Highway construction area monitoring system and method

Abstract

The invention discloses an expressway construction area monitoring system and method, and belongs to the technical field of construction monitoring, and the method comprises the following steps: determining the wearing compliance based on a first abnormality monitoring model; determining an equipment health value based on a second anomaly monitoring model; determining an instantaneous danger radius based on a third anomaly monitoring model; determining a first danger radius threshold value based on the equipment health value and the instantaneous danger radius; determining a second hazard radius threshold based on the wearing compliance and the instantaneous hazard radius; determining a final danger radius threshold value based on the first danger radius threshold value and the second danger radius threshold value; determining the Euclidean distance between the worker and the lifting hook based on the positioning model; and early warning information is determined based on the Euclidean distance between the worker and the lifting hook and the final danger radius threshold value. Safety early warning can be sent to constructors close to the tower crane in time, and therefore the construction risk of the constructors in a construction area is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of construction monitoring, and in particular to a highway construction area monitoring system and method. Background Art

[0002] When expanding or renovating highways or constructing bridges, tower cranes will intersect with on-site personnel in their operations. The equipment of the tower cranes themselves may create hidden dangers during operation, resulting in construction risks for on-site construction personnel. On the other hand, there are construction personnel walking or lingering under the tower cranes at the construction site. Especially at night or during low-visibility operations, the driver's vision is obstructed, causing personnel to be directly exposed to the tower crane's danger radius. Traditional isolation, command, and monitoring methods cannot accurately and timely eliminate the high overlap in time and space between the tower crane's "dynamic danger radius" and the "unpredictable movement" of personnel, resulting in a high-risk scenario of "mixed human and machine traffic and spatial overlap", which seriously threatens the safety of construction personnel. Summary of the Invention

[0003] To solve the above problems, the present invention provides a method for monitoring a highway construction area in a first aspect, comprising the following steps: determining wearing compliance based on a first anomaly monitoring model; determining a device health value based on a second abnormality monitoring model; determining an instantaneous danger radius based on a third abnormality monitoring model; Determining a first danger radius threshold based on the device health value and the instantaneous danger radius; determining a second danger radius threshold based on wearing compliance and the instantaneous danger radius; Determine a final danger radius threshold based on the first danger radius threshold and the second danger radius threshold; Determine the Euclidean distance between the worker and the hook based on the positioning model; The warning information is determined based on the Euclidean distance between the worker and the hook and the final danger radius threshold.

[0004] In some embodiments, determining wear compliance includes: Define single device compliance; By defining a binary variable To indicate whether construction workers are using personal protective equipment normally, the specific method is: ,in, For single device compliance; Calculate wearable compliance based on single device compliance; The overall compliance is calculated based on the weighted sum of the compliance of each device, as follows: ; in, For wearing compliance; The number of a single device; The total number of compliances for a single device; For the The importance weight of a single device; For construction workers compliance of a single device.

[0005] In some embodiments, determining the device health value includes: Obtaining status information of the target device during a first time period; Determining, based on the state information, a first health parameter of the target device at least at a time within a first time period; Determining a first health parameter sequence of the target device within a first time period based on the first health parameter at at least one moment; predicting, based on the first health parameter sequence, a second health parameter sequence of the target device in a second time period; Determining a device health value sequence of the target device based on the first health parameter sequence and the second health parameter sequence; Determine device warning information for at least one time point based on a device health value sequence and a device health value threshold of the target device; Based on the device health value sequence of the target device, the device health value of the target device at the current moment is determined.

[0006] In some embodiments, the first health parameter includes a crane tilt angle and a motor temperature; The first health parameter sequence is a sequence obtained by arranging the first health parameters of at least one moment in chronological order; A second health parameter sequence is determined based on the first health parameter sequence by a device state prediction model; Determining the second health parameter sequence includes: Obtain multiple equipment status prediction models; The equipment status model is a model reflecting the change of the first health parameter over time; For each of the plurality of device state prediction models, determining a fit between the first health parameter sequence and the device state prediction model; The degree of fit is used to characterize the degree of fit between the first health parameter sequence and the equipment status prediction model; determining a target device state prediction model from the plurality of device state prediction models based on a fit between the first health parameter sequence and each device state prediction model; Based on the target device state prediction model, a second health parameter sequence is determined.

