CN117542171A - Data processing method and device for mud leakage alarm - Google Patents
Data processing method and device for mud leakage alarm Download PDFInfo
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- CN117542171A CN117542171A CN202311706712.0A CN202311706712A CN117542171A CN 117542171 A CN117542171 A CN 117542171A CN 202311706712 A CN202311706712 A CN 202311706712A CN 117542171 A CN117542171 A CN 117542171A
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Abstract
The invention discloses a data processing method and a device for mud leakage alarm, wherein the method comprises the following steps: acquiring first mud sensing data corresponding to a pile casing in real time through a first sensing sensor group arranged on the inner wall of the pile casing in a bridge pile foundation construction area; acquiring second mud sensing data corresponding to the mud pit in real time through a second sensing sensor group arranged on the inner wall of the mud pit in the bridge pile foundation construction area; determining current construction operation of construction equipment in the bridge pile foundation construction area in real time; and determining a mud leakage judging result of the bridge pile foundation construction area based on a data prediction algorithm according to the first mud sensing data, the second mud sensing data and the current construction operation, and executing corresponding alarm operation according to the mud leakage judging result. Therefore, the method can more intelligently and accurately predict the slurry leakage, improve the supervision effect of the construction engineering and reduce the occurrence of engineering accidents.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a data processing method and device for mud leakage alarm.
Background
Along with the acceleration of the construction process of the infrastructure construction in China, bridges are widely used in China. The pile foundation is an important component of bridge engineering, bears all loads transmitted by the upper structure and transmits the loads of the upper structure and the lower structure to the stressed rock stratum; in order to ensure the safety and normal use of the full bridge, the pile foundation must have sufficient strength, rigidity and overall stability so that the bridge does not generate excessive horizontal displacement and uneven settlement. Slurry quality of slurry is particularly important during pile foundation construction, and leakage of slurry during pile foundation construction can cause serious pollution to river channel areas.
Because of reserving the navigation channel, the steel trestle of the pile foundation operation platform cannot be connected to be effectively supported, stability is fully considered in the construction process, the environmental protection of slurry leakage is guaranteed, theoretical research on technical control of slurry leakage alarm in the pile foundation construction process is lacking at present, and continuous discussion analysis is needed. The water level of the partial water area rises along with the water level of the seasonal rainy period, the water level becomes larger when the flow speed is increased, pollution to river water after slurry leakage can be amplified infinitely, and the occurrence of underground river or karst cave can cause larger operation risks.
However, when the slurry leakage alarm is realized in the prior art, the slurry leakage alarm is generally realized only by a manual supervision mode, the labor cost is high, the prediction cannot be performed, and the effect is poor. It can be seen that the prior art has defects and needs to be solved.
Disclosure of Invention
The invention aims to solve the technical problem of providing a data processing method and device for mud leakage alarm, which can more intelligently and accurately predict mud leakage, improve the supervision effect of construction engineering and reduce engineering accidents.
In order to solve the technical problem, a first aspect of the present invention discloses a data processing method for mud leakage alarm, the method comprising:
acquiring first mud sensing data corresponding to a pile casing in real time through a first sensing sensor group arranged on the inner wall of the pile casing in a bridge pile foundation construction area;
acquiring second mud sensing data corresponding to the mud pit in real time through a second sensing sensor group arranged on the inner wall of the mud pit in the bridge pile foundation construction area;
determining current construction operation of construction equipment in the bridge pile foundation construction area in real time;
and determining a mud leakage judging result of the bridge pile foundation construction area based on a data prediction algorithm according to the first mud sensing data, the second mud sensing data and the current construction operation, and executing corresponding alarm operation according to the mud leakage judging result.
As an optional implementation manner, in the first aspect of the present invention, the first sensing sensor set and the second sensing sensor set include a liquid level sensor, a temperature sensor, a humidity sensor, and an image sensor; the construction equipment comprises at least one of drilling machine equipment, water gun equipment, transportation equipment and slurry pump equipment; the current construction operation includes at least one of a rig rotation operation, a water gun firing operation, a transportation operation, and a mud pump work operation.
As an optional implementation manner, in the first aspect of the present invention, the determining, in real time, a current construction operation of construction equipment in the bridge pile foundation construction area includes:
acquiring equipment communication data sent by construction equipment in the bridge pile foundation construction area;
acquiring equipment image data corresponding to the construction equipment through an image sensing sensor arranged in the bridge pile foundation construction area;
acquiring project plan data of a current construction project;
and determining the current construction operation of the construction equipment according to the equipment communication data, the equipment image data and the project plan data.
As an optional implementation manner, in the first aspect of the present invention, the determining, according to the device communication data, the device image data, and the project plan data, a current construction operation of the construction device includes:
determining a current project stage corresponding to the current time point according to the project plan data and the current time point;
determining a phase construction operation set corresponding to the current project phase according to the current project phase and the corresponding relation between the preset phase and the construction operation;
inputting the equipment communication data and the equipment image data into a trained construction operation prediction algorithm model to obtain a predicted construction operation set; the construction operation prediction algorithm model is obtained through training a training data set comprising a plurality of training equipment communication data and training equipment image data and corresponding construction operation labels; the prediction construction operation set comprises a plurality of construction operations and prediction probabilities corresponding to each construction operation;
calculating an intersection of the stage construction operation set and the prediction construction operation set to obtain an intersection operation set;
and determining the construction operation with the highest prediction probability in the intersection operation set as the current construction operation of the construction equipment.
As an optional implementation manner, in the first aspect of the present invention, the determining, based on a data prediction algorithm, a mud leakage determination result of the bridge pile foundation construction area according to the first mud sensing data, the second mud sensing data and the current construction operation includes:
screening a target prediction algorithm model corresponding to the current construction operation from a plurality of candidate prediction algorithm models according to the current construction operation; the candidate prediction algorithm model or the target prediction algorithm model is obtained through training a training data set comprising a plurality of training mud sensing data and corresponding leakage labels;
inputting the first mud sensing data into the target prediction algorithm model to obtain a first leakage prediction possibility corresponding to the first mud sensing data;
inputting the second slurry sensing data into the target prediction algorithm model to obtain second leakage prediction possibility corresponding to the second slurry sensing data;
and determining a mud leakage judging result of the bridge pile foundation construction area according to the first leakage predicting possibility and the second leakage predicting possibility.
