CN113592369A - Utility tunnel operation management system based on data analysis - Google Patents
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Abstract
The invention discloses a comprehensive pipe gallery operation management system based on data analysis, relates to the technical field of comprehensive pipe gallery management, and solves the technical problems that the monitoring of a comprehensive pipe gallery is not stable and accurate due to the fact that the existing scheme does not analyze and screen a large amount of data and can not quantize the state of the comprehensive pipe gallery; according to the invention, the edge calculation method is introduced into the operation management of the comprehensive pipe rack, so that the monitoring strength and the monitoring stability of the whole comprehensive pipe rack system can be improved; the score of the pipe rack is obtained through the pipe rack evaluation model, so that the processing capacity of a large amount of data is improved, and the accuracy of health score is guaranteed; the method sets category labels for historical experience data and detection data, ensures the training precision of the pipe gallery evaluation model, and improves the data processing capacity and the data processing efficiency; according to the invention, the three-dimensional model of the comprehensive pipe gallery is established through the central processing unit, so that the visualization of the state of the comprehensive pipe gallery is facilitated, and a foundation is provided for the maintenance management of the comprehensive pipe gallery.
Description
Technical Field
The invention belongs to the field of comprehensive pipe gallery management, relates to a comprehensive pipe gallery management technology for data analysis, and particularly relates to a comprehensive pipe gallery operation management system based on data analysis.
Background
The utility tunnel is taken as a facility which can ensure the safe operation of underground pipelines and comprehensively utilize underground space, and more attention is paid, but the utility tunnel is also faced with a plurality of operation management problems after being built.
The management of utility tunnel at present stage mainly relies on the monitoring alarm system of piping lane self, but each monitoring alarm system is independent separately, does not have the linkage between the data, leads to the interior fault handling flow of piping lane complicated loaded down with trivial details. The existing scheme shares data information in the utility tunnel to develop business application, and provides decision support for the management of the utility tunnel through big data analysis.
The data volume collected in the existing scheme is large, a corresponding data grading screening function is not provided, and the state of the comprehensive pipe rack cannot be quantized, so that the monitoring of the comprehensive pipe rack is not stable and accurate enough; therefore, a utility tunnel operation management system which has a high-efficiency accurate data analysis function and is stable and reliable is needed.
Disclosure of Invention
The invention provides a comprehensive pipe gallery operation management system based on data analysis, which is used for solving the technical problem that the monitoring of a comprehensive pipe gallery is not stable and accurate enough due to the fact that the existing scheme does not analyze and screen a large amount of data and cannot quantize the state of the comprehensive pipe gallery.
The purpose of the invention can be realized by the following technical scheme: the utility model relates to a comprehensive pipe rack operation management system based on data analysis, which comprises a central processing unit and a data storage module;
the central processing unit is respectively communicated and/or electrically connected with the portal unit and the edge processors; each edge processor is respectively communicated and/or electrically connected with the data acquisition module and the personnel scheduling module; the portal unit comprises a pipe gallery operation unit and an entrance gallery pipeline unit;
the data acquisition module acquires detection data of the comprehensive pipe gallery through the acquisition sensor and respectively sends the detection data to the data storage module and the corresponding edge processor; the comprehensive pipe rack comprises a main pipe rack, branch pipe racks and reserved pipe racks;
the edge processor analyzes the detection data by combining the capability evaluation tag to obtain the abnormity of the pipe gallery; wherein, the capability evaluation label is used for representing the data processing capability of the edge processor;
the central processing unit is used for distributing the trained pipe gallery evaluation model to the plurality of edge processors and assisting the edge processors to finish data processing; the comprehensive pipe rack evaluation model is combined with the detection data to score the comprehensive pipe rack and is constructed based on the artificial intelligence model;
the personnel scheduling module schedules workers to maintain according to the abnormity of the pipe gallery or the grade of the pipe gallery; the central processing unit establishes a three-dimensional model of the comprehensive pipe rack and displays detection data, abnormal pipe racks and grading of the pipe racks in real time in the three-dimensional model of the comprehensive pipe rack.
