Wharf caisson risk sensing method, system and equipment

Abstract

The invention relates to the technical field of wharf caisson monitoring, and provides a wharf caisson risk sensing method, system and equipment. The wharf caisson risk sensing method comprises the steps of estimating the inclination direction and the inclination angle of a target wharf caisson to obtain inclination estimation data under the condition that the existence of an inclination risk is determined based on target inclination state data collected by an inclination sensor arranged on the target wharf caisson; determining associated wharf caissons based on the inclination estimation data, and acquiring target relative displacement data acquired by a displacement sensor between the target wharf caissons and the associated wharf caissons; and under the condition that the inclination estimation data is matched with the target relative displacement data, determining that the target wharf caisson inclines. By adopting the technical scheme, the inclination risk can be further verified based on the target relative displacement data under the condition of determining that the wharf caisson has the inclination risk based on the target inclination state data, so that accurate risk perception of the wharf caisson can be realized.

Description

Technical Field

[0001] This application relates to the technical field of wharf caisson monitoring, and in particular to a method, system and equipment for risk perception of wharf caissons. Background Art

[0002] Caissons are important hydraulic structures mainly used in the construction of wharves and breakwaters. Taking the construction of a wharf as an example, during the construction process, several caissons need to be closely arranged in the construction area, and sand, gravel or rubble are filled in the caissons to form a load-bearing and retaining wall structure.

[0003] During use, the shape and position of wharf caissons may change due to external forces, which may affect the stability of the wharf caisson structure. Therefore, it is necessary to accurately perceive the risks of wharf caissons during the use of the wharf to guide the maintenance work of the wharf. Summary of the Invention

[0004] To help achieve accurate risk perception of wharf caissons, this application provides a heating control method, cooking equipment and storage medium for cooking equipment.

[0005] In a first aspect, this application provides a method for risk perception of wharf caissons, adopting the following technical solutions: A method for risk perception of wharf caissons, which is used in the controller of a wharf caisson risk perception system. The wharf caisson perception system further includes an inclination sensor and a displacement sensor that are signal-connected to the controller. The inclination sensor is arranged on the wharf caisson to monitor the inclination state data of the wharf caisson, and the displacement sensor is arranged between adjacent wharf caissons to monitor the relative displacement data between the wharf caissons. The method includes: Determining whether there is an inclination risk for the target wharf caisson based on the target inclination state data collected by the inclination sensor arranged on the target wharf caisson; When it is determined that there is an inclination risk for the target wharf caisson, estimating the inclination direction and inclination angle of the target wharf caisson to obtain inclination estimation data; Determining associated wharf caissons based on the inclination estimation data, and obtaining target relative displacement data collected by the displacement sensor between the target wharf caisson and the associated wharf caissons. The associated wharf caissons are adjacent to the target wharf caisson; Determining whether the inclination estimation data matches the target relative displacement data; When the inclination estimation data matches the target relative displacement data, determining that the target wharf caisson is inclined.

[0006] By adopting the above technical solution, when it is determined that there is a risk of inclination of the wharf caisson based on the target inclination state data, the associated wharf caisson can be further determined based on the inclination data. When the inclination data matches the target relative displacement data of the target wharf caisson relative to the associated wharf caisson, it is determined that the target wharf caisson is inclined. In this way, the risk of inclination can be verified based on the target relative displacement data, and thus accurate risk perception of the wharf caisson can be achieved.

[0007] Optionally, determining whether the inclination prediction data matches the target relative displacement data includes: Determining an inclination conversion method based on the relative position relationship between the target wharf caisson and the associated wharf caisson; Converting the inclination prediction data based on the inclination conversion method to obtain displacement prediction data; Determining the matching relationship between the inclination prediction data and the target relative displacement data based on the matching relationship between the displacement prediction data and the target relative displacement data.

[0008] By adopting the above technical solution, the inclination prediction data can be accurately converted into displacement prediction data based on the inclination conversion method determined based on the relative position relationship between the target wharf caisson and the associated wharf caisson, which can help to determine the matching relationship between the inclination prediction data and the target relative displacement data.

[0009] Optionally, determining the matching relationship between the inclination prediction data and the target relative displacement data based on the matching relationship between the displacement prediction data and the target relative displacement data includes: When the displacement prediction data does not match the target relative displacement data, determining whether there is a risk of inclination of the associated wharf caisson based on the associated inclination state data collected by the inclination sensor arranged on the associated wharf caisson; When it is determined that there is a risk of inclination of the associated wharf caisson, predicting the inclination direction and inclination angle of the associated wharf caisson to obtain associated inclination data; Calibrating the inclination prediction data based on the associated inclination data to obtain inclination calibration data; Converting the inclination calibration data based on the inclination conversion method to obtain displacement calibration data; Determining the matching relationship between the inclination prediction data and the target relative displacement data based on the matching relationship between the displacement calibration data and the target relative displacement data.

[0010] By adopting the above technical solutions, the tilt prediction data can be calibrated based on the associated tilt data corresponding to the associated quay caisson, so as to reduce the influence of the tilt of the associated quay caisson on the tilt judgment of the target quay caisson, and thus contribute to improving the accuracy of the tilt judgment of the target quay caisson.

[0011] Optionally, determining the matching relationship between the tilt prediction data and the target relative displacement data based on the matching relationship between the displacement calibration data and the target relative displacement data includes: In the case where it is determined that the displacement calibration data does not match the target relative displacement data, determine the historical change situation of the displacement calibration data; Determine whether the historical change situation of the displacement calibration data is consistent with the historical change situation of the target relative displacement data; In the case where the historical change situation of the displacement calibration data is inconsistent with the historical change situation of the target relative displacement data, determine that the tilt prediction data does not match the target relative displacement data.

