CN118332168A - Intelligent supervision method and device based on artificial intelligence - Google Patents
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Abstract
The embodiment of the invention provides an intelligent supervision method and device based on artificial intelligence, and relates to the technical field of intelligent supervision technology. The method comprises the following steps: acquiring engineering supervision data; carrying out normalization processing on the engineering supervision data to obtain first supervision data; performing pixel gray value conversion processing on the first supervision data to obtain supervision gray distribution information; and performing distribution matching on the supervision gray level distribution information and preset gray level distribution information based on intelligent supervision of artificial intelligence, and determining that the engineering supervision data is abnormal under the condition that a distribution matching result does not accord with a preset distribution condition. The invention solves the problem of low engineering supervision efficiency, and further achieves the effect of improving the engineering supervision efficiency.
Description
Technical Field
The embodiment of the invention relates to the technical field of intelligent supervision, in particular to an intelligent supervision method and device based on artificial intelligence.
Background
With the further development of urban construction in China, for example, a large amount of construction projects are more and more invested, the construction period is more and more compact, and the operation and maintenance management requirements are higher, so that new challenges are brought to engineering project supervision.
The traditional engineering supervision is mainly carried out manually, and real-time monitoring and early warning are difficult to realize for a large amount of supervision data, so that the problem of insufficient rapid response and treatment for emergencies exists.
There is currently no better solution to the above problems.
Disclosure of Invention
The embodiment of the invention provides an intelligent supervision method and device based on artificial intelligence, which at least solve the problem of low engineering supervision data monitoring efficiency in the related technology.
According to one embodiment of the present invention, there is provided an artificial intelligence based intelligent supervision method including:
Acquiring engineering supervision data, wherein the engineering supervision data is obtained by acquiring data of an engineering site through a sensor deployed on the engineering site;
carrying out normalization processing on the engineering supervision data to obtain first supervision data;
Performing pixel gray value conversion processing on the first supervision data to obtain supervision gray distribution information;
And carrying out distribution matching on the supervision gray level distribution information and preset gray level distribution information through a first matching model, and determining that the engineering supervision data is abnormal under the condition that a distribution matching result does not accord with a preset distribution condition.
In an exemplary embodiment, after the pixel gray value conversion processing is performed on the first supervision data to obtain supervision gray distribution information, the method further includes:
constructing a supervision gray level distribution matrix according to a preset distribution matrix construction rule and the supervision gray level distribution information;
and calculating the correlation between the supervision gray distribution matrix and a preset gray distribution matrix, and determining that the engineering supervision data is abnormal under the condition that the correlation result does not accord with a preset correlation condition.
In an exemplary embodiment, before said normalizing the engineering management data to obtain first management data, the method further includes:
acquiring historical supervision data, wherein the historical supervision data comprises historical engineering environment data and historical engineering management data;
Determining a data filtering rule according to the historical engineering environment data and the historical engineering management data;
And carrying out data filtering processing on the engineering supervision data according to the data filtering rule, wherein the normalization processing is executed on the data filtering processing result.
In an exemplary embodiment, after the acquiring the history management data, the method further includes:
according to a preset space-time prediction model, performing prediction calculation on the historical supervision data to obtain supervision prediction data;
And carrying out matching processing on the supervision predicted data and the engineering supervision data, and determining that the engineering supervision data is abnormal under the condition that a matching processing result does not accord with a preset data evolution condition.
According to another embodiment of the present invention, there is provided an artificial intelligence based intelligent supervision apparatus including:
The data acquisition module is used for acquiring engineering supervision data, wherein the engineering supervision data is obtained by acquiring data of an engineering site through a sensor deployed on the engineering site;
The normalization conversion module is used for carrying out normalization processing on the engineering supervision data to obtain first supervision data;
The gray value conversion module is used for carrying out pixel gray value conversion processing on the first supervision data so as to obtain supervision gray distribution information;
The first matching module is used for carrying out distribution matching on the supervision gray level distribution information and preset gray level distribution information through a first matching model, and determining that the engineering supervision data is abnormal under the condition that a distribution matching result does not accord with a preset distribution condition.
In an exemplary embodiment, the apparatus further comprises:
The matrix construction module is used for constructing a supervision gray level distribution matrix according to preset distribution matrix construction rules and the supervision gray level distribution information after the pixel gray level value conversion processing is carried out on the first supervision data to obtain supervision gray level distribution information;
The correlation calculation module is used for calculating the correlation between the supervision gray level distribution matrix and a preset gray level distribution matrix, and determining that the engineering supervision data is abnormal under the condition that the correlation result does not accord with a preset correlation condition.