Preferably, after the detection data is collected, the detection data is subjected to data cleaning, repeated value removal and data filling.
Preferably, when the data is missing due to failure of the collecting sensor, the data filling is completed by manually collecting the data.
Preferably, the capability evaluation tag is obtained according to the basic information and the detection data of the edge processor, and comprises:
acquiring basic information of an edge processor; wherein, the basic information comprises the number of cores, the core frequency and the single clock period capacity;
calculating the performance parameters of the double-precision floating point according to the basic information, and marking the performance parameters as SFC;
counting the total amount of detection data received by the edge processor in unit time in real time, and marking the total amount as JSZ with the unit of kb; wherein the unit time is one minute;
when the double-precision floating point performance parameter SFC meets the condition that the SFC is more than or equal to alpha multiplied by JSZ, judging that the corresponding edge processor meets the data processing requirement, and setting the capacity evaluation tag to be 1; otherwise, judging that the corresponding edge processor does not meet the data processing requirement, and setting the capability evaluation label to be 0; and alpha is a real number which is larger than 0 and smaller than 1.
Preferably, the obtaining of the pipe gallery assessment model includes:
the central processing unit acquires historical experience data through a data storage module; the content of the historical experience data is consistent with that of the detection data, and the historical experience data comprises the historical experience data of the main pipe gallery, the historical experience data of the branch pipe gallery and the historical experience data of the reserved pipe gallery;
inserting category marks into historical experience data, and marking a pipe gallery score for each piece of historical experience data; wherein, the value of the pipe gallery score is [0, 9], and the pipe gallery score is an integer;
constructing an artificial intelligence model; the artificial intelligence model comprises a deep convolution neural network model and an RBF neural network model;
the training of the artificial intelligence model is completed through the historical experience data and the corresponding pipe gallery scoring, and the trained artificial intelligence model is marked as a pipe gallery evaluation model.
Preferably, the historical empirical data is updated periodically, and the pipe gallery assessment model is updated periodically and distributed.
Preferably, the category label is set at the head end or the tail end of the historical experience data or the detection data.
Preferably, when the capability evaluation label corresponding to the edge processor is 0, the detection data is analyzed by the central processing unit, and the pipe gallery score of the comprehensive pipe gallery is obtained.
Preferably, the scheduling of the staff by regions includes:
acquiring a comprehensive pipe gallery design drawing through a data storage module, and establishing a comprehensive pipe gallery plane model according to the comprehensive pipe gallery design drawing;
dividing the comprehensive pipe gallery plane model into a plurality of management areas; at least one emergency treatment unit is correspondingly configured in each management area, and not less than 2 workers are configured in each emergency treatment unit;
and associating the comprehensive pipe gallery in the management area with workers.
Preferably, the data acquisition module is also in communication connection with the central processing unit and the data storage module.
Preferably, the data acquisition module is in communication and/or electrical connection with the acquisition sensor; wherein, the acquisition sensor includes harmful gas detector, temperature sensor, level sensor, high definition digtal camera and access control ware.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention is provided with a central processing unit and an edge processor, the detection data is processed and the abnormity of the pipe gallery is obtained by combining the capability evaluation label of the edge processor, and meanwhile, the edge processor obtains the score of the pipe gallery by combining a management evaluation model; the edge calculation method is introduced into the operation management of the comprehensive pipe rack, so that the monitoring strength and the monitoring stability of the whole comprehensive pipe rack system can be improved; the pipe rack score is obtained through the pipe rack evaluation model, the processing capacity of a large amount of data is improved, and the accuracy of health score is guaranteed.
2. The method sets category labels for historical experience data and detection data, ensures the training precision of the pipe gallery evaluation model, classifies the data, and improves the data processing capacity and the data processing efficiency.