[0012] By adopting the above technical solutions, it can be determined whether the tilt prediction data matches the target relative displacement data based on whether the historical change situation of the displacement calibration data is consistent with the historical change situation of the target relative displacement data, which can help reduce the influence of the data acquisition error of the sensor on the matching judgment result, and thus contribute to improving the accuracy of the matching judgment result.

[0013] Optionally, after determining whether the tilt prediction data matches the target relative displacement data, it further includes: In the case where the tilt prediction data does not match the target relative displacement data, determine whether there is a first calibration caisson corresponding to the target quay caisson, where the first calibration caisson is adjacent to the target quay caisson and is symmetric with the associated quay caisson with respect to the target quay caisson; In the case where there is the first calibration caisson, perform an anomaly check on the tilt prediction data based on the first calibration relative displacement data collected by the displacement sensor arranged between the first calibration caisson and the target quay caisson to obtain a target anomaly check result; In the case where the target anomaly check result indicates that the tilt prediction data is abnormal, determine that the tilt sensor arranged on the target quay caisson is abnormal.

[0014] By adopting the above technical solutions, the tilt prediction data can be anomaly-checked by combining the first calibration relative displacement data collected by the displacement sensor between the target quay caisson and the corresponding first calibration caisson to perform an anomaly check on the tilt sensor, which can help assist in judging the reason for the mismatch between the tilt prediction data and the target relative displacement data.

[0015] Optionally, after performing anomaly verification on the tilt prediction data based on the first calibration relative displacement data collected by the displacement sensor disposed between the first calibration caisson and the target wharf caisson, the method further includes: When the target anomaly verification result indicates that the tilt prediction data is normal, determine whether there is a second calibration caisson corresponding to the associated wharf caisson, where the second calibration caisson is adjacent to the associated wharf caisson and is symmetric to the target wharf caisson with respect to the associated wharf caisson; When there is the second calibration caisson, perform anomaly verification on the associated tilt status data collected by the tilt sensor disposed on the associated wharf caisson based on the second calibration relative displacement data collected by the displacement sensor disposed between the second calibration caisson and the associated wharf caisson, to obtain an associated anomaly verification result; When the associated anomaly verification result indicates that the associated tilt status data is normal, determine that there is an anomaly in the position sensor between the target wharf caisson and the associated wharf caisson.

[0016] By adopting the above technical solution, the anomaly verification of the associated tilt status data corresponding to the associated wharf caisson can be performed in combination with the second calibration relative displacement data of the associated wharf caisson and the corresponding second calibration caisson, so as to further assist in judging the reason for the mismatch between the tilt prediction data and the target relative displacement data, and further help perform anomaly verification on the position sensor.

[0017] Optionally, after determining whether there is a first calibration caisson corresponding to the target wharf caisson, the method further includes: When there is no first calibration caisson, obtain the calibrated tilt status data collected by the tilt calibration component disposed on the target wharf caisson; Determine whether the target tilt status data matches the calibrated tilt status data; When the target tilt status data does not match the calibrated tilt status data, determine that the tilt monitoring of the target wharf caisson is abnormal.

[0018] By adopting the above technical solution, it can be determined whether there is an anomaly in the tilt monitoring of the target wharf caisson based on the matching relationship between the target tilt status data and the calibrated tilt status data collected by the tilt calibration component disposed on the target wharf caisson. In this way, it helps perform anomaly verification on the tilt sensor of the target wharf caisson, and further helps assist in judging the reason for the mismatch between the tilt prediction data and the target relative displacement data.

[0019] Optionally, the determining the associated wharf caisson based on the tilt prediction data includes: Determine the inclination direction of the target wharf caisson based on the inclination prediction data; Determine the associated wharf caisson that is adjacent to the target wharf caisson and located in the inclination direction.

[0020] In a second aspect, the present application provides a wharf caisson risk perception system, adopting the following technical solution: A wharf caisson risk perception system, the system includes a controller, and an inclination sensor and a displacement sensor that are signal-connected to the controller; the inclination sensor is arranged on the wharf caisson for monitoring the inclination state data of the wharf caisson; the displacement sensor is arranged between adjacent wharf caissons for monitoring the relative displacement data between the wharf caissons; the controller is used to execute any one of the wharf caisson risk perception methods provided in the first aspect.

[0021] In a third aspect, the present application provides an electronic device, adopting the following technical solution: An electronic device, the electronic device includes: At least one processor; A memory; At least one application program, wherein at least one application program is stored in the memory and is configured to be executed by at least one processor, and the at least one application program is configured to: execute any one of the wharf caisson risk perception methods provided in the first aspect. Description of the Drawings

[0022] Figure 1 is a schematic structural diagram of a wharf caisson risk perception system provided by an embodiment of the present application; Figures 2a - 2g is a schematic diagram of the installation position of the sensing component provided by an embodiment of the present application; Figure 3 is a schematic flowchart of a wharf caisson risk perception method provided by an embodiment of the present application; Figure 4 is a schematic flowchart of a data matching method provided by an embodiment of the present application; Figure 5 is a schematic flowchart of another data matching method provided by an embodiment of the present application; Figure 6 is a schematic flowchart of yet another data matching method provided by an embodiment of the present application; Figure 7 is a schematic flowchart of a matching result calibration method provided by an embodiment of the present application; Figure 8 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed Embodiments

[0023] In order to make the objectives, technical solutions, and advantages of this application more clearly understood, the following further elaborates on this application in conjunction with the accompanying Figures 1 - 8 drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0024] An embodiment of this application provides a risk perception system for a wharf caisson. Referring to Figure 1 , this system includes a controller 110, and an inclination sensor 120 and a displacement sensor 130 that are signal-connected to the controller 110.

