An abnormality early warning method and system for a slope adjuster

Abstract

The embodiment of the specification provides an abnormality early warning method and system of a slope adjuster, the method comprises the following steps: receiving a slope design parameter input by a user, and generating a slope adjusting instruction; acquiring positioning information of a driving device and the slope adjuster through a wireless router, and generating a moving instruction of the driving device; sending the slope adjusting instruction and the moving instruction to the driving device through the wireless router, and controlling the driving device to go to a position where the slope adjuster is located based on the moving instruction, and then adjusting a working parameter of the slope adjuster based on the slope adjusting instruction; acquiring sensing information of the slope adjuster through the wireless router, the sensing information at least comprising pressure sensing information; sending the sensing information to a server through the wireless router, so that the server determines an abnormal time point based on the sensing information; in response to receiving the abnormal time point determined by the server, displaying early warning information, the early warning information comprising a simulation image of the slope adjuster and an abnormal simulation point.

Description

[0001] Divisional Statement

[0002] This application is a divisional application of the Chinese application with the application number 202310723309.2 and the application date of 2023-06-19, and the title of "A control method and system of a slope adjuster". TECHNICAL FIELD

[0003] The present specification relates to the technical field of precast beam construction, in particular to an abnormal early warning method and system of a slope adjuster. BACKGROUND

[0004] In the construction of the wedge-shaped block at the end of the precast beam, the beam bottom slope is a key factor related to the quality of the precast beam construction. In the prior art, the hinged slope adjuster is provided with screw rod lifting devices at four corners. When the slope of the precast beam changes, in order to prevent the support from being biased and local stress concentration from occurring, the lifting height of the adjustable screw rod can be adjusted to adjust the longitudinal slope of the top plate to adapt to the slope required by the embedded steel plate. However, the operation needs to adjust the lifting devices of the screw rods at the four corners, and the operation is relatively cumbersome. The common slope adjuster realizes the inclination of the steel plate by manually adjusting the height of the screw rod. Manual adjustment needs to be measured while adjusting, which is time-consuming and inconvenient, and is not conducive to the future realization of unmanned precast of the beam body.

[0005] Therefore, providing an abnormal early warning method and system of a slope adjuster helps to improve the slope adjustment efficiency and reduce the labor cost. SUMMARY

[0006] One of the embodiments of the present specification provides an abnormality early warning method of a slope adjuster, the method comprising: receiving a slope design parameter input by a user, generating a slope adjustment instruction, the slope adjustment instruction comprising at least an operating parameter of the slope adjuster; obtaining positioning information of a driving device and the slope adjuster through a wireless router; generating a movement instruction of the driving device based on the positioning information, the movement instruction comprising a movement route of the driving device; sending the slope adjustment instruction and the movement instruction to the driving device through the wireless router, controlling the driving device to go to a position where the slope adjuster is located based on the movement instruction, and adjusting the operating parameter of the slope adjuster based on the slope adjustment instruction; obtaining sensing information of the slope adjuster through the wireless router, the sensing information comprising pressure sensing information and temperature sensing information obtained by a pressure sensor and a temperature sensor arranged on the slope adjuster; sending the sensing information to a server through the wireless router, the server processing a slope adjuster distribution graph through an abnormality identification model, and outputting abnormality probabilities of each node in the slope adjuster distribution graph at multiple time points, the abnormality identification model being a graph neural network model, the slope adjuster distribution graph being constructed based on slope adjuster distribution parameters, the sensing information at the multiple time points, a theoretical load bearing sequence, and distances between two contact points of each two slope adjusters and a pre-buried steel plate, the node corresponding to the slope adjuster; determining an abnormal time point based on the abnormality probabilities of the each node in the slope adjuster distribution graph at the multiple time points; in response to receiving the abnormal time point determined by the server, displaying early warning information, the early warning information comprising a simulation image of the slope adjuster and an abnormal simulation point.

[0007] One of the embodiments of the present specification provides an abnormality early warning system of a slope adjuster, the system comprising: a receiving module configured to receive a slope design parameter input by a user, generate a slope adjustment instruction, the slope adjustment instruction comprising at least an operating parameter of the slope adjuster; a positioning module configured to: obtain positioning information of a driving device and the slope adjuster through a wireless router; generate a movement instruction of the driving device based on the positioning information, the movement instruction comprising at least a movement route of the driving device; an operating module configured to send the slope adjustment instruction and the movement instruction to the driving device through the wireless router, control the driving device to go to a location of the slope adjuster based on the movement route, and adjust the operating parameter of the slope adjuster based on the slope adjustment instruction; an early warning module configured to: obtain sensing information of the slope adjuster through the wireless router, the sensing information comprising pressure sensing information and temperature sensing information obtained by a pressure sensor and a temperature sensor arranged on the slope adjuster; send the sensing information to a server through the wireless router, the server processes a slope adjuster distribution graph through an abnormality identification model, and outputs an abnormality probability of each node in the slope adjuster distribution graph at multiple time points, the abnormality identification model is a graph neural network model, the slope adjuster distribution graph is constructed based on slope adjuster distribution parameters, the sensing information at the multiple time points, a theoretical load bearing sequence, and a distance between two contact points of each two slope adjusters and a pre-buried steel plate, the node corresponds to the slope adjuster; determine an abnormal time point based on the abnormality probability of each node in the slope adjuster distribution graph at the multiple time points; in response to receiving the abnormal time point determined by the server, display early warning information, the early warning information comprising a simulation image of the slope adjuster and an abnormal simulation point.

[0008] One of the embodiments of the present specification provides an abnormality early warning device of a slope adjuster, the device comprising at least one processor and at least one memory; the at least one memory is configured to store computer instructions; the at least one processor is configured to execute the computer instructions to implement an abnormality early warning method of a slope adjuster.

