CN110908353A - Energy consumption monitoring and management method based on NB-IOT - Google Patents
Energy consumption monitoring and management method based on NB-IOT Download PDFInfo
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/4183—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by data acquisition, e.g. workpiece identification
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
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- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
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Abstract
The invention provides an energy consumption monitoring and management method based on NB-IOT, which comprises the following steps: step 101: the system inquires whether to actively set a threshold value, if so; step 102: setting a first threshold value; step 103: synchronously setting a main control device according to the first threshold value; step 104: monitoring to obtain energy consumption data; step 105: reporting energy consumption data; step 106: analyzing the energy consumption data; step 108: determining whether the first threshold is exceeded, yes; step 109: intervention command control; step 110: judging whether the intervention instruction is successful or not, and judging whether the intervention instruction is successful or not; step 111: sending an alarm; step 112: manually setting a second threshold value; step 113: sampling and analyzing, and monitoring to obtain energy consumption data; returning to the step 105; the first threshold comprises a first humidity threshold and a first temperature threshold; energy consumption data includes humidity and temperature. Has the advantages that: and data analysis and machine learning are carried out after data are collected by the cloud, and the temperature and humidity threshold value required by the area every day is automatically calculated according to the set threshold value to automatically control the optimized energy consumption.
Description
Technical Field
The invention relates to an energy consumption monitoring and management system and method based on NB-IOT, in particular to a system and method for monitoring energy consumption data in a region by adopting a distributed monitoring device network which is connected with each other through NB-IOT honeycomb and monitoring and adjusting in real time according to the energy consumption data.
Background
In the field needing regional monitoring, such as offices, storage places and the like, the traditional monitoring system depends on the connection of a line system, needs to carry out wiring planning, and causes safety problems due to line aging along with the long-time corrosion of the service time and the place environment; and the aging and updating of the line can not meet the requirements of modern society on intelligent home and office.
The traditional monitoring system largely depends on manpower to carry out manual regulation and control, the monitored historical data is redundant, and the statistical function of the traditional monitoring system according to the conditions of time, seasons, places and the like is not fully exerted. The conventional historical data is clumsy in statistical power, and processing and selection are not performed on a data structure, so that the statistical decision-making power is low, and even errors occur.
The NB-IoT narrowband Internet of things is an important branch of the Internet of things, is mainly used for constructing a cellular network, can be directly deployed in a GSM network, a UMTS network or an LTE network only by consuming a bandwidth of about 180kHz, has low deployment cost, and can ideally transform and upgrade the existing monitoring system.
Disclosure of Invention
The invention provides an energy consumption monitoring and managing system and method based on NB-IOT, wherein a main control monitoring node and one or more detection nodes form a multipoint humidity and temperature monitoring control and feedback system in an area through an NB-IOT cellular network, and temperature and humidity change data and active control information in a certain area are reported. Data analysis and machine learning are carried out after data are collected by the cloud, and the temperature and humidity threshold value required by the area every day can be automatically calculated according to the set threshold value, so that the optimized energy consumption can be automatically controlled.
The invention provides an energy consumption monitoring and management system based on NB-IOT, which comprises a control end, a monitoring end and an execution end, wherein the control end is connected with the execution end, the monitoring end is connected with the control end, the monitoring end forms an NB-IOT cellular network,
the execution end comprises a manager, a cloud end, a main control device and a control panel, wherein the manager is connected with the cloud end, the cloud end is connected with the main control device, and the main control device is connected with the main control panel;
the monitoring end comprises a temperature monitoring device and a humidity monitoring device, and is connected with the cloud end;
the execution end comprises a first main control node and a second node, the second node and the first main control node form an NB-IOT cellular network, and the first main control node is connected with the control panel.
Preferably, the monitoring end comprises a plurality of first temperature monitoring devices, a plurality of second temperature monitoring devices, a plurality of first humidity monitoring devices and a plurality of second humidity monitoring devices.
Preferably, the monitoring end is configured according to the execution end so as to form that the monitoring end uniformly surrounds the execution end.
Preferably, a human body monitoring device is also included.
Preferably, the human body monitoring device is configured according to each node of the execution end.
Preferably, the cloud end comprises a data acquisition module, a machine learning module, a dynamic scheduling module, a strategy generation module and an execution module.
Preferably, the control panel is electrically connected with the main control device.
Preferably, the main control device is wirelessly connected with the cloud.
Preferably, the manager is wirelessly connected with the cloud through the APP.
Preferably, the NB-IOT cellular network connection comprises a temperature monitoring device and a humidity monitoring device.