[0025] Among them, the inclination sensor 120 is arranged on the wharf caisson and is used to monitor the inclination state data of the wharf caisson. In one example, the inclination sensor 120 is selected as a wireless composite sensor. Specifically, the wireless composite sensor is a static application of a MEMS accelerometer. By using the components of the gravitational acceleration in the three axes of the MEMS sensor, the angles between each section of the sensor and the vertical and horizontal directions are calculated. Combining the equidistant / non-equidistant arrangement of the sensors, using the differential relationship between deflection and rotation angle, the displacements of the structure in the vertical and horizontal directions are solved through a high-precision deformation integration algorithm. In actual implementation, the wireless composite sensor can integrate data acquisition and transmission, be self-powered, and does not require cable connection. It is a multi-physical quantity (inclination, vibration, deformation, settlement, temperature, etc.) fusion perception sensor, with functions such as static measurement / high-frequency dynamic measurement, long-term passive wireless perception, low-power operation, and warning triggering. It can be installed with one key and is plug-and-play. Further, the wireless composite sensor can integrate advanced gravitational acceleration measurement technology, multiple filtering and noise reduction technologies, sensor temperature non-linear compensation technology, vibration filtering technology, and core algorithm model technology to realize real-time online monitoring of the three-dimensional deformation of the monitored object in the X, Y, and Z directions. In one instance, the wireless composite sensor is horizontally installed on the top surface of the caisson by pasting or welding, as Figure 2a shown.

[0026] The displacement sensor 130 is arranged between adjacent wharf caissons and is used to monitor the relative displacement data between the wharf caissons. In one example, the displacement sensor 130 is selected as a magnetostrictive displacement gauge, such as a magnetostrictive displacement gauge. Specifically, the magnetostrictive displacement gauge is based on the magnetostrictive principle. By using the induction of the external magnetic ring of the measuring rod containing the waveguide wire to the distance of the electronic bin, the displacement change of the structure is sensed through the change of the magnetic ring at different positions in the measuring rod. Optionally, the displacement sensor 130 can collect displacement data in different directions (such as the vertical direction and the horizontal direction) according to different installation methods. In one instance, two displacement sensors 130 are installed between adjacent wharf caissons, which are respectively used to collect the displacement in the horizontal direction and the displacement in the vertical direction. The installation method of the displacement sensor used to collect the displacement in the vertical direction is as shown in Figures 2b - 2dAs shown, the installation method of the displacement sensor for collecting the vertical displacement is as shown in the figure Figures 2e - 2g as shown.

[0027] The controller 110 is used to obtain the tilt state data collected by the tilt sensor assembly and the relative displacement data collected by the displacement sensor, and perform risk perception on the wharf caisson based on the tilt state data and the relative displacement data. In actual implementation, the controller 110 can be directly communicatively connected to the tilt sensor 120 and the displacement sensor 130, or can also be communicatively connected to the tilt sensor 120 and the displacement sensor 130 through other devices (such as: intelligent gateway). This embodiment does not limit the way of communicative connection between the controller 110 and the tilt sensor 120 and the displacement sensor 130.

[0028] This application also provides a method for risk perception of a wharf caisson, which is used in the controller of the above-mentioned wharf caisson risk perception system. Refer to Figure 3 , and this method includes the following steps: Step 201, determine whether there is a tilt risk for the target wharf caisson based on the target tilt state data collected by the tilt sensor arranged on the target wharf caisson.

[0029] Among them, the target wharf caisson refers to the caisson for which risk judgment is required. Specifically, the target wharf caisson can be pre-specified, or can also be randomly or determined from all wharf caissons according to a certain rule.

[0030] The tilt state data is used to reflect the tilt change of the wharf caisson. In one example, the tilt state data can include the displacements of the wharf caisson in different directions, so that it can be determined whether there is a tilt risk for the target wharf caisson based on the tilt state data.

[0031] Correspondingly, determining whether there is a tilt risk for the target wharf caisson based on the target tilt data includes: determining whether the displacement amount of the target wharf caisson in the horizontal direction is greater than a preset displacement amount threshold; if so, determining that the target wharf caisson has a tilt risk; if not, determining that the target wharf caisson does not have a tilt risk.

[0032] In actual implementation, more than two tilt sensors may be installed on a wharf caisson. At this time, the target tilt state data includes multiple items. Correspondingly, in the process of tilt risk judgment, different target tilt state data need to be comprehensively considered. For example: superimpose different target tilt state data to obtain the final tilt state data, and perform tilt risk judgment based on the final tilt state data.

[0033] Step 202, in the case of determining that the target wharf caisson has a tilt risk, estimate the tilt direction and tilt angle of the target wharf caisson to obtain tilt estimation data.

[0034] Wherein, the inclination direction refers to the orientation of the horizontal displacement of the target wharf caisson, that is, the direction in which the target wharf caisson undergoes horizontal displacement. In one example, for the convenience of describing the inclination direction, the inclination direction can be divided into four directions: the left and right longitudinal sections, the water-facing side, and the backwater side in combination with the installation position of the target wharf caisson.

[0035] The inclination angle refers to the angle of the target wharf caisson facing the inclination direction and can be determined based on the horizontal displacement of the target wharf caisson. In actual implementation, considering that there may be a certain deviation between the selected inclination direction and the actual inclination direction of the wharf caisson, in order to improve the accuracy of the inclination direction, the inclination angle determined based on the horizontal displacement can be corrected by combining the included angle between the selected inclination direction and the actual inclination direction, so as to calculate the inclination angle of the wharf caisson in the selected inclination direction, which can help improve the accuracy of the determined inclination angle.