[0009] One of the embodiments of the present specification provides a computer readable storage medium, the storage medium stores computer instructions, when the computer instructions are executed by a computer, an abnormality early warning method of a slope adjuster is implemented. BRIEF DESCRIPTION OF DRAWINGS

[0010] The present specification will be further illustrated in the form of exemplary embodiments, which will be described in detail with reference to the drawings. These embodiments are not limiting, and in these embodiments, the same numbers represent the same structures, wherein:

[0011] Figure 1 is an application scenario schematic diagram of a slope adjuster control system according to some embodiments of the present specification;

[0012] Figure 2 is an exemplary flowchart of a slope adjuster control method according to some embodiments of the present specification;

[0013] Figure 3 is an exemplary schematic diagram of applying a service life prediction model according to some embodiments of the present specification;

[0014] Figure 4 is an exemplary schematic diagram of applying an anomaly identification model according to some embodiments of the present specification. DETAILED DESCRIPTION

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some examples or embodiments of the present specification, and for those skilled in the art, the present specification can be applied to other similar scenarios without creative labor on the basis of these drawings. Unless it is clear from the language context or otherwise indicated, the same reference numbers in the drawings represent the same structures or operations.

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

[0017] As shown in the specification and claims, unless the context clearly indicates otherwise, "one", "a", "an", and / or "the" do not refer to the singular, but can also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements explicitly identified, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements.

[0018] Flowcharts are used in the present specification to illustrate the operations performed by the system according to the embodiments of the present specification. It should be understood that the preceding or subsequent operations are not necessarily performed in sequence. On the contrary, each step can be processed in reverse order or simultaneously. At the same time, other operations can be added to these processes, or one or more steps of the operation can be removed from these processes.

[0019] Figure 1 is an exemplary schematic diagram of the application scenario of the control system of the slope adjuster according to some embodiments of the present specification. As Figure 1As shown, the application scenario of the slope adjuster control system 100 can include a wireless router 110, a driving device 120, a slope adjuster 130, a server 140, a processor 150, a memory 160, and a terminal 170.

[0020] The wireless router 110 can be used to connect the components of the slope adjuster control system 100 and / or connect the system with external resource parts. The wireless router 110 enables communication between the components and other parts outside the system, facilitating exchange of data and / or information. For example, the terminal 170 can obtain positioning information of the driving device and the slope adjuster through the wireless router 110, and obtain sensing information of the slope adjuster through the wireless router 110. For another example, the terminal 170 can send slope adjustment instructions and movement instructions to the driving device through the wireless router 110, and send sensing information to the server.

[0021] The driving device 120 can adjust the slope adjuster 130. In some embodiments, the driving device 120 can adjust the working parameters of the slope adjuster 130. For example, the driving device 120 can obtain slope adjustment instructions sent by the terminal 170 through the wireless router 110, and adjust the working parameters of the slope adjuster 130 based on the slope adjustment instructions.

[0022] In some embodiments, the driving device 120 can move freely. For example, the driving device 120 obtains movement instructions sent by the wireless router 110, and goes to a specified location based on the movement instructions. For example, the specified location can be the location of the slope adjuster 130.

[0023] In some embodiments, the driving device 120 can have a positioning unit (which is used to obtain satellite information) and a communication unit (which is used to communicate with the wireless router 110) inside. The positioning information of the driving device 120 can be obtained through the built-in positioning unit, and the positioning information of the driving device 120 can be sent to the terminal 170 through the built-in communication unit and the wireless router 110.

[0024] The slope adjuster 130 can be used to adjust the slope size of the precast beam. When producing the precast beam, a pre-embedded steel plate is usually arranged below the precast beam. The slope adjuster 130 can adjust the slope size of the precast beam by adjusting the inclination angle of the pre-embedded steel plate.

[0025] In some embodiments, the slope adjuster 130 can be a numerical control hinged slope adjuster.

[0026] In some embodiments, the numerical control hinged slope adjuster can adjust the slope size of the precast beam by communicating with the terminal 170 (e.g., numerical control device). In some embodiments, the numerical control hinged slope adjuster can include a transmission rod, a numerical control device, and a hinged slope adjuster, the transmission rod connecting the numerical control device and the hinged slope adjuster, the upper hinge seat plate and the lower hinge seat plate of the hinged slope adjuster being mechanically connected by a pin shaft between the upper hinge seat steel plate single hinge ear and the lower hinge seat steel plate double hinge ear, and a screw jack being installed on the lower hinge seat plate; the screw jack is adjusted by the spline sleeve to meet the design longitudinal slope requirement.

[0027] An exemplary design longitudinal slope of the precast beam embedded steel plate under the numerical control hinged slope adjuster includes: connecting the numerical control device and the hinged slope adjuster together through the transmission rod, manually inputting the design longitudinal slope of the precast beam support embedded steel plate on the numerical control device, then operating the numerical control device, driving the transmission rod connected with the hinged slope adjuster by the motor in the numerical control device, synchronously lifting the screw jack in the hinged slope adjuster, until the upper hinge seat plate of the hinged slope adjuster is adjusted to the target position. The device has a simple overall structure, and accurately controls the design longitudinal slope of the precast beam support embedded steel plate through the numerical control hinged slope adjuster.

[0028] In some embodiments, the slope adjuster 130 can have a positioning unit (which is used to obtain satellite information) and a communication unit (which is used to communicate with the wireless router 110) inside. The positioning information of the slope adjuster 130 can be obtained through the built-in positioning unit, and the positioning information of the slope adjuster 130 can be sent to the terminal 170 through the built-in communication unit and the wireless router 110.

[0029] The server 140 can be used to manage resources and process data from at least one component of the slope adjuster control system 100 or an external data source (e.g., a cloud data center). For example, the server 140 receives the positioning information of the driving device 120 and the slope adjuster 130, and processes the data to generate the movement instruction of the driving device 120.

