CN113848303A - Steel structure building safety monitoring system and method based on big data - Google Patents
Steel structure building safety monitoring system and method based on big data Download PDFInfo
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Abstract
The invention discloses a steel structure building safety monitoring system and method based on big data, relating to the technical field of safety monitoring and comprising a data acquisition module, a data sorting module, a controller and a loss analysis module; the data collecting module is used for collecting contact parameter information of the steel structure component in real time, and the data sorting module is used for preprocessing the received contact parameter information to obtain contact loss information and storing the contact loss information to a database by stamping a time stamp; the loss analysis module is used for analyzing and processing the contact loss information with the timestamp stored in the database and generating a loss signal according to an analysis result; the safety management module acquires the position information of the corresponding steel structure component after receiving the loss signal and arranges maintenance personnel to overhaul and reinforce the corresponding steel structure component; the invention can send out alarm in time according to the loss coefficient, and carry out maintenance and reinforcement on the corresponding steel structure component, thereby improving the building safety.
Description
Technical Field
The invention relates to the technical field of safety monitoring, in particular to a steel structure building safety monitoring system and method based on big data.
Background
Compared with the traditional concrete building, the steel plate or the section steel replaces reinforced concrete, so that the steel structure building has higher strength and better shock resistance. Moreover, the components can be manufactured in a factory and installed on site, so that the construction period is greatly reduced; because the steel can be recycled, the construction waste can be greatly reduced, and the environment is more green and environment-friendly, so the steel is widely adopted by countries in the world and is applied to industrial buildings and civil buildings;
at present, steel structure buildings are increasingly mature in application to high-rise and super high-rise buildings, gradually become mainstream building processes, and are the development direction of future buildings; however, in the prior art, most of the construction process and the purchasing process of the building are optimized; after the construction of the steel structure building is finished and the steel structure building is put into use formally, the loss state of the steel structure building cannot be monitored in real time, and the damage or loss degree cannot be accurately judged by a manual detection method, so that great potential safety hazards are caused; based on the defects, the scheme provides a steel structure building safety monitoring system and method based on big data.
Disclosure of Invention
In order to solve the problems existing in the scheme, the invention provides a steel structure building safety monitoring system and method based on big data.
The purpose of the invention can be realized by the following technical scheme:
the steel structure building safety monitoring system based on big data is applied to steel structure components and comprises a data acquisition module, a data sorting module, a controller and a loss analysis module;
the data acquisition module is used for acquiring contact parameter information of the steel structure component in real time and transmitting the acquired contact parameter information to the data sorting module;
the data sorting module is used for preprocessing the received contact parameter information to obtain contact loss information, and stamping a time stamp on the contact loss information and storing the contact loss information into a database;
the loss analysis module is connected with the database and is used for analyzing and processing the contact loss information with the timestamp stored in the database to obtain a loss coefficient SH corresponding to the steel structure component; if the loss coefficient SH is larger than a preset coefficient threshold value, generating a loss signal;
the controller drives the alarm module to give an alarm after receiving the loss signal and sends the loss signal to the safety management module; and the safety management module acquires the position information of the corresponding steel structure component after receiving the loss signal and arranges maintenance personnel to overhaul and reinforce the corresponding steel structure component.
Further, the specific processing steps of the data sorting module are as follows:
the method comprises the following steps: periodically integrating the received contact parameter information by taking 24 hours as a period, and establishing a curve graph corresponding to the change of the contact parameters along with time;
step two: comparing each contact parameter in the contact parameter information with a corresponding parameter threshold;
if the contact parameter is larger than or equal to the corresponding parameter threshold, intercepting the corresponding curve segment in the corresponding curve graph, marking the curve segment as red and marking the curve segment as a loss curve segment;
step three: analyzing the loss curve segment to obtain a loss index corresponding to the contact parameter;
step four: and combining the loss indexes of all the contact parameters to obtain contact loss information.
Further, analyzing the loss curve segment, wherein the specific analysis steps are as follows:
for the same graph, counting the number of loss curve segments as C1; integrating the loss curve segment with time to obtain loss reference energy C2; the loss index Cs corresponding to the contact parameter is calculated by using the formula Cs-C1 × a1+ C2 × a2, where a1 and a2 are coefficient factors.
