CN116320833B - Heat supply pipe network monitoring method based on Internet of things technology - Google Patents
Heat supply pipe network monitoring method based on Internet of things technology Download PDFInfo
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- H—ELECTRICITY
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- H04Q9/00—Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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Abstract
The invention relates to the technical field of heat supply control, in particular to a heat supply network monitoring method based on the technology of the Internet of things, which comprises a server, an acquisition controller, a database, a monitoring module and a plurality of detection points, wherein each detection point is provided with a plurality of data sensors; the method also comprises the following steps: according to the set acquisition period, the server sends an instruction to the acquisition controller to acquire data of each data sensor and upload the data to the server, wherein the acquisition period is a time period; after the server receives the data, the data are sorted to form an orderly index format and stored in a database. The invention sets the collection period of different data sensors independently, the collection period of each data sensor is determined by the position and detection item of the detection point, the server is prevented from collecting data with lower risk frequently, the work load of the server is further reduced, the collection period of each data sensor can be regulated dynamically, and the collection efficiency is higher.
Description
Technical Field
The invention relates to the technical field of heat supply control, in particular to a heat supply pipe network monitoring method based on the internet of things technology.
Background
The urban heat supply system is a huge, closed and complex circulating system consisting of a heat source, a heat supply network and users (industrial and mining enterprises, schools, residential communities and the like), and the central heat supply system has the advantages of saving energy, reducing emission, improving urban environment, improving economic benefit and the like, and meets the development needs of modern society.
Along with the rapid development of information technology and the production trend of modern energy conservation and emission reduction, the technology of monitoring a heating pipe network in real time by utilizing the technology of the Internet of things is mature. The Chinese patent with publication number of CN107491020A provides a central heating intelligent control system, which comprises a central control system, a monitoring control system and a heating equipment system. The centralized control system comprises a prediction control system, an operation database and a decision database; the monitoring control system comprises a heat source plant DCS control system, a primary network SCADA system (data acquisition and monitoring control system), a plurality of heat exchange station SCADA systems (data acquisition and monitoring control system) and a heat storage device SCADA system.
The intelligent centralized heating control system can remarkably reduce the dependence on manpower monitoring, saves the manpower cost, and simultaneously is more efficient and convenient in monitoring work. However, in a heating pipe network with a large scale, the server frequently collects and processes information during actual operation, so that overload work is easy to occur, and the information amount processed by the server is exponentially increased whenever a new detecting element needs to be added, so that the processing efficiency of the server is easily reduced, and meanwhile, faults and omission are easy to occur, so that system breakdown occurs.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides a heat supply pipe network monitoring method based on the Internet of things technology, which can effectively solve the problem that the working efficiency is affected by the overlarge information quantity processed by a server of a monitoring system in the prior art.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
a heat supply pipe network monitoring method based on the technology of the Internet of things comprises the following steps: the method comprises the steps of establishing a server, an acquisition controller, a database, a monitoring module and a plurality of detection points, wherein a plurality of data sensors are arranged on each detection point; the method also comprises the following steps:
s100: according to a set acquisition period, the server sends an instruction to the acquisition controller to acquire data of each data sensor and upload the data to the server, wherein the acquisition period is a time period;
s200: after receiving the data, the server forms an orderly index format through arrangement and stores the orderly index format into a database;
s300: the monitoring module compares the received data with preset upper and lower thresholds for analysis, and extracts and records abnormal data;
s400: after the abnormal data is extracted and recorded, monitoring large screen feedback display and sending an alarm to a related service terminal are carried out according to a preset alarm mode;
in step S100, risk assessment is performed on the detection items corresponding to each data sensor, risk indexes of each detection item are confirmed, the acquisition period of the data sensor is adjusted according to the risk indexes, and calculation is performed according to the following model:wherein T is the acquisition period, the unit is seconds, and E is the risk index.
The method for monitoring a heating network based on the internet of things technology according to claim 1, wherein the risk index is determined by a fault probability and a degree of influence, the fault probability is obtained by intercepting the number of faults occurring in unit time, the degree of influence is assigned by human, and the risk index is calculated according to the following model:wherein P is the fault probability, and I is the influence degree. The larger the risk index is, the shorter the acquisition period is, and the acquisition period can be correspondingly prolonged for detection items with lower risk indexes, so that the total data quantity required to be processed by a server is reduced, the running load is lightened, the working efficiency of equipment is improved as a whole, the excessively quick loss of the equipment is avoided, and the occurrence of omission and faults is also avoided.
