Energy-saving operation control method for underground space Internet of Things devices

Abstract

An energy-saving operation control method based on underground space Internet of Things devices, which sets up an electronic fence in the target analysis area, detects the personnel changes in the detection area according to the electronic fence, calculates the number of personnel in the target analysis area and the number of devices in operation at the same time, obtains the activity coefficient of the target analysis area, judges the activity coefficient with the activity threshold, generates a deployment signal according to the judgment result, calculates the unit power consumption and the detection times in the acquisition device according to the deployment signal, and obtains the detection times when the total detection power consumption is the lowest according to the calculation result. Divide the duration of the fixed monitoring period by the detection times to obtain the adjusted interval duration, and start the acquisition device according to the adjusted interval duration. While reducing the device power consumption, the detection device can continuously detect the environment, preventing the situation of out-of-control caused by untimely detection when there are many people in the detection area.

Description

Technical Field

[0001] The present invention relates to the technical field of energy-saving operation of equipment, and particularly to an energy-saving operation control method for Internet of Things (IoT) devices based on underground space. Background Art

[0002] With the development of IoT technology, many physical devices are connected to the IoT, posing challenges to the energy-saving scheduling of the IoT device center.

[0003] The invention disclosed in the patent application publication number CN114879539A discloses a low-power operation method for a smart park under a power limit mode. The low-power operation method for the smart park under the power limit mode includes the following steps:

[0004] Step 1, information collection: collecting time information, light intensity, air humidity, functional system information, energy consumption of each device on the device layer, and total energy consumption of the park; Step 2, model analysis: starting and stopping each device in the park through a calculation model; Step 3, comprehensive energy consumption management: judging whether it is an emergency. If so, selecting an economic device energy consumption processing unit; if not, managing energy consumption according to the calculation model of the model analysis.

[0005] However, during the operation of IoT devices in underground spaces, the devices are set to run continuously, which will continuously increase the power consumption of the devices and increase the device operation cost. But when the devices adopt the method of regular detection, due to the non-fixed change situation of people in the underground space area, if the device detection time is set to a fixed detection time, when the number of people in the underground space is too large and the detection time is set unreasonably, the environmental monitoring may get out of control due to untimely device detection, and in severe cases, accidents may occur.

[0006] Therefore, in view of the above defects, the designer of the present invention, through painstaking research and design, and integrating the experience and achievements of being engaged in related industries for many years, has researched and designed an energy-saving operation control method for IoT devices based on underground space to overcome the above defects. Summary of the Invention

[0007] The purpose of the present invention is to provide an energy-saving operation control method for IoT devices based on underground space, solve the above-mentioned technical problems, effectively reduce the system power consumption, and avoid the occurrence of out-of-control situations.

[0008] To achieve the above purpose, the present invention discloses an energy-saving operation control method for IoT devices based on underground space, which is characterized in that the method specifically includes the following steps:

[0009] Step 1: Divide the underground space into different detection areas, and use the detection areas as the target analysis areas. Calculate the number of people and the number of equipment operations in the target analysis areas to obtain the activity coefficient of the target analysis areas. Judge the activity coefficient and the activity threshold, and generate a deployment signal according to the judgment result;

[0010] Step 2: According to the deployment signal, calculate the unit power consumption and the number of detections in the acquisition equipment, and obtain the number of detections when the total detection power consumption is the lowest according to the calculation result. Divide the duration of the fixed monitoring period by the number of detections to obtain the adjusted interval duration, and start the acquisition equipment according to the adjusted interval duration;

[0011] Step 3: According to the deployment signal, calculate the activity coefficient, the area information, and the unit power consumption to obtain the deployment frequency within the fixed detection period, and allocate tasks to the sensors in the detection equipment. That is, when receiving the deployment signal, obtain the unit power consumption Gd when the detection equipment collects data from the target analysis area, and then calculate the deployment signal and the unit power consumption as influencing factors to allocate tasks to the target analysis area.

[0012] Among them, the method for obtaining the detection area is as follows:

[0013] Obtain the floor plan of the underground space, and mark each working area as a detection area according to different working areas in the floor plan.