As an optional implementation manner, in the first aspect of the present invention, the selecting, according to the current construction operation, a target prediction algorithm model corresponding to the current construction operation from a plurality of candidate prediction algorithm models includes:
for each candidate prediction algorithm model, acquiring training construction operation information corresponding to all training mud sensing data in a training data set corresponding to the candidate prediction algorithm model;
calculating a weighted sum average value of the similarity between all the training construction operation information and the current construction operation to obtain a similarity parameter corresponding to the candidate prediction algorithm model; wherein, the weight corresponding to the similarity of each piece of training construction operation information is in direct proportion to the data detail degree of the training construction operation information; the data detail degree is a weighted sum value of the data quantity and the data category number; the weight of the number of data categories is greater than the weight of the data amount;
acquiring verification construction operation information corresponding to all verification data in the verification data set corresponding to the candidate prediction algorithm model;
calculating the average value of the prediction accuracy of the verification stage corresponding to the verification data corresponding to all the verification operation information which are the same as the current construction operation, and obtaining the verification accuracy parameters of the candidate prediction algorithm model;
Calculating a weighted sum average value of the similarity parameter and the verification accurate parameter to obtain a matching degree parameter corresponding to the candidate prediction algorithm model;
and determining the candidate prediction algorithm model with the highest matching degree parameter as a target prediction algorithm model corresponding to the current construction operation.
As an optional implementation manner, in the first aspect of the present invention, the determining, according to the first leakage prediction possibility and the second leakage prediction possibility, a mud leakage determination result of the bridge pile foundation construction area includes:
calculating a first height difference value between liquid level sensing data in the first mud sensing data and a preset casing liquid level parameter standard value;
calculating a second height difference value between liquid level sensing data in the second mud sensing data and a preset standard value of a liquid level parameter of a mud pit;
calculating a weighted summation value of the first leakage prediction possibility and the second leakage prediction possibility to obtain a slurry leakage characterization parameter corresponding to the bridge pile foundation construction area; wherein the weight of the first leakage prediction likelihood is proportional to the first height difference; the weight of the second leak prediction likelihood is proportional to the second height difference; the weight magnitude relation between the first leakage prediction probability and the second leakage prediction probability is the same as the magnitude relation between the first height difference value and the second height difference value;
And judging whether the slurry leakage characterization parameter is larger than a preset parameter threshold value, and obtaining a slurry leakage judgment result of the bridge pile foundation construction area.
As an optional implementation manner, in the first aspect of the present invention, the executing a corresponding alarm operation according to the mud leakage determination result includes:
when the mud leakage judging result is yes, sending an alarm instruction to alarm equipment for alarm;
when the mud leakage judging result is negative, the mud leakage characterization parameters are stored;
repeating the steps, if the mud leakage judging result is no, storing and obtaining the mud leakage characterization parameters corresponding to the bridge pile foundation construction area at a plurality of historical time points;
judging whether the slurry leakage characterization parameters corresponding to the historical time points meet a preset parameter rising rule or not to obtain a first judging result; the parameter rising rule is used for limiting a plurality of the slurry leakage characterization parameters to be in an increasing relation and limiting the parameter differences between the adjacent slurry leakage characterization parameters to be in an expanding trend;
according to the project plan data, determining a history project stage corresponding to each history time point;
Judging whether the historical project phases corresponding to the plurality of historical time points meet a preset phase change rule or not, and obtaining a second judging result; the stage change rule is used for limiting the condition that the stage transition from the construction preparation stage to the construction progress stage exists in a plurality of history project stages;
and when the first judging result and the second judging result are both yes, sending an alarm instruction to alarm equipment for alarm.
In a second aspect, the invention discloses a data processing apparatus for mud leakage warning, the apparatus comprising:
the first acquisition module is used for acquiring first mud sensing data corresponding to the pile casing in real time through a first sensing sensor group arranged on the inner wall of the pile casing in a bridge pile foundation construction area;
the second acquisition module is used for acquiring second mud sensing data corresponding to the mud pit in real time through a second sensing sensor group arranged on the inner wall of the mud pit in the bridge pile foundation construction area;
the determining module is used for determining the current construction operation of the construction equipment in the bridge pile foundation construction area in real time;
and the judging module is used for determining a mud leakage judging result of the bridge pile foundation construction area based on a data prediction algorithm according to the first mud sensing data, the second mud sensing data and the current construction operation, and executing corresponding alarm operation according to the mud leakage judging result.
As an alternative embodiment, in the second aspect of the present invention, the first sensing sensor group and the second sensing sensor group include a liquid level sensor, a temperature sensor, a humidity sensor, and an image sensor; the construction equipment comprises at least one of drilling machine equipment, water gun equipment, transportation equipment and slurry pump equipment; the current construction operation includes at least one of a rig rotation operation, a water gun firing operation, a transportation operation, and a mud pump work operation.
As an optional implementation manner, in the second aspect of the present invention, the determining module determines, in real time, a specific manner of a current construction operation of the construction equipment in the bridge pile foundation construction area, including:
acquiring equipment communication data sent by construction equipment in the bridge pile foundation construction area;
acquiring equipment image data corresponding to the construction equipment through an image sensing sensor arranged in the bridge pile foundation construction area;
acquiring project plan data of a current construction project;
and determining the current construction operation of the construction equipment according to the equipment communication data, the equipment image data and the project plan data.
As an optional implementation manner, in the second aspect of the present invention, the determining module determines a specific manner of a current construction operation of the construction device according to the device communication data, the device image data and the project plan data, including:
determining a current project stage corresponding to the current time point according to the project plan data and the current time point;
determining a phase construction operation set corresponding to the current project phase according to the current project phase and the corresponding relation between the preset phase and the construction operation;
inputting the equipment communication data and the equipment image data into a trained construction operation prediction algorithm model to obtain a predicted construction operation set; the construction operation prediction algorithm model is obtained through training a training data set comprising a plurality of training equipment communication data and training equipment image data and corresponding construction operation labels; the prediction construction operation set comprises a plurality of construction operations and prediction probabilities corresponding to each construction operation;
calculating an intersection of the stage construction operation set and the prediction construction operation set to obtain an intersection operation set;
And determining the construction operation with the highest prediction probability in the intersection operation set as the current construction operation of the construction equipment.