The energy consumption monitoring and management method based on the NB-IOT is further provided, and comprises the following steps:
step 101: the system inquires whether to actively set a threshold value, if so;
step 102: setting a first threshold value;
step 103: synchronously setting a main control device according to the first threshold value;
step 104: monitoring to obtain energy consumption data;
step 105: reporting energy consumption data;
step 106: analyzing the energy consumption data;
step 108: determining whether the first threshold is exceeded, yes;
step 109: intervention command control;
step 110: judging whether the intervention instruction is successful or not, and judging whether the intervention instruction is successful or not;
step 111: sending an alarm;
step 112: manually setting a second threshold value;
step 113: sampling and analyzing, and monitoring to obtain energy consumption data;
returning to the step 105;
the first threshold comprises a first humidity threshold and a first temperature threshold; the energy consumption data includes humidity and temperature.
Preferably, step 101 further comprises: forming an intelligent first learning threshold according to historical second threshold data;
in step 102, setting the first learning threshold; the first learning threshold includes a first learning humidity and a first learning temperature.
Preferably, step 101 further comprises: forming an intelligent second learning threshold based on historical said energy consumption data that caused the warning of step 111;
in step 102, the second learning threshold is set; the second learning threshold includes the second learning humidity and a second learning temperature.
Preferably, in step 104, energy consumption data is obtained using uniform monitoring point monitoring.
Preferably, in step 104, monitoring points are set according to the user concentration to monitor and obtain energy consumption data.
Preferably, in step 104, the energy consumption data is a statistical value.
Preferably, in step 104, the energy consumption data is a statistical average.
Preferably, in step 104, the energy consumption data is an average value within a statistical confidence interval
Preferably, in step 109, the intervention instruction includes shutdown and dynamic adjustment.
Preferably, in step 111, the alert includes an audible alert, a visual alert, and a tactile alert.
The invention provides an energy consumption monitoring and managing system and method based on NB-IOT, wherein a main control monitoring node and one or more detection nodes form a multipoint humidity and temperature monitoring control and feedback system in an area through an NB-IOT cellular network, and temperature and humidity change data and active control information in a certain area are reported. Data analysis and machine learning are carried out after data are collected by the cloud, and the temperature and humidity threshold value required by the area every day can be automatically calculated according to the set threshold value, so that the optimized energy consumption can be automatically controlled.
Drawings
FIG. 1 is a schematic diagram of an NB-IOT based energy consumption monitoring and management system of the present invention;
FIG. 2 is a schematic diagram of a cloud-based data analysis and machine learning system according to the present invention;
FIG. 3 is a flowchart of an energy consumption monitoring management method based on NB-IOT according to the present invention.
Detailed Description
The specific embodiments of the NB-IOT based energy consumption monitoring and management system and the method thereof according to the present invention are described in detail below with reference to the accompanying drawings.
In the drawings, the dimensional ratios of layers and regions are not actual ratios for the convenience of description. When a layer (or film) is referred to as being "on" another layer or substrate, it can be directly on the other layer or substrate, or intervening layers may also be present. In addition, when a layer is referred to as being "under" another layer, it can be directly under, and one or more intervening layers may also be present. In addition, when a layer is referred to as being between two layers, it can be the only layer between the two layers, or one or more intervening layers may also be present. Like reference numerals refer to like elements throughout. In addition, when two components are referred to as being "connected," they include physical connections, including, but not limited to, electrical connections, contact connections, and wireless signal connections, unless the specification expressly dictates otherwise.
The invention provides an energy consumption monitoring and management system based on NB-IOT, as shown in figure 1, comprising a control end (not shown), a monitoring end (not shown) and an execution end (not shown), wherein the control end is connected with the execution end, the monitoring end is connected with the control end, the monitoring end forms an NB-IOT cellular network, and is characterized in that,
the execution end comprises a manager 11, a cloud 12, a main control device 13 and a control panel 14, wherein the manager 11 is connected with the cloud 12, the cloud 12 is connected with the main control device 13, and the main control device 13 is connected with the main control panel 14;
the monitoring ends comprise a temperature monitoring device and a humidity monitoring device, and are connected with the cloud, as shown in fig. 1, in one embodiment, the monitoring ends comprise a first monitoring end 21, a second monitoring end 22, a third monitoring end 23, a fourth monitoring end 24, a fifth monitoring end 25, a sixth monitoring end 26, a seventh monitoring end 217, an eighth monitoring end 28 and a ninth monitoring end 29, and are connected through an NB-IOT cellular network, and the first monitoring end 21 serves as a main monitoring node and transmits energy consumption data to a temperature and humidity monitoring point 20;
the execution end comprises a first main control node 31 and second nodes 32, 33 and 34, the second nodes 32, 33 and 34 and the first main control node 31 form an NB-IOT cellular network, and the first main control node 31 is connected with the control panel 14.