Further, the contact parameter information comprises pressure data, vibration data and illumination data; the contact loss information includes a pressure loss index, a vibration loss index, and an illumination loss index.
Further, the specific step analysis of the loss analysis module is as follows:
s1: acquiring contact loss information of the system within thirty days before the current time according to the timestamp;
s2: sequentially marking the pressure loss index, the vibration loss index and the illumination loss index in the contact loss information as Fi, Zi and Gi; wherein i is 1, …, n; i represents the ith contact loss information;
calculating to obtain a loss value Hi of the steel structural component in the corresponding period by using a formula Hi-Fi multiplied by b1+ Zi multiplied by b2+ Gi multiplied by b 3; wherein b1, b2 and b3 are coefficient factors;
s3: comparing the loss value Hi with a preset loss threshold value, and counting the number of times that Hi is larger than the preset loss threshold value as L1; calculating the difference value between the corresponding Hi and a preset loss threshold value, and summing to obtain an excess loss total value L2;
s4: integrating the loss values Hi of all periods to obtain a loss value information group;
processing the loss value information group according to a preset rule to obtain a discrete degree LS;
s5: and calculating the loss coefficient SH of the corresponding steel structural component by using a formula SH (L1 × d1+ L2 × d2+ LS × d 3), wherein d1, d2 and d3 are coefficient factors.
Further, the loss value information group is processed according to a preset rule, specifically:
obtaining a standard deviation mu of the loss value information group according to a standard deviation calculation formula; traversing the loss value information group, marking the maximum value of Hi as Hmax, and marking the minimum value of Hi as Hmin;
calculating difference ratio Cb by using a formula Cb ═ Hmax-Hmin)/Hmin; the degree of dispersion LS of the loss value information sets is evaluated based on the standard deviation μ and the difference ratio Cb.
Further, the data acquisition module comprises a pressure sensor, a vibration sensor and an illumination sensor which are arranged on the steel structure component; wherein the steel structure component comprises a steel structure wall and a steel structure floor slab.
Further, the steel structure building safety monitoring method based on big data comprises the following steps:
v1: acquiring contact parameter information of the steel structure component in real time through a sensor group;
v2: the contact parameter information is periodically preprocessed to obtain contact loss information, and the contact loss information is stamped and stored in a database;
v3: analyzing and processing the contact loss information with the timestamp stored in the database to obtain a loss coefficient SH of the corresponding steel structure component; if the loss coefficient SH is larger than a preset coefficient threshold value, generating a loss signal;
v4: and when the loss signal is received, acquiring the position information of the corresponding steel structure component and arranging maintenance personnel to overhaul and reinforce the corresponding steel structure component.
Compared with the prior art, the invention has the beneficial effects that: the method comprises the steps that contact parameter information of a steel structure component is collected in real time through a data collection module, the collected contact parameter information is preprocessed, the received contact parameter information is periodically integrated by taking 24 hours as a period, loss indexes of corresponding contact parameters are obtained through calculation, and the loss indexes of all the contact parameters are combined to obtain contact loss information; and then analyzing and processing the contact loss information, acquiring the contact loss information thirty days before the current time of the system according to the timestamp, and calculating to obtain the loss coefficient SH of the corresponding steel structure component.
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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 block diagram of a steel structure building safety monitoring system based on big data.
FIG. 2 is a schematic flow diagram of the steel structure building safety monitoring method based on big data.
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.