[0007] In some embodiments, the device health value is a parameter used to reflect the overall health status of the device; The device health value is calculated as follows: ; in, is the device health value; is the initial device health value; is the normalized temperature, =Motor temperature value / motor temperature threshold; is the normalized inclination angle, =crane inclination angle / crane inclination angle threshold; are weight coefficients respectively.

[0008] In some embodiments, the instantaneous danger radius is a parameter used to describe the range of an area around the device whether there is danger when the device is running; The calculation method of the danger radius is: ; in, It is the instantaneous danger radius of tower crane operation; is the tower crane arm length; is the instantaneous swing angle; is the instantaneous angular velocity; is the initial swing angle; For safety margin.

[0009] In some embodiments, the first danger radius threshold is calculated as follows: ; in, is the first danger radius threshold; is the hazard radius expansion factor; in: ; is the adjustment coefficient, used to adjust the degree of expansion; The device health value.

[0010] In some embodiments, the second danger radius threshold is calculated as follows: ; in, is the first danger radius threshold; is the hazard radius expansion factor; in: ; is the adjustment coefficient, used to adjust the degree of expansion; The device health value.

[0011] In some embodiments, determining the final danger radius threshold based on the first danger radius threshold and the second danger radius threshold includes: The final danger radius threshold is determined by comparing the first danger radius threshold with the second danger radius threshold; the specific method is as follows: ;in, is the final danger radius threshold.

[0012] A second aspect of the present invention provides a highway construction area monitoring system, configured to execute any of the highway construction area monitoring methods described above, comprising: A first abnormality monitoring module, for determining wear compliance; A second abnormality monitoring module is used to determine the health value of the device; The third abnormality monitoring module is used to determine the instantaneous danger radius; A first determining module is configured to determine a first danger radius threshold value according to a device health value and an instantaneous danger radius; A second determining module is used to determine a second danger radius threshold according to wearing compliance and an instantaneous danger radius; A third determining module is configured to determine a final danger radius threshold according to the first danger radius threshold and the second danger radius threshold; Positioning module, used to determine the Euclidean distance between the worker and the hook; The early warning module is used to determine early warning information based on the Euclidean distance between the worker and the hook and the final danger radius threshold.

[0013] By adopting the above technical solution, the present invention mainly has the following technical effects: By comparing the equipment health value sequence with the equipment health value threshold, the equipment warning information is confirmed. When hidden dangers occur in the tower crane equipment after working for a long time, the warning information is issued in time to reduce the construction risk of construction workers. In addition, by comparing the first danger radius threshold with the second danger radius threshold to determine the final danger radius threshold, when the Euclidean distance between the worker and the hook is less than the final danger radius threshold, a safety warning information is generated in time, and a safety warning is issued to the construction workers near the tower crane, thereby effectively reducing the construction risk of construction workers in the construction area. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of a method for monitoring a highway construction area according to the present invention; Figure 2 is a circuit diagram of the identification submodule of the first abnormality monitoring module; Figure 3 is a circuit diagram of a power submodule of the first abnormality monitoring module; Figure 4 is a circuit diagram of the environment perception submodule in the second abnormality monitoring module; Figure 5 This is the circuit diagram of the positioning submodule of the positioning module; Figure 6 It is the circuit diagram of the alarm submodule of the early warning module. DETAILED DESCRIPTION

[0015] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0016] It should be understood that the "system" and "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0017] As used in this specification, unless the context clearly indicates otherwise, words such as "some" and "some" are not limited to the singular and may also include the plural. Generally speaking, the term "comprising" only indicates the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0018] Flowcharts are used throughout this specification to illustrate the operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0019] See also Figure 1 The first aspect of the present invention provides a method for monitoring a highway construction area, comprising the following steps: S1. Determine wearing compliance based on a first abnormality monitoring model; In some embodiments, wearing compliance is a parameter used to describe the extent to which construction workers correctly wear and use corresponding personal protective equipment in accordance with relevant laws and regulations, industry standards and internal corporate regulations at the construction site.

[0020] In some embodiments, construction workers should wear personal protective equipment such as hard hats, work clothes, and safety belts at the construction site. In some embodiments, the more personal protective equipment such as hard hats, work clothes, and safety belts a construction worker wears, the more complete the construction worker's protective measures are and the higher the compliance of the wear.

[0021] In some embodiments, the first anomaly monitoring model may be any deep learning model that can be used for target detection.