As an optional implementation manner, in the second aspect of the present invention, the determining module determines, based on a data prediction algorithm, a concrete mode of determining a mud leakage determination result of the bridge pile foundation construction area according to the first mud sensing data, the second mud sensing data, and the current construction operation, where the concrete mode includes:
screening a target prediction algorithm model corresponding to the current construction operation from a plurality of candidate prediction algorithm models according to the current construction operation; the candidate prediction algorithm model or the target prediction algorithm model is obtained through training a training data set comprising a plurality of training mud sensing data and corresponding leakage labels;
inputting the first mud sensing data into the target prediction algorithm model to obtain a first leakage prediction possibility corresponding to the first mud sensing data;
inputting the second slurry sensing data into the target prediction algorithm model to obtain second leakage prediction possibility corresponding to the second slurry sensing data;
And determining a mud leakage judging result of the bridge pile foundation construction area according to the first leakage predicting possibility and the second leakage predicting possibility.
In a second aspect of the present invention, the determining module, according to the current construction operation, selects a specific mode of the target prediction algorithm model corresponding to the current construction operation from a plurality of candidate prediction algorithm models, including:
for each candidate prediction algorithm model, acquiring training construction operation information corresponding to all training mud sensing data in a training data set corresponding to the candidate prediction algorithm model;
calculating a weighted sum average value of the similarity between all the training construction operation information and the current construction operation to obtain a similarity parameter corresponding to the candidate prediction algorithm model; wherein, the weight corresponding to the similarity of each piece of training construction operation information is in direct proportion to the data detail degree of the training construction operation information; the data detail degree is a weighted sum value of the data quantity and the data category number; the weight of the number of data categories is greater than the weight of the data amount;
acquiring verification construction operation information corresponding to all verification data in the verification data set corresponding to the candidate prediction algorithm model;
Calculating the average value of the prediction accuracy of the verification stage corresponding to the verification data corresponding to all the verification operation information which are the same as the current construction operation, and obtaining the verification accuracy parameters of the candidate prediction algorithm model;
calculating a weighted sum average value of the similarity parameter and the verification accurate parameter to obtain a matching degree parameter corresponding to the candidate prediction algorithm model;
and determining the candidate prediction algorithm model with the highest matching degree parameter as a target prediction algorithm model corresponding to the current construction operation.
In a second aspect of the present invention, the determining module determines, according to the first leakage prediction possibility and the second leakage prediction possibility, a concrete mode of a mud leakage determination result of the bridge pile foundation construction area, including:
calculating a first height difference value between liquid level sensing data in the first mud sensing data and a preset casing liquid level parameter standard value;
calculating a second height difference value between liquid level sensing data in the second mud sensing data and a preset standard value of a liquid level parameter of a mud pit;
calculating a weighted summation value of the first leakage prediction possibility and the second leakage prediction possibility to obtain a slurry leakage characterization parameter corresponding to the bridge pile foundation construction area; wherein the weight of the first leakage prediction likelihood is proportional to the first height difference; the weight of the second leak prediction likelihood is proportional to the second height difference; the weight magnitude relation between the first leakage prediction probability and the second leakage prediction probability is the same as the magnitude relation between the first height difference value and the second height difference value;
And judging whether the slurry leakage characterization parameter is larger than a preset parameter threshold value, and obtaining a slurry leakage judgment result of the bridge pile foundation construction area.
As an optional implementation manner, in the second aspect of the present invention, a specific manner of executing, by the determining module, a corresponding alarm operation according to the mud leakage determination result includes:
when the mud leakage judging result is yes, sending an alarm instruction to alarm equipment for alarm;
when the mud leakage judging result is negative, the mud leakage characterization parameters are stored;
repeating the steps, if the mud leakage judging result is no, storing and obtaining the mud leakage characterization parameters corresponding to the bridge pile foundation construction area at a plurality of historical time points;
judging whether the slurry leakage characterization parameters corresponding to the historical time points meet a preset parameter rising rule or not to obtain a first judging result; the parameter rising rule is used for limiting a plurality of the slurry leakage characterization parameters to be in an increasing relation and limiting the parameter differences between the adjacent slurry leakage characterization parameters to be in an expanding trend;
according to the project plan data, determining a history project stage corresponding to each history time point;
Judging whether the historical project phases corresponding to the plurality of historical time points meet a preset phase change rule or not, and obtaining a second judging result; the stage change rule is used for limiting the condition that the stage transition from the construction preparation stage to the construction progress stage exists in a plurality of history project stages;
and when the first judging result and the second judging result are both yes, sending an alarm instruction to alarm equipment for alarm.
In a third aspect the invention discloses another data processing apparatus for mud leakage warning, the apparatus comprising:
a memory storing executable program code;
a processor coupled to the memory;
the first sensing sensor group is respectively connected to the processor in a communication way and is arranged on the inner wall of the pile casing in the bridge pile foundation construction area, the second sensing sensor group is arranged on the inner wall of the mud pit in the bridge pile foundation construction area, and the construction equipment and the alarm equipment in the bridge pile foundation construction area;
the processor invokes the executable program code stored in the memory to perform some or all of the steps in the data processing method for mud leakage alarm disclosed in the first aspect of the present invention.
Compared with the prior art, the invention has the following beneficial effects:
therefore, the embodiment of the invention can acquire a plurality of sensing data by utilizing the plurality of sensing sensors arranged on the inner wall of the pile casing and the inner wall of the mud pit, and determine the risk of mud leakage by combining construction operation and an algorithm model, so that the mud leakage can be more intelligently and accurately predicted, the supervision effect of construction engineering is improved, and engineering accidents are reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a data processing method for mud leakage alarm according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a data processing device for mud leakage alarm according to an embodiment of the present invention.