As shown in fig. 1, the steel structure building safety monitoring system based on big data is applied to steel structure components and comprises a data acquisition module, a data sorting module, a database, a loss analysis module, a controller, an alarm module and a safety management module;
the data acquisition module is used for acquiring contact parameter information of the steel structure component in real time and transmitting the acquired contact parameter information to the data sorting module, and the contact parameter information comprises pressure data, vibration data and illumination data; the data acquisition module comprises a pressure sensor, a vibration sensor and an illumination sensor which are arranged on the steel structure component; the steel structure components comprise steel structure walls and steel structure floors;
the data sorting module is used for preprocessing the received contact parameter information to obtain contact loss information, and stamping a time stamp on the contact loss information and storing the contact loss information into a database; the specific treatment steps are as follows:
the method comprises the following steps: periodically integrating the received contact parameter information by taking 24 hours as a period, and establishing a curve graph corresponding to the change of the contact parameters along with time;
step two: comparing each contact parameter in the contact parameter information with a corresponding parameter threshold;
if the contact parameter is larger than or equal to the corresponding parameter threshold, intercepting the corresponding curve segment in the corresponding curve graph, marking the curve segment as red and marking the curve segment as a loss curve segment;
step three: for the same graph, counting the number of loss curve segments as C1; integrating the loss curve segment with time to obtain loss reference energy C2;
normalizing the quantity of the loss curve segments and the loss reference energy and taking the numerical values of the quantity and the loss reference energy;
calculating the loss index Cs corresponding to the contact parameters by using a formula Cs-C1 × a1+ C2 × a2, wherein a1 and a2 are coefficient factors;
step four: combining the loss indexes of all contact parameters to obtain contact loss information, wherein the contact loss information comprises a pressure loss index, a vibration loss index and an illumination loss index;
the loss analysis module is connected with the database and used for analyzing and processing the contact loss information with the timestamp stored in the database, and the method specifically comprises the following steps:
s1: acquiring contact loss information of the system within thirty days before the current time according to the timestamp;
s2: sequentially marking the pressure loss index, the vibration loss index and the illumination loss index in the contact loss information as Fi, Zi and Gi; wherein i is 1, …, n; i represents the ith contact loss information;
calculating to obtain a loss value Hi of the steel structural component in the corresponding period by using a formula Hi-Fi multiplied by b1+ Zi multiplied by b2+ Gi multiplied by b 3; wherein b1, b2 and b3 are coefficient factors;
s3: comparing the loss value Hi with a preset loss threshold value, if Hi is larger than the preset loss threshold value, marking the corresponding Hi as an influence loss value, counting the occurrence times of the influence loss value and marking the number as L1;
calculating the difference value between the influence loss value and a preset loss threshold value to obtain an excess loss value, and summing all the excess loss values to obtain an excess loss total value L2;
s4: integrating the loss values Hi of all periods to obtain a loss value information group;
obtaining a standard deviation mu of the loss value information group according to a standard deviation calculation formula; traversing the loss value information group, marking the maximum value of Hi as Hmax, and marking the minimum value of Hi as Hmin;
dividing the difference value between the maximum value Hmax and the minimum value Jmin by the minimum value Jmin to obtain a difference ratio Cb of the loss value information group, namely Cb is (Hmax-Hmin)/Hmin;
using formulas
ComputingObtaining a dispersion degree LS, wherein r1 and r2 are coefficient factors;
s5: normalizing the times of occurrence of the influence loss value, the over-consumption total value and the discrete degree and taking the numerical values; calculating the loss coefficient SH of the corresponding steel structural component by using a formula SH (L1 × d1+ L2 × d2+ LS × d 3), wherein d1, d2 and d3 are coefficient factors;
s6: comparing the loss factor SH with a preset factor threshold;
if the loss coefficient SH is larger than a preset coefficient threshold value, generating a loss signal;
the loss analysis module is used for sending a loss signal to the controller, and the controller drives the alarm module to give an alarm after receiving the loss signal and sends the loss signal to the safety management module;
the safety management module acquires the position information of the corresponding steel structure component after receiving the loss signal and arranges maintenance personnel to overhaul and reinforce the corresponding steel structure component, so that the building safety is improved;
as shown in fig. 2, the steel structure building safety monitoring method based on big data includes the following steps:
v1: acquiring contact parameter information of the steel structure component in real time through a sensor group;
v2: the contact parameter information is periodically preprocessed to obtain contact loss information, and the contact loss information is stamped and stored in a database;