[0022] In some embodiments, the first abnormality monitoring model can be a YOLO model. The YOLO model collects a large amount of image data including construction workers wearing and not wearing safety helmets, wearing and not wearing safety belts, and labels these data to train the YOLO model to identify these targets. After the model training is completed, it is deployed to the monitoring system of the construction site to analyze the images captured by the camera in real time to determine whether the construction workers comply with safety regulations.

[0023] In some embodiments, determining wear compliance includes: S101. Define single device compliance; In some embodiments, the single equipment compliance is used to describe whether construction workers use personal protective equipment normally according to construction requirements.

[0024] In some embodiments, a binary variable may be defined To indicate whether construction workers are using personal protective equipment normally, the specific method is: ,in, For single device compliance; In some embodiments, 0 or 1 can be used to describe whether the construction workers use personal protective equipment normally. For example, when When , it means that the construction workers have put on safety helmets; when When the alarm is raised, it means that the construction workers are not wearing protective clothing.

[0025] S102. Calculate wear compliance based on single device compliance; In some embodiments, after defining single-device compliance, wearing compliance may be calculated based on the recognition result of the first abnormality monitoring model.

[0026] In some embodiments, the comprehensive compliance can be calculated based on the weighted sum of the compliances of individual devices, specifically as follows: ; in, For wearing compliance; The number of a single device; The total number of compliances for a single device; For the The importance weight of a single device; For construction workers compliance of a single device.

[0027] It should be noted that, in order to facilitate subsequent calculations, when calculating the wearing compliance, a weight normalization method can be used to ensure that the wearing compliance does not exceed the range of [0,1].

[0028] S2. Determine the device health value based on the second abnormality monitoring model; In some embodiments, a device health value is a parameter used to describe the overall health and performance level of a device. By integrating multiple operating parameters, performance indicators, and fault diagnosis information of the device, it quantitatively reflects whether the device is in normal operation or whether there is a potential failure risk.

[0029] In some embodiments, determining the device health value includes: S201, obtaining status information of a target device in a first time period; The first time period may be a certain time period in history. For example, the first time period may be a time period of 10 seconds in history.

[0030] In some embodiments, the state information refers to key parameters of the device during operation. In some embodiments, the device state information may include: the tilt angle of the tower crane during operation and the motor temperature.

[0031] In some embodiments, an image of a tower crane can be captured by an image acquisition device (such as a camera, a video camera, etc.), and then the tower crane body is identified and segmented by an image processing algorithm to obtain a contour image of the tower body. Then, the straight line feature detection is performed on the tower body contour, and the average difference between the main contour straight line of the tower body and the preset item straight line is calculated to obtain the inclination angle of the tower body.

[0032] In some embodiments, a temperature sensor, such as a thermocouple or thermistor, can be installed on the surface or inside the tower crane motor to directly measure the temperature of the motor. The temperature sensor then converts the temperature signal into an electrical signal and transmits it to a display or recording device.

[0033] S202: Determine a first health parameter of the target device at least at one time within a first time period based on the status information; The first health parameter may be a parameter that characterizes the health of the target device in a certain period of time. In some embodiments, the first health parameter includes a tower crane tilt angle and a motor temperature.

[0034] S203. Determine a first health parameter sequence of the target device within a first time period based on the first health parameter at at least one moment; In some embodiments, the first health parameter sequence is a sequence of first health parameters at at least one moment arranged in chronological order, wherein the chronological order may be in ascending chronological order or in descending chronological order.

[0035] S204: predicting a second health parameter sequence of the target device in a second time period based on the first health parameter sequence; The second time period may be a time period in the future. For example, the second time period may be a time period in the future 60 seconds. In some embodiments, the second time period may be a time period after the first time period.

[0036] The second health parameter may be a health parameter of the target device within the second time period, and the second health parameter sequence is a sequence obtained by arranging the second health parameters of at least one moment in the second time period in chronological order. For example. The second health parameter sequence may include a second health parameter 1, a second health parameter 2, a second health parameter N... and so on within a certain time period in the future. In some embodiments, the second health parameter sequence includes at least one health parameter at a future moment. In some embodiments, the second health parameter sequence can be obtained by processing the first health parameter sequence through mathematical fitting, artificial intelligence, etc. In some embodiments, the second health parameter sequence can be determined by a device state prediction model based on the first health parameter sequence. The specific description of the device state prediction model will be further described below.