[0014] Among them, the number of people and the number of equipment operations in the target area analysis area are obtained through the acquisition equipment. The acquisition equipment includes an image acquisition device and sensors, and acquisition equipment is set in each target analysis area.

[0015] Among them, the specific method for obtaining the number of people in the target analysis area is as follows:

[0016] Set up an electronic fence at the entrance of each detection area. Judge the change of personnel in the detection area by detecting whether someone passes through the electronic fence. When there is a personnel change, generate an image wake-up signal, and identify the number of people in the detection area through image recognition. When the electronic fence does not detect a personnel change within t1 time, an image sleep signal will be generated at this time, and the number of people in the detection area is represented by the original data.

[0017] Among them, S1: Obtain the area information of the target analysis area, and at the same time, through the image monitor in the detection equipment, collect the activity information in the target analysis area in real time. The activity information refers to the number of people and the number of equipment operations existing in the target analysis area at this time. The area information refers to the designed volume and the equipment volume of the target analysis area. The designed volume refers to the overall space volume in the target analysis area, and the equipment volume is the space volume occupied by the operating equipment in the target analysis area;

[0018] S2: Then, the activity coefficient Hd is calculated using formula L1. The specific expression of formula L1 is as follows: where Rs is the number of personnel present in the target analysis area at this time, Bs represents the number of devices started in the target analysis area, Vk represents the designed volume, Vs represents the device volume, r1 represents the personnel influence coefficient, r2 is the device influence coefficient, r3 is the blank coefficient, η is the space coefficient, and α is the error coefficient.

[0019] Among them, the method for obtaining the deployment signal is:

[0020] When the activity coefficient is greater than the activity threshold, the detection device will be detected using a fixed detection period at this time. When the activity coefficient is less than or equal to the activity threshold, a deployment signal is generated.

[0021] Among them, the method for obtaining the adjustment interval duration is:

[0022] Take the detection area as the target analysis area, calculate the number of personnel in the target analysis area and the number of devices in operation, obtain the activity coefficient of the target analysis area, judge the activity coefficient and the activity threshold, and generate a deployment signal according to the judgment result; then, according to the deployment signal, calculate the unit power consumption and the number of detections in the acquisition device, and according to the calculation result, obtain the number of detections when the total detection power consumption is the lowest. Divide the duration of the fixed monitoring period by the number of detections to obtain the adjustment interval duration, and start the acquisition device according to the adjustment interval duration;

[0023] Perform linear simulation on the function change formula to obtain a linear graph, and according to the linear graph, obtain the corresponding number of detections Js when the detection power consumption Gz is the lowest;

[0024] Then divide the duration Tg of the fixed detection period by the number of detections Js to obtain the adjustment interval duration. Then, use the adjustment interval duration as the task deployment frequency within the fixed detection period to deploy tasks to the sensors in the detection device.

[0025] Among them, it also includes processing the collected data. The specific processing method is:

[0026] Obtain the collected data within the previous cycle time, compare the collected data with the normal data range, and obtain the collected data within the normal data range;

[0027] Take the activity coefficient as the independent variable and the collected data as the dependent variable to obtain the inertia curve of the collected data and the activity coefficient;

[0028] Substitute the activity coefficient into the inertia curve to obtain the theoretical acquisition data. At the same time, compare the real-time acquired data with the theoretical acquisition data. When the two data are consistent, an inertia signal will be generated, and then the inertia signal will be transmitted. Otherwise, it will be marked as abnormal data.

[0029] Among them, the method for processing abnormal data is as follows:

[0030] Obtain the number of consecutive occurrences of abnormal data. When the number of consecutive occurrences of abnormal data is less than N, the abnormal data will be transmitted at this time. When the number of consecutive occurrences of abnormal data is greater than or equal to N, a dynamic signal will be generated at this time, and the dynamic signal will be transmitted to the device processing center, and relevant staff will view the dynamic signal. N is the threshold.

[0031] From the above content, it can be seen that the energy-saving operation control method based on underground space Internet of Things devices of the present invention has the following effects:

[0032] 1. By setting up an electronic fence, detecting the changes of personnel in the detection area, and then generating wake-up signals and sleep signals for the image detection device, reducing the power consumption of the continuous operation of the image monitoring device.

