Millimeter wave radar-based bridge load monitoring method and related device

Abstract

The invention discloses a bridge load monitoring method based on a millimeter wave radar and a related device. The method comprises the following steps: acquiring expansion joint displacement dynamic change data fed back by a millimeter wave radar sensor in real time; performing baseline correction on the expansion joint displacement dynamic change data; traversing the instantaneous displacement fluctuation quantity of each moment in the expansion joint displacement dynamic change data, extracting the instantaneous displacement fluctuation quantity greater than a first threshold value and the corresponding moment, and collecting the instantaneous displacement fluctuation quantity and the corresponding moment into a queue; and obtaining bridge load information based on the queue. The millimeter wave radar sensor is adopted to collect the dynamic change data of the expansion joint displacement of the bridge at a high sampling rate, the instantaneous displacement fluctuation quantity of the expansion joint at each moment is obtained, the instantaneous displacement fluctuation quantity is analyzed, load monitoring of the bridge can be achieved, and compared with a traditional load monitoring method, the load monitoring method has the advantage that the load monitoring efficiency is improved. Through the non-contact monitoring method, the accuracy and effectiveness of monitoring data are ensured, the service life of the monitoring system is prolonged, and the precision of the monitoring system is improved.

Description

Technical Field

[0001] The present application belongs to the technical field of load monitoring, and specifically relates to a method and related device for bridge load monitoring based on millimeter wave radar. Background Art

[0002] Bridges are an important part of transportation infrastructure. Accurately and reliably collecting traffic information on bridges, including gross vehicle weight, vehicle axle weight, number of vehicle axles, and vehicle type, can provide data support for bridge operation management, bridge status assessment, and bridge specification improvement, which is of great significance. At present, bridge load monitoring mainly includes traffic flow monitoring system and weighing monitoring system. The main inspection data are cross-sectional traffic flow, vehicle type, axle weight, number of axles, gross vehicle weight, speed, etc. Among them, weighing monitoring adopts the method of non-stop weighing. The weighing measurement points are generally selected in the roadbed or the pavement layer of the concrete structure with stable support, covering all lanes. By monitoring the traffic flow and weight, the total amount of load required by the bridge in each period can be mastered, and data parameters for controlling the number and weight of vehicles can be provided for bridges with excessive loads.

[0003] However, the existing load monitoring methods have some limitations. On the one hand, weight sensors are used for load monitoring, and the weight sensors adopt contact measurement. Under long-term impact conditions, the accuracy of the weight sensors is significantly reduced and the service life is short. On the other hand, the weight sensors cannot distinguish oncoming vehicles and can only detect the weight of all vehicles at the monitoring position, and cannot effectively reflect the weight of vehicles passing in a specific direction. Summary of the invention

[0004] The purpose of the present application is to provide a method and related device for bridge load monitoring based on millimeter wave radar, so as to solve the technical problems in the prior art of using weight sensors for load monitoring, in which the accuracy of contact measurement is significantly reduced under long-term impact conditions, the service life is short, and the weight of vehicles passing in a specific direction cannot be effectively reflected.

[0005] In order to achieve the above-mentioned purpose, the first aspect of the present application provides a method for monitoring bridge load based on millimeter wave radar, wherein millimeter wave radar sensors and targets corresponding to the positions are respectively arranged at both ends of the target position of the bridge, and the method comprises:

[0006] Acquire dynamic change data of expansion joint displacement fed back in real time by the millimeter wave radar sensor;

[0007] Performing baseline correction on the dynamic change data of the expansion joint displacement;

[0008] Traversing the instantaneous displacement fluctuation at each moment in the dynamic change data of the expansion joint displacement, extracting the instantaneous displacement fluctuation greater than a first threshold and its corresponding time, and collecting them into a queue;

[0009] Based on the queue, bridge load information is obtained.

[0010] In one or more embodiments, the step of obtaining bridge load information based on the queue includes:

[0011] Based on the number of instantaneous displacement fluctuations in the queue, the heavy vehicle traffic flow of the bridge is obtained.

[0012] In one or more embodiments, the step of obtaining bridge load information based on the queue includes:

[0013] Based on the magnitude of the instantaneous displacement fluctuation in the queue, the load change data of the target position is obtained, and the load change data includes the weight of the heavy vehicles passing through the target position at each moment.

