CN114820825B - Green detection method and system for underground pipe gallery assembled structure - Google Patents
Green detection method and system for underground pipe gallery assembled structure Download PDFInfo
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Abstract
The invention discloses a green detection method and a green detection system for an underground pipe gallery assembly type structure, and relates to the field of data processing. The method comprises the following steps: the distributed corridor storehouse monitoring system is built, data acquisition of operating environments is carried out on distributed equipment in cabin assembly areas, preprocessing is carried out on the acquired data, energy consumption feature extraction is carried out on the preprocessed data, data comparison and analysis of forward continuous time sequences are carried out, abnormal marking is carried out on parts with obvious data fluctuation, and detection and output of abnormal energy consumption values are carried out. The technical problem that dynamic monitoring of energy consumption of each large power device is difficult to achieve, and quality control of each energy source data cannot be achieved is solved. The energy consumption of each large-scale power device is dynamically monitored when each cabin of the underground comprehensive pipe gallery is normally operated, quality control of each energy source data is guaranteed, and the technical effect of green detection of the energy consumption of the underground pipe gallery assembly type structure device is achieved.
Description
Technical Field
The invention relates to the field of data processing, in particular to a green detection method and system for an underground pipe gallery assembly type structure.
Background
The underground comprehensive pipe gallery is used as a life line project of a city, and a new concept of green city development is practiced. The appearance of the comprehensive pipe gallery gradually eliminates spider-web type overhead lines of main streets, and the urban ground landscape is obviously improved. The utility tunnel is a public tunnel which is built underground in a city and is used for intensively laying municipal pipelines such as electric power, communication, broadcast television, water supply and the like. The utility tunnel can effectively stop "zip fastener road" phenomenon, lets the technical staff need not to excavate the road surface repeatedly, just can salvage, maintain, dilatation transformation etc. all kinds of pipelines in the piping lane, reduces the pipeline time of salvageing simultaneously greatly. However, problems such as high power consumption of the apparatus are also successively caused.
However, in the prior art, when each cabin of the underground comprehensive pipe gallery is operated normally, the dynamic monitoring of the energy consumption of each large power device is difficult, so that the quality control of each energy data cannot be performed, and the technical problem of certain accident potential is brought while the energy consumption is greatly wasted.
Disclosure of Invention
The invention aims to provide a green detection method and a green detection system for an underground pipe gallery assembled structure, which are used for solving the technical problems that in the prior art, when normal equipment operation is carried out on each cabin of an underground pipe gallery, the dynamic monitoring on the energy consumption of each large-scale power equipment is difficult, the quality control on each energy data cannot be carried out, the energy consumption is greatly wasted, and meanwhile, certain accident potential is brought.
In view of the above problems, the present invention provides a green detection method and system for an underground piping and corridor assembled structure.
In a first aspect, the present invention provides a green detection method for an underground piping and corridor assembled structure, the method comprising: building a distributed gallery monitoring system, wherein the distributed gallery monitoring system comprises a data acquisition unit, a data analysis unit and an abnormity early warning unit; based on the BIM technology, carrying out simulation modeling on each cabin assembly area of a target urban underground pipe gallery to generate a three-dimensional simulation environment of the target urban underground pipe gallery; based on the data acquisition unit, acquiring data of the operation environment of each cabin in the three-dimensional simulation environment to obtain an operation data set of each cabin; uploading the cabin operation data sets to the data analysis unit, and performing feature extraction on the cabin operation data sets to obtain target extraction feature sets; comparing and analyzing the energy consumption data of the forward continuous time sequence of the target extraction feature set to obtain each extraction feature data analysis result; and uploading the analysis result of each extracted feature data to the abnormity early warning unit, and detecting and outputting an abnormity result.
In a second aspect, the present invention further provides a green detection system for an underground piping and corridor assembled structure, for performing the green detection method for the underground piping and corridor assembled structure according to the first aspect, wherein the system comprises: the system comprises a first building unit, a second building unit and a monitoring unit, wherein the first building unit is used for building a distributed type gallery monitoring system, and the distributed type gallery monitoring system comprises a data acquisition unit, a data analysis unit and an abnormity early warning unit; the first modeling unit is used for carrying out simulation modeling on each cabin assembly area of the target urban underground pipe gallery based on a BIM technology to generate a three-dimensional simulation environment of the target urban underground pipe gallery; the first acquisition unit is used for acquiring data of each cabin operation environment in the three-dimensional simulation environment based on the data acquisition unit to obtain each cabin operation data set; the first extraction unit is used for uploading each cabin operation data set to the data analysis unit, and performing feature extraction on each cabin operation data set to obtain a target extraction feature set; the first analysis unit is used for comparing and analyzing the energy consumption data of the forward continuous time sequence of the target extraction feature set to obtain each extraction feature data analysis result; and the first detection unit is used for uploading the analysis results of the extracted feature data to the abnormity early warning unit, and detecting and outputting the abnormity results.
In a third aspect, a green detection system for an underground piping and corridor assembled structure, comprising: a processor coupled to a memory for storing a program that, when executed by the processor, causes a system to perform the steps of the method of any of the first aspects above.
In a fourth aspect, a computer-readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of the method of any of the first aspects described above.