3. According to the invention, the three-dimensional model of the comprehensive pipe rack is established through the central processing unit, and the detection data, the abnormality of the pipe rack and the score of the pipe rack are displayed in the three-dimensional model of the comprehensive pipe rack in real time, so that the visualization of the state of the comprehensive pipe rack is facilitated, and a foundation is provided for the maintenance management of the comprehensive pipe rack.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings 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 some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a schematic diagram of the working steps of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, 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. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used herein is for the purpose of describing embodiments and is not intended to be limiting and/or limiting of the present disclosure; it should be noted that the singular forms "a," "an," and "the" include the plural forms as well, unless the context clearly indicates otherwise; also, although the terms first, second, etc. may be used herein to describe various elements, the elements are not limited by these terms, which are only used to distinguish one element from another.
Referring to fig. 1-2, the present application provides a data analysis-based utility tunnel operation management system, which includes a central processing unit and a data storage module, wherein the central processing unit is respectively in communication and/or electrical connection with a portal unit and a plurality of edge processors; each edge processor is respectively communicated and/or electrically connected with the data acquisition module and the personnel scheduling module.
The main purpose of the present application is to provide a stable and reliable comprehensive monitoring system, and an efficient and accurate data analysis and evaluation system.
The central processing unit is used for coordinating and managing the operation management system of the comprehensive pipe gallery and coordinating the work among all modules of the system; the data storage module is used for storing data generated by system acquisition or operation such as detection data and a pipe gallery evaluation model; the data acquisition module is connected with the acquisition sensor and used for acquiring detection data, transmitting the detection data to the corresponding edge processor and transmitting the detection data to the data storage module for storage; and the personnel scheduling module is used for scheduling staff to maintain according to the abnormal condition of the pipe gallery or the grading of the pipe gallery.
It is noted that the cpu is also connected to portal units, which include pipe gallery operating companies, porch pipeline units, city citizens, etc.
In the utility model provides a pair of utility tunnel operation management system based on data analysis, the data acquisition module is through the detection data of gathering the sensor acquisition utility tunnel to detect data and send to data storage module and corresponding marginal processor respectively.
In the embodiment, the data acquisition module is electrically connected with the acquisition sensor, comprises sensors capable of detecting the state of a pipe gallery and the safety state of personnel, such as a harmful gas detector, a temperature sensor, a liquid level sensor, a high-definition camera, an access controller and the like, and transmits the detection data to the data acquisition module in real time or periodically; if data of the pipe gallery state can be periodically sent to the data acquisition module, data related to the personnel safety state are sent to the data acquisition module in real time.
It is worth noting that utility tunnel in this application includes trunk piping lane, branch piping lane and reservation piping lane.
The main pipe gallery is generally arranged below the center of a road and is responsible for providing distribution service for the branch pipe galleries, the main contained pipelines are communication, cable television, electric power, fuel gas, tap water and the like, and some main pipe galleries bring rain and sewage systems into the main pipe gallery; the device is characterized by large structural section size, deep soil covering, stable system, large conveying capacity, high safety and high maintenance and detection requirements.
The branch pipe gallery is a channel communicated between the main pipe gallery and an end user, is generally arranged below sidewalks on two sides of a road, mainly contains pipelines for direct services such as communication, cable television, electric power, fuel gas, tap water and the like, and has more rectangular structural sections; the method is characterized by small effective section, low construction cost and high system stability and safety.
The reserved pipe gallery is characterized in that the internal space of the comprehensive pipe gallery is predicted by combining a city development plan, an associated region development plan, a region development requirement and a region environment restriction condition in a portal unit, and a reserved branch is finally determined.
In the utility model provides a pair of utility tunnel operation management system based on data analysis, after detecting data acquisition and accomplishing, carry out data cleaning, remove operations such as duplicate value, data filling to the detection data, guarantee the accuracy and the processibility of detection data.
Notably, data population includes automatic population and manual population. The automatic filling means that when the detection data are less in loss, the data filling is completed according to the relation between the front data and the rear data; the manual filling means that when the detection data are missing a lot, the data filling is completed by manually re-collecting the data, and if the collection sensor fails, the data filling is completed by a manual filling mode.
In the utility model provides a utility tunnel operation management system based on data analysis, after edge processor received the detection data, can acquire the ability evaluation label that self corresponds, combines ability evaluation label to carry out the analysis to the detection data and acquires the piping lane unusual.