In an exemplary embodiment, the apparatus further comprises:
the historical data acquisition module is used for acquiring historical supervision data before carrying out normalization processing on the engineering supervision data to obtain first supervision data, wherein the historical supervision data comprises historical engineering environment data and historical engineering management data;
The rule determining module is used for determining a data filtering rule according to the historical engineering environment data and the historical engineering management data;
And the filtering module is used for carrying out data filtering processing on the engineering supervision data according to the data filtering rule, and the normalization processing is carried out on the data filtering processing result.
In an exemplary embodiment, the apparatus further comprises:
The prediction calculation module is used for performing prediction calculation on the historical supervision data according to a preset space-time prediction model after the historical supervision data are acquired so as to obtain supervision prediction data;
And the second matching module is used for carrying out matching processing on the supervision predicted data and the engineering supervision data, and determining that the engineering supervision data is abnormal under the condition that a matching processing result does not accord with a preset data evolution condition.
According to a further embodiment of the invention, there is also provided a computer readable storage medium having stored therein a computer program, wherein the computer program is arranged to perform the steps of any of the method embodiments described above when run.
According to a further embodiment of the invention, there is also provided an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
According to the invention, the engineering supervision data is subjected to pixelation, so that whether the data are reasonable or not can be judged according to the gray distribution condition of the pixel points, the data are not required to be analyzed one by one, and the data analysis efficiency is improved, so that the problem of low engineering supervision efficiency can be solved, and the effect of improving the engineering supervision efficiency is achieved.
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FIG. 1 is a flow chart of an artificial intelligence based intelligent supervision method according to an embodiment of the invention;
FIG. 2 is a schematic diagram of an embodiment of the present invention;
Fig. 3 is a block diagram of an intelligent supervision device based on artificial intelligence according to an embodiment of the invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings in conjunction with the embodiments.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
In this embodiment, an artificial intelligence based intelligent supervision method is provided, fig. 1 is a flowchart of an artificial intelligence based intelligent supervision according to an embodiment of the present invention, and as shown in fig. 1, the flowchart includes the following steps:
step 100, acquiring engineering supervision data, wherein the engineering supervision data is obtained by acquiring data of an engineering site through a sensor deployed on the engineering site;
in the embodiment, the sensors are deployed in the key areas of the engineering site, and the construction progress and the material transportation condition are tracked in real time through the sensors, so that the engineering site is accurately monitored.
The data collected by the sensor comprises, but is not limited to, quality data detected by sensors such as a concrete strength tester and a steel bar scanner, temperature and humidity data and noise data detected by sensors such as a dust monitor; the sensor includes an integrated sensor with GPS positioning functionality and/or an integrated sensor with RFID tag functionality.
Specifically, the quality of the building materials is monitored by using sensors such as a concrete strength tester, a steel bar scanner and the like, so that the engineering quality standard is ensured to be met; or equipment such as a vibration sensor, a crack monitor and the like is arranged on a construction site and is used for detecting potential structural problems and potential safety hazards; or sensors such as a weather station, a dust monitor and the like are deployed to monitor the environmental conditions of a construction site, such as temperature, humidity, noise, air quality and the like.
After the sensor collects the related data, the data are sent to a central monitoring system in real time through a wireless network; the central monitoring system integrates the collected data to form a complete engineering supervision data set; the system then performs a preliminary analysis of the collected data to screen out critical information that requires further processing.
Step 200, carrying out normalization processing on the engineering supervision data to obtain first supervision data;
In this embodiment, since the dimensions and the data sizes of different detection data are different, normalization processing is required for the data, so that the influence of a large range of some characteristic factor values on the analysis result is avoided, and better data performance can be achieved when the data is performed through a machine learning algorithm in the following process.
For example, a min-max normalization process may be employed, and other normalization processes may be employed; it should be noted that, in order to ensure the efficiency of normalization processing, the data may be cleaned and filtered before normalization processing, so as to remove abnormal values and noise, or perform interpolation processing on missing data, so as to ensure the integrity of the data.
Wherein the min-max normalization process includes being implemented by the following equation 1:
(equation 1)
Wherein,Is the minimum value of the engineering supervision data,Is the maximum value of the engineering supervision data,And x is original engineering supervision data.
Step 300, performing pixel gray value conversion processing on the first supervision data to obtain supervision gray distribution information;
In this embodiment, after normalization processing, the relevant data values are usually between 0 and 255, so that the data can be considered to be converted into pixel points, so that the rationality of relevant supervision data can be judged by using the relevant learning model of image recognition through the gray value distribution condition of the pixel points, and the judgment efficiency of the supervision data is greatly improved.