FIG. 3 is a schematic diagram of another data processing apparatus for mud leakage alarm according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms "second," "second," and the like in the description and in the claims and in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, article, or device that comprises a list of steps or elements is not limited to the list of steps or elements but may, in the alternative, include other steps or elements not expressly listed or inherent to such process, method, article, or device.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The invention discloses a data processing method and a data processing device for mud leakage alarm, which can acquire a plurality of sensing data by utilizing a plurality of sensing sensors arranged on the inner wall of a casing and the inner wall of a mud pit, and determine the risk of mud leakage by combining construction operation and an algorithm model, so that the mud leakage can be predicted more intelligently and accurately, the supervision effect of construction engineering is improved, and engineering accidents are reduced. The following will describe in detail.
Referring to fig. 1, fig. 1 is a flow chart of a data processing method for mud leakage alarm according to an embodiment of the present invention. The data processing method for mud leakage alarm described in fig. 1 is applied to a data processing chip, a processing terminal or a processing server (wherein the processing server may be a local server or a cloud server). As shown in fig. 1, the data processing method for mud leakage alarm may include the following operations:
101. Through setting up the first sensing sensor group at the pile casing inner wall in bridge pile foundation construction area, acquire the first mud sensing data that the pile casing corresponds in real time.
102. And acquiring second mud sensing data corresponding to the mud pit in real time through a second sensing sensor group arranged on the inner wall of the mud pit in the bridge pile foundation construction area.
Optionally, the first sensor group and the second sensor group include a liquid level sensor, a temperature sensor, a humidity sensor, and an image sensor.
103. The current construction operation of the construction equipment in the bridge pile foundation construction area is determined in real time.
Optionally, the construction equipment includes at least one of drilling equipment, water gun equipment, transportation equipment, and mud pump equipment.
Optionally, the current construction operation includes at least one of a rig rotation operation, a water gun firing operation, a transportation operation, and a mud pump work operation.
104. And determining a mud leakage judging result of the bridge pile foundation construction area based on a data prediction algorithm according to the first mud sensing data, the second mud sensing data and the current construction operation, and executing corresponding alarm operation according to the mud leakage judging result.
Therefore, the embodiment of the invention can acquire a plurality of sensing data by utilizing the plurality of sensing sensors arranged on the inner wall of the casing and the inner wall of the mud pit, and determine the risk of mud leakage by combining construction operation and an algorithm model, so that the mud leakage can be more intelligently and accurately predicted, the supervision effect of construction engineering is improved, and engineering accidents are reduced.
As an alternative embodiment, in the above steps, determining, in real time, a current construction operation of the construction equipment in the bridge pile foundation construction area includes:
acquiring equipment communication data sent by construction equipment in a bridge pile foundation construction area;
acquiring equipment image data corresponding to construction equipment through an image sensing sensor arranged in a bridge pile foundation construction area;
acquiring project plan data of a current construction project;
the current construction operation of the construction equipment is determined based on the equipment communication data, the equipment image data, and the project plan data.
Through the embodiment, the current construction operation of the construction equipment can be determined according to the equipment communication data, the equipment image data and the project plan data, so that the current construction operation of the construction equipment can be accurately determined, and the accuracy of subsequent slurry leakage prediction is improved.
As an alternative embodiment, in the above step, determining the current construction operation of the construction equipment according to the equipment communication data, the equipment image data, and the project plan data includes:
determining a current project stage corresponding to a current time point according to project plan data and the current time point;
determining a phase construction operation set corresponding to the current project phase according to the corresponding relation between the current project phase and the preset phase and construction operation;
inputting the equipment communication data and the equipment image data into a trained construction operation prediction algorithm model to obtain a predicted construction operation set; the construction operation prediction algorithm model is obtained through training of a training data set comprising a plurality of training equipment communication data and training equipment image data and corresponding construction operation labels; the predicted construction operation set comprises a plurality of construction operations and a prediction probability corresponding to each construction operation;
calculating an intersection of the construction operation set and the prediction construction operation set at the stage to obtain an intersection operation set;
and determining the construction operation with the highest prediction probability in the intersection operation set as the current construction operation of the construction equipment.
Specifically, project plan data may include a plurality of project phases predicted by the current construction project and a completion time interval corresponding to each project phase. Alternatively, the correspondence between the stages and the construction operations may be predetermined by an operator according to historical experience or experimental data, which defines a combination of one or more construction operations to be performed in different project stages, and may be used to intersect with the predicted construction operations to improve the reliability of the prediction result.
Optionally, the prediction algorithm model in the present invention may be a neural network model of a CNN structure, an RNN structure, or an LTSM structure, and an operator may select the prediction algorithm model according to specific prediction scene characteristics and data characteristics, which is not limited herein.
Through the embodiment, the intersection of the stage construction operation set and the prediction construction operation set can be calculated, and the construction operation with the highest prediction probability in the intersection operation set is determined as the current construction operation of the construction equipment, so that the current construction operation of the construction equipment is comprehensively and accurately determined through project stage characteristics and image prediction results, and the accuracy of subsequent slurry leakage prediction is improved.
As an optional embodiment, in the step, determining, based on the data prediction algorithm, a mud leakage determination result of the bridge pile foundation construction area according to the first mud sensing data, the second mud sensing data and the current construction operation, includes:
screening a target prediction algorithm model corresponding to the current construction operation from a plurality of candidate prediction algorithm models according to the current construction operation; the candidate prediction algorithm model or the target prediction algorithm model is obtained through training a training data set comprising a plurality of training mud sensing data and corresponding leakage labels or not;
Inputting the first slurry sensing data into a target prediction algorithm model to obtain a first leakage prediction possibility corresponding to the first slurry sensing data;
inputting the second slurry sensing data into a target prediction algorithm model to obtain second leakage prediction possibility corresponding to the second slurry sensing data;
and determining a mud leakage judging result of the bridge pile foundation construction area according to the first leakage predicting possibility and the second leakage predicting possibility.