[0037] In some embodiments, the device state prediction model may be a machine learning model, such as a time series prediction model, etc. The input of the device state prediction model may include a first health parameter sequence, and the output may include a second health parameter sequence.

[0038] In some embodiments, the device state training model can be obtained through training. For example, the device state prediction model can be trained based on a large number of training samples with identifiers. The training samples may include a historical first health parameter sequence. The identifier may be a corresponding second health parameter sequence. The identifier may be obtained by manual annotation. The historical first health parameter sequence in the training sample is input into the device state training model; the second health parameter sequence output by the device state training model is obtained. A loss function is constructed based on the identified second health parameter sequence and the second health parameter sequence output by the device state training model, and the parameters of the device state training model are synchronously updated. Through parameter updating, a trained device state prediction model is obtained.

[0039] Through the device status prediction model described in some embodiments of this specification, intelligent prediction of the second health parameter sequence can be achieved.

[0040] Determining a second health parameter sequence for a second time period includes: Obtain multiple equipment status prediction models; The device state model can be a model that reflects the time-varying changes in the first health parameter. For example, the device state prediction model can include a linear prediction model, a nonlinear prediction model (such as a power law prediction model, an exponential prediction model, or a logarithmic prediction model), etc. In some embodiments, the device state prediction model can be obtained via a network or by calling from a storage device, database, etc.

[0041] For each of the plurality of device state prediction models, determining a fit between the first health parameter sequence and the device state prediction model; The degree of fit can represent the degree of fit between the first health parameter sequence and the device state prediction model. For example, the higher the degree of fit between the regression curve of the device state prediction and the observed values ​​of the first health parameter sequence, the higher the degree of fit can be.

[0042] determining a target device state prediction model from the plurality of device state prediction models based on a fit between the first health parameter sequence and each device state prediction model; In some embodiments, a device state prediction model with a high degree of fit may be selected from multiple device state prediction models as a target device state prediction model.

[0043] Based on the target device state prediction model, a second health parameter sequence is determined.

[0044] In some embodiments, the first health parameter sequence may be introduced into a target device state prediction model, and the second health parameter sequence may be determined through model fitting.

[0045] S205. Determine a device health value sequence of the target device based on the first health parameter sequence and the second health parameter sequence; In some embodiments, the health value sequence of the target device is a sequence of device health values ​​at at least one moment arranged in chronological order, wherein the chronological order can be in ascending chronological order or in descending chronological order.

[0046] In some embodiments, the device health value is a parameter used to reflect the overall health status of the device.

[0047] In some embodiments, the device health value is calculated as follows: ; in, is the device health value; is the initial device health value; is the normalized temperature, =Motor temperature value / motor temperature threshold; is the normalized inclination angle, =crane inclination angle / crane inclination angle threshold; are weight coefficients respectively; In some embodiments, the initial device health value It can be equal to 1 or decrease based on the age to ensure that the equipment health value does not exceed the range of [0,1].

[0048] In some embodiments, Used to characterize the impact of motor temperature deterioration on the health value of the target device. Used to characterize the impact of the deterioration of the tower crane inclination angle on the health value of the target equipment. Used to characterize the impact of the deterioration of the coupling between motor temperature and tower crane inclination on the health value of the target equipment.

[0049] In some embodiments, the motor temperature threshold, the tower crane inclination angle threshold, and the weight coefficient can be set based on historical data and multiple tests to build a database.

[0050] S206. Determine device warning information for at least one time point based on the device health value sequence and the device health value threshold of the target device; In some embodiments, the equipment warning information can be confirmed by comparing the equipment health value sequence with the equipment health value threshold. For example, when the equipment health value sequence is compared with the equipment health value threshold, if one or more data exceeds the equipment health value threshold, an equipment warning information is generated and the tower crane stops operating.

[0051] In some cases, equipment failures are often not sudden, but rather a gradual worsening process (e.g., a slow and continuous increase in motor temperature). By promptly capturing the changing trends of equipment parameters over time, potential failures can be predicted and the equipment can be shut down for inspection, effectively reducing construction risks for construction personnel.

[0052] S207: Determine the device health value of the target device at the current moment based on the device health value sequence of the target device; In some embodiments, after determining the device health value sequence of the target device, the device health value of the target device at the current moment may be determined, thereby determining the overall health status of the target device at the current moment.