[0030] In some embodiments, the server 140 can be a stand-alone server or a server group. The server group can be centralized or distributed (e.g., the server 140 can be a distributed system), and can be dedicated or simultaneously provided by other devices or systems. In some embodiments, the server 140 can be regional or remote. In some embodiments, the server 140 can be implemented on a cloud platform or provided in a virtual manner.

[0031] Processor 150 can be used to execute at least a portion of computer instructions to control ramp adjuster 130. Processor 150 can process data and / or information obtained from other devices or system components. Processor 150 can execute program instructions based on this data, information, and / or processing results to perform one or more functions described in this application.

[0032] In some embodiments, processor 150 may include one or more sub-processing devices (e.g., a single-core processing device or a multi-core multi-chip processing device). By way of example only, processor 150 may include a central processing unit (CPU), a digital signal processor (DSP), or any combination thereof. In some embodiments, processor 150 may be a component of terminal 170.

[0033] Memory 160 can be used to store data and / or computer instructions. In some embodiments, computer instructions may include ramp instructions and move instructions. Memory 160 may include one or more storage components, each of which may be a separate device or part of another device. In some embodiments, memory 160 may include random access memory (RAM), read-only memory (ROM), mass storage, removable memory, and any combination thereof. In some embodiments, memory 160 may be implemented on a cloud platform. In some embodiments, memory 160 may be a component of terminal 170.

[0034] Terminal 170 refers to one or more terminal devices or software used by the user.

[0035] In some embodiments, the terminal 170 can perform instruction conversion according to algorithm settings. For example, the terminal 170 can obtain the slope design parameters input by the user and convert them into slope adjustment instructions.

[0036] In some embodiments, terminal 170 may be one or any combination of other devices with input and / or output functions, such as CNC equipment, mobile equipment, tablet computer, laptop computer, desktop computer, etc.

[0037] It should be noted that the above description of the application scenarios of the slope adjuster control system 100 is for ease of description only and should not limit this specification to the scope of the embodiments described. It is understood that those skilled in the art, after understanding the principle of the slope adjuster control system 100, may arbitrarily combine the various components without departing from this principle.

[0038] Figure 2 This is an exemplary flowchart of a control method for a slope adjuster according to some embodiments of this specification. Figure 2As shown, the flow 200 includes the following steps. In some embodiments, the flow 200 can be performed by the terminal.

[0039] At step 210, a slope design parameter input by a user is received, and a slope adjustment instruction is generated.

[0040] The slope design parameter refers to a design slope when the precast beam is installed. In some embodiments, the slope design parameter can include a bridge deck transverse slope, a bridge deck longitudinal slope, and the like.

[0041] In some embodiments, the slope design parameter can be calculated by the user (for example, a relevant technical personnel) according to the bridge design, the site construction condition, and the like.

[0042] In some embodiments, the user can input the determined slope design parameter into the terminal (for example, the terminal 170). For example, the user can input the determined slope design parameter into a numerical control device, a mobile terminal, and the like.

[0043] The slope adjustment instruction refers to an operation instruction for adjusting the slope adjuster. In some embodiments, the slope adjustment instruction at least includes a working parameter of the slope adjuster. In some embodiments, when the number of slope adjusters includes multiple, the slope adjustment instruction can include the working parameters of the respective slope adjusters.

[0044] The working parameter of the slope adjuster refers to a structural parameter of the slope adjuster. For example, the working parameter of the slope adjuster can include an inclination angle of a top plate of the slope adjuster, and the like.

[0045] In some embodiments, the terminal can convert the slope design parameter input by the user based on a preset algorithm to obtain the working parameter of the slope adjuster. The preset algorithm can be based on mechanical principles, structural principles, and the like to convert the slope design parameter to obtain the working parameter of the slope adjuster.

[0046] In some embodiments, the terminal can generate a corresponding slope adjustment instruction based on the working parameter of the slope adjuster.

[0047] At step 220, the positioning information of the driving device and the slope adjuster is obtained through the wireless router.

[0048] For more information about the wireless router, the driving device, and the slope adjuster, see Figure 1 .

[0049] The positioning information refers to the location information. The positioning information of the driving device is referred to as the initial position, and the positioning information of the slope adjuster is referred to as the end position.

[0050] In some embodiments, the positioning information comprises at least longitude and latitude information of the locations where the driving device and / or the slope adjuster are located. In some embodiments, the terminal can determine the initial position and the end position by a wireless router and a positioning technology. Exemplary positioning technologies can include, but are not limited to, any combination of one or more of a Global Positioning System (GPS), a satellite positioning technology, etc.

[0051] At step 230, a movement instruction of the driving device is generated based on the positioning information.

[0052] The movement instruction refers to an instruction for controlling the movement of the driving device. For example, the movement instruction can control the driving device to move from the initial position to the end position.

[0053] In some embodiments, the movement instruction comprises at least a movement route of the driving device.

[0054] In some embodiments, the movement route comprises a path of the driving device from the initial position to the end position. In some embodiments, the number of the slope adjusters comprises a plurality, the end position comprises a plurality, and the movement route further comprises a path of the driving device from one end position to another end position.

[0055] In some embodiments, the terminal can process the positioning information of the driving device and the slope adjusters to obtain the movement route of the driving device. For example, the terminal can sort the end positions of the plurality of slope adjusters in ascending order according to the distances between the driving device and the slope adjusters, and generate a corresponding movement route according to the position sorting.

[0056] In some embodiments, the terminal can generate a corresponding slope adjustment instruction based on the movement route of the driving device.

[0057] At step 240, the slope adjustment instruction and the movement instruction are sent to the driving device through the wireless router to control the driving device to move to the locations where the slope adjusters are located based on the movement instruction, and to adjust the working parameters of the slope adjusters based on the slope adjustment instruction.