One or more technical schemes provided by the invention at least have the following technical effects or advantages:
by building a distributed type gallery monitoring system, data acquisition of an operating environment is carried out on equipment in each distributed cabin assembly area, acquired data are visually displayed in a three-dimensional simulation environment of a target city underground pipe gallery, the accuracy of sample data is improved by preprocessing each acquired data, energy consumption characteristic extraction is carried out on the preprocessed data, the extracted energy consumption characteristic is subjected to forward continuous time sequence data comparison analysis, dynamic monitoring can be carried out on a part with stable energy consumption difference data fluctuation, abnormal marking is carried out on a part with obvious data fluctuation, and detection and output of abnormal energy consumption values are carried out, so that green detection of energy consumption of the equipment with an underground pipe gallery assembly structure is realized, and the dynamic monitoring on the energy consumption of large-scale power equipment is realized when normal equipment is carried out on each cabin of the underground pipe gallery, ensure to carry out quality control to each energy data, realize carrying out green detection's technological effect to underground pipe gallery assembled structure equipment energy consumption.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can be obtained according to the provided drawings without inventive effort.
FIG. 1 is a schematic flow chart of a green detection method for an underground piping and corridor assembled structure according to the present invention;
FIG. 2 is a schematic flow chart illustrating feature extraction of the cabin operation data sets in the green detection method for the underground pipe gallery assembly structure according to the present invention;
FIG. 3 is a schematic flow chart of comparison analysis of energy consumption data of a forward continuous time sequence by the target extraction feature set in the green detection method of the underground piping lane fabricated structure according to the present invention;
FIG. 4 is a schematic structural diagram of a green detection system of an underground piping and corridor assembled structure according to the present invention;
fig. 5 is a schematic structural diagram of an exemplary electronic device of the present invention.
Description of reference numerals:
the system comprises a first building unit 11, a first modeling unit 12, a first acquisition unit 13, a first extraction unit 14, a first analysis unit 15, a first detection unit 16, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304 and a bus interface 305.
Detailed Description
The invention provides a green detection method and a green detection system for an underground pipe gallery assembly structure, and solves the technical problems that in the prior art, when normal equipment operation is carried out on each cabin of an underground pipe gallery, the dynamic monitoring on the energy consumption of each large-scale power equipment is difficult, the quality control on each energy data cannot be carried out, the energy consumption is greatly wasted, and meanwhile, certain accident potential is brought. Reached when carrying out normal equipment operation to each cabin of utility tunnel, carried out dynamic monitoring to the energy consumption of each large-scale power equipment, ensured to carry out quality control to each energy data, realized carrying out green detection's technological effect to utility tunnel assembled structure equipment energy consumption.
In the technical scheme of the invention, the data acquisition, storage, use, processing and the like all conform to relevant regulations of national laws and regulations.
In the following, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments of the present invention, and it should be understood that the present invention is not limited by the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention. It should be further noted that, for the convenience of description, only some but not all of the elements associated with the present invention are shown in the drawings.
The invention provides a green detection method for an underground pipe gallery assembled structure, which comprises the following steps: by building a distributed type gallery monitoring system, data acquisition of an operating environment is carried out on equipment in each distributed cabin assembly area, acquired data are visually displayed in a three-dimensional simulation environment of a target city underground pipe gallery, the accuracy of sample data is improved by preprocessing each acquired data, energy consumption characteristic extraction is carried out on the preprocessed data, the extracted energy consumption characteristic is subjected to forward continuous time sequence data comparison analysis, dynamic monitoring can be carried out on a part with stable energy consumption difference data fluctuation, abnormal marking is carried out on a part with obvious data fluctuation, and detection and output of abnormal energy consumption values are carried out, so that green detection of energy consumption of the equipment with an underground pipe gallery assembly structure is realized, and the dynamic monitoring on the energy consumption of large-scale power equipment is realized when normal equipment is carried out on each cabin of the underground pipe gallery, ensure to carry out quality control to each energy data, realize carrying out green detection's technological effect to underground pipe gallery assembled structure equipment energy consumption.
Having described the general principles of the invention, reference will now be made in detail to various non-limiting embodiments of the invention, examples of which are illustrated in the accompanying drawings.
Example one
Referring to fig. 1, the present invention provides a green detection method for an underground piping and corridor assembled structure, which specifically includes the following steps:
step S100: building a distributed gallery monitoring system, wherein the distributed gallery monitoring system comprises a data acquisition unit, a data analysis unit and an abnormity early warning unit;
step S200: carrying out simulation modeling on each cabin assembly area of a target urban underground pipe gallery based on a BIM technology to generate a three-dimensional simulation environment of the target urban underground pipe gallery;
particularly, the underground comprehensive pipe gallery is used as a life line project of a city, and a new concept of green city development is practiced. The appearance of the comprehensive pipe gallery gradually eliminates spider-web type overhead lines of main streets, and the urban ground landscape is obviously improved. The utility tunnel is a public tunnel which is built underground in a city and is used for intensively laying municipal pipelines such as electric power, communication, broadcast television, water supply and the like. The utility tunnel can effectively stop "zip fastener road" phenomenon, lets the technical staff need not to excavate the road surface repeatedly, just can salvage, maintain, dilatation transformation etc. all kinds of pipelines in the piping lane, reduces the pipeline time of salvageing simultaneously greatly. However, problems such as high power consumption of the apparatus are also successively caused.
However, in the prior art, when each cabin of the underground comprehensive pipe gallery is operated normally, the dynamic monitoring of the energy consumption of each large power device is difficult, so that the quality control of each energy data cannot be performed, and the technical problem of certain accident potential is brought while the energy consumption is greatly wasted.