[0053] S3. Determine the instantaneous danger radius based on the third abnormality monitoring model; In some embodiments, the instantaneous danger radius is a parameter used to describe the range of an area around a device to determine whether there is danger when the device is running.

[0054] In some embodiments, the instantaneous danger radius can be the instantaneous danger radius of a tower crane operation. The instantaneous danger radius of a tower crane operation is the maximum ground radius where a suspended object (including hooks, slings, and slings) may fall or sweep due to swinging, falling off, or sudden movement at any moment of the tower crane operation. This is a dynamic, real-time safety warning distance. In some embodiments, the danger radius is calculated as follows: ; in, It is the instantaneous danger radius of tower crane operation; is the tower crane arm length; is the instantaneous swing angle; is the instantaneous angular velocity; is the initial swing angle; is a safety margin; In some embodiments, the instantaneous swing angle of a tower crane refers to the instantaneous deflection angle of the hook or load relative to the vertical (plumb line) during the swing process. In some embodiments, an industrial camera can be used to synchronously capture the cooperation mark on the rope or load. The rope's three-dimensional vector can then be obtained through image processing to calculate the instantaneous swing angle of the tower crane.

[0055] In some embodiments, the instantaneous angular velocity is the rate of change of the swing angle. In some embodiments, a three-axis MEMS gyroscope can be installed on the tower crane hook or the slewing platform to obtain the instantaneous angular velocity of the swing in real time.

[0056] In some embodiments, the initial swing angle refers to the angle at which the hook deviates from its equilibrium position at the "initial moment." In some embodiments, by setting the initial swing angle, the impact of wind loads or other external disturbances can be quantified, thereby improving the accuracy of subsequent dynamic response analysis.

[0057] S4. Determine a first danger radius threshold based on the device health value and the instantaneous danger radius; In some embodiments, the first danger radius threshold refers to a parameter used to determine whether the device is in a dangerous area during operation.

[0058] In some embodiments, the less healthy the device, the more the instantaneous danger radius expands.

[0059] In some embodiments, the first danger radius threshold is calculated as follows: ; in, is the first danger radius threshold; is the hazard radius expansion factor; in: ; is the adjustment coefficient, used to adjust the degree of expansion; The device health value.

[0060] S5. Determine a second danger radius threshold based on wearing compliance and the instantaneous danger radius; In some embodiments, the second danger radius threshold refers to a parameter used to determine whether the device is in a dangerous area during operation.

[0061] In some embodiments, the lower the wearing compliance, the more the instantaneous danger radius expands.

[0062] In some embodiments, the second danger radius threshold is calculated as follows: ; in, is the first danger radius threshold; is the hazard radius expansion factor; in: ; is the adjustment coefficient, used to adjust the degree of expansion; For wear compliance.

[0063] S6. Determine a final danger radius threshold based on the first danger radius threshold and the second danger radius threshold; In some embodiments, after determining the first danger radius threshold and the second danger radius threshold, the final danger radius threshold may be determined by comparing the first danger radius threshold with the second danger radius threshold, specifically in the following manner: ; in, is the final danger radius threshold.

[0064] In some embodiments, by selecting the largest value between the first danger radius threshold and the second danger radius threshold as the final danger radius threshold, the safety radius covers all potential dangers, thereby ensuring zero missed judgments and avoiding safety blind spots.

[0065] S7. determining the Euclidean distance between the worker and the hook based on the positioning model; In some embodiments, the positioning model can be any model that can be used to determine the Euclidean distance between the worker and the hook. In some embodiments, a base station installed on the hook can receive the signal emitted by the signal source worn by the construction worker, and the distance between the signal source (the tag worn by the worker) and the base station (the UWB anchor point installed on the hook) can be calculated by obtaining the time it takes for the measurement signal to reach each base station.

[0066] In some embodiments, data can be transmitted by sending and receiving extremely narrow pulses with nanosecond or even sub-nanosecond duration. These pulse signals are very short in the time domain, and the corresponding frequency domain bandwidth is very wide, typically reaching the GHz level.

[0067] S8, determining safety warning information based on the Euclidean distance between the worker and the hook and the final danger radius threshold; In some embodiments, safety warning information can be confirmed by comparing the Euclidean distance between the worker and the hook with a final danger radius threshold. For example, the Euclidean distance between the worker and the hook is compared with the final danger radius threshold. If the Euclidean distance between the worker and the hook is less than the final danger radius threshold, a safety warning message is generated to alert construction workers near the tower crane to the danger radius of the tower crane, thereby further effectively reducing construction risks for construction workers.