In the heat supply network monitoring method based on the internet of things, the server adjusts the acquisition period according to the abnormal data, the server intercepts the data of each data sensor in unit time, analyzes the time distribution rule of the abnormal data, and sets the acquisition period under different time according to the time distribution rule.
In the heat supply pipe network monitoring method based on the internet of things, the server shortens the acquisition period in the time when the abnormal data occur at high frequency, and prolongs the acquisition period in the time when the abnormal data occur at low frequency.
In the heat supply network monitoring method based on the internet of things, when the data in the unit time is intercepted, the unit time is determined to be 30 days.
Furthermore, after the new statistical period is finished, the data in all previous statistical periods can be combined for calculation, and the obtained abnormal frequency is more accurate.
In the above heat supply network monitoring method based on the internet of things, the data sensor includes, but is not limited to, a temperature sensor, a pressure sensor, and a flow sensor.
In the heat supply network monitoring method based on the internet of things, the server generalizes the data of the data sensor in each detection point, determines the data types, wherein the data types comprise temperature, pressure and flow, sets different association indexes among different data types, and cooperatively adjusts the acquisition period of various types of data according to the association indexes.
In the heat supply network monitoring method based on the internet of things, the correlation index of the temperature data to the pressure data is as followsThe adjustment value of the acquisition period of the temperature data is +.>The adjustment value of the acquisition period of the pressure data isCalculated according to the following model: />Wherein S is the number of the corresponding detection point.
Compared with the prior art, the invention sets the collection period of different data sensors independently, the collection period of each data sensor is determined by the position and the detection item of the detection point, the server is prevented from collecting data with lower risk frequently, the workload of the server is further reduced, the collection period of each data sensor can be adjusted dynamically, the collection efficiency is higher, and the system operation is more intelligent.
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 evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a flow chart of the present invention;
fig. 2 is a system block diagram of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention is further described below with reference to examples.
Embodiment one:
a heat supply pipe network monitoring method based on the technology of the Internet of things comprises the following steps: the method comprises the steps of establishing a server, an acquisition controller, a database, a monitoring module and a plurality of detection points, wherein a plurality of data sensors are arranged on each detection point; the method also comprises the following steps:
s100: according to a set acquisition period, the server sends an instruction to the acquisition controller to acquire data of each data sensor and upload the data to the server, wherein the acquisition period is a time period;
s200: after receiving the data, the server forms an orderly index format through arrangement and stores the orderly index format into a database;
s300: the monitoring module compares the received data with preset upper and lower thresholds for analysis, and extracts and records abnormal data;
s400: after the abnormal data is extracted and recorded, monitoring large screen feedback display and sending an alarm to a related service terminal are carried out according to a preset alarm mode;
in step S100, risk assessment is performed on the detection items corresponding to each data sensor, risk indexes of each detection item are confirmed, the acquisition period of the data sensor is adjusted according to the risk indexes, and calculation is performed according to the following model:wherein T is the acquisition period, the unit is seconds, and E is the risk index.
For example, if the risk index E is 0.01, the risk index T is 10 seconds, the risk index E is 0.0025, and the risk index T is 20 seconds, so that the larger the risk index is, the shorter the acquisition period is, the acquisition period can be correspondingly prolonged for a detection item with a lower risk index, the total data amount required to be processed by the server is reduced, the running load is lightened, the working efficiency of the equipment is improved as a whole, the excessive quick loss of the equipment is avoided, and the occurrence of omission and faults is avoided.
Further, the risk index is determined by fault probability and influence degree, the fault probability is obtained by intercepting the number of times of faults in unit time, the influence degree is assigned by people, and the risk index is calculated according to the following model:wherein P is the fault probability, and I is the influence degree.
For example, if the failure probability P is 0.2 and the influence degree is 0.05, the risk index is 1%, that is, 0.01, and the acquisition period T is 10 seconds. The fault probability P is obtained according to the statistical result of 6 equipment faults occurring within 30 days, and the influence degree is confirmed by a management and control personnel by combining experience and actual conditions, wherein the numerical range is 0.01-1.
Embodiment two:
besides the scheme, the server adjusts the acquisition period according to the occurrence time of the abnormal data, intercepts the data of each data sensor in unit time, analyzes the time distribution rule of the abnormal data, and sets the acquisition period under different time according to the time distribution rule.