[0036] Step 203: Determine the associated wharf caisson based on the inclination prediction data, and obtain the target relative displacement data collected by the displacement sensor between the target wharf caisson and the associated wharf caisson.

[0037] Wherein, the associated wharf caisson is adjacent to the target wharf caisson.

[0038] The target relative displacement data is used to indicate the displacement of the wharf caisson relative to the associated wharf caisson. Specifically, the target relative displacement data may include the displacement in the vertical direction and / or the displacement in the horizontal direction, which is specifically determined by the installation method of the displacement sensor.

[0039] In one example, determining the associated wharf caisson based on the inclination prediction data includes: determining the inclination direction of the target wharf caisson based on the inclination prediction data; and determining the wharf caisson adjacent to the target wharf caisson and located in the inclination direction as the associated wharf caisson.

[0040] Furthermore, in the case where the target wharf caisson is located at the edge of the construction area, there may be a wharf caisson in the inclination direction of the target wharf caisson. At this time, the wharf caisson located in the opposite direction of the inclination direction of the target wharf caisson can be determined as the associated wharf caisson.

[0041] In another example, determining the associated wharf caisson based on the inclination prediction data includes: determining the wharf caisson located in the direction perpendicular to the inclination direction of the target wharf imaging as the associated wharf caisson. At this time, the associated wharf caisson can be one or two.

[0042] Step 204: Determine whether the inclination prediction data matches the target relative displacement data.

[0043] Specifically, when the caisson of the target dock is tilted, its relative position relationship with the adjacent associated dock caisson also changes, and there is a certain corresponding relationship between the two. Therefore, the tilt risk can be verified based on the matching relationship between the tilt prediction data and the target relative displacement data.

[0044] Step 205: When the tilt prediction data matches the target relative displacement data, determine that the caisson of the target dock is tilted.

[0045] Furthermore, when the tilt prediction data matches the target relative displacement data, it also includes: generating tilt prompt information based on the tilt prediction data to facilitate the monitoring and maintenance of the caisson of the target dock.

[0046] Optionally, when the tilt prediction data does not match the target relative displacement data, it can be directly determined that the target caisson is not tilted, or the tilt of the target caisson can be further verified based on other methods.

[0047] The implementation principle of the dock caisson risk perception method provided by the embodiments of the present application is as follows: determining whether there is a tilt risk of the caisson of the target dock based on the target tilt state data collected by the tilt sensor set on the caisson of the target dock; when it is determined that there is a tilt risk of the caisson of the target dock, predicting the tilt direction and tilt angle of the caisson of the target dock to obtain tilt prediction data; determining the associated dock caisson based on the tilt prediction data, and obtaining the target relative displacement data collected by the displacement sensor between the caisson of the target dock and the associated dock caisson, and the associated dock caisson is adjacent to the caisson of the target dock; determining whether the tilt prediction data matches the target relative displacement data; when the tilt prediction data matches the target relative displacement data, determining that the caisson of the target dock is tilted. In the above technical solution, when it is determined that there is a tilt risk of the dock caisson based on the target tilt state data, the associated dock caisson is further determined based on the tilt data, and when the tilt data matches the target relative displacement data of the caisson of the target dock relative to the associated dock caisson, it is determined that the caisson of the target dock is tilted. In this way, the tilt risk can be verified based on the target relative displacement data, and thus accurate risk perception of the dock caisson can be achieved.

[0048] In some embodiments, referring to Figure 4 , step 204, determining whether the tilt prediction data matches the target relative displacement data includes the following steps: Step 301: Determine the tilt conversion method based on the relative position relationship between the caisson of the target dock and the associated dock caisson.

[0049] Among them, the tilt conversion method is used to convert the tilt data into the displacement calibration data of the target quay caisson relative to the associated quay caisson, and the tilt conversion methods corresponding to different relative position relationships are preset in advance. Specifically, since the tilt data is referenced based on the initial state of the target quay caisson, while the displacement data needs to be referenced based on the position of the associated quay caisson, the relative position relationship between the target quay caisson and the associated quay caisson will affect the determination of the displacement data, and the tilt conversion method needs to be determined based on the relative position relationship.

[0050] Step 302, convert the tilt prediction data based on the tilt conversion method to obtain displacement prediction data.

[0051] Among them, the displacement prediction data is used to indicate the displacement data of the target quay caisson relative to the associated quay caisson, specifically including the displacement direction and the displacement amount. For example: the displacement direction includes towards the associated quay caisson, away from the associated quay caisson, etc.

[0052] Step 303, determine the matching relationship between the tilt prediction data and the target relative displacement data based on the matching relationship between the displacement prediction data and the target relative displacement data.

[0053] Among them, the target relative displacement data is used to indicate the displacement data of the target quay caisson relative to the associated quay caisson, specifically including the displacement direction and the displacement amount.

[0054] Correspondingly, determining the matching between the tilt prediction data and the target relative displacement data based on the matching relationship between the displacement prediction data and the target relative displacement data includes: determining whether the displacement direction indicated by the displacement prediction data is the same as the displacement direction indicated by the target relative displacement data; if not, determining that the displacement prediction data and the target relative displacement data do not match; if so, further determining whether the difference between the displacement amount indicated by the displacement prediction data and the displacement amount indicated by the target relative displacement data is less than a preset difference value threshold; if so, determining that the displacement prediction data and the target relative displacement data match; if not, determining that the displacement prediction data and the target relative displacement data do not match.