The capability evaluation label is mainly used for representing the data processing capability of the corresponding edge processor, and the problem that the data processing capability of the edge processor is insufficient and the detection data cannot be processed in time due to the fact that a large amount of detection data are collected instantly or in a short time is avoided.
The capability evaluation tag of the embodiment is obtained by combining the basic information and the detection data of the edge processor, and includes:
acquiring basic information of an edge processor; the basic information comprises the number of cores, the core frequency and the single clock period capacity;
calculating the performance parameters of the double-precision floating point according to the basic information, and marking the performance parameters as SFC;
counting the total amount of detection data received by the edge processor in unit time in real time, and marking the total amount as JSZ with the unit of kb; wherein the unit time is one minute;
when the double-precision floating point performance parameter SFC meets the condition that the SFC is more than or equal to alpha multiplied by JSZ, judging that the corresponding edge processor meets the data processing requirement, and setting the capacity evaluation tag to be 1; otherwise, judging that the corresponding edge processor does not meet the data processing requirement, and setting the capability evaluation label to be 0. It is to be noted that, during the comparison analysis, the double-precision floating-point performance parameter SFC and the total amount JSZ of the detected data are removed from the dimension, and the values are compared.
In other preferred embodiments, the capability evaluation tag can be set according to the total amount of the detection data; if the detected total data JSZ exceeds the total data threshold, setting the capability evaluation tag to be 0, otherwise, setting the capability evaluation tag to be 1; wherein the data total threshold is set empirically.
The double-precision floating-point performance parameter is used for measuring the capability of scientific calculation of a processor, namely the capability of processing 64-bit decimal floating data, such as:
1) the length of a single instruction of a processor supporting the AVX2 is 256 bits, each intel core is assumed to include 2 FMAs, and one FMA can perform multiplication or addition Operations 2 times Per clock cycle, so that the processor can perform Floating Point Operations Per Second (256 bits × 2FMA × 2M/a/64=16 Floating Point Operations, also called 16FLOPs, 1 clock cycle Per core;
2) the length of a single instruction of a processor supporting AVX512 is 512 bits, each intel core is assumed to include 2 FMAs, and one FMA can perform a multiplication or addition operation 2 times per clock cycle, so that the processor can perform a floating point operation 512 bits by 2FMA by 2M/a/64=32 times per 1 clock cycle of the core, which is also referred to as 32 FLOPs.
When the capability evaluation label is 1, analyzing and detecting data through a corresponding edge processor to acquire the abnormity of the pipe gallery, and when the capability evaluation label is 0, analyzing and detecting data through a central processing unit to acquire the abnormity of the pipe gallery; wherein, the abnormity of the pipe gallery comprises temperature abnormity, liquid level abnormity, harmful gas abnormity and the like.
In the utility model provides a utility tunnel operation management system based on data analysis, central processing unit is used for distributing trained piping lane aassessment model to a plurality of edge treater, and supplementary edge treater accomplishes data processing.
Pipe gallery evaluation model combines detection data to grade utility tunnel, and the acquirement of pipe gallery evaluation model includes:
the central processing unit acquires historical experience data through the data storage module; the content of the historical experience data is consistent with that of the detection data, and the historical experience data comprises the historical experience data of the main pipe gallery, the historical experience data of the branch pipe gallery and the historical experience data of the reserved pipe gallery;
inserting category labels for historical experience data; in the embodiment, the category mark is inserted at the head end or the tail end of the historical experience data, and in other preferred embodiments, the category mark can also be inserted in the middle of the historical experience data;
marking a pipe gallery score for each experience historical data; in the other preferred embodiments, the scoring of the pipe gallery can be marked in a machine marking mode;
constructing an artificial intelligence model based on the deep convolutional neural network model;
the training of the artificial intelligence model is completed through the historical experience data and the scoring of the corresponding pipe gallery, the trained artificial intelligence model is marked as a pipe gallery evaluation model, then the pipe gallery evaluation model is distributed to the edge processor, and meanwhile the pipe gallery evaluation model is sent to the data storage module to be stored. Notably, historical empirical data is updated periodically, and the piping lane assessment model is updated periodically and distributed.