In order to solve the problems in the prior art, the application provides a green detection method for an underground pipe gallery assembly type structure. By building a distributed type gallery monitoring system, data acquisition of an operating environment is carried out on equipment in each distributed cabin assembly area, acquired data are visually displayed in a three-dimensional simulation environment of a target city underground pipe gallery, the accuracy of sample data is improved by preprocessing each acquired data, energy consumption characteristic extraction is carried out on the preprocessed data, the extracted energy consumption characteristic is subjected to forward continuous time sequence data comparison analysis, dynamic monitoring can be carried out on a part with stable energy consumption difference data fluctuation, abnormal marking is carried out on a part with obvious data fluctuation, and detection and output of abnormal energy consumption values are carried out, so that green detection of energy consumption of the equipment with an underground pipe gallery assembly structure is realized, and the dynamic monitoring on the energy consumption of large-scale power equipment is realized when normal equipment is carried out on each cabin of the underground pipe gallery, ensure to carry out quality control to each energy data, realize carrying out green detection's technological effect to underground pipe gallery assembled structure equipment energy consumption.
The distributed corridor storehouse monitoring system is used for carrying out energy consumption monitoring on all cabin assembled equipment of a target city underground pipe gallery, and data collected by a monitoring system of each cabin are transmitted back to the distributed corridor storehouse monitoring system in a video baseband, optical fibers, microwaves and other wireless transmission modes, so that energy consumption analysis is carried out on the collected data, and abnormal energy consumption values are marked, tracked and output early warning is carried out. The data acquisition unit is used for driving a monitoring system of each cabin and dynamically acquiring equipment operation environment data of each cabin, wherein the equipment operation environment data comprises ventilation data, monitoring data, lighting data, drainage data, security data and the like of each cabin; the data analysis unit is used for extracting energy consumption characteristics of the acquired data and comparing and analyzing the extracted energy consumption data; the abnormity early warning unit is used for marking abnormal values of the energy consumption data comparison analysis results and outputting the abnormal marking results, so that dynamic detection of equipment energy consumption is realized, and green detection of the underground pipe gallery assembly structure is realized.
Furthermore, simulation modeling can be carried out on each cabin assembly area of the target urban underground pipe gallery based on the BIM technology, and a three-dimensional simulation environment of the target urban underground pipe gallery is generated. The method is characterized in that a three-dimensional mode is adopted to simulate the underground comprehensive pipe gallery, data monitoring and video monitoring of water, electricity, gas, heat, communication pipelines, harmful gas concentration and temperature and humidity are integrated, an overall map of the layout of the pipe gallery can be displayed through a loading interface after modeling, monitoring data are arranged on two sides of the interface, the three-dimensional landscape of the urban overall comprehensive pipe gallery is displayed, and three-dimensional model data of pipe gallery bodies, pipelines, equipment and the like are displayed, edited and stored. Each cabin assembly area has covered each regional node of this target city utility tunnel, including trunk utility tunnel and branch utility tunnel, wherein, trunk utility tunnel is used for holding the utility tunnel that city trunk engineering pipeline adopted independent subdivision mode to construct, and branch utility tunnel is used for holding the utility tunnel that city ration engineering pipeline adopted single-deck or two cabin modes to construct. Three-dimensional simulation environment carries out virtual simulation's result to each cabin of this target city underground pipe gallery promptly, includes that the environmental parameter, the equipment state to the piping lane inside carry out real-time supervision, in time handle inside alarm information of piping lane and remote scheduling work.
Step S300: based on the data acquisition unit, acquiring data of the operation environment of each cabin in the three-dimensional simulation environment to obtain an operation data set of each cabin;
further, step S300 includes:
step S310: based on various sensor monitoring systems embedded in the cabin assembly areas, acquiring data of the operating environment of each cabin to obtain a primary acquired data set;
step S320: performing data preprocessing on the primary collected data set to obtain a processed standard data set;
step S330: and carrying out time element acquisition marking on the processed standard data set to generate the operation data set of each cabin.
Wherein, step S320 includes:
step S321: obtaining a first characteristic data set according to the primary acquisition data set;
step S322: performing centralized processing on the first characteristic data set to obtain a second characteristic data set;
step S323: obtaining a first covariance matrix of the second feature data set;
step S324: calculating the first covariance matrix to obtain a first eigenvalue and a first eigenvector of the first covariance matrix;
step S325: and projecting the first feature data set to the first feature vector to obtain a first dimension reduction data set, wherein the first dimension reduction data set is the feature data set obtained after dimension reduction of the first feature data set.
Specifically, after the three-dimensional simulation environment is generated through simulation, a monitoring system of each cabin needs to be driven to dynamically acquire equipment operation environment data of each cabin. Specifically, the accessible multiple sensor monitoring system of embedding in each cabin assembled area is right each cabin operational environment carries out data acquisition, and is exemplary, usable humiture sensor, oxygen sensor, carbon monoxide sensor, carbon dioxide sensor, hydrogen sulfide, methane sensor, pressure sensor etc. carry out dynamic monitoring to the gas environment in each cabin to the energy consumption condition of the ventilation equipment in each cabin of analysis, through illumination sensor etc. carry out dynamic monitoring to the illumination environment in each cabin, thereby the energy consumption condition of the illumination equipment in each cabin of analysis etc.. The primary collected data set is a collected functional performance consumption data set, and covers energy consumption data of ventilation equipment, energy consumption data of monitoring equipment, energy consumption data of lighting equipment, energy consumption data of drainage equipment, energy consumption data of security equipment and the like.