[0055] In the above embodiments, the tilt prediction data is accurately converted into displacement prediction data based on the tilt conversion method determined according to the relative position relationship between the target quay caisson and the associated quay caisson, which can help to determine the matching relationship between the tilt prediction data and the target relative displacement data.

[0056] Further, referring to Figure 5 , step 301, determining the matching relationship between the tilt prediction data and the target relative displacement data based on the matching relationship between the displacement prediction data and the target relative displacement data includes: Step 401: When the displacement prediction data does not match the target relative displacement data, determine whether there is a tilt risk for the associated wharf caisson based on the associated tilt status data collected by the tilt sensors installed on the associated wharf caisson.

[0057] Specifically, the method for determining whether there is a risk for the associated wharf caisson based on the associated tilt status data can be analogous to the method for determining whether there is a risk for the associated wharf caisson based on the target tilt status data in Step 201 above, and will not be elaborated here.

[0058] Step 402: When it is determined that there is a tilt risk for the associated wharf caisson, estimate the tilt direction and tilt angle of the associated wharf caisson to obtain associated tilt data.

[0059] Specifically, the method for determining the associated tilt data can be analogous to the method for determining the tilt prediction data in Step 202 above.

[0060] Step 403: Calibrate the tilt prediction data based on the associated tilt data to obtain tilt calibration data.

[0061] Optionally, calibrating the tilt prediction data based on the associated tilt data includes: when the tilt direction indicated by the associated tilt data is the same as the tilt direction indicated by the tilt prediction data, determining the absolute value of the difference between the tilt angle indicated by the associated tilt data and the tilt angle indicated by the tilt prediction data as the tilt angle corresponding to the tilt calibration data, and the tilt direction of the tilt calibration data is the same as the tilt direction indicated by the tilt prediction data.

[0062] When the tilt direction indicated by the associated tilt data is opposite to the tilt direction indicated by the tilt prediction data, determining the sum of the tilt angle indicated by the associated tilt data and the tilt angle indicated by the tilt prediction data as the tilt angle corresponding to the tilt calibration data, and the tilt direction corresponding to the tilt calibration data is the same as the tilt direction indicated by the tilt prediction data.

[0063] Step 404: Convert the tilt calibration data based on the tilt conversion method to obtain displacement calibration data.

[0064] Specifically, the tilt conversion method is the tilt conversion method determined in Step 301 above, and the method for converting the tilt calibration data based on the tilt conversion method can be analogous to the method for converting the tilt prediction data based on the tilt conversion method in Step 302 above, and will not be elaborated in this embodiment.

[0065] Step 405: Determine the matching relationship between the tilt prediction data and the target relative displacement data based on the matching relationship between the displacement calibration data and the target relative displacement data.

[0066] Specifically, the method for determining the matching relationship between the tilt prediction data and the target relative displacement data based on the matching relationship between the displacement calibration data and the target relative displacement data can be analogous to the specific implementation corresponding to step 303 above, and will not be elaborated here.

[0067] In the above implementation, when it is determined that the displacement prediction data does not match the target relative displacement data, it is possible to determine whether there is a tilt risk for the associated wharf caisson based on the associated tilt data, and when it is determined that there is a tilt risk for the associated wharf caisson, the tilt prediction data can be calibrated based on the associated tilt data corresponding to the associated wharf caisson, so as to reduce the impact of the tilt of the associated wharf caisson on the tilt judgment of the target wharf caisson, and thus can help improve the accuracy of the tilt judgment of the target wharf caisson.

[0068] Furthermore, referring to Figure 6 , step 405, determining the matching relationship between the tilt prediction data and the target relative displacement data based on the matching relationship between the displacement calibration data and the target relative displacement data includes: Step 501, when it is determined that the displacement calibration data does not match the target relative displacement data, determine the historical change situation of the displacement calibration data.

[0069] Optionally, determining the historical change situation of the positioning calibration data includes: obtaining the target tilt state data and the associated tilt state data collected historically; determining the historical tilt prediction data based on the target tilt state data collected historically, and determining the historical associated tilt data based on the associated tilt state data collected historically; calibrating the historical tilt prediction data based on the historical associated tilt data to obtain the historical tilt calibration data; and converting the historical tilt calibration data based on the tilt conversion method to obtain the historical displacement calibration data, so as to obtain the historical change situation of the positioning calibration data.

[0070] Step 502, determine whether the historical change situation of the displacement calibration data is consistent with the historical change situation of the target relative displacement data.

[0071] In an example, determining whether the historical change situation of the displacement calibration data is consistent with the historical change situation of the target relative displacement data includes: determining whether the historical fluctuation situation of the displacement calibration data is consistent with the historical fluctuation situation of the target relative displacement data; if not, it is determined that the historical change situation of the displacement calibration data is inconsistent with the historical change situation of the target relative displacement data.

[0072] In another example, it is determined whether the historical change of the displacement calibration data is consistent with the historical change of the target relative displacement data, including: determining whether the historical inflection points of the displacement calibration data are consistent with the historical inflection points of the target relative displacement data; if not, it is determined that the historical change of the displacement calibration data is consistent with the historical change of the target relative displacement data.

[0073] Among them, the historical inflection point can be determined based on the data curve after fitting the historical data into a data curve. For example: the point with a curvature greater than the preset curvature threshold in the data curve is determined as the inflection point.

[0074] Step 503, in the case where the historical change of the displacement calibration data is inconsistent with the historical change of the target relative displacement data, it is determined that the tilt prediction data does not match the target relative displacement data.