[0033] 2. Calculate the changes of personnel and the number of operating devices in the underground space to obtain the activity coefficient, make a judgment based on the activity coefficient, generate a fixed detection period and a deployment signal according to the judgment result. At the same time, when the deployment signal is detected, calculate the activity coefficient as an influencing factor to obtain the adjustment interval duration, and then use the adjustment interval duration as the task deployment frequency within the fixed detection period to deploy tasks for the sensors in the detection device, so that the detection device can adjust the acquisition frequency according to the changes of personnel in the detection area, enabling the detection device to adapt to the changes of personnel, reducing the power consumption of the device while enabling the detection device to continuously detect the environment, and preventing out-of-control situations caused by untimely detection when there are many people in the detection area.

[0034] 3. Process the acquisition data, process the data transmission of the Internet of Things device, and transmit the inertia signal instead of the acquisition data, thereby reducing the data transmission volume and further reducing the power consumption of the Internet of Things device during the transmission process.

[0035] The detailed content of the present invention can be obtained through the following description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Shows the flow framework diagram of the energy-saving operation control method based on underground space Internet of Things devices of the present invention.

[0037] Figure 2Shows the transmission process framework diagram of the data collection of the present invention. Detailed implementation manners

[0038] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0039] See Figure 1 and 2 , which shows the energy-saving operation control method of the present invention based on underground space Internet of Things devices.

[0040] In the embodiments thereof, the energy-saving operation control method based on underground space Internet of Things devices specifically includes the following steps:

[0041] Step 1: Obtain the floor plan of the underground space. According to the floor plan of the underground space, divide the underground space into multiple detection areas according to different working areas of the floor plan, and at the same time obtain the activity information and area information in the detection areas, that is, detection devices are arranged in the detection areas. In a specific embodiment, several rooms are arranged in the underground space, each room is marked as a detection area, and detection devices are arranged in each detection area. In this embodiment, the detection devices include sensors and image monitors. The image monitor is used to detect the device operation and the number of people in the detection area. The sensors here include device detection sensors and environmental detection sensors. The device detection sensors are used to detect device operation, and the environmental detection sensors are used to detect the environmental data of the target analysis area. At the same time, the data detected by the environmental detection sensors is marked as collected data.

[0042] Step 2: Calculate the activity information to obtain an activity coefficient, and at the same time compare the activity coefficient with an activity threshold to obtain a deployment signal. Specifically, select any detection area as the target analysis area, and detect the activity coefficient in the target analysis area through the image monitor. The specific detection method of the activity coefficient is as follows:

[0043] S1: Obtain the area information of the target analysis area, and at the same time, through the image monitor in the detection device, collect the activity information in the target analysis area in real time. The activity information refers to the number of people and the number of device operations existing in the target analysis area at this time. The area information refers to the designed volume and device volume of the target analysis area. The designed volume refers to the overall space volume in the target analysis area, and the device volume is the space volume occupied by the operating devices in the target analysis area.

[0044] S2: Then, the activity coefficient Hd is calculated using formula L1. The specific expression of formula L1 is as follows: Where Rs is the number of personnel present in the target analysis area at this time, Bs represents the number of equipment startups in the target analysis area, Vk represents the designed volume, Vs represents the equipment volume, r1 represents the personnel influence coefficient, r2 is the equipment influence coefficient, r3 is the blank coefficient, η is the space coefficient, and α is the error coefficient.

[0045] The activity coefficient Hd is compared with the activity threshold. When the activity coefficient is greater than the activity threshold, the detection equipment will be detected using a fixed detection cycle. In this embodiment, the fixed detection cycle is set by relevant technical personnel in the field. When the activity coefficient is less than or equal to the activity threshold, a deployment signal will be generated. The activity threshold is set by professional technical personnel in the field.

[0046] It should be further noted that during the operation of the equipment, energy conversion will occur. At the same time, in this embodiment, the underground space is set as a coal mine, so the equipment mainly collects data from the coal mine, which will affect the air in the detection area during equipment operation. Therefore, the operation of the equipment needs to be regarded as one of the influencing factors.

