C)=P(B|C)P(a|C)P(ω|C).

[0121] P(B,a,ω) is the joint distribution of the data. The overall probability distribution can be expressed as the product of their respective marginal probabilities, P(B,a,ω)=P(B)P(a)P(ω). Here, the three states P(B)P(a)P(ω) are considered independent, and the probability density function is obtained through the historical data obtained by the corresponding sensors.

[0122] S203: The central control system receives the risk results sent by the edge server layer, and when there is a risk, issues an early warning guidance plan instruction to the corresponding upper edge server;

[0123] S204: The upper edge server receives the early warning induction plan instruction issued by the central control system layer, sends it to the corresponding intelligent perception unit for execution.

[0124] Warning induction program instructions, the intelligent sensing unit within 500m displays red to warn,

[0125] The intelligent sensing unit displays yellow for warning within 500-1000m.

[0126] The embodiment of the present invention also provides a road slope collapse perception detection system, including: a perception node layer, an edge server, and a central control system, wherein the edge server includes a local edge server and an upper edge server;

[0127] Perception node layer: includes intelligent perception units deployed along the slope route, which perceive slope data information in real time and send it to the local edge server; and receive and execute the early warning induction program instructions sent by the upper edge server;

[0128] Specifically, the sensor network includes a geomagnetic sensor, an acceleration sensor, and a three-axis angular momentum sensor.

[0129] The installation method of the intelligent sensing unit is flexible. It can be installed at the slope monitoring point or deployed on the road surface or near the guardrail to ensure the best monitoring effect.

[0130] In the embodiment of the present invention, the intelligent sensing unit can be installed on the slopes, guardrails or other suitable supporting positions along the highway through the basic fixed structure to achieve monitoring of key slope areas.

[0131] Each intelligent sensing unit has an independent power supply system, equipped with solar panels and low-power power modules, which can achieve long-term stable operation without relying on external power supply. Solar power supply combined with internal battery energy storage allows the device to maintain normal operation in bad weather and at night, adapting to various environmental conditions.

[0132] Edge server: including an edge server set up on the roadside, which receives slope information sent by the perception node layer, performs real-time calculation of slope collapse risk based on multi-source data, determines the risk result, and uploads the risk result to the central control system. The upper edge server receives the early warning induction plan instruction issued by the central control system layer and sends it to the corresponding intelligent perception unit;

[0133] The edge server is set on the roadside and receives data collected by the intelligent sensing unit within the corresponding range. Multiple edge servers are managed by the same central control system layer. For the convenience of scheme description, the structure and function of the edge server and the upper edge server in the embodiment of the present invention are the same, and they are distinguished according to the direction of the road.

[0134] Central control system layer: receives risk results sent by the edge server layer, and when there is a risk, issues early warning guidance plan instructions to the corresponding upper edge server.

[0135] Warning induction program instructions, the intelligent sensing unit within 500m displays red to warn,

[0136] The intelligent sensing unit displays yellow for warning within 500-1000m.

[0137] Let vehicles that have not yet arrived receive early warning information and respond in advance.

[0138] like Figure 3 As shown, an embodiment of the present invention provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, a road slope collapse perception and detection method provided by an embodiment of the present invention is implemented.

[0139] In the specific implementation process, when the processor 320 executes the computer program 311, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0140] Since the electronic device introduced in this embodiment is a device used to implement a data processing device in the embodiment of the present invention, based on the method introduced in the embodiment of the present invention, technical personnel in this field can understand the specific implementation method of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present invention is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present invention falls within the scope of protection of the present invention.

[0141] Example 1

[0142] The monitoring point is a slope. The intelligent sensing unit is deployed at the slope monitoring point along the slope. The intelligent sensing unit integrates a sensor network including a geomagnetic sensor, an acceleration sensor, and a three-axis angular momentum sensor. It senses the slope data information in real time and sends it to the local edge server. It also receives and executes the early warning induction program instructions sent by the upper edge server.

[0143] Each intelligent sensing unit has an independent power supply system, equipped with solar panels and low-power power modules, which can achieve long-term stable operation without relying on external power supply. Solar power supply combined with internal battery energy storage allows the device to maintain normal operation in bad weather and at night, adapting to various environmental conditions.