Through the embodiment, a more targeted prediction model can be obtained through screening, and the mud leakage judging result of the bridge pile foundation construction area is determined according to the prediction results of the mud sensing data at two positions, so that the mud leakage can be predicted more intelligently and accurately, the supervision effect of construction engineering is improved, and engineering accidents are reduced.
As an optional embodiment, in the step, selecting, according to the current construction operation, a target prediction algorithm model corresponding to the current construction operation from a plurality of candidate prediction algorithm models, including:
for each candidate prediction algorithm model, acquiring training construction operation information corresponding to all training mud sensing data in a training data set corresponding to the candidate prediction algorithm model;
Calculating a weighted sum average value of the similarity between all training construction operation information and the current construction operation to obtain a similarity parameter corresponding to the candidate prediction algorithm model; wherein, the weight corresponding to the similarity of each training construction operation information is in direct proportion to the data detail degree of the training construction operation information; the data detail degree is a weighted sum value of the data quantity and the data category number; the weight of the number of data categories is greater than the weight of the data amount;
acquiring verification construction operation information corresponding to all verification data in the verification data set corresponding to the candidate prediction algorithm model;
counting the average value of the prediction accuracy of the verification stage corresponding to the verification data corresponding to all the verification operation information which are the same as the current construction operation, and obtaining the verification accuracy parameters of the candidate prediction algorithm model;
calculating a weighted sum average value of the similarity parameter and the verification accurate parameter to obtain a matching degree parameter corresponding to the candidate prediction algorithm model;
and determining the candidate prediction algorithm model with the highest matching degree parameter as a target prediction algorithm model corresponding to the current construction operation.
Alternatively, the similarity calculation in the present invention can be implemented by a vector distance algorithm.
Alternatively, the weights in the present invention may be determined by a weight assignment algorithm or by an operator based on experience or experiment.
Through the embodiment, the matching degree between the candidate prediction algorithm model and the current construction operation can be calculated through the similarity calculation of the training data set and the verification data set of each candidate prediction algorithm model, and the more targeted prediction model is obtained through screening, so that the slurry leakage can be predicted more intelligently and accurately, the supervision effect of the construction engineering is improved, and the occurrence of engineering accidents is reduced.
As an alternative embodiment, in the step, determining the mud leakage judgment result of the bridge pile foundation construction area according to the first leakage prediction possibility and the second leakage prediction possibility includes:
calculating a first height difference value between liquid level sensing data in the first mud sensing data and a preset casing liquid level parameter standard value;
calculating a second height difference value between liquid level sensing data in the second mud sensing data and a preset standard value of a liquid level parameter of the mud pit;
calculating a weighted sum value of the first leakage prediction possibility and the second leakage prediction possibility to obtain a slurry leakage characterization parameter corresponding to the bridge pile foundation construction area; wherein the weight of the first leakage prediction likelihood is proportional to the first height difference; the weight of the second leakage prediction likelihood is proportional to the second altitude difference; the weight magnitude relation between the first leakage prediction probability and the second leakage prediction probability is the same as the magnitude relation between the first height difference value and the second height difference value;
And judging whether the slurry leakage characterization parameter is larger than a preset parameter threshold value, and obtaining a slurry leakage judgment result of the bridge pile foundation construction area.
Alternatively, the standard values of the fluid level parameters of the casing and the standard values of the fluid level parameters of the mud pit can be determined by operators according to experience or experimental values, or can be measured and calculated in site construction and input into a system for storage and subsequent calculation.
Through the embodiment, the weight of the slurry leakage possibility of different positions can be determined in advance by preferentially measuring and calculating the liquid level height sensing data and the standard value, and the weighted summation value of the first leakage prediction possibility and the second leakage prediction possibility is adjusted and calculated according to the weight, so that the slurry leakage characterization parameters corresponding to the bridge pile foundation construction area are obtained, the slurry leakage can be predicted more intelligently and accurately, the supervision effect of construction engineering is improved, and engineering accidents are reduced.
As an optional embodiment, in the step, performing a corresponding alarm operation according to the mud leakage determination result includes:
when the mud leakage judging result is yes, sending an alarm instruction to alarm equipment for alarm;
when the mud leakage judging result is negative, storing the mud leakage characterization parameters;
Repeating the steps, if the mud leakage judging result is no, storing and obtaining mud leakage characterization parameters corresponding to the bridge pile foundation construction area at a plurality of historical time points;
judging whether slurry leakage characterization parameters corresponding to a plurality of historical time points meet a preset parameter ascending rule or not, and obtaining a first judgment result; the parameter rising rule is used for limiting a plurality of slurry leakage characterization parameters to be in an increasing relation and limiting the parameter difference between adjacent slurry leakage characterization parameters to be in an expanding trend;
according to project plan data, determining a history project stage corresponding to each history time point;
judging whether the historical project phases corresponding to the historical time points meet a preset phase change rule or not, and obtaining a second judging result; the stage change rule is used for limiting the condition that the stage transition from the construction preparation stage to the construction is carried out in a plurality of history project stages;
and when the first judging result and the second judging result are both yes, sending an alarm instruction to alarm equipment for alarm.
Alternatively, the parameter rising rule and the stage change rule may be determined by an operator according to experience or experiment, for example, may be determined by analyzing and fitting historical construction data, and may be defined rules for accurate data correspondence, or may be a mathematical expression or a mathematical relationship model obtained by fitting, which is not limited in the present invention.
Through the embodiment, under the condition that mud leakage alarm does not occur at a plurality of time points, whether possible dangerous situations of mud leakage exist or not can be judged and alarm is carried out according to the data of a plurality of historical time points through the parameter rising rules and the stage change rules, so that mud leakage can be more intelligently and accurately predicted, the supervision effect of construction engineering is improved, and engineering accidents are reduced.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a data processing device for mud leakage alarm according to an embodiment of the present invention. The data processing device for mud leakage alarm described in fig. 2 is applied to a data processing chip, a processing terminal or a processing server (wherein the processing server may be a local server or a cloud server). As shown in fig. 2, the data processing apparatus for mud leakage alarm may include:
the first acquisition module 201 is configured to acquire, in real time, first mud sensing data corresponding to a pile casing through a first sensing sensor group disposed on an inner wall of the pile casing in a bridge pile foundation construction area;
the second acquisition module 202 is configured to acquire, in real time, second mud sensing data corresponding to the mud pit through a second sensor group disposed on an inner wall of the mud pit in the bridge pile foundation construction area;
A determining module 203, configured to determine, in real time, a current construction operation of construction equipment in a bridge pile foundation construction area;
and the judging module 204 is used for determining a mud leakage judging result of the bridge pile foundation construction area based on the data prediction algorithm according to the first mud sensing data, the second mud sensing data and the current construction operation, and executing corresponding alarm operation according to the mud leakage judging result.