After the primary collection data set is obtained, it needs to be processed frequently because it covers a large and cumbersome amount of data. Specifically, the extracted feature data, that is, the primary collected data set, may be subjected to a digitization process, and a feature data set matrix is constructed to obtain the first feature data set. And then carrying out centralization processing on each feature data in the first feature data set, firstly solving an average value of each feature in the first feature data set, then subtracting the average value of each feature from each feature for all samples, and then obtaining a new feature value, wherein the second feature data set is formed by the new feature values, and is a data matrix. By the covariance formula:
and operating the second characteristic data set to obtain a first covariance matrix of the second characteristic data set. Wherein the content of the first and second substances,
characteristic data in the second characteristic data set;
is the average value of the characteristic data;
the total amount of sample data in the second feature data set. Then, through matrix operation, the eigenvalue and the eigenvector of the first covariance matrix are solved, and each eigenvalue corresponds to one eigenvector. And selecting the largest first K characteristic values and the corresponding characteristic vectors from the obtained first characteristic vectors, and projecting the original characteristics in the first characteristic data set onto the selected characteristic vectors to obtain the first characteristic data set after dimension reduction. The feature data are subjected to dimensionality reduction processing through a principal component analysis method, and redundant data are removed on the premise of ensuring the information quantity, so that the sample quantity of the feature data is reduced, the loss of the information quantity after dimensionality reduction is minimum, and the operation speed of a training model on the data is accelerated. The processed standard data set may be obtained after a simplified pre-processing of the primary collected data set. And generating each cabin operation data set by carrying out acquisition marking of a time element on the data. And marking time nodes on each processed standard data, so that the data can be compared and analyzed subsequently according to a forward time node development sequence, the operation data set of each cabin is the final obtained result of the acquired data, and the subsequent data analysis and processing are performed on the basis.
Step S400: uploading the cabin operation data sets to the data analysis unit, and performing feature extraction on the cabin operation data sets to obtain target extraction feature sets;
further, as shown in fig. 2, step S400 includes:
step S410: acquiring historical energy consumption characteristics of operation data of each cabin;
step S420: taking the historical energy consumption characteristics as identification information, and constructing an energy consumption characteristic extraction model;
step S430: uploading the operation data sets of all cabins as input data to the energy consumption feature extraction model, and training the input data based on the identification information to obtain a training result, wherein the training result comprises a model extraction feature set;
step S440: obtaining a target characteristic energy consumption value set corresponding to the model extraction characteristic set;
step S450: obtaining each operation data energy consumption value set of each cabin operation data set;
step S460: determining a preset energy consumption threshold value according to the target characteristic energy consumption value set;
step S470: screening the energy consumption value sets of the operation data based on the preset energy consumption threshold value to obtain an additional energy consumption characteristic set;
step S480: and inputting the additional energy consumption feature set into the energy consumption feature extraction model, and performing optimization training on the energy consumption feature extraction model.
Specifically, the preprocessed cabin operation data sets are obtained and analyzed by the data analysis unit. Specifically, feature extraction can be performed on the operation data set of each cabin, and historical energy consumption features of the operation data of each cabin are acquired through acquisition, wherein the historical energy consumption features can be acquired according to historical report logs of the operation data of each cabin and historical energy consumption statistical features of the same equipment in different areas, and include energy consumption features of ventilation equipment, energy consumption features of monitoring equipment, energy consumption features of lighting equipment and the like, for example, no matter people exist or do not exist, if a lighting lamp of the underground pipe gallery keeps a lighting state for a long time, certain energy consumption is caused to the lighting equipment, specifically, the lighting can be controlled through an embedded automatic control induction module arranged at the top of the pipe gallery, infrared induction can be set to light when people pass by, and the lighting is turned off within a set time after people leave; or manual control equipment such as intelligent panel, can inlay the wall and install at piping lane subregion entrance, can realize automatic control like this, avoid causing too big energy consumption to lighting equipment.
The historical energy consumption characteristics are used as identification information to construct an energy consumption characteristic extraction model, the energy consumption characteristic extraction model can extract characteristics of input ventilation data, monitoring data, lighting data and the like, training can be carried out on the input data based on the identification information to obtain a training result, the training result comprises a model extraction characteristic set, namely the energy consumption characteristic set extracted through the model, and due to the fact that the result obtained through the model has certain limitation, an additional characteristic set close to the model extraction characteristic set can be determined through a difference value between dynamically changed energy consumption values, and exemplarily, the energy consumption characteristics of drainage equipment, the energy consumption characteristics of security equipment and the like can be obtained.
Specifically, a target characteristic energy consumption value set corresponding to the model extraction characteristic set may be obtained, that is, a specific energy consumption value set corresponding to the energy consumption characteristic set extracted by the model is obtained, for example, generally, the energy consumption of the ventilation equipment tends to 900KWH, the energy consumption of the monitoring equipment tends to 1700KWH, and the like on average in one day, and meanwhile, each operation data energy consumption value set of each cabin operation data set is obtained, which may be understood as the operation data energy consumption value set of other operation equipment except the ventilation equipment and the monitoring equipment, including the operation energy consumption data of the lighting equipment, the operation energy consumption data of the drainage equipment, and the like. Generally, the average energy consumption of lighting equipment tends to 1350KWH and the energy consumption of drainage equipment tends to 600KWH per day, and then, according to the target characteristic energy consumption value set, a preset energy consumption threshold value is determined, which is a threshold value interval that normally fluctuates in an upper interval and a lower interval of the target characteristic energy consumption value set, and here, an explanation may be given by taking [1200,1800] as an example, and further, based on the preset energy consumption threshold value, each operation data energy consumption value set may be screened to obtain an additional energy consumption characteristic set, that is, energy consumption values in an interval of [1200,1800] are screened to obtain an additional energy consumption characteristic set, where the additional energy consumption characteristic set includes energy consumption characteristics of the lighting equipment. Since the energy consumption feature extraction model does not include extraction training of energy consumption features of the lighting device, but the energy consumption value of the energy consumption feature extraction model is close to the target feature energy consumption value set, in order to ensure the update performance of the model training, the additional energy consumption feature set can be input into the energy consumption feature extraction model, and the energy consumption feature extraction model is optimized and trained, so that the energy consumption feature extraction model performs incremental optimization on the additional energy consumption feature set, and the real-time update performance of the model is ensured. And the target extraction feature set is a feature set obtained after data fusion is carried out on the model extraction feature set and the extra energy consumption feature set.