[0047] Step 3: According to the deployment signal, calculate the activity coefficient, regional information, and unit power consumption to obtain the deployment frequency within the fixed detection cycle, and allocate tasks to the sensors in the detection equipment. That is, when the deployment signal is received, the unit power consumption Gd during the collection of the target analysis area by the detection equipment is obtained. Then, the deployment signal and the unit power consumption are used as influencing factors for calculation to allocate tasks to the target analysis area. The specific task allocation method is as follows:

[0048] ST31: When the deployment signal is received, first obtain the regional blank coefficient Hk of the target analysis area, where The regional blank coefficient is the activity coefficient when the number of personnel and the number of equipment startups in the target analysis area are both 0.

[0049] ST32: Then, obtain the activity coefficient of the target analysis area, and divide the regional blank coefficient by the activity coefficient to obtain the regional detection value Jc.

[0050] ST33: Obtain the duration of the fixed detection cycle and mark it as Tg. Use the regional detection value as the influencing value to obtain the function change formula L2 of the detection power consumption and the number of detections. The specific expression of L2 is as follows: Gz is the total detection power consumption within the fixed detection cycle, Js represents the number of detections within the fixed detection cycle, Td is the time for the detection equipment to perform one detection on the target analysis area, and a1 and a2 are the influencing factors respectively.

[0051] After that, the function variation L2 is linearly simulated to obtain a linear graph. According to the linear graph, when the detection power consumption Gz is the smallest, the corresponding detection times Js are obtained. In this embodiment, the linear simulation is calculated using the Matlab system.

[0052] ST34: Then, divide the duration Tg of the fixed detection period by the detection times Js to obtain the adjustment interval duration. Then, use the adjustment interval duration as the task allocation frequency within the fixed detection period to allocate tasks to the sensors in the detection device.

[0053] In another embodiment of the present invention, the difference from the above embodiment is that in this embodiment, the activity information in step two is collected by means of an electronic fence. The specific method for collecting the activity information is as follows:

[0054] S11. Set an electronic fence at the boundary of each detection area. Specifically, the electronic fence is set at the entrance of the detection area, and the electronic fence encloses the detection area into a closed space.

[0055] S12. The electronic fence is used to detect whether there are people moving in the detection area, that is, by judging whether there are people passing through the electronic fence to determine whether there are personnel changes in the detection area. When the electronic fence detects personnel changes in the detection area, an image wake-up signal is generated, and the image monitoring device is started to detect the people in the detection area. When the electronic fence does not detect personnel change activities in the detection area within the time t1, an image sleep signal is transmitted to make the image monitor enter the sleep state. t1 is a threshold value, which is specifically set by those skilled in the art. By using the electronic fence to judge the activities of the people inside the detection area, wake-up signals and sleep signals are generated for the image detection device, reducing the power consumption of the continuous operation of the image monitoring device.

[0056] In another embodiment, on the basis of the above two embodiments, it further includes processing and transmitting the collected data of the sensors in the detection device. At the same time, refer to Figure 2 , and the specific processing method is as follows:

[0057] F1: First, perform inertial analysis on the collected data to obtain an inertial curve within the fixed detection period. The specific method for obtaining the inertial curve is as follows:

[0058] Obtain the collected data within the previous cycle time, compare the collected data with the normal data range, and obtain the collected data within the normal data range, where the normal data range is set by those skilled in the art.

[0059] After that, linear fitting is performed on the collected data and the activity coefficient, that is, the activity coefficient is used as the independent variable and the collected data is used as the dependent variable to obtain the inertia curve of the collected data and the activity coefficient;

[0060] F2: Then substitute the activity coefficient into the inertia curve to obtain the theoretical collected data. At the same time, compare the collected data obtained in real time with the theoretical collected data. When the two data are consistent, an inertia signal will be generated, and then the inertia signal will be transmitted, thereby reducing the data transmission volume, and further reducing the power consumption of the Internet of Things device during the transmission process. On the contrary, mark it as abnormal data. First, obtain the number of consecutive occurrences of abnormal data. When the number of consecutive occurrences of abnormal data is less than N, the abnormal data will be transmitted at this time. When the number of consecutive occurrences of abnormal data is greater than or equal to N, a dynamic signal will be generated at this time, and the dynamic signal will be transmitted to the device processing center, and relevant staff will view the dynamic signal. N is a threshold value, and the specific value is set by those skilled in the art.