Inserting category labels into the detection data before obtaining the score of the pipe rack through the pipe rack evaluation model and the detection data; the category labels in this embodiment include 1, 2 and 3, where 1 represents historical empirical data or sensed data for the main pipe lane, 2 represents historical empirical data or sensed data for the branch pipe lane, and 3 represents historical empirical data or sensed data for the reserved pipe lane.
In the utility model provides a pair of utility tunnel operation management system based on data analysis, personnel scheduling module is according to the unusual or pipe gallery of pipe gallery mark scheduling staff and is maintained the maintenance.
Before the dispatching of workers, acquiring a comprehensive pipe gallery design drawing through a data storage module, and establishing a comprehensive pipe gallery plane model according to the comprehensive pipe gallery design drawing;
dividing the comprehensive pipe gallery plane model into a plurality of management areas; at least one emergency treatment unit is correspondingly configured in each management area, and at least 2 workers are configured in each emergency treatment unit;
and associating the comprehensive pipe gallery in the management area with workers.
The emergency processing unit in the embodiment specifically refers to a unit capable of providing professional equipment and professional workers when a pipe rack is abnormal or the pipe rack is abnormally rated; the size of the divided management area is determined according to the actual situation.
The most important point is that the management area needs to be associated with the staff in the corresponding emergency processing unit, and when the management area is abnormal or the pipe gallery is abnormally rated, the associated staff can be dispatched in time.
The central processing unit can also establish the three-dimensional model of utility tunnel according to the planning drawing of utility tunnel, will detect data, the pipe gallery is unusual, the pipe gallery score shows in real time in the three-dimensional model, makes things convenient for managers in time to early warn and accomplish the dispatch.
The data in the above formulas are all calculated by removing dimensions and taking numerical values thereof, the formulas are obtained by acquiring a large amount of data and performing software simulation to obtain the formulas closest to the real conditions, and the preset parameters and the preset threshold values in the formulas are set by the technicians in the field according to the actual conditions or obtained by simulating a large amount of data.
The working principle of the invention is as follows:
the data acquisition module acquires the detection data of the comprehensive pipe gallery through the acquisition sensor and respectively sends the detection data to the data storage module and the corresponding edge processor.
Acquiring basic information of the edge processor, calculating a double-precision floating point performance parameter according to the basic information, counting the total amount of detection data received by the edge processor in unit time in real time, and analyzing and comparing the double-precision floating point performance parameter and the total amount of the detection data to acquire a capability evaluation label.
The edge processor analyzes the detection data by combining the capability evaluation label to obtain the abnormity of the pipe gallery; the central processing unit is used for distributing the trained pipe gallery evaluation model to a plurality of the edge processor, the personnel scheduling module is used for scheduling staff to maintain according to the abnormal condition of the pipe gallery or the grading of the pipe gallery.
The central processing unit establishes a three-dimensional model of the comprehensive pipe rack, and displays detection data, abnormal pipe racks and grading of the pipe racks in real time in the three-dimensional model of the comprehensive pipe rack.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (9)
1. The utility model discloses a comprehensive pipe gallery operation management system based on data analysis, which comprises a central processing unit and a data storage module, and is characterized in that the central processing unit is respectively communicated and/or electrically connected with a portal unit and a plurality of edge processors; each edge processor is respectively communicated and/or electrically connected with the data acquisition module and the personnel scheduling module; the portal unit comprises a pipe gallery operation unit and an entrance gallery pipeline unit;
the data acquisition module acquires detection data of the comprehensive pipe gallery through the acquisition sensor and respectively sends the detection data to the data storage module and the corresponding edge processor; the comprehensive pipe rack comprises a main pipe rack, branch pipe racks and reserved pipe racks;
the edge processor analyzes the detection data by combining the capability evaluation tag to obtain the abnormity of the pipe gallery; the capability evaluation tag is used for representing the data processing capability of the edge processor, and values of the capability evaluation tag comprise 0 and 1;
the central processing unit is used for distributing the trained pipe gallery evaluation model to the plurality of edge processors and assisting the edge processors to finish data processing; the comprehensive pipe rack evaluation model is combined with the detection data to score the comprehensive pipe rack and is constructed based on the artificial intelligence model;
the personnel scheduling module schedules workers to maintain according to the abnormity of the pipe gallery or the grade of the pipe gallery; the central processing unit establishes a three-dimensional model of the comprehensive pipe rack and displays detection data, abnormal pipe racks and grading of the pipe racks in real time in the three-dimensional model of the comprehensive pipe rack.