[0014] In one or more embodiments, it further includes:

[0015] determining whether there is a heavy vehicle with a weight greater than a second threshold in the load change data;

[0016] If so, an overweight warning is sent, and the passing time of the heavy vehicle with a weight greater than the second threshold at the target location is recorded.

[0017] In one or more embodiments, the step of obtaining the load change data of the target position based on the magnitude of the instantaneous displacement fluctuation in the queue includes:

[0018] The instantaneous displacement fluctuations in the queue are traversed, and the instantaneous displacement fluctuations at the traversed target moment are input into a mapping relationship to obtain the weight of the heavy vehicle passing through the target position at the target moment. The mapping relationship is used to describe the linear relationship between the expansion joint displacement fluctuation at the target position and the weight of the passing vehicle.

[0019] In one or more embodiments, the step of obtaining the load change data of the target position based on the magnitude of the instantaneous displacement fluctuation in the queue includes:

[0020] Traversing the instantaneous displacement fluctuations in the queue, inputting the traversed instantaneous displacement fluctuations at the target moment into a pre-trained deep learning neural network model, and obtaining the weight of the heavy vehicle passing through the target position at the target moment;

[0021] The training method of the deep learning neural network model includes:

[0022] A sample target set is obtained and divided into a sample training set and a sample verification set, wherein the sample target set includes a plurality of expansion joint displacement fluctuations at the target positions that have been marked with weight labels;

[0023] Using the displacement fluctuation of the expansion joint at the target position in the sample training set as the input of the deep learning neural network model, and updating the weight of the deep learning neural network model along the direction of gradient descent based on the loss function and the weight label until the loss function converges;

[0024] The effectiveness of the deep learning neural network model is tested using the sample validation set to obtain optimal model parameters.

[0025] In order to achieve the above-mentioned purpose, the second aspect of the present application provides a bridge load monitoring device based on millimeter wave radar, wherein millimeter wave radar sensors and targets corresponding to the positions are respectively arranged at both ends of the target position of the bridge, and the device comprises:

[0026] An acquisition module, used to acquire the dynamic change data of the expansion joint displacement fed back in real time by the millimeter wave radar sensor;

[0027] A correction module, used for performing baseline correction on the dynamic change data of the expansion joint displacement;

[0028] A data extraction module, used to traverse the instantaneous displacement fluctuation at each moment in the dynamic change data of the expansion joint displacement, and extract the instantaneous displacement fluctuation greater than a first threshold and its corresponding time, and collect them into a queue;

[0029] The analysis output module is used to obtain bridge load information based on the queue.

[0030] In order to achieve the above-mentioned purpose, the third aspect of the present application provides a bridge load monitoring system based on millimeter wave radar, comprising:

[0031] a target disposed at one end of the bridge at a target location;

[0032] A millimeter wave radar sensor is arranged at the other end of the target position of the bridge and corresponds to the target position, and the millimeter wave radar sensor is used to detect the distance between the target and the millimeter wave radar sensor in real time to obtain dynamic change data of expansion joint displacement;

[0033] The controller is used to obtain the dynamic change data of the expansion joint displacement fed back by the millimeter wave radar sensor in real time, perform baseline correction on the dynamic change data of the expansion joint displacement, traverse the instantaneous displacement fluctuation at each moment in the dynamic change data of the expansion joint displacement, extract the instantaneous displacement fluctuation greater than a first threshold and its corresponding moment, collect them into a queue, and obtain bridge load information based on the queue.

[0034] In order to achieve the above-mentioned object, the fourth aspect of the present application provides an electronic device, including:

[0035] at least one processor; and

[0036] A memory storing instructions, which, when executed by the at least one processor, enables the at least one processor to execute the method for bridge load monitoring based on millimeter wave radar as described in any of the above embodiments.

[0037] In order to achieve the above-mentioned objectives, the fifth aspect of the present application provides a machine-readable storage medium storing executable instructions, which, when executed, enable the machine to execute the method for bridge load monitoring based on millimeter-wave radar as described in any of the above-mentioned embodiments.