v3: analyzing and processing the contact loss information with the timestamp stored in the database to obtain a loss coefficient SH of the corresponding steel structure component; if the loss coefficient SH is larger than a preset coefficient threshold value, generating a loss signal;
v4: and when the loss signal is received, acquiring the position information of the corresponding steel structure component and arranging maintenance personnel to overhaul and reinforce the corresponding steel structure component.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The working principle of the invention is as follows:
during work, the data acquisition module is used for acquiring contact parameter information of a steel structure component in real time, the data sorting module is used for preprocessing the received contact parameter information, periodically integrating the received contact parameter information by taking 24 hours as a period, and establishing a curve graph of the corresponding contact parameter changing along with time; if the contact parameter is larger than or equal to the corresponding parameter threshold, intercepting the corresponding curve segment in the corresponding curve graph, marking the curve segment as red and marking the curve segment as a loss curve segment; calculating to obtain loss indexes corresponding to the contact parameters by combining the number of the loss curve segments and the loss reference energy, and combining the loss indexes of all the contact parameters to obtain contact loss information; time stamping the contact loss information and storing the contact loss information into a database;
the loss analysis module is used for analyzing and processing the contact loss information with the timestamp stored in the database, and acquiring the contact loss information of the system within thirty days before the current time according to the timestamp; calculating to obtain a loss value Hi of the steel structure part in the corresponding period according to the pressure loss index, the vibration loss index and the illumination loss index in the contact loss information; counting the number L1 of Hi > a preset loss threshold value and a corresponding total excess loss value L2; and then integrating the loss values Hi of all periods to obtain a loss value information group, calculating to obtain a discrete degree LS, calculating to obtain a loss coefficient SH of the corresponding steel structure component by using a formula SH (L1 × d1+ L2 × d2+ LS × d 3), generating a loss signal if the loss coefficient SH is larger than a preset coefficient threshold, driving an alarm module to give an alarm after the controller receives the loss signal, obtaining the position information of the corresponding steel structure component by a safety management module after the safety management module receives the loss signal, and arranging maintenance personnel to overhaul and reinforce the corresponding steel structure component, so that the building safety is improved.
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 preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (8)
1. The steel structure building safety monitoring system based on big data is applied to steel structure components and is characterized by comprising a data acquisition module, a data sorting module, a controller and a loss analysis module;
the data acquisition module is used for acquiring contact parameter information of the steel structure component in real time and transmitting the acquired contact parameter information to the data sorting module;
the data sorting module is used for preprocessing the received contact parameter information to obtain contact loss information, and stamping a time stamp on the contact loss information and storing the contact loss information into a database;
the loss analysis module is connected with the database and is used for analyzing and processing the contact loss information with the timestamp stored in the database to obtain a loss coefficient SH corresponding to the steel structure component; if the loss coefficient SH is larger than a preset coefficient threshold value, generating a loss signal;
the controller drives the alarm module to give an alarm after receiving the loss signal and sends the loss signal to the safety management module; and the safety management module acquires the position information of the corresponding steel structure component after receiving the loss signal and arranges maintenance personnel to overhaul and reinforce the corresponding steel structure component.
2. The big data based steel structure building safety monitoring system according to claim 1, wherein the data sorting module comprises the following specific processing steps:
the method comprises the following steps: periodically integrating the received contact parameter information by taking 24 hours as a period, and establishing a curve graph corresponding to the change of the contact parameters along with time;
step two: comparing each contact parameter in the contact parameter information with a corresponding parameter threshold;
if the contact parameter is larger than or equal to the corresponding parameter threshold, intercepting the corresponding curve segment in the corresponding curve graph, marking the curve segment as red and marking the curve segment as a loss curve segment;
step three: analyzing the loss curve segment to obtain a loss index corresponding to the contact parameter;
step four: and combining the loss indexes of all the contact parameters to obtain contact loss information.
3. The big data based steel structure building safety monitoring system according to claim 2, wherein the loss curve segment is analyzed by the following specific analysis steps:
for the same graph, counting the number of loss curve segments as C1; integrating the loss curve segment with time to obtain loss reference energy C2; the loss index Cs corresponding to the contact parameter is calculated by using the formula Cs-C1 × a1+ C2 × a2, where a1 and a2 are coefficient factors.
