Alarm method and device for water meter internet-of-things equipment

Abstract

The invention relates to an alarm method and device for water meter internet-of-things equipment, and the method comprises the steps: carrying out the internal monitoring of different impact forces in different directions on the water meter internet-of-things equipment of the same specification, obtaining monitoring data, associating the monitoring data with corresponding impact data, and carrying out the collection of a large amount of data, thereby achieving the alarm of the water meter internet-of-things equipment. Cutting a large amount of collected data based on the occurrence of impact to further strengthen the relevance between the data and the impact data, then counting detection data associated with different impact data to obtain prediction detection data of different impact data, creating an impact data prediction model, and predicting the impact data according to the prediction detection data. The impact data prediction model is trained through a large amount of sample data, so that the impact data prediction model can obtain the function of accurate impact data by analyzing the monitored data, and the obtained impact data is compared with the impact capable of being borne by the water meter Internet of Things device so as to send alarm information.

Description

Technical Field

[0001] This application relates to the technical field of water meter Internet of Things devices, and particularly to an alarm method and device for water meter Internet of Things devices. Background Art

[0002] An Internet of Things device is installed on a water meter. The Internet of Things device can collect the pulse information of the water meter to read the data of the water meter, and the Internet of Things sends the collected water meter data to the host computer.

[0003] Currently, the usage environments of some water meters are in outdoor public places or some relatively concealed places. When installing an Internet of Things device on this type of water meter, the Internet of Things device is easily interfered by external forces, damaged, or stolen, and it is difficult to be discovered in a timely manner. Summary of the Invention

[0004] Based on this, in view of the problem that the usage environment of traditional water meters is not stable enough, and thus the security of the Internet of Things device cannot be guaranteed after installing the Internet of Things device on the water meter, it is necessary to provide an alarm method and device for water meter Internet of Things devices.

[0005] This application provides an alarm method for water meter Internet of Things devices, including:

[0006] Obtain sample data of multiple samples, where the sample data includes monitoring data and impact data;

[0007] Create a time-monitoring data curve for each sample data based on the monitoring data in each sample data;

[0008] Crop the time-monitoring data curve of each sample data based on the impact data of each sample data to obtain the cropped time-monitoring data curve of each sample data;

[0009] Calculate the predicted time-monitoring data curve of the impact data of each sample data based on the cropped time-monitoring data curves of all sample data corresponding to the impact data of each sample data;

[0010] Create an impact data prediction model;

[0011] Use the time-monitoring data curve of each sample data, the cropped time-monitoring data curve of each sample data, the impact data of each sample data, and the predicted time-monitoring data curve of the impact data of each sample data as training data to train the impact data prediction model;

[0012] Obtain a to-be-tested sample data;

[0013] Analyze the to-be-tested sample data to obtain the to-be-tested monitoring data and impact data in the to-be-tested sample data;

[0014] Input the obtained monitoring data to be measured into the impact data prediction model, start the impact data prediction model, and obtain the predicted impact data output by the impact data prediction model;

[0015] If the predicted impact data is greater than or equal to the impact resistance data, send the first alarm message.

[0016] Furthermore, select a monitoring data;

[0017] Analyze the monitoring data to obtain the magnetic field position monitoring data at all positions in the monitoring data;

[0018] Select a magnetic field position monitoring data;

[0019] Create a time-magnetic field position curve based on the time dimension;

[0020] Return the selected magnetic field position monitoring data until each magnetic field position monitoring data has been selected once;

[0021] Return the selected monitoring data until each monitoring data has been selected once.

[0022] Furthermore, the time-monitoring data curve of each sample data is cropped based on the impact data of each sample data to obtain the cropped time-monitoring data curve of each sample data, including:

[0023] Select a monitoring data;

[0024] Analyze the monitoring data to obtain all the impact data in the monitoring data, and the impact data includes the impact direction and the impact force value;

[0025] Select an impact force value in an impact force direction;

[0026] Analyze the impact force value in the impact force direction to obtain the start time node and the end time node when the impact force value in the impact force direction occurs;

[0027] Select the time-magnetic field position curve of a position;

[0028] Crop the corresponding time interval in the time-magnetic field position curve of this position based on the time interval formed by the start time node and the end time node when the impact force value in this impact force direction occurs to obtain the cropped first curve;

[0029] Return the selected time-magnetic field position curve of a position until the time-magnetic field position curve of each position has been selected once;

[0030] Return an impact force value for the selected one impact force direction until each impact force value for each impact force direction has been selected once;

[0031] Return the selected one monitoring data until each monitoring data has been selected once.

[0032] Furthermore, select an impact force value for one impact direction;

[0033] Select a magnetic field position;

[0034] Analyze the magnetic field position to obtain all the first curves after clipping corresponding to the magnetic field position;

[0035] Incorporate all the first curves after clipping obtained into the same coordinate system to obtain the first fusion curve;

[0036] Return the selected one magnetic field position until each magnetic field position has been selected once;

[0037] Return an impact force value for the selected one impact direction until each impact force value for each impact direction has been selected once.

[0038] Furthermore, calculating the predicted time-monitoring data curve of the impact data of each sample data based on the time-monitoring data curves after clipping of all sample data corresponding to the impact data of each sample data further includes:

[0039] Select a time node in one first fusion curve;

[0040] Analyze the time node to obtain all the magnetic field position values corresponding to the time node;

[0041] Filter all the magnetic field position values to obtain the maximum magnetic field position value, and use the obtained maximum magnetic field position value as the maximum boundary value of the predicted magnetic field position value range for this time node;

[0042] Filter all the magnetic field position values to obtain the minimum magnetic field position value, and use the obtained minimum magnetic field position value as the minimum boundary value of the predicted magnetic field position value range for this time node;

[0043] Return the selected one time node in one first fusion curve until each time node in each first fusion curve has been selected once.