[0038] Different from the prior art, the beneficial effects of this application are:

[0039] The present application adopts a millimeter-wave radar sensor to collect the dynamic change data of the expansion joint displacement of the bridge at a high sampling rate, and obtains the instantaneous displacement fluctuation of the expansion joint at each moment. By analyzing the instantaneous displacement fluctuation, the load monitoring of the bridge can be realized. Compared with the traditional load monitoring method, the accuracy and effectiveness of the monitoring data are guaranteed by the non-contact monitoring method, and the service life and accuracy of the monitoring system are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 It is a schematic diagram of the application scenarios of various embodiments of the present application;

[0042] Figure 2 It is a flow chart of an implementation method of a bridge load monitoring method based on millimeter wave radar of the present application;

[0043] Figure 3 It is a schematic diagram of an implementation method of the dynamic change data of the expansion joint displacement in the present application;

[0044] Figure 4 It is a schematic diagram of an implementation method of the present application after baseline correction of the dynamic change data of the expansion joint displacement;

[0045] Figure 5 It is a schematic diagram of an implementation method of the linear relationship between displacement fluctuation amount and vehicle weight of the present application;

[0046] Figure 6It is a structural schematic diagram of an implementation method of a device for bridge load monitoring based on millimeter wave radar of the present application;

[0047] Figure 7 It is a structural schematic diagram of an implementation scheme of a bridge load monitoring system based on millimeter wave radar of the present application;

[0048] Figure 8 It is a schematic structural diagram of an embodiment of the electronic device of the present application. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application.

[0050] This application is mainly used in bridge load monitoring scenarios. For details, please refer to Figure 1 , Figure 1 It is a schematic diagram of the application scenarios of various embodiments of the present application. Figure 1 As shown, the bridge deck 10 is installed on the column 20, and an elastic support 30 is provided between the bridge deck 10 and the column 20. When the bridge deck 10 is subjected to a load, the elastic support 30 will undergo elastic or inelastic instantaneous deformation, which will cause the width of the expansion joint 100 between the elastic support 30 and the column 20 to change. Therefore, the instantaneous change in the displacement of the expansion joint 100 can indirectly reflect the change in the bridge load.

[0051] The present application enables bridge maintenance personnel to obtain the load data of the bridge by setting the corresponding millimeter wave radar sensor 200 and target 300 on the elastic support 30 and the column 20, and analyzing the change data of the expansion joint 100 fed back in real time by the millimeter wave radar sensor 200. The arrangement position of the millimeter wave radar sensor 200 and the target 300 can be the position of the elastic support 30 on the bridge that has the largest response to the displacement change of the expansion joint 100 caused by the vehicle load, and the position can be selected based on experiments or bridge maintenance history to improve the monitoring accuracy and precision of the present application.

[0052] It should be noted that the target positions for the two opposite directions of traffic on the bridge are not the same. Therefore, a single target position can realize bridge deck load monitoring in a specific direction, thereby effectively reflecting the weight of vehicles passing in a specific direction and solving the problems existing in load monitoring through weight sensors in the prior art.

[0053] The following is a detailed description of the bridge load monitoring method based on millimeter wave radar in this application. Figure 2 , Figure 2 It is a flow chart of an implementation method of a bridge load monitoring method based on millimeter wave radar in the present application.

[0054] like Figure 2 As shown, the method includes:

[0055] S100, obtaining dynamic change data of expansion joint displacement fed back in real time by a millimeter wave radar sensor.

[0056] The millimeter wave radar sensor has a high sampling rate and can output the dynamic displacement of the expansion joint at a high sampling rate. At the same time, the millimeter wave radar sensor is an electronic wireless connection. Compared with the traditional weight sensor method, it has lower requirements for installation, high reliability, and is not easy to damage.

[0057] In addition, compared with the traditional pull-rope displacement meter for monitoring the displacement of expansion joints, the millimeter-wave radar sensor of the present application is completely different. The sampling rate of the traditional pull-rope displacement meter is very low. It is used to monitor the change trend of the expansion joint caused by factors such as temperature and humidity, so as to provide reference for bridge maintenance personnel, but it cannot observe the instantaneous change of the expansion joint displacement.

[0058] The millimeter wave radar sensor of the present application is used to monitor the instantaneous change of the expansion joint displacement, and the purposes and working methods of the two are completely different.

[0059] The millimeter wave radar sensor provides real-time feedback of the distance between it and the target during operation, that is, the dynamic change data of the expansion joint displacement. This data includes the correlation between the expansion joint displacement of the bridge and time. Figure 3 , Figure 3 It is a schematic diagram of an implementation method of the dynamic change data of expansion joint displacement in the present application.