In this embodiment, the monitoring terminals include a plurality of temperature monitoring devices and a plurality of humidity monitoring devices, as shown in fig. 1, the 9 monitoring terminals, the first monitoring terminal 21, the second monitoring terminal 22, the third monitoring terminal 23, the fourth monitoring terminal 24, the fifth monitoring terminal 25, the sixth monitoring terminal 26, the seventh monitoring terminal 217, the eighth monitoring terminal 28, and the ninth monitoring terminal 29 all include a temperature monitoring device and a humidity monitoring device for data of energy consumption data, the energy consumption data includes temperature t and humidity h, as shown in fig. 1, the 9 monitoring terminals, the first monitoring terminal 21, the second monitoring terminal 22, the third monitoring terminal 23, the fourth monitoring terminal 24, the fifth monitoring terminal 25, the sixth monitoring terminal 26, the seventh monitoring terminal 217, the eighth monitoring terminal 28, and the ninth monitoring terminal 29 respectively output (t1, h1), (t2, h2), (t3, h3), (t4, h4), (t5, h5), (t6, h6) (t7, h7), (t8, h8), (t9, h 9). In the embodiment, because the temperature and the humidity are in an inverse correlation relationship with a high probability, the purpose of simultaneously monitoring the temperature and the humidity and the energy consumption can be achieved only by considering the temperature, and a binary bit type temperature control algorithm can be adopted for control in places with low requirements, such as a factory; in demanding locations, such as laboratories, the PID algorithm is used as follows:
where kp is the proportional gain and T1 is the integration time constant; TD-differential time constant; u (t) is the output signal of the PID controller; e (t) is the difference between the given value r (t) and the measured value.
In this embodiment, the monitoring end is configured according to the execution end, so that the monitoring end uniformly surrounds the execution end. As shown in fig. 1, the 9 monitoring terminals uniformly surround the first master node 31 of the execution terminal and 4 nodes of the second nodes 32, 33 and 34.
In this embodiment, the device further comprises a human body monitoring device, and the human body monitoring device may be an infrared sensor, a sensory recognition circuit, a 4G radar sensor, a pyroelectric sensor, or the like. As shown in fig. 1, a first person monitoring device 41, a second person monitoring device 42, a third person monitoring device 43, and a fourth person monitoring device 44 are included, and preferably, the first person monitoring device 41, the second person monitoring device 42, the third person monitoring device 43, and the fourth person monitoring device 44 are connected via an NB-IOT cellular network. The first person monitoring device 41 serves as a master control node to transmit the person monitoring data to the control end.
In this embodiment, the human body monitoring device is configured according to each node of the execution end. As shown in fig. 1, the execution end includes a first master node 31 and second nodes 32, 33 and 34, and allocates and configures a first person monitoring device 41, a second person monitoring device 42, a third person monitoring device 43 and a fourth person monitoring device 44.
In order to obtain a key decision basis from the monitored data, machine learning needs to be performed on the monitored data, as shown in fig. 2, the cloud 12 includes a data acquisition module 51, a machine learning module 52, a dynamic scheduling module 54, a policy generation module 55, and an execution module 53. The data acquisition module 51 receives each monitoring data, after preliminary processing, transmits the data to the machine learning module 52 for deep learning, outputs an accurate data model, transmits the data model to the strategy generation module 55 to generate an energy consumption control strategy according to various conditions, such as time, season, cargo in and out conditions, office number and the like, and transmits the energy consumption control strategy to the dynamic scheduling module 54, the dynamic scheduling module 54 sends an instruction to the execution module 53 in real time according to the energy consumption control strategy, and the execution module 53 is connected with the main control device 13 to send an instruction to an execution end in real time.
In this embodiment, the control panel 14 is electrically connected to the main control device 13.
In this embodiment, the main control device 13 is wirelessly connected to the cloud 12.
In this embodiment, the manager 11 is wirelessly connected to the cloud 12 through the APP.
The invention also provides an energy consumption monitoring and management method based on NB-IOT, as shown in FIG. 3, comprising:
step 100: the system is initially set up. This step is a preliminary step when the system is first used.