The conversion processing of the gray values may be to generate pixel points of corresponding gray values in a preset pixel area according to the data values of the supervision data, so as to form a supervision gray distribution map (as shown in fig. 2), or of course, histogram statistics may be performed on the converted supervision gray distribution map to obtain distribution information of pixel gray, where the histogram shows the occurrence frequency of each gray value in the image, and may be used to analyze brightness distribution and contrast of the image; the supervision gray level distribution information comprises position information of the pixel points and gray level values of the pixel points; after obtaining the supervision gray-scale distribution map, edge detection, defect recognition, and the like (for example, by analyzing a peak in the gray-scale histogram, a specific region in the image or a collection target of corresponding raw data may be identified) may be performed on the image, and further judgment may be made based on these data.
And 400, carrying out distribution matching on the supervision gray scale distribution information and preset gray scale distribution information through a first matching model, and determining that the engineering supervision data is abnormal under the condition that a distribution matching result does not accord with a preset distribution condition.
In the embodiment, the distribution information is matched through the matching model, so that the data analysis efficiency can be effectively improved, and errors caused by manual processing are reduced.
The first matching model can be a K-nearest neighbor (KNN), a Support Vector Machine (SVM), a neural network and the like which can perform gray distribution similarity calculation; the preset gray level distribution information is obtained by carrying out big data statistics calculation on the historical data.
Specifically, the method comprises the following steps:
S1, establishing preset gray level distribution information: carrying out big data statistics on historical data, expert experience and engineering standards to establish a normal gray level distribution information model;
s2, building a matching model: selecting or training a matching model, such as K-nearest neighbor (KNN), a Support Vector Machine (SVM), a neural network and the like, for comparing the similarity of actual gray distribution and preset gray distribution;
S3, a distribution matching process: inputting the extracted gray level distribution information into a first matching model, and performing matching degree calculation with preset gray level distribution information, for example, using measuring methods such as Euclidean distance, manhattan distance or cosine similarity;
S4, abnormality judgment: comparing the matching degree with a preset threshold value, and judging that the matching degree is abnormal if the matching degree is lower than the threshold value; the preset threshold value can be adjusted according to the safety requirement of engineering and historical data.
S5, exception handling: once an anomaly is detected, the system automatically triggers an alarm and notifies a supervisor to perform field inspection; meanwhile, the system can record abnormal information, including time, position, gray level distribution difference and the like, for subsequent analysis and audit.
In an alternative embodiment, after said performing pixel gray value conversion processing on said first supervision data to obtain supervision gray distribution information, the method further includes:
Step 301, constructing a supervision gray level distribution matrix according to a preset distribution matrix construction rule and the supervision gray level distribution information;
Step 302, calculating the correlation between the supervision gray level distribution matrix and a preset gray level distribution matrix, and determining that the engineering supervision data is abnormal if the correlation result does not meet a preset correlation condition.
In this embodiment, in addition to matching the distribution situation of the gray values, a corresponding distribution matrix may be constructed according to the distribution situation of the gray values of the pixel points, and then the correlation of the matrix is calculated to determine the rationality of the data, so that the erroneous determination of the distribution situation by the first matching model is avoided.
For example, according to a preset distribution matrix construction rule, a gray histogram is converted into a matrix form (if the number of gray levels is 256 (0-255), a 256×1 matrix is constructed, each element represents the number of pixels of the corresponding gray level), then the correlation between the supervision gray distribution matrix and the preset gray distribution matrix is calculated using an algorithm such as pearson correlation coefficient or cosine similarity, and then the correlation calculation result is compared with a set correlation threshold value, and if the calculated correlation is lower than the threshold value, the difference between the supervision gray distribution matrix and the preset matrix is considered to be beyond the normal range, thereby judging that an abnormality exists.
Wherein, the correlation can be calculated by the following formula 2, and the correlation is judged based on the Pearson (Pearson) correlation coefficient:
(equation 2)
Wherein,AndRespectively the (i, j) th element in the substation matrix,AndIs the average value of the elements.
In an alternative embodiment, before said normalizing the engineering management data to obtain first management data, the method further includes:
step 201, acquiring historical supervision data, wherein the historical supervision data comprises historical engineering environment data and historical engineering management data;
Step 202, determining a data filtering rule according to the historical engineering environment data and the historical engineering management data;
and 203, performing data filtering processing on the engineering supervision data according to the data filtering rule, wherein the normalization processing is performed on the data filtering processing result.