4. The big data based steel structure building safety monitoring system according to claim 2, wherein the contact parameter information comprises pressure data, vibration data, and illumination data; the contact loss information includes a pressure loss index, a vibration loss index, and an illumination loss index.
5. The big data based steel structure building safety monitoring system according to claim 4, wherein the specific steps of the loss analysis module are as follows:
s1: acquiring contact loss information of the system within thirty days before the current time according to the timestamp;
s2: sequentially marking the pressure loss index, the vibration loss index and the illumination loss index in the contact loss information as Fi, Zi and Gi; wherein i is 1, …, n; i represents the ith contact loss information;
calculating to obtain a loss value Hi of the steel structural component in the corresponding period by using a formula Hi-Fi multiplied by b1+ Zi multiplied by b2+ Gi multiplied by b 3; wherein b1, b2 and b3 are coefficient factors;
s3: comparing the loss value Hi with a preset loss threshold value, and counting the number of times that Hi is larger than the preset loss threshold value as L1; calculating the difference value between the corresponding Hi and a preset loss threshold value, and summing to obtain an excess loss total value L2;
s4: integrating the loss values Hi of all periods to obtain a loss value information group;
processing the loss value information group according to a preset rule to obtain a discrete degree LS;
s5: and calculating the loss coefficient SH of the corresponding steel structural component by using a formula SH (L1 × d1+ L2 × d2+ LS × d 3), wherein d1, d2 and d3 are coefficient factors.
6. The big data based steel structure building safety monitoring system according to claim 5, wherein the loss value information group is processed according to a preset rule, specifically:
obtaining a standard deviation mu of the loss value information group according to a standard deviation calculation formula; traversing the loss value information group, marking the maximum value of Hi as Hmax, and marking the minimum value of Hi as Hmin;
calculating difference ratio Cb by using a formula Cb ═ Hmax-Hmin)/Hmin; the degree of dispersion LS of the loss value information sets is evaluated based on the standard deviation μ and the difference ratio Cb.
7. The big data based steel structure building safety monitoring system according to claim 1, wherein the data acquisition module comprises a pressure sensor, a vibration sensor and an illumination sensor arranged on a steel structure component; wherein the steel structure component comprises a steel structure wall and a steel structure floor slab.
8. Steel construction building safety monitoring method based on big data, its characterized in that includes:
v1: acquiring contact parameter information of the steel structure component in real time through a sensor group;
v2: the contact parameter information is periodically preprocessed to obtain contact loss information, and the contact loss information is stamped and stored in a database;
v3: analyzing and processing the contact loss information with the timestamp stored in the database to obtain a loss coefficient SH of the corresponding steel structure component; if the loss coefficient SH is larger than a preset coefficient threshold value, generating a loss signal;
v4: and when the loss signal is received, acquiring the position information of the corresponding steel structure component and arranging maintenance personnel to overhaul and reinforce the corresponding steel structure component.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114593845A (en) * | 2022-03-04 | 2022-06-07 | 蚌埠高灵传感系统工程有限公司 | Load sensor safety monitoring system based on internet |
| CN116909226A (en) * | 2023-07-14 | 2023-10-20 | 湖南新世纪陶瓷有限公司 | Method and system for controlling ceramic surface treatment equipment |
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2021
- 2021-09-18 CN CN202111111148.9A patent/CN113848303A/en not_active Withdrawn
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114593845A (en) * | 2022-03-04 | 2022-06-07 | 蚌埠高灵传感系统工程有限公司 | Load sensor safety monitoring system based on internet |
| CN114593845B (en) * | 2022-03-04 | 2024-05-28 | 蚌埠高灵传感系统工程有限公司 | Load sensor safety monitoring system based on internet |
| CN116909226A (en) * | 2023-07-14 | 2023-10-20 | 湖南新世纪陶瓷有限公司 | Method and system for controlling ceramic surface treatment equipment |
| CN116909226B (en) * | 2023-07-14 | 2024-03-12 | 湖南新世纪陶瓷有限公司 | Method and system for controlling ceramic surface treatment equipment |
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