[0144] The edge server is set up on the side of the slope road. This edge server receives the slope information sent by the perception node layer, performs real-time calculation of the slope collapse risk based on multi-source data, determines the risk results, and uploads the risk results to the central control system. The upper edge server receives the early warning induction plan instructions issued by the central control system layer and sends them to the corresponding intelligent perception unit;

[0145] The edge server is set on the roadside and receives data collected by the intelligent sensing unit within the corresponding range. Multiple edge servers are managed by the same central control system layer. For the convenience of scheme description, the structure and function of the edge server and the upper edge server in the embodiment of the present invention are the same, and they are distinguished according to the direction of the road.

[0146] Central control system layer: receives risk results sent by the edge server layer, and when there is a risk, issues early warning guidance plan instructions to the corresponding upper edge server.

[0147] Warning induction program instructions, the intelligent sensing unit within 500m displays red to warn,

[0148] The intelligent sensing unit displays yellow for warning within 500-1000m.

[0149] Let vehicles that have not yet arrived receive early warning information and respond in advance.

[0150] Embodiment 2:

[0151] The monitoring point is the road surface, and the road collapse is identified. The intelligent sensing unit is deployed at the road surface monitoring point. The intelligent sensing unit integrates the sensor network, including geomagnetic sensors, acceleration sensors, and three-axis angular momentum sensors; it senses the slope data information in real time and sends it to the local edge server; and receives and executes the early warning induction program instructions sent by the upper edge server;

[0152] Each intelligent sensing unit has an independent power supply system, equipped with solar panels and low-power power modules, which can achieve long-term stable operation without relying on external power supply. Solar power supply combined with internal battery energy storage allows the device to maintain normal operation in bad weather and at night, adapting to various environmental conditions.

[0153] The edge server is set up on the roadside of the monitored road surface. This edge server receives the monitored road surface information sent by the perception node layer, performs real-time calculation of the monitored road surface collapse risk based on multi-source data, determines the risk results, and uploads the risk results to the central control system. The upper edge server receives the early warning induction plan instructions issued by the central control system layer and sends them to the corresponding intelligent perception unit;

[0154] The edge server is set on the roadside and receives data collected by the intelligent sensing unit within the corresponding range. Multiple edge servers are managed by the same central control system layer. For the convenience of scheme description, the structure and function of the edge server and the upper edge server in the embodiment of the present invention are the same, and they are distinguished according to the direction of the road.

[0155] Central control system layer: receives risk results sent by the edge server layer, and when there is a risk, issues early warning guidance plan instructions to the corresponding upper edge server.

[0156] Warning induction program instructions, the intelligent sensing unit within 500m displays red to warn,

[0157] The intelligent sensing unit displays yellow for warning within 500-1000m.

[0158] Let vehicles that have not yet arrived receive early warning information and respond in advance.

[0159] See also Figure 4 , Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention.

[0160] like Figure 4 As shown, this embodiment provides a computer-readable storage medium 400, on which a computer program 411 is stored. When the computer program 411 is executed by a processor, a road slope collapse perception and detection method provided by an embodiment of the present invention is implemented;

[0161] In the specific implementation process, when the computer program 411 is executed by the processor, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0162] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0163] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0164] (1) The present invention proposes a road slope collapse sensing detection method, comprising the following steps: an intelligent sensing unit collects road slope information in real time, and the intelligent sensing unit integrates a sensor network; based on the road slope information collected in real time by the intelligent sensing unit, various parameter characteristics are calculated, and the road slope risk is evaluated based on the various parameter characteristics; the comprehensive road collapse risk is determined based on multi-sensor data fusion calculation; when there is a road slope risk or / and a comprehensive road collapse risk, an early warning induction scheme is activated. The present invention proposes a road slope collapse sensing detection method, which can achieve high-precision and real-time monitoring of the geological structure of the slope, and is suitable for slope safety monitoring in high-risk areas such as highways and mountain roads.

[0165] (2) The multi-sensor fusion technology adopted in the present invention improves the accuracy of slope collapse perception. Through the intelligent analysis algorithm, it can effectively reduce false alarms and missed alarms, greatly improving traffic safety.

[0166] The above is only a specific implementation of the present invention, but the design concept of the present invention is not limited thereto. Any non-substantial changes to the present invention using this concept shall be deemed as an infringement of the protection scope of the present invention.