[0075] Optionally, in the case where the historical change of the displacement calibration data is consistent with the historical change of the target relative displacement data, it can be directly determined that the tilt prediction data matches the target relative displacement data, or it can also be further determined whether the tilt prediction data matches the target relative displacement data based on other methods.

[0076] In the above technical solution, since in the case where the displacement calibration data does not match the target relative displacement data, it is further determined whether the tilt prediction data matches the target relative displacement data based on whether the historical change of the displacement calibration data is consistent with the historical change of the target relative displacement data, which can help reduce the influence of the data acquisition error of the sensor on the matching judgment result, and thus can help improve the accuracy of the matching judgment result.

[0077] In some embodiments, referring to Figure 7 , after step 204 of determining whether the tilt prediction data matches the relative displacement data, it further includes: Step 601, in the case where the tilt prediction data does not match the target relative displacement data, it is determined whether there is a first calibration caisson corresponding to the target wharf caisson.

[0078] Among them, the first calibration caisson is adjacent to the target wharf caisson and is symmetric with the associated wharf caisson with respect to the target wharf caisson. For example: if the associated wharf caisson is in the upstream direction of the target wharf caisson, then the first calibration caisson is in the downstream direction of the target wharf caisson. Another example: if the associated wharf caisson is on the left longitudinal section of the target wharf caisson, then the first calibration caisson is on the right longitudinal section of the target wharf caisson.

[0079] In actual implementation, the target wharf caisson may be located at the edge of the construction area, and in this case, there may be no first calibration caisson for the target wharf caisson. Therefore, it is necessary to determine whether there is a corresponding first calibration caisson for the target wharf caisson.

[0080] Step 602: In the case where there is a first calibration caisson, perform an anomaly check on the tilt prediction data based on the first calibrated relative displacement data collected by the displacement sensor arranged between the first calibration caisson and the target wharf caisson, to obtain a target anomaly check result.

[0081] Specifically, performing an anomaly check on the tilt prediction data based on the first calibrated relative displacement data collected by the displacement sensor arranged between the first calibration caisson and the target wharf caisson includes: determining whether the tilt prediction data matches the first calibrated relative displacement data; if so, determining that the tilt prediction data has no anomaly; if not, determining that the tilt prediction data has an anomaly.

[0082] Among them, the method for determining whether the tilt prediction data matches the first calibrated relative displacement data can be analogous to the method for determining whether the tilt prediction data matches the target relative displacement data in step 204, and will not be elaborated here.

[0083] Step 603: In the case where the target anomaly check result indicates that the tilt prediction data has an anomaly, determine that the tilt sensor arranged on the target wharf caisson has an anomaly.

[0084] Optionally, in the case where the anomaly check result indicates that the tilt prediction data has no anomaly, it can be directly determined that the target relative displacement data has an anomaly, or other methods can also be used to further judge the reason for the mismatch between the tilt prediction data and the target relative displacement data.

[0085] In the above technical solution, in the case where the tilt prediction data does not match the target relative displacement data, the first calibrated relative displacement data collected by the displacement sensor between the target wharf caisson and the corresponding first calibration caisson can be further combined to perform an anomaly check on the tilt prediction data, so as to perform an anomaly check on the tilt sensor, and thus it can help to assist in judging the reason for the mismatch between the tilt prediction data and the target relative displacement data.

[0086] Further, continue to refer to Figure 7 , after performing an anomaly check on the tilt prediction data based on the first calibrated relative displacement data collected by the displacement sensor arranged between the first calibration caisson and the target wharf caisson in step 602, it further includes: Step 604: In the case where the target anomaly check result indicates that the tilt prediction data has no anomaly, determine whether there is a second calibration caisson corresponding to the associated wharf caisson.

[0087] Among them, the second calibration caisson is adjacent to the associated wharf caisson and symmetric to the target wharf caisson with respect to the associated wharf caisson. For example, if the target wharf caisson is located in the water-facing direction of the associated wharf caisson, then the second calibration caisson is located in the water-backing direction of the associated wharf caisson. Another example: if the target wharf caisson is located on the left longitudinal section of the associated wharf caisson, then the second calibration caisson is located on the right longitudinal section of the associated wharf caisson.

[0088] Step 605, in the case of the existence of the second calibration caisson, perform an anomaly check on the associated inclination state data collected by the inclination sensor provided on the associated wharf caisson based on the second calibration relative displacement data collected by the displacement sensor provided between the second calibration caisson and the associated wharf caisson, to obtain an associated anomaly check result.

[0089] Specifically, performing an anomaly check on the associated inclination state data collected by the inclination sensor provided on the associated wharf caisson based on the second calibration relative displacement data collected by the displacement sensor provided between the second calibration caisson and the associated wharf caisson includes: estimating the inclination direction and inclination angle of the associated wharf caisson based on the associated inclination state to obtain associated inclination data; determining whether the associated inclination data matches the second calibration relative displacement data; if so, determining that the associated inclination state data is normal; if not, determining that the associated inclination state data is abnormal.

[0090] Step 606, in the case where the associated anomaly check result indicates that the associated inclination state data is normal, determine that there is an anomaly in the position sensor between the target wharf caisson and the associated wharf caisson.

[0091] Specifically, since in the case where the target check result indicates that the inclination state estimation data is normal, that is, the reason for the mismatch between the inclination estimation data and the target relative displacement data may be abnormal target relative displacement data or abnormal associated inclination state data. Therefore, in the case where the associated anomaly check result indicates that the associated inclination state data is normal, it means that the reason for the mismatch between the inclination estimation data and the target relative displacement data is probably abnormal target relative displacement data. At this time, it can be determined that there is an anomaly in the position sensor between the target wharf caisson and the associated wharf caisson.