Step S500: comparing and analyzing the energy consumption data of the forward continuous time sequence of the target extraction feature set to obtain each extraction feature data analysis result;
step S600: and uploading the analysis result of each extracted feature data to the abnormity early warning unit, and detecting and outputting an abnormity result.
Further, as shown in fig. 3, step S500 includes:
step S510: performing data fusion on the model extraction feature set and the additional energy consumption feature set to generate the target extraction feature set;
step S520: based on the logic of the forward continuous time sequence, performing point-by-point acquisition on the energy consumption data of the target extraction characteristic set to obtain each target extraction characteristic energy consumption data set, wherein each target extraction characteristic energy consumption data set has a continuous time characteristic;
step S530: calculating the energy consumption difference value of each time node in the energy consumption data set of each target extraction characteristic compared with the energy consumption data of the previous time node to obtain the energy consumption difference value set of each time node;
step S540: determining each extracted feature data analysis result according to each time node energy consumption difference set;
step S550: the abnormal early warning unit is embedded with a data fluctuation monitoring response;
step S560: based on the data fluctuation monitoring response, monitoring data fluctuation on the input energy consumption difference value set of each time node to obtain data fluctuation results of each energy consumption difference value;
step S570: judging whether the energy consumption difference data fluctuation results meet a preset data fluctuation threshold value or not;
step S580: and if the energy consumption difference data fluctuation results meet the preset data fluctuation threshold, carrying out abnormal marking on the meeting parts, and outputting the abnormal energy consumption difference data fluctuation results.
Specifically, after the target extraction feature set is obtained, the energy consumption data of the forward continuous time sequence may be compared and analyzed with the extracted feature data, and for example, a certain day is used as a first time node, and a second day after the day is changed is obtained through the forward continuous time sequence and is used as a second time node until an nth time node. And then based on the logic of the forward continuous time sequence, performing point-by-point acquisition on the energy consumption data of the target extraction characteristic set to obtain each target extraction characteristic energy consumption data set, illustratively, performing point-by-point acquisition on the energy consumption values of the monitoring equipment from a first time node to a second time node to an Nth time node, generating the energy consumption value distribution of the energy consumption value of the monitoring equipment at each continuous time node, and so on, sequentially acquiring the energy consumption value distribution of other equipment at each continuous time node to form each target extraction characteristic energy consumption data set.
Furthermore, the energy consumption data difference of each time node in the target extracted characteristic energy consumption data set compared with the previous time node may be calculated, and the energy consumption value of the monitoring device is taken as an example to explain here, the energy consumption data difference of the monitoring device of the second time node compared with the first time node, the energy consumption data difference of the monitoring device of the third time node compared with the second time node, and the energy consumption data difference of the monitoring device of the nth time node compared with the nth-1 time node may be calculated, and the energy consumption data difference of the monitoring device of each time node forms the energy consumption difference set of each time node, and also represents the analysis result of each extracted characteristic data.
Further, after determining the analysis result of each extracted feature data, deep data fluctuation analysis can be performed on each extracted feature data. Specifically, data fluctuation analysis can be performed through data fluctuation monitoring response embedded in the abnormality early warning unit. The data fluctuation monitoring response performs fluctuation analysis on input data through an integrated monitoring algorithm, and performs fluctuation analysis on an energy consumption difference set of each time node through a preset data fluctuation threshold, wherein the preset data fluctuation threshold represents a threshold outside a set standard fluctuation interval, illustratively, the standard fluctuation interval can be set as [500, 1000], if the preset data fluctuation threshold exceeds the interval, the preset data fluctuation threshold can be divided into energy consumption differences which are more than or equal to 1000KWH, each energy consumption difference data fluctuation result reflects the actual fluctuation situation of the energy consumption data difference of the monitoring equipment of each time node, whether the actual fluctuation situation of the energy consumption data difference of the monitoring equipment of each time node exceeds the set 1000KWH is judged by judging whether the energy consumption difference data fluctuation result meets the preset data fluctuation threshold or not, if the energy consumption data of the equipment exceeds the set 1000KWH, the equipment works normally, the energy consumption data difference value of the monitoring equipment of the exceeding time node is marked abnormally, the fluctuation result of the abnormal energy consumption difference value data is output, and green detection of the energy consumption of each equipment running state is achieved.