In this embodiment, the supervision data is filtered based on the history data to reduce data interference.
Wherein the historical data comprises all historical supervision data which is retrieved from a database and related to the current project, such as historical project environment data (such as temperature, humidity, wind power and the like) and historical project management data (such as construction progress, material use, safety accident records and the like); the data filtering rules can be determined by analyzing historical supervision data and identifying common noise patterns, abnormal values and uncorrelated characteristics, and the rules can comprise removing abnormal values beyond a certain range, ignoring data uncorrelated with engineering quality or safety, smoothing noise data and the like;
in an alternative embodiment, after the acquiring the historical proctoring data, the method further comprises:
step 101, according to a preset space-time prediction model, performing prediction calculation on the historical supervision data to obtain supervision prediction data;
And 102, carrying out matching processing on the supervision predicted data and the engineering supervision data, and determining that the engineering supervision data is abnormal under the condition that a matching processing result does not accord with a preset data evolution condition.
In this embodiment, in general, the project supervision data will change along with the change of the project progress, and these changes are related to the qualification of the construction unit and the supply condition of the supplier, so that in the case of the construction unit and the supplier being specific, the supervision data under the project progress of the specific project can be predicted based on the historical data by the time sequence analysis model, thereby realizing the dynamic supervision of the project progress.
For example, the time-space prediction model is firstly trained through historical data, such as time series analysis (ARIMA), a machine learning model (random forest, support vector regression, etc.), or a deep learning model (LSTM network), and then engineering progress data of each month or each day is input into the trained model to obtain a predicted result, and then the predicted result is matched with the actual engineering progress, so as to judge whether the progress is reasonable.
From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
In this embodiment, an intelligent supervision device based on artificial intelligence is further provided, and the device is used to implement the foregoing embodiments and preferred embodiments, which are not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
FIG. 3 is a block diagram of an intelligent supervision device based on artificial intelligence according to an embodiment of the invention, as shown in FIG. 3, the device includes:
the data acquisition module 31 is configured to acquire engineering supervision data, where the engineering supervision data is obtained by acquiring data of an engineering site through a sensor deployed on the engineering site;
The normalization conversion module 32 is configured to normalize the engineering supervision data to obtain first supervision data;
the gray value conversion module 33 is configured to perform pixel gray value conversion processing on the first supervision data to obtain supervision gray distribution information;
The first matching module 34 is configured to perform distribution matching on the supervision gray scale distribution information and preset gray scale distribution information through a first matching model, and determine that the engineering supervision data is abnormal if a distribution matching result does not conform to a preset distribution condition.
In an alternative embodiment, the apparatus further comprises:
The matrix construction module is used for constructing a supervision gray level distribution matrix according to preset distribution matrix construction rules and the supervision gray level distribution information after the pixel gray level value conversion processing is carried out on the first supervision data to obtain supervision gray level distribution information;
The correlation calculation module is used for calculating the correlation between the supervision gray level distribution matrix and a preset gray level distribution matrix, and determining that the engineering supervision data is abnormal under the condition that the correlation result does not accord with a preset correlation condition.
In an alternative embodiment, the apparatus further comprises:
the historical data acquisition module is used for acquiring historical supervision data before carrying out normalization processing on the engineering supervision data to obtain first supervision data, wherein the historical supervision data comprises historical engineering environment data and historical engineering management data;
The rule determining module is used for determining a data filtering rule according to the historical engineering environment data and the historical engineering management data;
And the filtering module is used for carrying out data filtering processing on the engineering supervision data according to the data filtering rule, and the normalization processing is carried out on the data filtering processing result.
In an alternative embodiment, the apparatus further comprises:
The prediction calculation module is used for performing prediction calculation on the historical supervision data according to a preset space-time prediction model after the historical supervision data are acquired so as to obtain supervision prediction data;
And the second matching module is used for carrying out matching processing on the supervision predicted data and the engineering supervision data, and determining that the engineering supervision data is abnormal under the condition that a matching processing result does not accord with a preset data evolution condition.
It should be noted that each of the above modules may be implemented by software or hardware, and for the latter, it may be implemented by, but not limited to: the modules are all located in the same processor; or the above modules may be located in different processors in any combination.
Embodiments of the present invention also provide a computer readable storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the method embodiments described above when run.
In one exemplary embodiment, the computer readable storage medium may include, but is not limited to: a usb disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing a computer program.