As an alternative embodiment, the first and second sensor sets include a liquid level sensor, a temperature sensor, a humidity sensor, and an image sensor; the construction equipment comprises at least one of drilling machine equipment, water gun equipment, transportation equipment and slurry pump equipment; the current construction operations include at least one of a rig rotation operation, a water gun firing operation, a transportation operation, and a mud pump work operation.
As an alternative embodiment, the determining module 203 determines in real time a specific manner of the current construction operation of the construction equipment in the bridge pile foundation construction area, including:
acquiring equipment communication data sent by construction equipment in a bridge pile foundation construction area;
acquiring equipment image data corresponding to construction equipment through an image sensing sensor arranged in a bridge pile foundation construction area;
Acquiring project plan data of a current construction project;
the current construction operation of the construction equipment is determined based on the equipment communication data, the equipment image data, and the project plan data.
As an alternative embodiment, the determining module 203 determines a specific manner of the current construction operation of the construction apparatus according to the apparatus communication data, the apparatus image data, and the project plan data, including:
determining a current project stage corresponding to a current time point according to project plan data and the current time point;
determining a phase construction operation set corresponding to the current project phase according to the corresponding relation between the current project phase and the preset phase and construction operation;
inputting the equipment communication data and the equipment image data into a trained construction operation prediction algorithm model to obtain a predicted construction operation set; the construction operation prediction algorithm model is obtained through training of a training data set comprising a plurality of training equipment communication data and training equipment image data and corresponding construction operation labels; the predicted construction operation set comprises a plurality of construction operations and a prediction probability corresponding to each construction operation;
calculating an intersection of the construction operation set and the prediction construction operation set at the stage to obtain an intersection operation set;
And determining the construction operation with the highest prediction probability in the intersection operation set as the current construction operation of the construction equipment.
As an alternative embodiment, the determining module 204 determines, based on the data prediction algorithm and based on the first mud sensing data, the second mud sensing data, and the current construction operation, a concrete mode of a mud leakage determination result of the bridge pile foundation construction area, including:
screening a target prediction algorithm model corresponding to the current construction operation from a plurality of candidate prediction algorithm models according to the current construction operation; the candidate prediction algorithm model or the target prediction algorithm model is obtained through training a training data set comprising a plurality of training mud sensing data and corresponding leakage labels or not;
inputting the first slurry sensing data into a target prediction algorithm model to obtain a first leakage prediction possibility corresponding to the first slurry sensing data;
inputting the second slurry sensing data into a target prediction algorithm model to obtain second leakage prediction possibility corresponding to the second slurry sensing data;
and determining a mud leakage judging result of the bridge pile foundation construction area according to the first leakage predicting possibility and the second leakage predicting possibility.
As an optional embodiment, the determining module 204, according to the current construction operation, screens out a specific mode of the target prediction algorithm model corresponding to the current construction operation from a plurality of candidate prediction algorithm models, including:
for each candidate prediction algorithm model, acquiring training construction operation information corresponding to all training mud sensing data in a training data set corresponding to the candidate prediction algorithm model;
calculating a weighted sum average value of the similarity between all training construction operation information and the current construction operation to obtain a similarity parameter corresponding to the candidate prediction algorithm model; wherein, the weight corresponding to the similarity of each training construction operation information is in direct proportion to the data detail degree of the training construction operation information; the data detail degree is a weighted sum value of the data quantity and the data category number; the weight of the number of data categories is greater than the weight of the data amount;
acquiring verification construction operation information corresponding to all verification data in the verification data set corresponding to the candidate prediction algorithm model;
counting the average value of the prediction accuracy of the verification stage corresponding to the verification data corresponding to all the verification operation information which are the same as the current construction operation, and obtaining the verification accuracy parameters of the candidate prediction algorithm model;
Calculating a weighted sum average value of the similarity parameter and the verification accurate parameter to obtain a matching degree parameter corresponding to the candidate prediction algorithm model;
and determining the candidate prediction algorithm model with the highest matching degree parameter as a target prediction algorithm model corresponding to the current construction operation.
As an alternative embodiment, the determining module 204 determines a concrete mode of the mud leakage determination result of the bridge pile foundation construction area according to the first leakage prediction possibility and the second leakage prediction possibility, including:
calculating a first height difference value between liquid level sensing data in the first mud sensing data and a preset casing liquid level parameter standard value;
calculating a second height difference value between liquid level sensing data in the second mud sensing data and a preset standard value of a liquid level parameter of the mud pit;
calculating a weighted sum value of the first leakage prediction possibility and the second leakage prediction possibility to obtain a slurry leakage characterization parameter corresponding to the bridge pile foundation construction area; wherein the weight of the first leakage prediction likelihood is proportional to the first height difference; the weight of the second leakage prediction likelihood is proportional to the second altitude difference; the weight magnitude relation between the first leakage prediction probability and the second leakage prediction probability is the same as the magnitude relation between the first height difference value and the second height difference value;
And judging whether the slurry leakage characterization parameter is larger than a preset parameter threshold value, and obtaining a slurry leakage judgment result of the bridge pile foundation construction area.