[0060] like Figure 3 As shown in Figure 3, the displacement of the expansion joint changes with time, which is caused by the passage of heavy vehicles and can represent the flow rate and weight of heavy vehicles passing on the bridge.

[0061] S200, performing baseline correction on the dynamic change data of the expansion joint displacement.

[0062] There are fluctuations and data drift in the change of expansion joint displacement, and there are errors in the baseline of expansion joint displacement at each moment, such as Figure 3 As shown, in order to reduce deviation and improve the accuracy and reliability of data analysis, it is also necessary to perform baseline correction on the dynamic change data of expansion joint displacement.

[0063] Among them, the baseline correction can adopt polynomial fitting, other fitting methods, Mann-Kendall test and other methods commonly used in the art, all of which can achieve the effect of this embodiment and will not be repeated again.

[0064] After baseline correction, the displacement of the expansion joint at each moment is on the same basis and can be applied to the next analysis operation. Figure 3 For example, the dynamic change data of expansion joint displacement after baseline correction is as follows Figure 4 As shown, Figure 4 It is a schematic diagram of an implementation method of the dynamic change data of expansion joint displacement after baseline correction of the present application.

[0065] S300, traversing the instantaneous displacement fluctuation at each moment in the dynamic change data of the expansion joint displacement, extracting the instantaneous displacement fluctuation greater than a first threshold and its corresponding moment, and collecting them into a queue.

[0066] After baseline correction, data related to bridge load, that is, data associated with the passage of heavy vehicles, can be extracted from the dynamic change data of expansion joint displacement.

[0067] Since the instantaneous expansion joint fluctuation amount is proportional to the weight of the passing vehicle, a first threshold value of the instantaneous expansion joint fluctuation amount can be set. When the fluctuation amount is greater than the first threshold value, the passing vehicle is determined to be a heavy vehicle. When the fluctuation amount is less than the first threshold value, it is determined that the expansion joint fluctuation is caused by a light vehicle or detection error, thereby simplifying the data and improving the effectiveness and accuracy of monitoring.

[0068] Among them, the first threshold can be set based on actual needs according to the construction specifications and historical traffic data of the bridge, and the effect of this implementation can be achieved.

[0069] Collecting the extracted instantaneous displacement fluctuations and their corresponding moments into a queue, and converting the original complex change data into point value data, helps to simplify subsequent analysis operations.

[0070] S400: Obtain bridge load information based on the queue.

[0071] Each instantaneous displacement fluctuation in the queue is caused by the passage of heavy vehicles. Since the displacement fluctuation of the expansion joint is positively correlated with the weight of the passing vehicles, the bridge load information can be obtained based on the queue.

[0072] Specifically, in one embodiment, the heavy vehicle traffic flow of the bridge can be obtained based on the number of instantaneous displacement fluctuations in the queue.

[0073] In another embodiment, the load change data of the target position may also be obtained based on the magnitude of the instantaneous displacement fluctuation in the queue.

[0074] The load variation data includes the weight of heavy vehicles passing through the target position of the bridge at each moment.

[0075] The method for obtaining load change data is introduced in detail below.

[0076] In one embodiment, a linear relationship between the displacement fluctuation of the expansion joint and the weight of the passing vehicle, i.e., a mapping relationship, can be constructed for the target position of the target bridge. The mapping relationship can be obtained through preliminary experiments or through simulation methods.

[0077] By traversing the instantaneous displacement fluctuation in the queue and inputting the traversed instantaneous displacement fluctuation at the target time into the mapping relationship, the weight of the heavy vehicle passing the target position at the target time can be obtained.

[0078] Specifically, see Figure 5 , Figure 5 Schematic diagram of an implementation method of the linear relationship between displacement fluctuation and vehicle weight in this application. Figure 5 As shown in the figure, the displacement fluctuation and passing vehicle weight data obtained through previous experiments show that the displacement fluctuation and passing vehicle weight are linearly correlated as a whole. Based on the above data, the linear relationship between the two can be fitted. By bringing the instantaneous displacement fluctuation into the linear relationship, the weight of the passing vehicle can be obtained.

[0079] In another embodiment, a deep learning neural network model may be constructed for the target position of the target bridge, and the model is used to describe the functional relationship between the displacement fluctuation of the expansion joint at the target position and the weight of the passing vehicle.

[0080] By using the instantaneous displacement fluctuation in the queue and inputting the instantaneous displacement fluctuation at the target moment into a pre-trained deep learning neural network model, the weight of the heavy vehicle passing through the target position at the target moment can be obtained.