An embodiment of the invention also provides an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
In an exemplary embodiment, the electronic apparatus may further include a transmission device connected to the processor, and an input/output device connected to the processor.
Specific examples in this embodiment may refer to the examples described in the foregoing embodiments and the exemplary implementation, and this embodiment is not described herein.
It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may be implemented in program code executable by computing devices, so that they may be stored in a storage device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than that shown or described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An artificial intelligence based intelligent supervision method is characterized by comprising the following steps:
Acquiring engineering supervision data, wherein the engineering supervision data is obtained by acquiring data of an engineering site through a sensor deployed on the engineering site;
carrying out normalization processing on the engineering supervision data to obtain first supervision data;
Performing pixel gray value conversion processing on the first supervision data to obtain supervision gray distribution information;
And carrying out distribution matching on the supervision gray level distribution information and preset gray level distribution information through a first matching model, and determining that the engineering supervision data is abnormal under the condition that a distribution matching result does not accord with a preset distribution condition.
2. The method of claim 1, wherein after said performing pixel gray value conversion processing on said first supervision data to obtain supervision gray distribution information, said method further comprises:
constructing a supervision gray level distribution matrix according to a preset distribution matrix construction rule and the supervision gray level distribution information;
and calculating the correlation between the supervision gray distribution matrix and a preset gray distribution matrix, and determining that the engineering supervision data is abnormal under the condition that the correlation result does not accord with a preset correlation condition.
3. The method of claim 1, wherein prior to said normalizing said engineering proctorial data to obtain first proctorial data, the method further comprises:
acquiring historical supervision data, wherein the historical supervision data comprises historical engineering environment data and historical engineering management data;
Determining a data filtering rule according to the historical engineering environment data and the historical engineering management data;
And carrying out data filtering processing on the engineering supervision data according to the data filtering rule, wherein the normalization processing is executed on the data filtering processing result.
4. A method according to claim 3, wherein after the acquisition of the history management data, the method further comprises:
according to a preset space-time prediction model, performing prediction calculation on the historical supervision data to obtain supervision prediction data;
And carrying out matching processing on the supervision predicted data and the engineering supervision data, and determining that the engineering supervision data is abnormal under the condition that a matching processing result does not accord with a preset data evolution condition.
5. Intelligent supervision device based on artificial intelligence, its characterized in that includes:
The data acquisition module is used for acquiring engineering supervision data, wherein the engineering supervision data is obtained by acquiring data of an engineering site through a sensor deployed on the engineering site;
The normalization conversion module is used for carrying out normalization processing on the engineering supervision data to obtain first supervision data;
The gray value conversion module is used for carrying out pixel gray value conversion processing on the first supervision data so as to obtain supervision gray distribution information;
The first matching module is used for carrying out distribution matching on the supervision gray level distribution information and preset gray level distribution information through a first matching model, and determining that the engineering supervision data is abnormal under the condition that a distribution matching result does not accord with a preset distribution condition.
6. The apparatus of claim 5, wherein the apparatus further comprises:
The matrix construction module is used for constructing a supervision gray level distribution matrix according to preset distribution matrix construction rules and the supervision gray level distribution information after the pixel gray level value conversion processing is carried out on the first supervision data to obtain supervision gray level distribution information;
The correlation calculation module is used for calculating the correlation between the supervision gray level distribution matrix and a preset gray level distribution matrix, and determining that the engineering supervision data is abnormal under the condition that the correlation result does not accord with a preset correlation condition.
7. The apparatus of claim 5, wherein the apparatus further comprises:
the historical data acquisition module is used for acquiring historical supervision data before carrying out normalization processing on the engineering supervision data to obtain first supervision data, wherein the historical supervision data comprises historical engineering environment data and historical engineering management data;
The rule determining module is used for determining a data filtering rule according to the historical engineering environment data and the historical engineering management data;
And the filtering module is used for carrying out data filtering processing on the engineering supervision data according to the data filtering rule, and the normalization processing is carried out on the data filtering processing result.
8. The apparatus of claim 7, wherein the apparatus further comprises:
The prediction calculation module is used for performing prediction calculation on the historical supervision data according to a preset space-time prediction model after the historical supervision data are acquired so as to obtain supervision prediction data;
And the second matching module is used for carrying out matching processing on the supervision predicted data and the engineering supervision data, and determining that the engineering supervision data is abnormal under the condition that a matching processing result does not accord with a preset data evolution condition.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a computer program, wherein the computer program is arranged to execute the method of any of the claims 1 to 4 when run.
10. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the method of any of the claims 1 to 4.
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