As an alternative embodiment, the specific manner of executing the corresponding alarm operation by the judging module 204 according to the mud leakage judging result includes:
when the mud leakage judging result is yes, sending an alarm instruction to alarm equipment for alarm;
when the mud leakage judging result is negative, storing the mud leakage characterization parameters;
repeating the steps, if the mud leakage judging result is no, storing and obtaining mud leakage characterization parameters corresponding to the bridge pile foundation construction area at a plurality of historical time points;
judging whether slurry leakage characterization parameters corresponding to a plurality of historical time points meet a preset parameter ascending rule or not, and obtaining a first judgment result; the parameter rising rule is used for limiting a plurality of slurry leakage characterization parameters to be in an increasing relation and limiting the parameter difference between adjacent slurry leakage characterization parameters to be in an expanding trend;
according to project plan data, determining a history project stage corresponding to each history time point;
judging whether the historical project phases corresponding to the historical time points meet a preset phase change rule or not, and obtaining a second judging result; the stage change rule is used for limiting the condition that the stage transition from the construction preparation stage to the construction is carried out in a plurality of history project stages;
And when the first judging result and the second judging result are both yes, sending an alarm instruction to alarm equipment for alarm.
Specific technical details and technical effects of the modules and steps in the above embodiment may refer to corresponding expressions in the first embodiment, and are not described herein.
Referring to fig. 3, fig. 3 is a schematic diagram illustrating another data processing apparatus for mud leakage alarm according to an embodiment of the present invention. The data processing device for mud leakage alarm described in fig. 3 is applied to a data processing chip, a processing terminal or a processing server (wherein the processing server may be a local server or a cloud server). As shown in fig. 3, the data processing apparatus for mud leakage alarm may include:
a memory 301 storing executable program code;
a processor 302 coupled with the memory 301;
a first sensing sensor set 303 arranged on the inner wall of a pile casing in a bridge pile foundation construction area, a second sensing sensor set 304 arranged on the inner wall of a mud pit in the bridge pile foundation construction area, construction equipment 305 in the bridge pile foundation construction area and alarm equipment 306 which are respectively connected to a processor 302 in a communication manner;
wherein the processor 302 invokes executable program code stored in the memory 301 for performing the steps of the data processing method for mud leakage warning described in embodiment one.
In a fourth embodiment, the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps of the data processing method for mud leakage alarm described in the first embodiment.
In a fifth embodiment, the present invention discloses a computer program product comprising a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the data processing method for mud leakage alarm described in the first embodiment.
The foregoing describes certain embodiments of the present disclosure, other embodiments being within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. Furthermore, the processes depicted in the accompanying drawings do not necessarily have to be in the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, devices, non-transitory computer readable storage medium embodiments, the description is relatively simple, as it is substantially similar to method embodiments, with reference to portions of the description of method embodiments being relevant.
The apparatus, the device, the nonvolatile computer readable storage medium and the method provided in the embodiments of the present disclosure correspond to each other, and therefore, the apparatus, the device, and the nonvolatile computer storage medium also have similar advantageous technical effects as those of the corresponding method, and since the advantageous technical effects of the method have been described in detail above, the advantageous technical effects of the corresponding apparatus, device, and nonvolatile computer storage medium are not described herein again.
In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., a field programmable gate array (Field Programmable gate array, FPGA)) is an integrated circuit whose logic function is determined by the user programming the device. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but not just one of the hdds, but a plurality of kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware DescriptionLanguage), confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), lava, lola, myHDL, PALASM, RHDL (RubyHardware Description Language), etc., VHDL (Very-High-SpeedIntegrated Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in one or more software and/or hardware elements when implemented in the present specification.
It will be appreciated by those skilled in the art that the present description may be provided as a method, system, or computer program product. Accordingly, the present specification embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description embodiments may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present description is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
Finally, it should be noted that: the embodiment of the invention discloses a data processing method and a device for mud leakage alarm, which are disclosed by the embodiment of the invention only as a preferred embodiment of the invention, and are only used for illustrating the technical scheme of the invention, but not limiting the technical scheme; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme recorded in the various embodiments can be modified or part of technical features in the technical scheme can be replaced equivalently; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. A data processing method for mud leakage warning, the method comprising:
Acquiring first mud sensing data corresponding to a pile casing in real time through a first sensing sensor group arranged on the inner wall of the pile casing in a bridge pile foundation construction area;
acquiring second mud sensing data corresponding to the mud pit in real time through a second sensing sensor group arranged on the inner wall of the mud pit in the bridge pile foundation construction area;
determining current construction operation of construction equipment in the bridge pile foundation construction area in real time;
and determining a mud leakage judging result of the bridge pile foundation construction area based on a data prediction algorithm according to the first mud sensing data, the second mud sensing data and the current construction operation, and executing corresponding alarm operation according to the mud leakage judging result.
2. The data processing method for mud leakage alarm according to claim 1, wherein the first and second sensor groups include a liquid level sensor, a temperature sensor, a humidity sensor, and an image sensor; the construction equipment comprises at least one of drilling machine equipment, water gun equipment, transportation equipment and slurry pump equipment; the current construction operation includes at least one of a rig rotation operation, a water gun firing operation, a transportation operation, and a mud pump work operation.
3. The data processing method for mud leakage alarm according to claim 1, wherein the determining in real time a current construction operation of construction equipment in the bridge pile foundation construction area comprises:
acquiring equipment communication data sent by construction equipment in the bridge pile foundation construction area;
acquiring equipment image data corresponding to the construction equipment through an image sensing sensor arranged in the bridge pile foundation construction area;
acquiring project plan data of a current construction project;
and determining the current construction operation of the construction equipment according to the equipment communication data, the equipment image data and the project plan data.
4. A data processing method for mud leakage warning according to claim 3, wherein said determining a current construction operation of the construction equipment based on the equipment communication data, equipment image data and the project plan data comprises:
determining a current project stage corresponding to the current time point according to the project plan data and the current time point;
determining a phase construction operation set corresponding to the current project phase according to the current project phase and the corresponding relation between the preset phase and the construction operation;
Inputting the equipment communication data and the equipment image data into a trained construction operation prediction algorithm model to obtain a predicted construction operation set; the construction operation prediction algorithm model is obtained through training a training data set comprising a plurality of training equipment communication data and training equipment image data and corresponding construction operation labels; the prediction construction operation set comprises a plurality of construction operations and prediction probabilities corresponding to each construction operation;
calculating an intersection of the stage construction operation set and the prediction construction operation set to obtain an intersection operation set;
and determining the construction operation with the highest prediction probability in the intersection operation set as the current construction operation of the construction equipment.