The server shortens the acquisition period in the time when the abnormal data occur at high frequency, and prolongs the acquisition period in the time when the abnormal data occur at low frequency.
For example, the frequency of abnormality of the data of a certain data sensor is higher between 14 and 15 times of the day, and the frequency of abnormality at other times is significantly reduced compared with the time period, so that the acquisition period of the data sensor can be slightly shortened between 14 and 15 times, the frequency of detection in unit time is higher, the frequency of detection in other times is greatly reduced, and the acquisition period is prolonged, thereby further reducing the operation load of the server, and simultaneously, the safety of the whole system is not affected.
It should be noted that the probability of failure P, which is the probability of a failure of a device, and the frequency of occurrence of abnormal data, which is only a value detected by a data sensor exceeding a preset threshold range, belong to two different concepts, and when abnormal data occurs, it does not necessarily represent a failure of a device.
And when the data in the unit time is intercepted, determining the unit time as 30 days. The method takes 30 days as a statistical period, the server automatically counts and analyzes the occurrence frequency of abnormal data every 30 days, the acquisition period is adjusted according to the obtained result, the frequency can be adjusted in time to determine the acquisition period, the accuracy of the system in operation is improved, meanwhile, the statistical period span of 30 days is enough, and the data accuracy is higher.
Furthermore, after the new statistical period is finished, the data in all previous statistical periods can be combined for calculation, so that the obtained abnormal frequency is more accurate.
In the present invention, the data sensor includes, but is not limited to, a temperature sensor, a pressure sensor, a flow sensor.
Embodiment III:
unlike the first and second embodiments, the acquisition period can also be determined in the following manner:
the server generalizes the data of the data sensor in each detection point, determines the data types, wherein the data types comprise temperature, pressure and flow, different association indexes are set among different data types, and the acquisition period of various types of data is adjusted in a cooperative mode according to the association indexes.
The correlation index of temperature data to pressure data isThe adjustment value of the acquisition period of the temperature data is +.>The adjustment value of the acquisition period of the pressure data is +.>Calculated according to the following model: />Wherein S is corresponding toEach detection point is numbered in advance, the number is marked in a mode from S001 to S999, the order of magnitude of the number can be correspondingly adjusted according to the number of the detection points, and when the number of all the detection points exceeds 999, a digital bit is added for numbering.
When the temperature of a certain detection point is changed, the pressure data of the position is easily influenced to change, so that the correlation index of the temperature data to the pressure dataLarger, e.g.)>The acquisition period of the temperature data is currently 20 seconds, the acquisition period of the pressure data is 30 seconds, and the server shortens the data acquisition period of the temperature sensor of the acquisition point to 10 seconds at a certain time, so that the temperature sensor is adjusted to be a value->The adjustment value of the acquisition period of the pressure data was 5 seconds, and the acquisition period was changed to 25 seconds.
Similarly, the correlation index of the temperature data to the flow data example is smaller, which is that，/>Set to 0.2, for example, the initial acquisition period of flow data is 40 seconds, when the adjustment value of the acquisition period of temperature data +.>When the temperature is 10 seconds, the acquisition period of the temperature data after adjustment is 10 seconds, and the acquisition period of the flow data is adjusted by a value +.>That is, the adjusted flow data acquisition period is changed to 38 seconds, the adjustment amplitude is smaller, and the processing load on the server is less increased.
Wherein the number range of the association index is between 0 and 1. The greater the correlation between the detected items, the greater the value of the correlation index.
The method flexibly adjusts the acquisition period of each detection item in the same detection point according to the mutual relevance of the detection items, can acquire data more intelligently, automatically realizes the adjustment of the acquisition period, ensures that the acquired data is more accurate, and can reasonably adjust the processing load of the server.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; these modifications or substitutions do not depart from the essence of the corresponding technical solutions from the protection scope of the technical solutions of the embodiments of the present invention.