[0068] A second aspect of the present invention provides a highway construction area monitoring system for executing the above-mentioned highway construction area monitoring method, comprising: A first abnormality monitoring module, for determining wear compliance; A second abnormality monitoring module is used to determine the health value of the device; The third abnormality monitoring module is used to determine the instantaneous danger radius; A first determining module is configured to determine a first danger radius threshold value according to a device health value and an instantaneous danger radius; A second determining module is used to determine a second danger radius threshold according to wearing compliance and an instantaneous danger radius; A third determining module is configured to determine a final danger radius threshold according to the first danger radius threshold and the second danger radius threshold; Positioning module, used to determine the Euclidean distance between the worker and the hook; The early warning module is used to determine early warning information based on the Euclidean distance between the worker and the hook and the final danger radius threshold.

[0069] In some embodiments, Figure 2 This is the circuit diagram of the identification submodule of the first anomaly monitoring module. VCC represents the positive and negative power inputs, GND represents the ground line, PA10 and PA9 represent general-purpose GPIO pins, TX and RX represent serial port transmit pins, K210 represents the chip used for machine vision speech recognition, and u180 represents the device number.

[0070] Figure 3 This is the circuit diagram of the power submodule of the first abnormality monitoring module. VCC-3.3 represents the 3.3V power input, VBAT is the real-time clock backup power pin, VDD-1, VDD-2, and VDD-3 are the digital circuit power pins, VSS-1, VSS-2, and VSS-3 are the digital circuit ground pins, VDDA is the analog circuit power pin, VSSA is the analog ground pin, GND is the ground line, STM32F103C8T6 is the chip signal, and u182B is the device number.

[0071] In some embodiments, Figure 4 This is the circuit diagram of the environmental sensing submodule in the second abnormality monitoring module, where VDD is the positive power supply, GND is the ground wire, DO is the single bus data port, R9 is the pull-up resistor, US is the 5V power supply, PAS is the single bus, and DS18B20 is the temperature sensor.

[0072] In some embodiments, Figure 5 This is the circuit diagram of the positioning module's positioning submodule, where U2 is the device number, ATGM332D-5N is the positioning chip, RF-IN is the chip's RF input pin, SDA is the communication bus, and SCL is I 2C clock, VCC-RF is the power supply for the internal LNA, nRESET is a low-level reset, IPPS is a pulse-per-second signal, VCC is the power supply, V-BCKP is the backup power supply, TXD1 is the master UART, TXD2 is the applied UART, TxD1 is UART0 transmission, RxD1 is UART0 reception, NC / Reserved is unconnected or a test point, and GND is ground.

[0073] In some embodiments, Figure 6 This is the circuit diagram of the alarm submodule of the early warning module, where VCC is the power supply, IN is the drive signal, PA8 is the oscillation transistor, u184 is the device number, GND is the ground, and BEEP is the buzzer.

[0074] A third aspect of the present invention provides a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the aforementioned highway construction area monitoring method.

[0075] While the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure is merely illustrative and does not limit this specification. Although not explicitly stated herein, various modifications, improvements, and revisions to this specification may be made by those skilled in the art. Such modifications, improvements, and revisions are suggested in this specification and remain within the spirit and scope of the exemplary embodiments of this specification.

[0076] This specification also uses specific terms to describe the embodiments of this specification. For example, "some embodiments" refers to a feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that "some embodiments" may be mentioned twice or multiple times in different places in this specification. Furthermore, certain features, structures, or characteristics in one or more embodiments of this specification may be appropriately combined.

[0077] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly described and illustrated in this specification.

Claims

1. A method for monitoring a highway construction area, characterized in that: The following steps are involved: determining wearing compliance based on a first anomaly monitoring model; determining a device health value based on a second abnormality monitoring model; determining an instantaneous danger radius based on a third abnormality monitoring model; Determining a first danger radius threshold based on the device health value and the instantaneous danger radius; determining a second danger radius threshold based on wearing compliance and the instantaneous danger radius; Determine a final danger radius threshold based on the first danger radius threshold and the second danger radius threshold; Determine the Euclidean distance between the worker and the hook based on the positioning model; The warning information is determined based on the Euclidean distance between the worker and the hook and the final danger radius threshold.