5. The data processing method for mud leakage alarm according to claim 1, wherein the determining the mud leakage judgment result of the bridge pile foundation construction area based on a data prediction algorithm according to the first mud sensing data, the second mud sensing data and the current construction operation comprises:
screening a target prediction algorithm model corresponding to the current construction operation from a plurality of candidate prediction algorithm models according to the current construction operation; the candidate prediction algorithm model or the target prediction algorithm model is obtained through training a training data set comprising a plurality of training mud sensing data and corresponding leakage labels;
Inputting the first mud sensing data into the target prediction algorithm model to obtain a first leakage prediction possibility corresponding to the first mud sensing data;
inputting the second slurry sensing data into the target prediction algorithm model to obtain second leakage prediction possibility corresponding to the second slurry sensing data;
and determining a mud leakage judging result of the bridge pile foundation construction area according to the first leakage predicting possibility and the second leakage predicting possibility.
6. The method for data processing for mud leakage alarm according to claim 5, wherein the selecting a target prediction algorithm model corresponding to the current construction operation from a plurality of candidate prediction algorithm models according to the current construction operation comprises:
for each candidate prediction algorithm model, acquiring training construction operation information corresponding to all training mud sensing data in a training data set corresponding to the candidate prediction algorithm model;
calculating a weighted sum average value of the similarity between all the training construction operation information and the current construction operation to obtain a similarity parameter corresponding to the candidate prediction algorithm model; wherein, the weight corresponding to the similarity of each piece of training construction operation information is in direct proportion to the data detail degree of the training construction operation information; the data detail degree is a weighted sum value of the data quantity and the data category number; the weight of the number of data categories is greater than the weight of the data amount;
Acquiring verification construction operation information corresponding to all verification data in the verification data set corresponding to the candidate prediction algorithm model;
calculating the average value of the prediction accuracy of the verification stage corresponding to the verification data corresponding to all the verification operation information which are the same as the current construction operation, and obtaining the verification accuracy parameters of the candidate prediction algorithm model;
calculating a weighted sum average value of the similarity parameter and the verification accurate parameter to obtain a matching degree parameter corresponding to the candidate prediction algorithm model;
and determining the candidate prediction algorithm model with the highest matching degree parameter as a target prediction algorithm model corresponding to the current construction operation.
7. The method for data processing of mud leakage alarm according to claim 5, wherein determining the mud leakage judgment result of the bridge pile foundation construction area according to the first leakage prediction possibility and the second leakage prediction possibility comprises:
calculating a first height difference value between liquid level sensing data in the first mud sensing data and a preset casing liquid level parameter standard value;
calculating a second height difference value between liquid level sensing data in the second mud sensing data and a preset standard value of a liquid level parameter of a mud pit;
Calculating a weighted summation value of the first leakage prediction possibility and the second leakage prediction possibility to obtain a slurry leakage characterization parameter corresponding to the bridge pile foundation construction area; wherein the weight of the first leakage prediction likelihood is proportional to the first height difference; the weight of the second leak prediction likelihood is proportional to the second height difference; the weight magnitude relation between the first leakage prediction probability and the second leakage prediction probability is the same as the magnitude relation between the first height difference value and the second height difference value;
and judging whether the slurry leakage characterization parameter is larger than a preset parameter threshold value, and obtaining a slurry leakage judgment result of the bridge pile foundation construction area.
8. The method for processing data for mud leakage alarm according to claim 7, wherein said executing a corresponding alarm operation according to the mud leakage judgment result comprises:
when the mud leakage judging result is yes, sending an alarm instruction to alarm equipment for alarm;
when the mud leakage judging result is negative, the mud leakage characterization parameters are stored;
repeating the steps, if the mud leakage judging result is no, storing and obtaining the mud leakage characterization parameters corresponding to the bridge pile foundation construction area at a plurality of historical time points;
Judging whether the slurry leakage characterization parameters corresponding to the historical time points meet a preset parameter rising rule or not to obtain a first judging result; the parameter rising rule is used for limiting a plurality of the slurry leakage characterization parameters to be in an increasing relation and limiting the parameter differences between the adjacent slurry leakage characterization parameters to be in an expanding trend;
according to the project plan data, determining a history project stage corresponding to each history time point;
judging whether the historical project phases corresponding to the plurality of historical time points meet a preset phase change rule or not, and obtaining a second judging result; the stage change rule is used for limiting the condition that the stage transition from the construction preparation stage to the construction progress stage exists in a plurality of history project stages;
and when the first judging result and the second judging result are both yes, sending an alarm instruction to alarm equipment for alarm.
9. A data processing apparatus for mud leakage warning, the apparatus comprising:
the first acquisition module is used for acquiring first mud sensing data corresponding to the pile casing in real time through a first sensing sensor group arranged on the inner wall of the pile casing in a bridge pile foundation construction area;
The second acquisition module is used for acquiring second mud sensing data corresponding to the mud pit in real time through a second sensing sensor group arranged on the inner wall of the mud pit in the bridge pile foundation construction area;
the determining module is used for determining the current construction operation of the construction equipment in the bridge pile foundation construction area in real time;
and the judging module is used for determining a mud leakage judging result of the bridge pile foundation construction area based on a data prediction algorithm according to the first mud sensing data, the second mud sensing data and the current construction operation, and executing corresponding alarm operation according to the mud leakage judging result.
10. A data processing apparatus for mud leakage warning, the apparatus comprising:
a memory storing executable program code;
a processor coupled to the memory;
the first sensing sensor group is respectively connected to the processor in a communication way and is arranged on the inner wall of the pile casing in the bridge pile foundation construction area, the second sensing sensor group is arranged on the inner wall of the mud pit in the bridge pile foundation construction area, and the construction equipment and the alarm equipment in the bridge pile foundation construction area;
The processor invokes the executable program code stored in the memory to perform the data processing method for mud leakage warning as set forth in any one of claims 1-8.
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| CN202311706712.0A CN117542171B (en) | 2023-12-13 | 2023-12-13 | Data processing method and device for mud leakage alarm |
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