[0061] Some of the data in the above formula are calculated by removing the dimension and taking their numerical values. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through a large amount of data simulation.

[0062] Thus, the present invention divides the underground space into different detection areas, and uses the detection areas as target analysis areas. Calculate the number of personnel and the number of device operations in the target analysis area to obtain the activity coefficient of the target analysis area. Judge the activity coefficient and the activity threshold, and generate a deployment signal according to the judgment result; then, according to the deployment signal, calculate the unit power consumption and the number of detections in the collection device, and according to the calculation result, obtain the number of detections when the total detection power consumption is the lowest. Divide the duration of the fixed monitoring period by the number of detections to obtain the adjustment interval duration, and start the collection device according to the adjustment interval duration.

[0063] Obviously, the above description and record are only examples and not intended to limit the disclosure, application or use of the present invention. Although it has been described in the embodiments and illustrated in the drawings, the present invention does not limit the specific examples described in the embodiments and illustrated in the drawings as the currently considered best mode for implementing the teachings of the present invention. The scope of the present invention will include any embodiment falling within the foregoing description and the appended claims.

Claims

1. An energy-saving operation control method based on underground space Internet of Things equipment, characterized in that: The method specifically includes the following steps: Step 1: Divide the underground space into different detection areas, and use the detection areas as target analysis areas. Calculate the number of personnel and the number of equipment in the target analysis area to obtain the activity coefficient of the target analysis area. Judge the activity coefficient and the activity threshold, and generate a deployment signal based on the judgment result. Step 2: According to the deployment signal, the unit power consumption and the number of detections in the acquisition device are calculated, and according to the calculation result, the number of detections when the total detection power consumption is the lowest is obtained, and the length of the fixed monitoring cycle is divided by the number of detections to obtain the adjustment interval length, and the acquisition device is started according to the adjustment interval length; Step 3: According to the deployment signal, the activity coefficient, the area information and the unit power consumption are calculated to obtain the deployment frequency within the fixed detection period, and the task of the sensor in the detection equipment is deployed. That is, when the deployment signal is received, the unit power consumption Gd of the detection equipment when collecting the target analysis area is obtained, and then the deployment signal and the unit power consumption are calculated as influencing factors to deploy the task of the target analysis area; The activity coefficient is calculated as follows: S1: Acquire the regional information of the target analysis area, and collect the activity information of the target analysis area in real time through the image monitor in the detection device, wherein the activity information refers to the number of people existing in the target analysis area at this time and the number of equipment running, and the regional information refers to the design volume and equipment volume of the target analysis area, wherein the design volume refers to the overall spatial volume in the target analysis area, and the equipment volume refers to the spatial volume occupied by the running equipment in the target analysis area; S2: Then use formula L1 to calculate the activity coefficient Hd. The specific expression of formula L1 is: Where Rs is the number of personnel in the target analysis area at this time, Bs represents the number of equipment started in the target analysis area, Vk represents the design volume, Vs represents the equipment volume, r1 represents the personnel influence coefficient, r2 represents the equipment influence coefficient, r3 represents the blank coefficient, η represents the space coefficient, and α represents the error coefficient; The specific task allocation method is as follows: ST31: When the deployment signal is received, first obtain the regional blank coefficient Hk of the target analysis area, where The regional blank coefficient is the activity coefficient when the number of personnel and the number of equipment started in the target analysis area are both 0; ST32: Then, the activity coefficient of the target analysis area is obtained, and the regional blank coefficient is divided by the activity coefficient to obtain the regional detection value Jc; ST33: Obtain the duration of the fixed detection cycle and mark it as Tg. Use the regional detection value as the impact value to obtain the function variation formula L2 of the detection power consumption and the number of detections. The specific expression of L2 is: Gz is the total detection power consumption in a fixed detection period, Js represents the number of detections in a fixed detection period, Td is the time it takes for the detection device to detect the target analysis area once, and a1 and a2 are influencing factors respectively; Then, the function variation L2 is subjected to linear simulation to obtain a linear graph, and based on the linear graph, the corresponding number of detections Js when the detection power consumption Gz is minimum is obtained; ST34: Then divide the duration of the fixed detection cycle Tg by the number of detections Js to obtain the adjustment interval duration, and then use the adjustment interval duration as the task allocation frequency within the fixed detection cycle to perform task allocation on the sensors in the detection device.