2. A highway construction area monitoring method according to claim 1, characterized in that: Determining wear compliance includes: Define single device compliance; By defining a binary variable To indicate whether construction workers are using personal protective equipment normally, the specific method is: ， in, For single device compliance; Calculate wearable compliance based on single device compliance; The overall compliance is calculated based on the weighted sum of the compliance of each device, as follows: ； in, For wearing compliance; The number of a single device; The total number of compliances for a single device; For the The importance weight of a single device; For construction workers compliance of a single device.

3. A highway construction area monitoring method according to claim 1, characterized in that: Determining the device health value includes: Obtaining status information of the target device during a first time period; Determining, based on the state information, a first health parameter of the target device at least at a time within a first time period; Determining a first health parameter sequence of the target device within a first time period based on the first health parameter at at least one moment; predicting, based on the first health parameter sequence, a second health parameter sequence of the target device in a second time period; Determining a device health value sequence of the target device based on the first health parameter sequence and the second health parameter sequence; Determine device warning information for at least one time point based on a device health value sequence and a device health value threshold of the target device; Based on the device health value sequence of the target device, the device health value of the target device at the current moment is determined.

4. A highway construction area monitoring method according to claim 3, characterized in that: The first health parameter includes the tilt angle of the tower crane and the motor temperature; The first health parameter sequence is a sequence obtained by arranging the first health parameters of at least one moment in chronological order; A second health parameter sequence is determined based on the first health parameter sequence by a device state prediction model; Determining the second health parameter sequence includes: Obtain multiple equipment status prediction models; The equipment status model is a model reflecting the change of the first health parameter over time; For each of the plurality of device state prediction models, determining a fit between the first health parameter sequence and the device state prediction model; The degree of fit is used to characterize the degree of fit between the first health parameter sequence and the equipment status prediction model; determining a target device state prediction model from the plurality of device state prediction models based on a fit between the first health parameter sequence and each device state prediction model; Based on the target device state prediction model, a second health parameter sequence is determined.

5. A highway construction area monitoring method according to claim 4, characterized in that: The device health value is a parameter used to reflect the overall health status of the device; The device health value is calculated as follows: ； in, is the device health value; is the initial device health value; is the normalized temperature, =Motor temperature value / motor temperature threshold; is the normalized inclination angle, =crane inclination angle / crane inclination angle threshold; are weight coefficients respectively.

6. A highway construction area monitoring method according to claim 1, characterized in that: The instantaneous danger radius is a parameter used to describe the range of the area around the equipment that is dangerous during operation. The instantaneous danger radius is calculated as follows: ； in, It is the instantaneous danger radius of tower crane operation; is the tower crane arm length; is the instantaneous swing angle; is the instantaneous angular velocity; is the initial swing angle; For safety margin.

7. A highway construction area monitoring method according to claim 5, characterized in that: The first danger radius threshold is calculated as follows: ； in, is the first danger radius threshold; is the hazard radius expansion factor; in: ； is the adjustment coefficient, used to adjust the degree of expansion; The device health value.

8. A highway construction area monitoring method according to claim 7, characterized in that: The second danger radius threshold is calculated as follows: ； in, is the first danger radius threshold; is the hazard radius expansion factor; in: ； is the adjustment coefficient, used to adjust the degree of expansion; The device health value.

9. A highway construction area monitoring method according to claim 8, characterized in that: Determining the final danger radius threshold based on the first danger radius threshold and the second danger radius threshold includes: The final danger radius threshold is determined by comparing the first danger radius threshold with the second danger radius threshold; the specific method is as follows: ； in, is the final danger radius threshold.

10. A highway construction area monitoring system, characterized in that: A method for monitoring a highway construction area according to any one of claims 1 to 9, comprising: A first abnormality monitoring module, for determining wear compliance; A second abnormality monitoring module is used to determine the health value of the device; The third abnormality monitoring module is used to determine the instantaneous danger radius; A first determining module is configured to determine a first danger radius threshold value according to a device health value and an instantaneous danger radius; A second determining module is used to determine a second danger radius threshold according to wearing compliance and an instantaneous danger radius; A third determining module is configured to determine a final danger radius threshold according to the first danger radius threshold and the second danger radius threshold; Positioning module, used to determine the Euclidean distance between the worker and the hook; The early warning module is used to determine early warning information based on the Euclidean distance between the worker and the hook and the final danger radius threshold.

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