[0081] Specifically, the training method of the deep learning neural network model may include:

[0082] A sample target set is obtained and divided into a sample training set and a sample verification set, wherein the sample target set includes displacement fluctuations of expansion joints at several target positions marked with weight labels;

[0083] The displacement fluctuation of the expansion joint at the target position in the sample training set is used as the input of the deep learning neural network model, and based on the loss function and weight label, the weight of the deep learning neural network model is updated along the direction of gradient descent until the loss function converges;

[0084] The effectiveness of the deep learning neural network model is tested with a sample validation set to obtain the optimal model parameters.

[0085] Among them, the sample target set can be obtained through preliminary experimental methods or simulation methods.

[0086] In one embodiment, an overweight warning may be performed based on the load change data. Specifically, the method further includes:

[0087] S500: Determine whether there is a heavy vehicle with a weight greater than a second threshold in the load change data.

[0088] If yes, then:

[0089] S600: Send an overweight warning, and record the passing time of a heavy vehicle with a weight greater than a second threshold at the target location.

[0090] It is understandable that when the system determines that the weight of a passing vehicle is greater than the set second threshold, an overweight warning can be sent and the passing time can be recorded, thereby facilitating maintenance personnel to track overweight vehicles.

[0091] Based on the methods of the above-mentioned implementation modes, the dynamic change data of the expansion joint displacement fed back by the millimeter-wave radar sensor in real time can be analyzed to obtain the heavy vehicle traffic flow of the bridge and the weight distribution of the passing vehicles, and realize overweight warning; compared with the traditional load monitoring method, the contactless monitoring method is used to ensure the accuracy and effectiveness of the monitoring data, and improve the service life and accuracy of the monitoring system.

[0092] This application also provides a bridge load monitoring device based on millimeter wave radar, please refer to Figure 6 , Figure 6 It is a structural schematic diagram of an implementation scheme of a device for bridge load monitoring based on millimeter wave radar in the present application.

[0093] The two ends of the target position of the bridge are respectively equipped with millimeter-wave radar sensors and targets corresponding to the positions, such as Figure 6 As shown, the device includes an acquisition module 21, a correction module 22, a data extraction module 23 and an analysis and output module 24.

[0094] The acquisition module 21 is used to obtain the dynamic change data of the expansion joint displacement fed back by the millimeter wave radar sensor in real time;

[0095] The correction module 22 is used to perform baseline correction on the dynamic change data of the expansion joint displacement;

[0096] The data extraction module 23 is used to traverse the instantaneous displacement fluctuation at each moment in the dynamic change data of the expansion joint displacement, and extract the instantaneous displacement fluctuation greater than the first threshold and its corresponding time, and collect them into a queue;

[0097] The analysis output module 24 is used to obtain bridge load information based on the queue.

[0098] As above Figures 1 to 5 , a method for monitoring bridge loads based on millimeter wave radar according to an embodiment of this specification is described. The details mentioned in the above description of the method embodiment are also applicable to the device for monitoring bridge loads based on millimeter wave radar in the embodiment of this specification. The above device for monitoring bridge loads based on millimeter wave radar can be implemented by hardware, or by software, or by a combination of hardware and software.

[0099] This application also provides a bridge load monitoring system based on millimeter wave radar, please refer to Figure 7 , Figure 7 It is a structural schematic diagram of an implementation scheme of a bridge load monitoring system based on millimeter wave radar in the present application.

[0100] like Figure 7 As shown, the system includes a target 300 arranged at one end of the target position of the bridge, and a millimeter-wave radar sensor 200 arranged at the other end of the target position of the bridge, wherein the millimeter-wave radar sensor 200 and the target 300 have corresponding positions, and the millimeter-wave radar sensor 200 is used to detect the distance between the target 300 and the millimeter-wave radar sensor 200 in real time to obtain dynamic change data of the expansion joint displacement.

[0101] The system also includes a controller 400, which is used to obtain the dynamic change data of the expansion joint displacement fed back by the millimeter wave radar sensor 200 in real time, perform baseline correction on the dynamic change data of the expansion joint displacement, traverse the instantaneous displacement fluctuation at each moment in the dynamic change data of the expansion joint displacement, and extract the instantaneous displacement fluctuation greater than the first threshold and its corresponding moment, collect them into a queue, and obtain bridge load information based on the queue.