[0058] In some embodiments, after the terminal 170 sends the movement instruction to the driving device 120 via the wireless router 110, the driving device 120 moves from the initial position to one or more end positions according to the set movement route based on the movement instruction.

[0059] In some embodiments, after the terminal 170 sends the slope adjustment instruction to the driving device 120 via the wireless router 110, the driving device 120 can adjust the slope adjuster to the set working parameter based on the slope adjustment instruction.

[0060] In some embodiments of the present specification, the driving device 120 adjusts the slope adjuster according to the set parameters, and the user only needs to input the required design slope, without any other operation, so that the adjustment operation is simpler, the work efficiency is improved, and the labor cost is greatly reduced.

[0061] It should be noted that the above description of the process is only for example and illustration, and does not limit the scope of the present specification. Those skilled in the art can make various modifications and changes to the process under the guidance of the present specification. However, these modifications and changes are still within the scope of the present specification.

[0062] In some embodiments, the number of slope adjusters is at least two. In some embodiments, the terminal can send the received slope design parameter to the server through the wireless router, so that the server determines the slope adjuster distribution parameter based on at least the slope design parameter, the slope adjuster distribution parameter including the deployment position of the at least two slope adjusters; receive the server-determined slope adjuster distribution parameter, and based on the slope adjuster distribution parameter, deploy the at least two slope adjusters to the corresponding deployment position.

[0063] The slope adjuster distribution parameter refers to a parameter related to the distribution of multiple slope adjusters. In some embodiments, the slope adjuster distribution parameter includes the deployment position of each slope adjuster.

[0064] The deployment position refers to the installation position of the slope adjuster. For example, the slope adjuster is deployed at the four corners of the precast beam.

[0065] The server can determine the slope adjuster distribution parameter based on the slope design parameter in various ways. In some embodiments, the server can preset the corresponding relationship between different slope design parameters and different slope adjuster distribution parameters based on prior knowledge or historical data, etc., and determine the current slope adjuster distribution parameter based on the slope design parameter and the corresponding relationship.

[0066] In some embodiments, the server can determine the slope adjuster distribution parameter based on at least one of the structural features, material features, pouring features, and slope design parameters of the target object.

[0067] In some embodiments, the target object refers to a precast beam to be adjusted in slope.

[0068] The structural feature refers to a feature related to the physical structure of the target object. For example, the structural feature can include the cross-sectional height, width, and thickness, etc.

[0069] The material feature refers to a feature related to the material composition of the target object. For example, the material feature can include the composition of the reinforcing steel, steel plate, etc.

[0070] The pouring feature refers to a feature related to the pouring of the target object. For example, the pouring feature can include a pouring method (e.g., layer-by-layer pouring, etc.), a pouring speed, a pouring temperature, a pouring amount, etc.

[0071] The structural feature, the material feature, and the pouring feature can be obtained through user input, database query, etc.

[0072] The server can determine the slope adjuster distribution parameter in various ways. In some embodiments, the server can determine the slope adjuster distribution parameter through a vector database based on at least one of the structural feature, the material feature, the pouring feature, and the slope design parameter of the target object.

[0073] In some embodiments, a target feature vector can be determined based on at least one of the structural feature, the material feature, the pouring feature, and the slope design parameter of the target object. In some embodiments, an associated feature vector can be determined through a vector database based on the target feature vector.

[0074] The vector database refers to a database for storing, indexing, and querying vectors. In some embodiments, the vector database can include a reference feature vector corresponding to a distribution object, the distribution object being a slope adjuster distribution parameter, and the reference feature vector being a feature vector constructed based on at least one of the structural feature, the material feature, the pouring feature, and the slope design parameter of the target object.

[0075] In some embodiments, the correspondence between the reference feature vector in the vector database and the distribution object can be determined based on stability. For example, for a certain reference feature vector, a slope adjuster distribution parameter that can make the stability meet a stability condition can be selected as the distribution object corresponding to the reference feature vector. The stability refers to the stability degree of the slope adjuster during the pouring process. For example, the stability degree indicates the probability of the slope adjuster falling over during the pouring process.

[0076] In some embodiments, the stability can be obtained based on experiments. For example, a certain reference feature vector can be used as an experimental condition, a plurality of slope adjusters can be deployed according to the slope adjuster distribution parameter, the proportion of the number of times of the precast beam falling over to the total number of deployments can be counted, and the stability can be determined according to the conversion relationship between the proportion and the stability.

[0077] In some embodiments, the stability can also be obtained based on simulation software. For example, a certain reference feature vector can be used as a simulation condition, a plurality of slope adjusters can be deployed according to the slope adjuster distribution parameter, the proportion of the number of times of the precast beam falling over to the total number of deployments can be counted, and the stability can be determined according to the conversion relationship between the proportion and the stability. At the same time, external force factors (e.g., wind force, etc.) can also be applied irregularly during the simulation.

[0078] In some embodiments, the target feature vector can be used to determine a reference feature vector meeting a preset condition from the vector database, and the reference feature vector meeting the preset condition can be determined as the associated feature vector. The distribution object corresponding to the associated feature vector can be determined as the slope adjuster distribution parameter corresponding to the target feature vector. The preset condition can refer to a judgment condition for determining the associated feature vector. In some embodiments, the preset condition can include a vector distance less than a distance threshold, a minimum vector distance, etc.

[0079] In some embodiments of the present specification, the slope adjuster distribution parameter can be determined by at least one of the structural feature, the material feature, the pouring feature of the target object, and the slope design parameter, so as to accurately adjust the slope of the target object, and thus the slope of the target object meets the design requirement.

[0080] In some embodiments, the server can generate a plurality of candidate slope adjuster distribution parameters based on the matching result of the vector database, and determine the slope adjuster distribution parameter based on the service life distribution of each candidate slope adjuster distribution parameter.