2. The data analysis based utility tunnel operation management system of claim 1, wherein after the inspection data collection, data cleaning, duplicate value removal and data population are performed on the inspection data.
3. The data analysis based utility tunnel operations management system of claim 2, characterized in that data population is accomplished by manual data acquisition when data loss is caused by acquisition sensor failure.
4. The data analysis based utility tunnel operation management system of claim 1, wherein the capability assessment tag is obtained from the basic information and detection data of the edge processor, comprising:
acquiring basic information of an edge processor; wherein, the basic information comprises the number of cores, the core frequency and the single clock period capacity;
calculating the performance parameters of the double-precision floating point according to the basic information, and marking the performance parameters as SFC;
counting the total amount of detection data received by the edge processor in unit time in real time, and marking the total amount as JSZ with the unit of kb; wherein the unit time is one minute;
when the double-precision floating point performance parameter SFC meets the condition that the SFC is more than or equal to alpha multiplied by JSZ, judging that the corresponding edge processor meets the data processing requirement, and setting the capacity evaluation tag to be 1; otherwise, judging that the corresponding edge processor does not meet the data processing requirement, and setting the capability evaluation label to be 0; and alpha is a real number which is larger than 0 and smaller than 1.
5. The data analysis based utility tunnel operation management system of claim 1, wherein the obtaining of the pipe tunnel assessment model comprises:
the central processing unit acquires historical experience data through a data storage module; the content of the historical experience data is consistent with that of the detection data, and the historical experience data comprises the historical experience data of the main pipe gallery, the historical experience data of the branch pipe gallery and the historical experience data of the reserved pipe gallery;
inserting category marks into historical experience data, and marking a pipe gallery score for each piece of historical experience data; wherein, the value of the pipe gallery score is [0, 9], and the pipe gallery score is an integer;
constructing an artificial intelligence model; the artificial intelligence model comprises a deep convolution neural network model and an RBF neural network model;
the training of the artificial intelligence model is completed through the historical experience data and the corresponding pipe gallery scoring, and the trained artificial intelligence model is marked as a pipe gallery evaluation model.
6. The data analysis based utility tunnel operation management system of claim 5, characterized in that the category labels are provided at the head end or tail end of historical empirical data or test data.
7. The utility tunnel operation management system based on data analysis of claim 1, characterized in that, when the ability assessment tag that the edge processor corresponds to is 0, then through central processing unit to the detection data analysis, obtain utility tunnel's pipe gallery score.
8. The data analysis-based utility tunnel operation management system of claim 1, wherein the staff members schedule by zone, comprising:
acquiring a comprehensive pipe gallery design drawing through a data storage module, and establishing a comprehensive pipe gallery plane model according to the comprehensive pipe gallery design drawing;
dividing the comprehensive pipe gallery plane model into a plurality of management areas; at least one emergency treatment unit is correspondingly configured in each management area, and not less than 2 workers are configured in each emergency treatment unit;
and associating the comprehensive pipe gallery in the management area with workers.