[0092] Optionally, in the case where the associated anomaly check result indicates that the associated inclination state data is normal, it can be directly determined that there is an anomaly in the inclination sensor provided on the target wharf caisson.

[0093] In the above technical solution, when the target anomaly verification result indicates that the tilt prediction data is normal, the second calibration relative displacement data of the associated wharf caisson and the corresponding second calibration caisson can be further combined to perform anomaly verification on the associated tilt state data corresponding to the associated wharf caisson, so as to further assist in judging the reason for the mismatch between the tilt prediction data and the target relative displacement data, and thus can help the position sensor perform anomaly verification.

[0094] Further, continue to refer to Figure 7 , in step 601, after determining whether there is a first calibration caisson corresponding to the target wharf caisson, it further includes: Step 607, when there is no first calibration caisson, obtain the calibrated tilt state data collected by the tilt calibration component set on the target wharf caisson.

[0095] Specifically, during the installation of the wharf caisson, for the wharf caisson installed in a special position (such as: the edge of the construction area), a tilt calibration component will be redundantly installed to collect the calibrated tilt state data. In actual implementation, the type of the tilt calibration component can be the same as the type of the tilt sensor, so that it is convenient to compare the calibrated tilt state data with the target tilt state data.

[0096] Step 608, determine whether the target tilt state data matches the calibrated tilt state data.

[0097] In an example, the type of the tilt calibration component is the same as the type of the tilt sensor. Determining whether the target tilt state data matches the calibrated tilt state data includes: determining whether the difference value between the target tilt state data and the calibrated tilt state data is less than or equal to the difference value threshold; if so, determine that the target tilt state data matches the calibrated tilt state data; if not, determine that the target tilt state data does not match the calibrated tilt state data.

[0098] In actual implementation, when the type of the tilt calibration component is different from the type of the tilt sensor, it is necessary to first determine the calibrated tilt data based on the calibrated tilt state data. The calibrated tilt data includes the tilt direction and the tilt angle, and then compare the tilt prediction state with the calibrated tilt data to obtain the matching relationship between the target tilt state data and the calibrated tilt state data.

[0099] Step 609, when the target tilt state data does not match the calibrated tilt state data, determine that the tilt monitoring of the target wharf caisson is abnormal.

[0100] Among them, the tilt monitoring anomaly is used to indicate that at least one of the tilt sensor and the tilt calibration component corresponding to the target wharf caisson is abnormal.

[0101] Optionally, when the target tilt state data matches the calibrated tilt state data, it can be determined that there is no abnormality in the tilt monitoring of the target quay caisson, and a target anomaly verification result indicating that the tilt sensor is normal can be generated.

[0102] In the above embodiment, in the case where there is no first calibrated caisson corresponding to the target quay caisson, the matching relationship between the target tilt state data and the calibrated tilt state data collected by the tilt calibration component provided on the target quay caisson can be further used to determine whether there is an abnormality in the tilt monitoring of the target quay caisson. This can help verify the abnormality of the tilt sensor of the target quay caisson, and further help assist in determining the reason for the mismatch between the tilt prediction data and the target relative displacement data.

[0103] The embodiment of the present application also provides an electronic device. As Figure 8 shown, Figure 8 The electronic device 700 shown includes: a processor 701 and a memory 703. Among them, the processor 701 and the memory 703 are connected, such as connected through a bus 702. Optionally, the electronic device 700 may further include a transceiver 704. It should be noted that in practical applications, the transceiver 704 is not limited to one, and the structure of the electronic device 700 does not constitute a limitation to the embodiment of the present application.

[0104] The processor 701 may be a CPU (Central Processing Unit, central processor), a general-purpose processor, a DSP (Digital Signal Processor, data signal processor), an ASIC (Application Specific Integrated Circuit, application-specific integrated circuit), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in combination with the disclosure of the present application. The processor 701 may also be a combination that implements computing functions, such as a combination including one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0105] The bus 702 may include a path for transmitting information between the above components. The bus 702 may be a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus or an EISA (Extended Industry Standard Architecture, extended industry standard structure) bus, etc. The bus 702 may be divided into an address bus, a data bus, etc. For the sake of representation, Figure 8 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0106] The memory 703 can be a ROM (Read Only Memory), or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory), or other types of dynamic storage devices that can store information and instructions. It can also be an EEPROM (Electrically Erasable Programmable Read Only Memory), a magnetic disk storage medium, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0107] The memory 703 is used to store the application program code for implementing the solution of this application and is controlled by the processor 701 for execution. The processor 701 is used to execute the application program code stored in the memory 703 to implement the content shown in the foregoing method embodiments.

[0108] Among them, the electronic device includes but is not limited to: mobile terminals such as mobile phones, laptop computers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), etc., and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 8 The illustrated electronic device is only an example and should not impose any limitations on the functions and usage scope of the embodiments of this application.

[0109] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limitation and can be executed in other orders.

[0110] The above are only some implementation manners of this application. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the principle of this application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of this application.