[0081] The candidate slope adjuster distribution parameter refers to a parameter that may be confirmed as the slope adjuster distribution parameter. In some embodiments, the server can match a plurality of associated feature vectors meeting a preset condition through the vector database to obtain corresponding slope adjuster distribution parameters, and randomly move each slope adjuster by a small amplitude to generate a plurality of candidate slope adjuster distribution parameters.

[0082] The service life distribution refers to the remaining service life of each slope adjuster under the candidate slope adjuster distribution parameter.

[0083] The server can determine the service life distribution in various ways. In some embodiments, the server can preset the corresponding relationship between the historical use times, the different historical load sequences, the different current load positions, and the different service lives (i.e., the remaining service life after completing the current load) of different slope adjusters based on prior knowledge or historical data, etc. The service life of a certain slope adjuster can be determined based on the historical use times, the historical load sequences, the current load position of the slope adjuster, and the corresponding relationship. After determining the service life of each of the plurality of slope adjusters, the service life distribution can be obtained by comprehensively considering the service life of all slope adjusters. The current load position refers to the position of the slope adjuster relative to the precast beam during the current use. The historical load sequence refers to the theoretical load sequence during the historical use. For more information about the theoretical load sequence, see the relevant description below.

[0084] In some embodiments, the server can also determine the service life distribution through a machine learning model. For more information about this embodiment, see Figure 3 .

[0085] In some embodiments, the server can select, as the preferred slope adjuster distribution parameter, the candidate slope adjuster distribution parameter corresponding to the longest overall remaining service life of the service life distribution or the most uniform service life distribution, and the like. The overall remaining service life refers to the sum of the remaining service lives of the plurality of slope adjusters in the service life distribution.

[0086] In some embodiments of the present specification, by considering the service life distribution of each candidate slope adjuster distribution parameter to determine the preferred slope adjuster distribution parameter, the situation of each candidate slope adjuster distribution parameter can be better understood, thereby reducing the safety hazards in the slope adjustment process of the precast beam.

[0087] In some embodiments, the server can send the determined slope adjuster distribution parameter to the terminal, and the terminal can control the driving device to deploy the at least two slope adjusters to the corresponding deployment positions based on the slope adjuster distribution parameter.

[0088] In some embodiments of the present specification, the slope adjuster distribution parameter is determined based on the slope design parameter, and the slope adjusters are deployed according to the slope adjuster distribution parameter, which can improve the stability of the slope adjusters, the accuracy and quality of the precast beam, and the production efficiency and reduce the production cost.

[0089] Figure 3 is an exemplary schematic diagram of applying the service life prediction model according to some embodiments of the present specification.

[0090] Referring to Figure 3 In some embodiments, the server can predict the estimated remaining service life of each slope adjuster under each set of candidate slope adjuster distribution parameters by using the service life prediction model 330 to obtain the service life distribution 340. For example, the service life distribution 340 includes the estimated service life 341 of the slope adjuster 1, …, the estimated remaining service life 342 of the slope adjuster n.

[0091] The service life prediction model 330 can be a machine learning model. For example, a Deep Neural Networks (DNN) type, a Convolutional Neural Networks (CNN) model, or the like, or any combination thereof.

[0092] In some embodiments, the input of the service life prediction model 330 can include the historical use features 320 and the current use features 310 of the slope adjuster, and the output is the estimated remaining service life of the slope adjuster.

[0093] The historical use features refer to the features related to the historical use of the slope adjuster.

[0094] In some embodiments, the historical usage features include at least a historical usage times. Correspondingly, the historical usage features 320 input to the service life prediction model 330 can further include a historical usage times 321.

[0095] In some embodiments, the historical usage features of the slope adjuster further include historical cumulative usage data of the slope adjuster. Correspondingly, the historical usage features 320 input to the service life prediction model 330 can further include a historical cumulative usage data 322.

[0096] The historical cumulative usage data refers to data related to each historical usage. The historical cumulative usage data can include a historical load data sequence and a historical slope angle data sequence. The historical load data sequence includes a sequence of load data (e.g., at least one of load amount and load position) at each historical usage. The historical slope angle data sequence includes a sequence of slope angle data at each historical usage. In some embodiments, the historical cumulative usage data can be obtained based on historical usage data statistics of the slope adjuster.

[0097] In some embodiments of the present disclosure, the historical usage features of the slope adjuster further include historical cumulative usage data of the slope adjuster, which can provide valuable reference information for the estimated remaining service life of the slope adjuster.

[0098] The current usage features refer to features related to the current usage of the slope adjuster. In some embodiments, the current usage features include at least a slope angle. Correspondingly, the current usage features 310 input to the service life prediction model 330 can further include a slope angle 311.

[0099] In some embodiments, the current usage features of the slope adjuster further include a theoretical load sequence. Correspondingly, the current usage features 310 input to the service life prediction model 330 can further include a theoretical load sequence 312.

[0100] The theoretical load sequence refers to a sequence of load data of the slope adjuster at each time point from the start of pouring concrete to the solidification of the concrete.

[0101] In some embodiments, the theoretical load sequence can be determined based on the deployment position of the slope adjuster and the pouring characteristics. The deployment position of the slope adjuster can be the deployment position of each candidate slope adjuster in the candidate slope adjuster distribution parameters.

[0102] In some embodiments, the server can preset a correspondence between different slope adjuster deployment positions, different pouring characteristics, and different theoretical load sequences based on historical data, etc., determine the current theoretical load sequence based on the current slope adjuster deployment position, the pouring characteristics, and the correspondence. In some embodiments, the server can also determine the theoretical load sequence based on simulation experiments.

[0103] In some embodiments of the present specification, the current use features of the slope adjuster further include a theoretical load bearing sequence, which can provide valuable reference information for the estimated remaining service life of the slope adjuster.