[0102] This application also provides an electronic device, see Figure 8 , Figure 8 It is a schematic structural diagram of an embodiment of the electronic device of the present application.

[0103] like Figure 8 As shown, the electronic device 40 may include at least one processor 41, a memory 42 (e.g., a non-volatile memory), a memory 43, and a communication interface 44, and the at least one processor 41, the memory 42, the memory 43, and the communication interface 44 are connected together via a bus 45. At least one processor 41 executes at least one computer-readable instruction stored or encoded in the memory 42.

[0104] It should be understood that the computer executable instructions stored in the memory 42, when executed, cause at least one processor 41 to perform the above combined operations in various embodiments of the present specification. Figure 1-Figure 5 Describes the various operations and functions.

[0105] In the embodiments of the present specification, the electronic device 40 may include, but is not limited to: a personal computer, a server computer, a workstation, a desktop computer, a laptop computer, a notebook computer, a mobile electronic device, a smart phone, a tablet computer, a cellular phone, a personal digital assistant (PDA), a handheld device, a messaging device, a wearable electronic device, a consumer electronic device, and the like.

[0106] According to one embodiment, a program product such as a machine-readable medium is provided. The machine-readable medium may have instructions (i.e., the above-mentioned elements implemented in the form of software), which, when executed by a machine, causes the machine to perform the above-mentioned combination of various embodiments of this specification. Figure 1-Figure 5 Specifically, a system or device equipped with a readable storage medium may be provided, on which a software program code implementing the functions of any of the above-mentioned embodiments is stored, and a computer or processor of the system or device reads and executes the instructions stored in the readable storage medium.

[0107] In this case, the program code itself read from the machine-readable medium can realize the function of any one of the above embodiments, and thus the machine-readable code and the machine-readable storage medium storing the machine-readable code constitute part of this specification.

[0108] Examples of readable storage media include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD-RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer or a cloud via a communication network.

[0109] Those skilled in the art should understand that the various embodiments disclosed above can be modified and altered in various ways without departing from the essence of the invention. Therefore, the protection scope of this specification should be defined by the appended claims.

[0110] It should be noted that not all steps and units in the above-mentioned processes and system structure diagrams are necessary, and some steps or units can be ignored according to actual needs. The execution order of each step is not fixed and can be determined as needed. The device structure described in the above-mentioned embodiments can be a physical structure or a logical structure, that is, some units may be implemented by the same physical client, or some units may be implemented by multiple physical clients, or some components in multiple independent devices may be implemented together.

[0111] In the above embodiments, the hardware unit or module can be realized by mechanical or electrical means. For example, a hardware unit, module or processor can include permanent dedicated circuit or logic (such as special processor, FPGA or ASIC) to complete the corresponding operation. The hardware unit or processor can also include programmable logic or circuit (such as general-purpose processor or other programmable processor), which can be temporarily set by software to complete the corresponding operation. Specific implementation (mechanical method or dedicated permanent circuit or temporary circuit) can be determined based on cost and time consideration.

[0112] The specific embodiments described above in conjunction with the accompanying drawings describe exemplary embodiments, but do not represent all embodiments that can be implemented or fall within the scope of protection of the claims. The term "exemplary" used throughout this specification means "used as an example, instance or illustration" and does not mean "preferred" or "having advantages" over other embodiments. For the purpose of providing an understanding of the described technology, the specific embodiments include specific details. However, these technologies can be implemented without these specific details. In some instances, in order to avoid making the concepts of the described embodiments difficult to understand, well-known structures and devices are shown in block diagram form.

[0113] The above description of the present disclosure is provided to enable any person of ordinary skill in the art to implement or use the present disclosure. Various modifications to the present disclosure will be apparent to those of ordinary skill in the art, and the general principles corresponding to the present disclosure may be applied to other variations without departing from the scope of protection of the present disclosure. Therefore, the present disclosure is not limited to the examples and designs described herein, but is consistent with the widest range of principles and novel features disclosed herein.

Claims

1. A method for monitoring bridge load based on millimeter wave radar, characterized in that: Millimeter-wave radar sensors and targets corresponding to the positions are respectively arranged at both ends of the target position of the bridge, and the method includes: Acquire dynamic change data of expansion joint displacement fed back in real time by the millimeter wave radar sensor; Performing baseline correction on the dynamic change data of the expansion joint displacement; Traversing the instantaneous displacement fluctuation at each moment in the dynamic change data of the expansion joint displacement, extracting the instantaneous displacement fluctuation greater than a first threshold and its corresponding time, and collecting them into a queue; Based on the queue, bridge load information is obtained.