2. The energy-saving operation control method based on underground space Internet of Things equipment according to claim 1 is characterized in that The method for obtaining the detection area is: The plan design drawing of the underground space is obtained, and each working area is marked as a detection area according to different working areas in the plan design drawing.

3. The energy-saving operation control method based on underground space Internet of Things equipment according to claim 1 is characterized in that The number of personnel and the number of equipment in operation in the target area analysis area are acquired through a collection device, which includes an image collection device and a sensor, and a collection device is provided in each target analysis area.

4. The energy-saving operation control method based on underground space Internet of Things equipment according to claim 1 is characterized in that The specific method for obtaining the number of people in the target analysis area is: An electronic fence is set at the entrance of each detection area. By detecting whether someone passes through the electronic fence, the change of personnel in the detection area is judged. When there is a change of personnel, an image wake-up signal is generated, and the number of people in the detection area is detected by image recognition. When the electronic fence does not detect a change of personnel within t1 time, an image sleep signal will be generated, and the number of people in the detection area is represented as the original data.

5. The energy-saving operation control method based on underground space Internet of Things equipment according to claim 1 is characterized in that The method for obtaining the deployment signal is: When the activity coefficient is greater than the activity threshold, a fixed detection cycle will be used to detect the detection device. When the activity coefficient is less than or equal to the activity threshold, a deployment signal is generated.

6. The energy-saving operation control method based on underground space Internet of Things equipment according to claim 1 is characterized in that The method for obtaining the adjustment interval duration is: The detection area is taken as the target analysis area, the number of personnel in the target analysis area and the number of equipment in operation are calculated to obtain the activity coefficient of the target analysis area, the activity coefficient and the activity threshold are judged, and a deployment signal is generated according to the judgment result; Then, according to the adjustment signal, the unit power consumption and the number of detections in the collection device are calculated, and according to the calculation result, the number of detections when the total detection power consumption is the lowest is obtained, and the length of the fixed monitoring cycle is divided by the number of detections to obtain the adjustment interval length, and the collection device is started according to the adjustment interval length; Perform linear simulation on the function variation to obtain a linear graph, and based on the linear graph, obtain the corresponding number of detections Js when the detection power consumption Gz is minimum; Then divide the duration of the fixed detection cycle Tg by the number of detections Js to obtain the adjustment interval duration, and then use the adjustment interval duration as the task allocation frequency within the fixed detection cycle to perform task allocation on the sensors in the detection equipment.

7. The energy-saving operation control method based on underground space Internet of Things equipment according to claim 1 is characterized in that It also includes processing the collected data. The specific processing method is: Acquire the collected data within the previous cycle time, compare the collected data with the normal data range, and obtain the collected data within the normal data range; Taking the activity coefficient as the independent variable and the collected data as the dependent variable, the inertia curve of the collected data and the activity coefficient is obtained; Substitute the activity coefficient into the inertia curve to obtain the theoretical collection data. At the same time, compare the real-time collection data with the theoretical collection data. When the two data are consistent, an inertia signal will be generated and then transmitted. Otherwise, it will be marked as abnormal data.

8. The energy-saving operation control method based on underground space Internet of Things equipment according to claim 7 is characterized in that The method for processing abnormal data is: The number of consecutive occurrences of abnormal data is obtained. When the number of consecutive occurrences of abnormal data is less than N, the abnormal data will be transmitted. When the number of consecutive occurrences of abnormal data is greater than or equal to N, a dynamic signal will be generated and transmitted to the equipment processing center, and the dynamic signal will be checked by relevant staff. N is the threshold.

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