[0104] In some embodiments, the service life prediction model 330 can be trained by a plurality of first training samples with first labels.

[0105] In some embodiments, the first training sample can include sample historical use features and sample current use features of the sample slope adjuster, and the first label can be the sample estimated remaining service life. In some embodiments, the first training sample can be obtained by historical data analysis, for example, the time interval between the actual retirement time of the sample slope adjuster in the historical data and the corresponding historical time is taken as the first label corresponding to the first training sample. Wherein, the corresponding historical time refers to the historical time corresponding to the sample current use features in the first training sample.

[0106] In some embodiments of the present specification, by processing the historical use features and the current use features using the service life prediction model, the self-learning ability of the machine learning model can be utilized to find the rules from a large amount of slope adjuster use data, obtain the correlation between the estimated remaining service life and the use features, and improve the accuracy and efficiency of determining the estimated remaining service life.

[0107] In some embodiments, the terminal can obtain the sensing information of the slope adjuster through the wireless router; send the sensing information to the server through the wireless router, so that the server determines the abnormal time point based on the sensing information; in response to receiving the abnormal time point determined by the server, display the warning information.

[0108] The sensing information refers to the information obtained by the sensor arranged on the slope adjuster. In some embodiments, at least one pressure sensor is arranged on the slope adjuster, and correspondingly, the sensing information at least includes pressure sensing information.

[0109] In some embodiments, a temperature sensor is further arranged on the slope adjuster, and correspondingly, the sensing information further includes temperature sensing information.

[0110] In some embodiments of the present specification, a temperature sensor is further arranged on the slope adjuster, which can monitor the temperature change of the surface of the slope adjuster in real time, improve the dimension of the monitoring information, and facilitate more accurate warning later.

[0111] The abnormal time point refers to the time when the sensing information is abnormal.

[0112] The server can determine the abnormal time point in various ways. In some embodiments, the server can determine the normal range of various sensing information based on historical data, and determine the time corresponding to the sensing information exceeding the normal range as the abnormal time point.

[0113] In some embodiments, the server can determine the abnormal time point 440 through the abnormality identification model 420.

[0114] Figure 4 is an exemplary schematic diagram of applying an abnormality identification model according to some embodiments of the present specification.

[0115] The abnormality identification model 420 is a machine learning model. In some embodiments, the abnormality identification model 420 is a graph neural network model (GNN). The abnormality identification model 420 can also be other graph models, such as a graph convolutional neural network model (GCNN), or adding other processing layers to the graph neural network model, modifying the processing method thereof, etc.

[0116] In some embodiments, the input of the abnormality identification model includes the slope adjuster distribution graph 410, and the output includes the abnormal probability 430 of each node in the slope adjuster distribution graph at each time point. Each time point is a historical time point. is the abnormal probability 430 of the corresponding node at each time point corresponding to the node output in the GNN. The abnormal probability 430 can be in the form of a vector, including the abnormal probability of the corresponding node at each time point.

[0117] In some embodiments, the server can construct the slope adjuster distribution graph 410 based on the slope adjuster distribution parameters 411, the sensing information 412 at multiple time points, the theoretical load bearing sequence 413, and the distance 414 between the two contact points of each two slope adjusters and the embedded steel plate. The slope adjuster distribution graph 410 is a data structure composed of nodes and edges, and the edges connect the nodes. The nodes and edges can have attributes. It should be noted that each slope adjuster can have one contact point with the preset steel plate, and the above distance 414 is the distance between the two contact points.

[0118] In some embodiments, the nodes of the slope adjuster distribution graph 410 can correspond to each slope adjuster. The node features can reflect the relevant attributes of the corresponding slope adjuster. For example, the node features include the sensing information 412 at multiple time points and the theoretical load bearing sequence 413. For more information about the theoretical load bearing sequence 413, see Figure 3 and related content.

[0119] In some embodiments, when the distance between the two contact points of the two slope adjusters and the embedded steel plate is less than a preset distance, there is an edge between the nodes corresponding to the two slope adjusters. The preset distance can be obtained by manual setting. The edge features can reflect the relevant attributes of the corresponding two slope adjusters. For example, the edge features include the distance 414 between the contact points of the two slope adjusters and the embedded steel plate.

[0120] In some embodiments, the edge feature of the slope adjuster distribution graph 410 further includes a thickness distribution 415 of the embedded steel plate located on the line between the two contact points of the two slope adjusters and the embedded steel plate.

[0121] The thickness distribution is used to describe the thickness distribution of the embedded steel plate between the two contact points. For example, the distance between the two contact points is D (unit: cm), the thickness of the embedded steel plate is H (unit: mm), and there is a hollow hole with a width of L (unit: cm) in the middle, then the thickness distribution is [(0, a1, H), (a1, a1+L, 0), (a1+L, a2, H)], where a1

[0122] The hollow structure can affect the force transmission. In some embodiments of the present specification, the edge feature further includes the thickness distribution of the embedded steel plate, which is beneficial to more accurately identify abnormal situations.

[0123] The features of the nodes and edges can be determined based on the basic data by various methods. The data source can be the method described in other embodiments, or other methods. The data can include current data, or historical slope adjustment data.

[0124] The anomaly identification model can be trained based on the second training sample with the second label. The second training sample can be a historical slope adjuster distribution graph determined based on historical data, and the second label can be obtained by manually labeling the fault tracing and fault positioning of the historical data, wherein the time label of the real fault occurrence is labeled as 1, and the closer the time label is to the time of the real fault occurrence, the closer the label can be to 1.

[0125] In some embodiments, the server can determine the time point 440 of the anomaly as the time point when the anomaly probability 430 is greater than the anomaly threshold. The anomaly threshold can be obtained by manual setting.