Claims

1. A road slope collapse perception and detection method, characterized in that: The steps include: The intelligent sensing unit collects road slope information in real time, and the intelligent sensing unit integrates a sensor network; calculates various parameter characteristics based on the road slope information collected in real time by the intelligent sensing unit, and evaluates the road slope risk based on the various parameter characteristics; Determine the comprehensive road collapse risk based on multi-sensor data fusion calculation; When there is a risk of road slope and / or comprehensive road collapse, the early warning induction plan is activated.

2. A road slope collapse sensing and detection method according to claim 1, characterized in that: The sensor network includes a geomagnetic sensor, an acceleration sensor, and a three-axis angular momentum sensor.

3. A road slope collapse sensing and detection method according to claim 1, characterized in that: According to the road slope information collected in real time by the intelligent sensing unit, the characteristics of each parameter are calculated, and the road slope risk is evaluated according to the characteristics of each parameter, specifically: The local magnetic field intensity B(t) of the slope is collected by the geomagnetic sensor, and the geomagnetic change rate is calculated: If the geomagnetic change rate exceeds the preset geomagnetic change rate threshold T B , then it is judged that there is a displacement risk on the road slope; The acceleration a in each direction is collected by the acceleration sensor x ,a y ,a z , a x is the acceleration in the x direction, a y is the acceleration in the y direction, a z is the acceleration in the z direction, then the acceleration value a(t): Calculate the rate of change of acceleration: If the acceleration rate exceeds the preset acceleration rate threshold T a , then it is judged that there is a sliding risk on the road slope: The angular velocity ω in each direction is collected by the angular momentum sensor x ,ω y ,ω z ,ω x is the angular velocity in the x direction, ω y is the angular velocity in the y direction, ω z is the angular velocity in the z direction; The angular velocity value ω(t) is: Calculate the angular velocity change rate ω(t) to determine the slope posture change: If the angular velocity change rate exceeds the preset angular velocity change threshold T ω , it is determined that there is a risk of tilting of the road slope.

4. A road slope collapse sensing and detection method according to claim 1, characterized in that: The comprehensive road collapse risk is determined based on multi-sensor data fusion calculation, specifically: The posterior probability of collapse is calculated using the Bayesian formula: When the posterior probability exceeds the set threshold, the system determines that there is a risk of slope collapse and issues a warning signal; where P(C) is the set prior probability of slope collapse, P(B, a, ω) is the overall probability distribution of sensor data, P(B, a, ω|C) is the likelihood function of slope collapse, and P(B), P(a), and P(ω) are the probability density functions of the magnetic field intensity, acceleration, and angular velocity collected by the geomagnetic sensor, acceleration sensor, and angular momentum sensor, respectively.

5. A road slope collapse sensing and detection method according to claim 1, characterized in that: When there is a risk of road slope or / and comprehensive road collapse, the early warning induction plan is activated, specifically: Send warnings to corresponding intelligent sensing units; The intelligent sensing unit within 500m will display red to warn you. The intelligent sensing unit displays yellow for warning within 500-1000m.

6. A road slope collapse perception detection method, characterized in that: include: The intelligent sensing unit deployed along the slope route senses the slope data information in real time and sends it to the edge server; This edge server receives the slope information sent by the perception node layer, performs real-time calculation of slope collapse risk based on multi-source data, determines the risk results, and uploads the risk results to the central control system; The central control system receives the risk results sent by the edge server layer, and when there is a risk, it issues early warning guidance plan instructions to the corresponding upper edge server; The upper edge server receives the early warning induction plan instructions issued by the central control system layer, sends them to the corresponding intelligent perception unit for execution.

7. A road slope collapse sensing and detection system, characterized in that: include: Perception node layer, edge server, central control system, where the edge server includes the current edge server and the upper edge server; Perception node layer: includes intelligent perception units deployed along the slope route, which perceive slope data information in real time and send it to the local edge server; and receive and execute the early warning induction program instructions sent by the upper edge server; Edge server: including an edge server set up on the roadside, which receives slope information sent by the perception node layer, performs real-time calculation of slope collapse risk based on multi-source data, determines the risk result, and uploads the risk result to the central control system. The upper edge server receives the early warning induction plan instruction issued by the central control system layer and sends it to the corresponding intelligent perception unit; Central control system layer: receives risk results sent by the edge server layer, and when there is a risk, issues early warning guidance plan instructions to the corresponding upper edge server.

8. An electronic device, characterized in that: include: A memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method steps of any one of claims 1 to 5 when executing the computer program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps of any one of claims 1 to 5 are implemented.

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