In summary, the green detection method for the underground pipe gallery assembly type structure provided by the invention has the following technical effects:
1. by building a distributed type gallery monitoring system, data acquisition of an operating environment is carried out on equipment in each distributed cabin assembly area, acquired data are visually displayed in a three-dimensional simulation environment of a target city underground pipe gallery, the accuracy of sample data is improved by preprocessing each acquired data, energy consumption characteristic extraction is carried out on the preprocessed data, the extracted energy consumption characteristic is subjected to forward continuous time sequence data comparison analysis, dynamic monitoring can be carried out on a part with stable energy consumption difference data fluctuation, abnormal marking is carried out on a part with obvious data fluctuation, and detection and output of abnormal energy consumption values are carried out, so that green detection of energy consumption of the equipment with an underground pipe gallery assembly structure is realized, and the dynamic monitoring on the energy consumption of large-scale power equipment is realized when normal equipment is carried out on each cabin of the underground pipe gallery, ensure to carry out quality control to each energy data, realize carrying out green detection's technological effect to underground pipe gallery assembled structure equipment energy consumption.
2. After the target extraction characteristic set is obtained, forward continuous time sequence energy consumption data comparison analysis can be carried out on the extracted characteristic data, energy consumption data difference values of time nodes in the target extraction characteristic energy consumption data set and the time nodes above can be calculated, data fluctuation analysis is carried out through data fluctuation monitoring responses embedded in the abnormity early warning unit, fluctuation analysis is carried out on the energy consumption difference value set of the time nodes based on a preset data fluctuation threshold value, the operation state of each device is evaluated, and the energy consumption green detection of the operation state of each device is achieved by outputting the abnormal energy consumption difference value data fluctuation results.
Example two
Based on the same inventive concept as the green detection method of the assembled structure of the underground pipe gallery in the foregoing embodiment, the present invention further provides a green detection system of the assembled structure of the underground pipe gallery, please refer to fig. 4, where the system includes:
the system comprises a first building unit 11, wherein the first building unit 11 is used for building a distributed corridor monitoring system, and the distributed corridor monitoring system comprises a data acquisition unit, a data analysis unit and an abnormity early warning unit;
the first modeling unit 12 is used for performing simulation modeling on each cabin assembly area of the target urban underground pipe gallery based on a BIM technology to generate a three-dimensional simulation environment of the target urban underground pipe gallery;
the first acquisition unit 13 is configured to perform data acquisition on each cabin operation environment in the three-dimensional simulation environment based on the data acquisition unit to obtain each cabin operation data set;
a first extraction unit 14, where the first extraction unit 14 is configured to upload the cabin operation data sets to the data analysis unit, and perform feature extraction on the cabin operation data sets to obtain a target extraction feature set;
the first analysis unit 15 is configured to perform forward continuous time sequence energy consumption data comparison analysis on the target extracted feature set to obtain each extracted feature data analysis result;
and the first detection unit 16 is configured to upload the analysis results of each extracted feature data to the abnormality early warning unit, and detect and output an abnormality result.
Further, the system further comprises:
the second acquisition unit is used for acquiring data of the operating environment of each cabin based on various sensor monitoring systems embedded in the cabin assembly areas to obtain a primary acquisition data set;
the first processing unit is used for carrying out data preprocessing on the primary collected data set to obtain a processed standard data set;
and the first marking unit is used for collecting and marking time elements for the processed standard data set to generate the operation data set of each cabin.
Further, the system further comprises:
a first obtaining unit configured to obtain a first feature data set according to the primary collected data set;
the second processing unit is used for carrying out centralized processing on the first characteristic data set to obtain a second characteristic data set;
a second obtaining unit configured to obtain a first covariance matrix of the second feature data set;
the first operation unit is used for operating the first covariance matrix to obtain a first eigenvalue and a first eigenvector of the first covariance matrix;
a third obtaining unit, configured to project the first feature data set to the first feature vector to obtain a first dimension-reduced data set, where the first dimension-reduced data set is a feature data set obtained after dimension reduction of the first feature data set.
Further, the system further comprises:
the third acquisition unit is used for acquiring historical energy consumption characteristics of the operation data of each cabin;
a first construction unit, configured to construct an energy consumption feature extraction model by using the historical energy consumption features as identification information;
and the fourth obtaining unit is used for uploading the operation data sets of all the cabins as input data to the energy consumption feature extraction model, training the input data based on the identification information, and obtaining a training result, wherein the training result comprises a model extraction feature set.
Further, the system further comprises:
a fifth obtaining unit, configured to obtain a target characteristic energy consumption value set corresponding to the model extraction characteristic set;
a sixth obtaining unit, configured to obtain each set of operation data energy consumption values of each set of cabin operation data;
the first determining unit is used for determining a preset energy consumption threshold value according to the target characteristic energy consumption value set;
the first screening unit is used for screening the energy consumption value sets of the operation data based on the preset energy consumption threshold value to obtain an additional energy consumption characteristic set;
a first optimization unit, configured to input the additional energy consumption feature set to the energy consumption feature extraction model, and perform optimization training on the energy consumption feature extraction model.
Further, the system further comprises:
the first fusion unit is used for carrying out data fusion on the model extraction feature set and the extra energy consumption feature set to generate the target extraction feature set;
the fourth acquisition unit is used for acquiring the energy consumption data of the target extraction characteristic set point by point based on the logic of the forward continuous time sequence to obtain each target extraction characteristic energy consumption data set, and each target extraction characteristic energy consumption data set has a continuous time characteristic;
a seventh obtaining unit, configured to obtain an energy consumption difference set of each time node by calculating an energy consumption data difference of each time node in the target extraction characteristic energy consumption data set;
and the second determining unit is used for determining each extracted feature data analysis result according to the energy consumption difference set of each time node.