[0126] In some embodiments of the present specification, the interaction between the slope adjusters is considered when determining the anomaly probability of each slope adjuster at each time point, which can make the determined anomaly probability of each slope adjuster at each time point more consistent with the actual situation, and improve the accuracy of the anomaly time point.

[0127] In some embodiments, the early warning information comprises a simulation image of the slope adjuster and an abnormal simulation point. The abnormal simulation point refers to a point in the simulation image of the slope adjuster where the abnormality occurs. For example, the abnormal simulation point can be the position of the slope adjuster where the abnormality occurs. The simulation image of the slope adjuster refers to a simulation image generated by deploying a plurality of slope adjusters according to the distribution parameters of the slope adjusters, and reflecting the deployment situation. The simulation image of the slope adjuster can reflect the deployment positions and relative positions of the plurality of slope adjusters.

[0128] In some embodiments, the terminal can display the early warning information in response to receiving the abnormal time point determined by the server. The form of the early warning information can include, but is not limited to, sound and image, etc.

[0129] According to some embodiments of the present specification, by determining the abnormal time point based on the sensing information and displaying the early warning information to the operator in a timely manner, the safety during the slope adjustment process can be improved. The early warning information comprises a simulation image of the slope adjuster and an abnormal simulation point, which can help the operator better understand the abnormal situation.

[0130] Some embodiments of the present specification provide a control device of a slope adjuster. The device comprises at least one processor and at least one memory. The at least one memory is configured to store computer instructions. The at least one processor is configured to execute at least part of the computer instructions to implement the control method of the slope adjuster according to any one of the embodiments of the present specification.

[0131] Some embodiments of the present specification provide a computer readable storage medium. The storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the control method of the slope adjuster according to any one of the embodiments of the present specification.

[0132] The above has described the basic concepts. It is obvious that the above detailed disclosure is only used as an example and does not limit the present specification. Although it is not explicitly stated herein, those skilled in the art can make various modifications, improvements and corrections to the present specification. Such modifications, improvements and corrections are suggested in the present specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of the present specification.

[0133] Meanwhile, specific terms are used in the present specification to describe the embodiments of the present specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" means a certain feature, structure or characteristic related to at least one embodiment of the present specification. Therefore, it should be emphasized and noted that "an embodiment" or "one embodiment" or "one alternative embodiment" mentioned in different positions in the present specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present specification can be properly combined.

[0134] Furthermore, the order of the processing elements and sequences described in this specification are not intended to be construed as a limitation, unless specifically stated, but are included to provide a complete description of one or more embodiments of the present specification. Regardless of the particular sequence of processing elements, or the like, described in this specification, such sequence is included to provide a complete description of the embodiments of the specification, but is not intended to limit the scope or application of the specification. Although various embodiments of the disclosure have been discussed through various examples in the above disclosure, it should be understood that such details are merely for the purpose of illustration and additional claims can not be limited to the embodiments disclosed. Rather, the claims are intended to cover all modifications and equivalent arrangements that come within the spirit and scope of the embodiments of the specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented by software solutions, such as installing the described system on existing servers or mobile devices.

[0135] Similarly, it should be noted that, in order to simplify the description of the disclosure disclosed in this specification and to help understand one or more embodiments of the disclosure, in the foregoing description of the embodiments of the specification, various features are sometimes combined into one embodiment, figure or description thereof. However, this method of disclosure does not mean that the features required by the specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiments disclosed above.

[0136] Some embodiments use numerical values to describe components, quantities of attributes, and the like. It should be understood that such numerical values used in the description of the embodiments are, in some examples, modified by the adjectives "about", "approximately", or "generally". Unless otherwise stated, "about", "approximately", or "generally" indicates that the stated numerical value allows for a ±20% variation. Accordingly, in some embodiments, the numerical parameters in the specification and claims are approximations that can vary depending on the desired properties of the individual embodiments. In some embodiments, numerical parameters should be considered in the context of the number of significant digits and errors inherent to measurement using conventional measuring equipment. Although numerical ranges and parameters setting forth the broad scope of the embodiments of the specification are approximations, in specific embodiments, such numerical values are set forth as precisely as possible.

[0137] Each patent, patent application, patent publication, and other material, such as articles, books, specifications, publications, documents, and the like, cited in this specification are hereby incorporated by reference in their entirety for the purpose of providing clarity to the disclosure of the specification. Application history documents that are inconsistent with or otherwise contradictory to the content of this specification, and documents that limit the scope of the claims of this specification, whether currently or later attached hereto, are excluded to the extent of such inconsistency, contradiction, or limitation. It is specifically noted that, to the extent there is a description, definition, and / or terminology in the attached material that is inconsistent or otherwise in conflict with the description, definitions, and / or terminology set forth in this specification, the description, definitions, and / or terminology set forth in this specification shall control.

[0138] Finally, it should be understood that the embodiments described herein are only given by way of example and that other modifications can occur to persons skilled in the art. Therefore, the scope of the present description is not intended to be limited to the embodiments described herein but is only limited by the claims that follow.