2. The method according to claim 1, characterized in that: The step of obtaining bridge load information based on the queue includes: Based on the number of instantaneous displacement fluctuations in the queue, the heavy vehicle traffic flow of the bridge is obtained.

3. The method according to claim 1, characterized in that The step of obtaining bridge load information based on the queue includes: Based on the magnitude of the instantaneous displacement fluctuation in the queue, the load change data of the target position is obtained, and the load change data includes the weight of the heavy vehicles passing through the target position at each moment.

4. The method according to claim 3, characterized in that Also includes: determining whether there is a heavy vehicle with a weight greater than a second threshold in the load change data; If so, an overweight warning is sent, and the passing time of the heavy vehicle with a weight greater than the second threshold at the target location is recorded.

5. The method according to claim 3, characterized in that: The step of obtaining the load change data of the target position based on the magnitude of the instantaneous displacement fluctuation in the queue includes: The instantaneous displacement fluctuations in the queue are traversed, and the instantaneous displacement fluctuations at the traversed target moment are input into a mapping relationship to obtain the weight of the heavy vehicle passing through the target position at the target moment. The mapping relationship is used to describe the linear relationship between the expansion joint displacement fluctuation at the target position and the weight of the passing vehicle.

6. The method according to claim 3, characterized in that The step of obtaining the load change data of the target position based on the magnitude of the instantaneous displacement fluctuation in the queue includes: Traversing the instantaneous displacement fluctuations in the queue, inputting the traversed instantaneous displacement fluctuations at the target moment into a pre-trained deep learning neural network model, and obtaining the weight of the heavy vehicle passing through the target position at the target moment; The training method of the deep learning neural network model includes: A sample target set is obtained and divided into a sample training set and a sample verification set, wherein the sample target set includes a plurality of expansion joint displacement fluctuations at the target positions that have been marked with weight labels; Using the displacement fluctuation of the expansion joint at the target position in the sample training set as the input of the deep learning neural network model, and updating the weight of the deep learning neural network model along the direction of gradient descent based on the loss function and the weight label until the loss function converges; The effectiveness of the deep learning neural network model is tested using the sample validation set to obtain optimal model parameters.

7. A bridge load monitoring device based on millimeter wave radar, characterized in that: Millimeter-wave radar sensors and targets corresponding to the positions are arranged at both ends of the target position of the bridge, and the device includes: An acquisition module, used to acquire the dynamic change data of the expansion joint displacement fed back in real time by the millimeter wave radar sensor; A correction module, used for performing baseline correction on the dynamic change data of the expansion joint displacement; A data extraction module, used to traverse the instantaneous displacement fluctuation at each moment in the dynamic change data of the expansion joint displacement, and extract the instantaneous displacement fluctuation greater than a first threshold and its corresponding time, and collect them into a queue; The analysis output module is used to obtain bridge load information based on the queue.

8. A bridge load monitoring system based on millimeter wave radar, characterized in that: include: a target disposed at one end of the bridge at a target location; A millimeter wave radar sensor is arranged at the other end of the target position of the bridge and corresponds to the target position, and the millimeter wave radar sensor is used to detect the distance between the target and the millimeter wave radar sensor in real time to obtain dynamic change data of expansion joint displacement; The controller is used to obtain the dynamic change data of the expansion joint displacement fed back by the millimeter wave radar sensor in real time, perform baseline correction on the dynamic change data of the expansion joint displacement, traverse the instantaneous displacement fluctuation at each moment in the dynamic change data of the expansion joint displacement, extract the instantaneous displacement fluctuation greater than a first threshold and its corresponding moment, collect them into a queue, and obtain bridge load information based on the queue.

9. An electronic device, comprising: at least one processor; as well as A memory storing instructions, which, when executed by the at least one processor, enables the at least one processor to execute the method for bridge load monitoring based on millimeter wave radar as described in any one of claims 1 to 6.

10. A machine-readable storage medium storing executable instructions, which, when executed, enable the machine to perform the method for bridge load monitoring based on millimeter-wave radar as claimed in any one of claims 1 to 6.

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