Further, the system further comprises:
the first embedding unit is used for embedding the data fluctuation monitoring response into the abnormality early warning unit;
the first monitoring unit is used for monitoring data fluctuation on the basis of the data fluctuation monitoring response to the input energy consumption difference value sets of the time nodes to obtain data fluctuation results of the energy consumption difference values;
the first judging unit is used for judging whether the energy consumption difference data fluctuation results meet a preset data fluctuation threshold value or not;
and the first output unit is used for marking the meeting part abnormally and outputting the abnormal energy consumption difference data fluctuation result if the energy consumption difference data fluctuation results meet the preset data fluctuation threshold value.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on the difference from other embodiments, the foregoing green detection method and specific example of the first embodiment in fig. 1 are also applicable to the green detection system of the underground piping and duct assembled structure of the present embodiment, and through the foregoing detailed description of the green detection method of the underground piping and duct assembled structure, a person skilled in the art can clearly know the green detection system of the underground piping and duct assembled structure of the present embodiment, so for the brevity of the description, detailed description is not repeated here. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Exemplary electronic device
The electronic device of the present invention is described below with reference to fig. 5.
Fig. 5 illustrates a schematic structural diagram of an electronic device according to the present invention.
Based on the inventive concept of the method for green inspection of an underground piping and corridor assembled structure as in the previous embodiment, the present invention further provides a green inspection system of an underground piping and corridor assembled structure, on which a computer program is stored, which when executed by a processor implements the steps of any one of the methods for green inspection of an underground piping and corridor assembled structure as described above.
Where in fig. 5 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
The invention provides a green detection method for an underground pipe gallery assembled structure, which comprises the following steps: building a distributed gallery monitoring system, wherein the distributed gallery monitoring system comprises a data acquisition unit, a data analysis unit and an abnormity early warning unit; carrying out simulation modeling on each cabin assembly area of a target urban underground pipe gallery based on a BIM technology to generate a three-dimensional simulation environment of the target urban underground pipe gallery; based on the data acquisition unit, acquiring data of the operation environment of each cabin in the three-dimensional simulation environment to obtain an operation data set of each cabin; uploading the cabin operation data sets to the data analysis unit, and performing feature extraction on the cabin operation data sets to obtain target extraction feature sets; comparing and analyzing the energy consumption data of the forward continuous time sequence of the target extraction feature set to obtain each extraction feature data analysis result; and uploading the analysis result of each extracted feature data to the abnormity early warning unit, and detecting and outputting an abnormity result. The problem of among the prior art when carrying out normal equipment operation to each cabin of utility tunnel, because be difficult to carry out dynamic monitoring to each large-scale power equipment's energy consumption, lead to unable quality control to each energy data, when causing the energy consumption very big waste, bring certain accident hidden danger is solved. By building a distributed type gallery monitoring system, data acquisition of an operating environment is carried out on equipment in each distributed cabin assembly area, acquired data are visually displayed in a three-dimensional simulation environment of a target city underground pipe gallery, the accuracy of sample data is improved by preprocessing each acquired data, energy consumption characteristic extraction is carried out on the preprocessed data, the extracted energy consumption characteristic is subjected to forward continuous time sequence data comparison analysis, dynamic monitoring can be carried out on a part with stable energy consumption difference data fluctuation, abnormal marking is carried out on a part with obvious data fluctuation, and detection and output of abnormal energy consumption values are carried out, so that green detection of energy consumption of the equipment with an underground pipe gallery assembly structure is realized, and the dynamic monitoring on the energy consumption of large-scale power equipment is realized when normal equipment is carried out on each cabin of the underground pipe gallery, ensure to carry out quality control to each energy data, realize carrying out green detection's technological effect to underground pipe gallery assembled structure equipment energy consumption.
The invention also provides an electronic device, which comprises a processor and a memory;
the memory is used for storing;
the processor is configured to execute the method according to any one of the first embodiment through calling.
The invention also provides a computer program product comprising a computer program and/or instructions which, when executed by a processor, performs the steps of the method of any of the above embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely software embodiment, an entirely hardware embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention is in the form of a computer program product that may be embodied on one or more computer-usable storage media having computer-usable program code embodied therewith. And such computer-usable storage media include, but are not limited to: various media capable of storing program codes, such as a usb disk, a portable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk Memory, a Compact Disc Read-Only Memory (CD-ROM), and an optical Memory.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 a system 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 an instruction system 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. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the present invention and its equivalent technology, it is intended that the present invention also include such modifications and variations.
Claims (8)
1. A green inspection method of an underground piping and corridor fabricated structure, the method comprising:
building a distributed gallery monitoring system, wherein the distributed gallery monitoring system comprises a data acquisition unit, a data analysis unit and an abnormity early warning unit;
carrying out simulation modeling on each cabin assembly area of a target urban underground pipe gallery based on a BIM technology to generate a three-dimensional simulation environment of the target urban underground pipe gallery;
based on the data acquisition unit, acquiring data of the operation environment of each cabin in the three-dimensional simulation environment to obtain an operation data set of each cabin;
uploading the cabin operation data sets to the data analysis unit, and performing feature extraction on the cabin operation data sets to obtain target extraction feature sets;
comparing and analyzing the energy consumption data of the forward continuous time sequence of the target extraction feature set to obtain each extraction feature data analysis result;
uploading the analysis result of each extracted feature data to the abnormality early warning unit, and detecting and outputting an abnormality result;
the performing feature extraction on each cabin operation data set comprises:
acquiring historical energy consumption characteristics of operation data of each cabin;
taking the historical energy consumption characteristics as identification information, and constructing an energy consumption characteristic extraction model;
uploading the operation data sets of all cabins as input data to the energy consumption feature extraction model, and training the input data based on the identification information to obtain a training result, wherein the training result comprises a model extraction feature set;
obtaining a target characteristic energy consumption value set corresponding to the model extraction characteristic set;
obtaining each operation data energy consumption value set of each cabin operation data set;
determining a preset energy consumption threshold value according to the target characteristic energy consumption value set;
screening the energy consumption value sets of the operation data based on the preset energy consumption threshold value to obtain an additional energy consumption characteristic set;
and inputting the additional energy consumption feature set into the energy consumption feature extraction model, and performing optimization training on the energy consumption feature extraction model.