Claims

1. A method for abnormality early warning of a slope adjuster, characterized in that, The method is executed by a terminal and includes: receiving a user-inputted slope design parameter, generating a slope adjustment instruction, the slope adjustment instruction including at least a working parameter of a slope adjuster; the number of the slope adjuster is at least two; sending the received slope design parameter to a server through a wireless router, so that the server determines a slope adjuster distribution parameter based on at least the slope design parameter, the slope adjuster distribution parameter including a deployment position of at least two slope adjusters, the slope adjuster distribution parameter being determined based on at least one of a structure feature, a material feature, a pouring feature of a target object and the slope design parameter, the target object referring to a prefabricated beam to be adjusted in slope; receiving the slope adjuster distribution parameter determined by the server, and deploying at least two slope adjusters to the corresponding deployment position based on the slope adjuster distribution parameter; obtaining positioning information of a driving device and the slope adjuster through the wireless router; generating a moving instruction of the driving device based on the positioning information, the moving instruction including a moving route of the driving device; sending the slope adjustment instruction and the moving instruction to the driving device through the wireless router, so that the driving device moves to a position where the slope adjuster is located based on the moving instruction, and adjusts the working parameter of the slope adjuster based on the slope adjustment instruction; obtaining sensing information of the slope adjuster through the wireless router, the sensing information including pressure sensing information and temperature sensing information obtained by a pressure sensor and a temperature sensor arranged on the slope adjuster; sending the sensing information to the server through the wireless router, the server processing a slope adjuster distribution graph through an anomaly identification model to output an abnormal probability of each node in the slope adjuster distribution graph at multiple time points, the anomaly identification model being a graph neural network model, the slope adjuster distribution graph being constructed based on the slope adjuster distribution parameter, the sensing information at multiple time points, a theoretical load bearing sequence and a distance between two contact points of each two slope adjusters and a pre-buried steel plate, the node corresponding to the slope adjuster; determining an abnormal time point based on the abnormal probability of each node in the slope adjuster distribution graph at the multiple time points; in response to receiving the abnormal time point determined by the server, displaying warning information, the warning information including a simulation image of the slope adjuster and an abnormal simulation point.

2. The method of claim 1, wherein, The node feature of the node includes the sensing information at multiple time points and the theoretical load bearing sequence, an edge is an edge between the nodes corresponding to two slope adjusters when a distance between the two contact points of the two slope adjusters and the pre-buried steel plate is less than a preset distance, and an edge feature includes the distance between the two contact points of the two slope adjusters and the pre-buried steel plate.

3. The method of claim 1, wherein, The method further includes: determining a target feature vector based on at least one of the structure feature, the material feature, the pouring feature and the slope design parameter of the target object; Based on the target feature vector, a matching result is determined by matching in a vector database, the vector database including reference feature vectors and corresponding slope adjuster distribution parameters, the matching result including a slope adjuster distribution parameter corresponding to the target feature vector; Based on the matching result, a plurality of candidate slope adjuster distribution parameters are generated; Based on the service life distribution of each of the plurality of candidate slope adjuster distribution parameters, the slope adjuster distribution parameter is determined.

4. An abnormality early warning system for a slope adjuster, characterized by comprising: The system comprises: A receiving module for receiving user input slope design parameters and generating slope adjustment instructions, the slope adjustment instructions including at least the working parameters of the slope adjuster; the number of slope adjusters is at least two; A determining module for sending the received slope design parameters to a server through a wireless router, so that the server determines a slope adjuster distribution parameter based on at least the slope design parameters, the slope adjuster distribution parameter including the deployment positions of at least two slope adjusters, the slope adjuster distribution parameter being determined based on at least one of the structural features, material features, pouring features of a target object and the slope design parameters, the target object being a precast beam to be sloped; A deployment module for receiving the slope adjuster distribution parameter determined by the server and deploying at least two slope adjusters to the corresponding deployment positions based on the slope adjuster distribution parameter; A positioning module for: Obtaining positioning information of a driving device and the slope adjuster through the wireless router; Generating a movement instruction of the driving device based on the positioning information, the movement instruction including at least the movement route of the driving device; A working module for sending the slope adjustment instructions and the movement instructions to the driving device through the wireless router, controlling the driving device to go to the position of the slope adjuster based on the movement route, and adjusting the working parameters of the slope adjuster based on the slope adjustment instructions; An early warning module for: Obtaining sensing information of the slope adjuster through the wireless router, the sensing information including pressure sensing information and temperature sensing information obtained by pressure sensors and temperature sensors arranged on the slope adjuster; Sending the sensing information to the server through the wireless router, the server processing a slope adjuster distribution graph through an anomaly recognition model to output an abnormal probability of each node in the slope adjuster distribution graph at multiple time points, the anomaly recognition model being a graph neural network model, the slope adjuster distribution graph being constructed based on the slope adjuster distribution parameter, the sensing information at multiple time points, a theoretical load sequence, and the distance between two contact points of each two slope adjusters and a pre-embedded steel plate, the node corresponding to the slope adjuster; Determining an abnormal time point based on the abnormal probability of each node in the slope adjuster distribution graph at the multiple time points; In response to receiving the abnormal time point determined by the server, displaying early warning information, the early warning information including a simulation image of the slope adjuster and an abnormal simulation point.

5. The system of claim 4, wherein, The node features of the nodes include the sensing information and the theoretical load-bearing sequence at multiple time points, and an edge is connected between two nodes corresponding to the two slope adjusters when the distance between the two contact points of the two slope adjusters and the embedded steel plate is less than a preset distance, and an edge feature includes the distance between the two contact points of the two slope adjusters and the embedded steel plate.

6. The system of claim 4, wherein, The determination module is further configured to: determine a target feature vector based on at least one of the structure feature, the material feature, the pouring feature, and the slope design parameter of the target object; match and determine a matching result in a vector database based on the target feature vector, the vector database including reference feature vectors and corresponding slope adjuster distribution parameters, and the matching result including a slope adjuster distribution parameter corresponding to the target feature vector; generate a plurality of candidate slope adjuster distribution parameters based on the matching result; determine the slope adjuster distribution parameter based on the service life distribution of each of the plurality of candidate slope adjuster distribution parameters.

7. An abnormal early warning device for a slope adjuster, characterized in that, The device includes at least one processor and at least one memory; The at least one memory is configured to store computer instructions; The at least one processor is configured to execute at least part of the computer instructions to implement the abnormality early warning method of the slope adjuster according to any one of claims 1-3.

8. A computer-readable storage medium, the storage medium storing computer instructions, when a computer reads the computer instructions in the storage medium, the computer executes the abnormality early warning method of the slope adjuster according to any one of claims 1-3.

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