Step 101: the system inquires whether to actively set a threshold value, if so;
step 102: setting a first threshold value;
step 103: synchronously setting a main control device according to the first threshold value;
step 104: monitoring to obtain energy consumption data;
step 105: reporting energy consumption data;
step 106: analyzing the energy consumption data;
step 108: determining whether the first threshold is exceeded, yes;
step 109: intervention command control;
step 110: judging whether the intervention instruction is successful or not, and judging whether the intervention instruction is successful or not;
step 111: sending an alarm;
step 112: manually setting a second threshold value;
step 113: sampling and analyzing, and monitoring to obtain energy consumption data;
returning to the step 105; so as to carry out real-time cyclic energy consumption control.
The first threshold comprises a first humidity threshold and a first temperature threshold; the energy consumption data includes humidity and temperature.
In this embodiment, step 101 further includes: forming an intelligent first learning threshold according to historical second threshold data;
in step 102, setting the first learning threshold; the first learning threshold includes a first learning humidity and a first learning temperature.
In this embodiment, step 108 further includes, before step 107: it is determined whether the user previously preset a threshold.
In this embodiment, step 101 further includes: forming an intelligent second learning threshold based on historical said energy consumption data that caused the warning of step 111;
in step 102, the second learning threshold is set; the second learning threshold includes the second learning humidity and a second learning temperature.
In this embodiment, in step 104, energy consumption data is obtained by monitoring with uniform monitoring points.
In the embodiment, in step 104, energy consumption data is obtained by setting monitoring points according to the user concentration.
In this embodiment, in step 104, the energy consumption data is a statistical value.
In this embodiment, in step 104, the energy consumption data is a statistical average.
In this embodiment, the energy consumption data is an average value within a statistical confidence interval in step 104.
In this embodiment, in step 109, the intervention instruction includes shutdown and dynamic adjustment.
In this embodiment, the alert includes an audible alert, a visual alert, and a tactile alert, step 111.
The invention provides an energy consumption monitoring and managing system and method based on NB-IOT, wherein a main control monitoring node and one or more detection nodes form a multipoint humidity and temperature monitoring control and feedback system in an area through an NB-IOT cellular network, and temperature and humidity change data and active control information in a certain area are reported. Data analysis and machine learning are carried out after data are collected by the cloud, and the temperature and humidity threshold value required by the area every day can be automatically calculated according to the set threshold value, so that the optimized energy consumption can be automatically controlled.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. An energy consumption monitoring and management method based on NB-IOT comprises the following steps:
step 101: the system inquires whether to actively set a threshold value, if so;
step 102: setting a first threshold value;
step 103: synchronously setting a main control device according to the first threshold value;
step 104: monitoring to obtain energy consumption data;
step 105: reporting energy consumption data;
step 106: analyzing the energy consumption data;
step 108: determining whether the first threshold is exceeded, yes;
step 109: intervention command control;
step 110: judging whether the intervention instruction is successful or not, and judging whether the intervention instruction is successful or not;
step 111: sending an alarm;
step 112: manually setting a second threshold value;
step 113: sampling and analyzing, and monitoring to obtain energy consumption data;
returning to the step 105;
the first threshold comprises a first humidity threshold and a first temperature threshold; the energy consumption data includes humidity and temperature.
2. The method of claim 1,
step 101 is preceded by: forming an intelligent first learning threshold according to historical second threshold data;
in step 102, setting the first learning threshold; the first learning threshold includes a first learning humidity and a first learning temperature.
3. The method of claim 1,
step 101 is preceded by: forming an intelligent second learning threshold based on historical said energy consumption data that caused the warning of step 111;
in step 102, the second learning threshold is set; the second learning threshold includes the second learning humidity and a second learning temperature.
4. The method of claim 1, wherein the energy consumption data is obtained using uniform monitoring point monitoring in step 104.
5. The method of claim 1, wherein the energy consumption data is obtained by monitoring the set monitoring points according to the user concentration in step 104.
6. The method of claim 1, wherein the energy consumption data is statistical in step 104.
7. The method of claim 1, wherein the energy consumption data is a statistical average in step 104.
8. The method of claim 1, wherein the energy consumption data is an average value within a statistical confidence interval in step 104.
9. The method of claim 1, wherein the intervention instruction comprises shutdown and dynamic adjustment in step 109.
10. The method of claim 1, wherein the alert comprises an audible alert, a visual alert, and a tactile alert in step 111.
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2019
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| US9471946B1 (en) * | 2012-01-12 | 2016-10-18 | Enerallies, Inc. | Energy management computer system |
| CN106444541A (en) * | 2016-11-23 | 2017-02-22 | 天津生态城绿色建筑研究院有限公司 | Intellectualized monitoring and operation optimization method and system of green building |
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Application publication date: 20200324 |