2. The method of claim 1, wherein the collecting data of the cabin operation environments in the three-dimensional simulation environment comprises:
based on various sensor monitoring systems embedded in the cabin assembly areas, acquiring data of the operating environment of each cabin to obtain a primary acquired data set;
performing data preprocessing on the primary collected data set to obtain a processed standard data set;
and carrying out time element acquisition marking on the processed standard data set to generate the operation data set of each cabin.
3. The method of claim 2, wherein said pre-processing the data by the primary acquisition data set comprises:
obtaining a first characteristic data set according to the primary acquisition data set;
performing decentralized processing on the first characteristic data set to obtain a second characteristic data set;
obtaining a first covariance matrix of the second feature data set;
calculating the first covariance matrix to obtain a first eigenvalue and a first eigenvector of the first covariance matrix;
and projecting the first feature data set to the first feature vector to obtain a first dimension reduction data set, wherein the first dimension reduction data set is the feature data set obtained after dimension reduction of the first feature data set.
4. The method of claim 1, wherein the forward continuous time series energy consumption data alignment analysis by the target extracted feature set comprises:
performing data fusion on the model extraction feature set and the additional energy consumption feature set to generate the target extraction feature set;
based on the logic of the forward continuous time sequence, performing point-by-point acquisition on the energy consumption data of the target extraction characteristic set to obtain each target extraction characteristic energy consumption data set, wherein each target extraction characteristic energy consumption data set has a continuous time characteristic;
calculating the energy consumption difference value of each time node in the energy consumption data set of each target extraction characteristic compared with the energy consumption data of the previous time node to obtain the energy consumption difference value set of each time node;
and determining the analysis result of each extracted characteristic data according to the energy consumption difference set of each time node.
5. The method of claim 4, wherein the method comprises:
the abnormal early warning unit is embedded with a data fluctuation monitoring response;
based on the data fluctuation monitoring response, monitoring data fluctuation on the input energy consumption difference value set of each time node to obtain data fluctuation results of each energy consumption difference value;
judging whether the energy consumption difference data fluctuation results meet a preset data fluctuation threshold value or not;
and if the energy consumption difference data fluctuation results meet the preset data fluctuation threshold, carrying out abnormal marking on the meeting parts, and outputting the abnormal energy consumption difference data fluctuation results.
6. A green detection system for an underground piping and corridor assembled structure, the system comprising:
the system comprises a first building unit, a second building unit and a monitoring unit, wherein the first building unit is used for building a distributed type gallery monitoring system, and the distributed type gallery monitoring system comprises a data acquisition unit, a data analysis unit and an abnormity early warning unit;
the system comprises a first modeling unit, a second modeling unit and a third modeling unit, wherein the first modeling unit is used for carrying out simulation modeling on each cabin assembly area of a target urban underground pipe gallery based on a BIM technology to generate a three-dimensional simulation environment of the target urban underground pipe gallery;
the first acquisition unit is used for acquiring data of each cabin operation environment in the three-dimensional simulation environment based on the data acquisition unit to obtain each cabin operation data set;
the first extraction unit is used for uploading each cabin operation data set to the data analysis unit, and performing feature extraction on each cabin operation data set to obtain a target extraction feature set;
the first analysis unit is used for comparing and analyzing the energy consumption data of the forward continuous time sequence of the target extraction feature set to obtain each extraction feature data analysis result;
the first detection unit is used for uploading the analysis results of the extracted feature data to the abnormality early warning unit, and detecting and outputting the abnormality results;
the third acquisition unit is used for acquiring historical energy consumption characteristics of the operation data of each cabin;
the first construction unit is used for constructing an energy consumption feature extraction model by taking the historical energy consumption features as identification information;
a fourth obtaining unit, configured to upload the cabin operation data sets as input data to the energy consumption feature extraction model, train the input data based on the identification information, and obtain a training result, where the training result includes a model extraction feature set;
a fifth obtaining unit, configured to obtain a target characteristic energy consumption value set corresponding to the model extraction characteristic set;
a sixth obtaining unit, configured to obtain each set of operation data energy consumption values of each set of cabin operation data;
the first determining unit is used for determining a preset energy consumption threshold value according to the target characteristic energy consumption value set;
the first screening unit is used for screening the energy consumption value sets of the operation data based on the preset energy consumption threshold value to obtain an additional energy consumption characteristic set;
a first optimization unit, configured to input the additional energy consumption feature set to the energy consumption feature extraction model, and perform optimization training on the energy consumption feature extraction model.
7. A green detection system of an underground piping and corridor assembled structure, comprising: a processor coupled to a memory, the memory for storing a program that, when executed by the processor, causes a system to perform the steps of the method of any of claims 1 to 5.
8. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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