Claims (4)
1. The utility model provides a heating network monitoring system based on internet of things, includes server, collection controller, database, monitoring module and a plurality of check point, every be equipped with a plurality of data sensor on the check point, its characterized in that, the system during operation is following step:
s100: according to a set acquisition period, the server sends an instruction to the acquisition controller to acquire data of each data sensor and upload the data to the server, wherein the acquisition period is a time period;
s200: after receiving the data, the server forms an orderly index format through arrangement and stores the orderly index format into a database;
s300: the monitoring module compares the received data with preset upper and lower thresholds for analysis, and extracts and records abnormal data;
s400: after the abnormal data is extracted and recorded, monitoring large screen feedback display and sending an alarm to a related service terminal are carried out according to a preset alarm mode;
in step S100, risk assessment is performed on the detection items corresponding to each data sensorEstimating the risk index of each detection item, adjusting the acquisition period of the data sensor according to the risk index, and calculating according to the following model:；
wherein T is an acquisition period, the unit is seconds, and E is a risk index;
the risk index is determined by fault probability and influence degree, the fault probability is obtained by intercepting the number of times of faults in unit time, the influence degree is assigned by people, and the risk index is calculated according to the following model:；
wherein P is the fault probability, and I is the influence degree;
the server adjusts the acquisition period according to the abnormal data, intercepts the data of each data sensor in unit time, analyzes the time distribution rule of the abnormal data, and sets the acquisition period under different time according to the time distribution rule;
the server generalizes the data of the data sensor in each detection point, determines the data types, wherein the data types comprise temperature, pressure and flow, different association indexes are set among different data types, and the acquisition period of various types of data is adjusted in a cooperative mode according to the association indexes;
the correlation index of temperature data to pressure data isThe adjustment value of the acquisition period of the temperature data is +.>The adjustment value of the acquisition period of the pressure data is +.>Calculated according to the following model: />；
Wherein S is the number of the corresponding detection point.
2. The heating network monitoring system based on the internet of things technology according to claim 1, wherein the server shortens the collection period in the time when the abnormal data occur at high frequency and extends the collection period in the time when the abnormal data occur at low frequency.
3. The heating network monitoring system based on the internet of things according to claim 2, wherein the unit time is determined to be 30 days when intercepting the data in the unit time.
4. The heating network monitoring system based on the internet of things technology according to claim 1, wherein the data sensor comprises a temperature sensor, a pressure sensor and a flow sensor.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103327295A (en) * | 2013-05-16 | 2013-09-25 | 苏州阔地网络科技有限公司 | Dynamic adjusting method and system for video monitoring |
| KR20190096706A (en) * | 2018-02-09 | 2019-08-20 | 주식회사 케이티 | Method and Apparatus for Monitoring Abnormal of System through Service Relevance Tracking |
| CN115525001A (en) * | 2021-06-24 | 2022-12-27 | 浙江博泽能源科技有限公司 | Safety monitoring method and system based on Internet of things |
| CN115865992A (en) * | 2023-03-02 | 2023-03-28 | 中国建材检验认证集团湖南有限公司 | Wisdom water conservancy on-line monitoring system |
| CN115914289A (en) * | 2022-11-07 | 2023-04-04 | 周妙 | Intelligent management system based on Internet of things technology |
| CN116089213A (en) * | 2022-12-30 | 2023-05-09 | 天翼云科技有限公司 | Cloud platform resource monitoring method and device, electronic equipment and readable storage medium |
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103327295A (en) * | 2013-05-16 | 2013-09-25 | 苏州阔地网络科技有限公司 | Dynamic adjusting method and system for video monitoring |
| KR20190096706A (en) * | 2018-02-09 | 2019-08-20 | 주식회사 케이티 | Method and Apparatus for Monitoring Abnormal of System through Service Relevance Tracking |
| CN115525001A (en) * | 2021-06-24 | 2022-12-27 | 浙江博泽能源科技有限公司 | Safety monitoring method and system based on Internet of things |
| CN115914289A (en) * | 2022-11-07 | 2023-04-04 | 周妙 | Intelligent management system based on Internet of things technology |
| CN116089213A (en) * | 2022-12-30 | 2023-05-09 | 天翼云科技有限公司 | Cloud platform resource monitoring method and device, electronic equipment and readable storage medium |
| CN115865992A (en) * | 2023-03-02 | 2023-03-28 | 中国建材检验认证集团湖南有限公司 | Wisdom water conservancy on-line monitoring system |
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Effective date of registration: 20231024 Address after: Room 501, Comprehensive Office Building, No. 8 Xiji Road (Factory A), Huangpu District, Guangzhou City, Guangdong Province, 510000 Patentee after: Guangzhou Hengyun Thermal Energy Group Co.,Ltd. Address before: Room 403, Building C3, No. 182 Science Avenue, Huangpu District, Guangzhou City, Guangdong Province, 510000 Patentee before: GUANGZHOU NIKEY ELECTRIC TECHNOLOGY Co.,Ltd. Patentee before: Guangzhou Hengyun Thermal Energy Group Co.,Ltd. |