Claims

1. A method for risk perception of quay caissons, characterized in that, In the controller of the risk perception system for wharf caissons, the wharf caisson perception system further includes an inclination sensor and a displacement sensor that are signal-connected to the controller. The inclination sensor is arranged on the wharf caisson and is used to monitor the inclination state data of the wharf caisson. The displacement sensor is arranged between adjacent wharf caissons and is used to monitor the relative displacement data between the wharf caissons. The method includes: Determine whether there is an inclination risk for the target wharf caisson based on the target inclination state data collected by the inclination sensor arranged on the target wharf caisson; In the case of determining that there is an inclination risk for the target wharf caisson, estimate the inclination direction and inclination angle of the target wharf caisson to obtain inclination estimation data; Determine the associated wharf caisson based on the inclination estimation data, and obtain the target relative displacement data collected by the displacement sensor between the target wharf caisson and the associated wharf caisson. The associated wharf caisson is adjacent to the target wharf caisson; Determine whether the inclination estimation data matches the target relative displacement data; In the case of the inclination estimation data matching the target relative displacement data, determine that the target wharf caisson is inclined.

2. The method according to claim 1, characterized in that, The determination of whether the inclination estimation data matches the target relative displacement data includes: Determine the inclination conversion method based on the relative position relationship between the target wharf caisson and the associated wharf caisson; Convert the inclination estimation data based on the inclination conversion method to obtain displacement estimation data; Determine the matching relationship between the inclination estimation data and the target relative displacement data based on the matching relationship between the displacement estimation data and the target relative displacement data.

3. The method according to claim 2, wherein The determination of the matching relationship between the inclination estimation data and the target relative displacement data based on the matching relationship between the displacement estimation data and the target relative displacement data includes: In the case of the displacement estimation data not matching the target relative displacement data, determine whether there is an inclination risk for the associated wharf caisson based on the associated inclination state data collected by the inclination sensor arranged on the associated wharf caisson; In the case of determining that there is an inclination risk for the associated wharf caisson, estimate the inclination direction and inclination angle of the associated wharf caisson to obtain associated inclination data; Calibrate the inclination estimation data based on the associated inclination data to obtain inclination calibration data; Convert the inclination calibration data based on the inclination conversion method to obtain displacement calibration data; Determine the matching relationship between the inclination estimation data and the target relative displacement data based on the matching relationship between the displacement calibration data and the target relative displacement data.

4. The method according to claim 3, characterized in that The determination of the matching relationship between the inclination estimation data and the target relative displacement data based on the matching relationship between the displacement calibration data and the target relative displacement data includes: In the case of determining that the displacement calibration data does not match the target relative displacement data, determine the historical change situation of the displacement calibration data; Determine whether the historical change situation of the displacement calibration data is consistent with the historical change situation of the target relative displacement data; In the case where the historical change of the displacement calibration data is inconsistent with the historical change of the target relative displacement data, it is determined that the tilt prediction data does not match the target relative displacement data.

5. The method according to claim 3, characterized in that, After determining whether the tilt prediction data matches the target relative displacement data, it further includes: In the case where the tilt prediction data does not match the target relative displacement data, it is determined whether there is a first calibration caisson corresponding to the target dock caisson, the first calibration caisson is adjacent to the target dock caisson and is symmetric with the associated dock caisson with respect to the target dock caisson; In the case where there is the first calibration caisson, the tilt prediction data is subjected to anomaly verification based on the first calibration relative displacement data collected by the displacement sensor provided between the first calibration caisson and the target dock caisson, and a target anomaly verification result is obtained; In the case where the target anomaly verification result indicates that the tilt prediction data is abnormal, it is determined that the tilt sensor provided on the target dock caisson is abnormal.

6. The method according to claim 5, wherein After the tilt prediction data is subjected to anomaly verification based on the first calibration relative displacement data collected by the displacement sensor provided between the first calibration caisson and the target dock caisson, it further includes: In the case where the target anomaly verification result indicates that the tilt prediction data is not abnormal, it is determined whether there is a second calibration caisson corresponding to the associated dock caisson, the second calibration caisson is adjacent to the associated dock caisson and is symmetric with the target dock caisson with respect to the associated dock caisson; In the case where there is the second calibration caisson, the associated tilt state data collected by the tilt sensor provided on the associated dock caisson is subjected to anomaly verification based on the second calibration relative displacement data collected by the displacement sensor provided between the second calibration caisson and the associated dock caisson, and an associated anomaly verification result is obtained; In the case where the associated anomaly verification result indicates that the associated tilt state data is not abnormal, it is determined that the position sensor between the target dock caisson and the associated dock caisson is abnormal.

7. The method according to claim 5, characterized in that After determining whether there is a first calibration caisson corresponding to the target dock caisson, it further includes: In the case where there is no first calibration caisson, the calibrated tilt state data collected by the tilt calibration component provided on the target dock caisson is obtained; Determine whether the target tilt state data matches the calibrated tilt state data; In the case where the target tilt state data does not match the calibrated tilt state data, it is determined that the tilt monitoring of the target dock caisson is abnormal.

8. The method according to claim 1, wherein Determining the associated dock caisson based on the tilt prediction data includes: Determining the tilt direction of the target dock caisson based on the tilt prediction data; The dock caisson adjacent to the target dock caisson and located in the tilt direction is determined as the associated dock caisson.

9. A risk perception system for a quay caisson, characterized in that, The system includes a controller, as well as an inclination sensor and a displacement sensor that are signal-connected to the controller; the inclination sensor is disposed on the wharf caisson for monitoring the inclination state data of the wharf caisson; the displacement sensor is disposed between adjacent wharf caissons for monitoring the relative displacement data between the wharf caissons; the controller is used to execute the wharf caisson risk perception method according to any one of claims 1 to 8.

10. An electronic device, characterized in that, The electronic device includes: At least one processor; A memory; At least one application program, wherein at least one application program is stored in the memory and is configured to be executed by at least one processor, and the at least one application program is configured to: execute the wharf caisson risk perception method according to any one of claims 1 to 8.

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