9. The data analysis based utility tunnel operation management system of claim 1, wherein the data acquisition module is in communication and/or electrical connection with an acquisition sensor; wherein, the acquisition sensor includes harmful gas detector, temperature sensor, level sensor, high definition digtal camera and access control ware.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114063011A (en) * | 2021-11-11 | 2022-02-18 | 北京中航科电测控技术股份有限公司 | Positioning tracking system based on RFID |
| CN114353866A (en) * | 2021-12-21 | 2022-04-15 | 济南轨道交通集团有限公司 | Multi-element comprehensive quantitative detection method for health state of pipe gallery structure |
| CN114860803A (en) * | 2022-04-27 | 2022-08-05 | 中煤科工集团重庆智慧城市科技研究院有限公司 | Analysis system for tunnel pipe gallery management |
| CN116167748A (en) * | 2023-04-20 | 2023-05-26 | 中国市政工程西南设计研究总院有限公司 | Urban underground comprehensive pipe gallery operation and maintenance method, system and device and electronic equipment |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20080075446A (en) * | 2007-02-12 | 2008-08-18 | 오정호 | Blind branch outlet fitting system |
| CN107016502A (en) * | 2017-03-30 | 2017-08-04 | 中建地下空间有限公司 | A kind of pipe gallery intelligent management system |
| CN108243246A (en) * | 2017-12-25 | 2018-07-03 | 北京市天元网络技术股份有限公司 | A kind of edge calculations resource regulating method, edge device and system |
| CN109491301A (en) * | 2019-01-23 | 2019-03-19 | 东莞固高自动化技术有限公司 | Industry internet intelligent controller based on edge calculations architectural framework |
| CN110158648A (en) * | 2019-05-29 | 2019-08-23 | 青岛市政空间开发集团有限责任公司 | A kind of Urban Underground pipe gallery O&M monitoring management system |
| CN111964731A (en) * | 2020-09-08 | 2020-11-20 | 江苏燕宁工程科技集团有限公司 | Pipe gallery monitoring and warning device and method |
| CN212567515U (en) * | 2020-09-08 | 2021-02-19 | 江苏燕宁工程科技集团有限公司 | Piping lane monitoring and alarm device |
-
2021
- 2021-09-29 CN CN202111149080.3A patent/CN113592369B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20080075446A (en) * | 2007-02-12 | 2008-08-18 | 오정호 | Blind branch outlet fitting system |
| CN107016502A (en) * | 2017-03-30 | 2017-08-04 | 中建地下空间有限公司 | A kind of pipe gallery intelligent management system |
| CN108243246A (en) * | 2017-12-25 | 2018-07-03 | 北京市天元网络技术股份有限公司 | A kind of edge calculations resource regulating method, edge device and system |
| CN109491301A (en) * | 2019-01-23 | 2019-03-19 | 东莞固高自动化技术有限公司 | Industry internet intelligent controller based on edge calculations architectural framework |
| CN110158648A (en) * | 2019-05-29 | 2019-08-23 | 青岛市政空间开发集团有限责任公司 | A kind of Urban Underground pipe gallery O&M monitoring management system |
| CN111964731A (en) * | 2020-09-08 | 2020-11-20 | 江苏燕宁工程科技集团有限公司 | Pipe gallery monitoring and warning device and method |
| CN212567515U (en) * | 2020-09-08 | 2021-02-19 | 江苏燕宁工程科技集团有限公司 | Piping lane monitoring and alarm device |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114063011A (en) * | 2021-11-11 | 2022-02-18 | 北京中航科电测控技术股份有限公司 | Positioning tracking system based on RFID |
| CN114063011B (en) * | 2021-11-11 | 2022-11-22 | 北京中航科电测控技术股份有限公司 | Positioning tracking system based on RFID |
| CN114353866A (en) * | 2021-12-21 | 2022-04-15 | 济南轨道交通集团有限公司 | Multi-element comprehensive quantitative detection method for health state of pipe gallery structure |
| CN114353866B (en) * | 2021-12-21 | 2023-11-03 | 济南轨道交通集团有限公司 | Multi-element comprehensive quantitative detection method for health state of pipe gallery structure |
| CN114860803A (en) * | 2022-04-27 | 2022-08-05 | 中煤科工集团重庆智慧城市科技研究院有限公司 | Analysis system for tunnel pipe gallery management |
| CN116167748A (en) * | 2023-04-20 | 2023-05-26 | 中国市政工程西南设计研究总院有限公司 | Urban underground comprehensive pipe gallery operation and maintenance method, system and device and electronic equipment |
| CN116167748B (en) * | 2023-04-20 | 2023-08-25 | 中国市政工程西南设计研究总院有限公司 | Urban underground comprehensive pipe gallery operation and maintenance method, system and device and electronic equipment |
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