[0044] Furthermore, calculating the predicted time-monitoring data curve of the impact data of each sample data based on the time-monitoring data curves after clipping of all sample data corresponding to the impact data of each sample data further includes:

[0045] Select one first fusion curve;

[0046] Analyze the first fusion curve to obtain the maximum boundary value and the minimum boundary value of the predicted magnetic field position value range at each time node in the first fusion curve;

[0047] Connect the maximum boundary values of the predicted magnetic field position value ranges at each time node based on the chronological order to obtain a first prediction curve;

[0048] Connect the minimum boundary values of the predicted magnetic field position value ranges at each time node based on the chronological order to obtain a second prediction curve;

[0049] Return the selected first fusion curve until each first fusion curve has been selected once.

[0050] Further, the control method of the water meter IoT device further includes:

[0051] Obtain the initial magnetic field position;

[0052] Wait for a preset time;

[0053] Obtain the magnetic field position and compare the obtained magnetic field position with the initial magnetic field position;

[0054] If the obtained magnetic field position is different from the initial magnetic field position, return to execute the step of waiting for the preset time;

[0055] If the number of times of returning to execute the step of waiting for the preset time is greater than or equal to N times, where N is greater than or equal to 2 and N is a positive integer, then send a second alarm message.

[0056] Further, the control method of the water meter IoT device further includes:

[0057] Select a magnetic field position;

[0058] Analyze the magnetic field position to obtain the magnetic field intensity of the magnetic field position;

[0059] Obtain a preset magnetic field intensity;

[0060] Determine whether the magnetic field intensity of the magnetic field position is less than the preset magnetic field intensity;

[0061] If the magnetic field intensity of the magnetic field position is less than the preset magnetic field intensity, then send a third alarm message;

[0062] Return to select a magnetic field position until each magnetic field position has been selected once.

[0063] This application also provides a water meter IoT device, including:

[0064] A housing, disposed on one side of the water meter, and fixedly connected to the water meter;

[0065] A water meter detection component, fixedly disposed in the housing, and used for detecting the operation data of the water meter;

[0066] A plurality of monitoring components are provided, and the monitoring components are disposed inside the housing;

[0067] A processing device is fixedly disposed inside the housing, and each monitoring component is communicatively connected to the processing device. The processing device is used to execute the control method of the water meter IoT device as described above.

[0068] Further, a first detection component is disposed inside the housing, and the first detection component is used to monitor the monitoring data in the first direction of the housing;

[0069] A second detection component is disposed inside the housing, and the second detection component is used to monitor the monitoring data in the second direction of the housing;

[0070] A third detection component is disposed inside the housing, and the third detection component is used to monitor the monitoring data in the third direction of the housing.

[0071] The present application relates to an alarm method and device for water meter IoT equipment. By internally monitoring the same specification of water meter IoT devices under different impacts in different directions, monitoring data is obtained, and the monitoring data is associated with the corresponding impact data. Through a large amount of data collection, the collected large amount of data is trimmed based on the occurrence of the impact, further strengthening the correlation between the data and the impact data. Then, the detection data associated with different impact data is statistically analyzed to obtain the predicted detection data of different impact data, creating an impact data prediction model. The impact data prediction model is trained with a large amount of sample data so that the impact data prediction model can obtain the function of accurate impact data by analyzing the monitored data, and the obtained impact data is compared with the impact that the water meter IoT device itself can withstand to send an alarm message. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a flowchart of an alarm method for water meter IoT equipment provided by an embodiment of the present application.

[0073] Figure 2 It is a structural schematic diagram of a water meter IoT device provided by an embodiment of the present application.

[0074] Figure 3 It is a schematic diagram of the positional relationship between the monitoring component and the housing in the water meter IoT device provided by an embodiment of the present application.

[0075] Figure 4 Schematic diagram of the positional relationship between the first detection member and the second detection member in the water meter IoT device provided by an embodiment of the present application.

[0076] Figure 5 Schematic diagram of the positional relationship between the first spring and the contact rod in the water meter IoT device provided by an embodiment of the present application.

[0077] Figure 6 Schematic diagram of the positional relationship between the second magnet and the second magnetic field sensor in the water meter IoT device provided by an embodiment of the present application.

[0078] Figure 7 Schematic diagram of the positional relationship between the third magnet and the third magnetic field sensor in the water meter IoT device provided by an embodiment of the present application.

[0079] Reference numerals:

[0080] 11. Housing; 12. Monitoring component; 121. First detection member; 121a. First positioning cylinder;

[0081] 121b. First magnetic field sensor; 121c. First magnet; 121d. First spring;

[0082] 121e. Contact rod; 122. Second detection member; 122a. Second positioning cylinder; 122b. Second spring;

[0083] 122c. Third spring; 122d. Second magnet; 122e. Second magnetic field sensor;

[0084] 123. Third detection member; 123a. Third positioning cylinder; 123b. Fourth spring; 123c. Fifth spring;

[0085] 123d. Third magnet; 123e. Third magnetic field sensor; 13. Processing device;

[0086] 14. Water meter detection component. Detailed implementation manners

[0087] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0088] As Figure 1 shown, in an embodiment of the present application, the control method and device of the water meter IoT device include the following S001 to S010:

[0089] S001. Obtain sample data of multiple samples, where the sample data includes monitoring data and impact data.

[0090] S002, Create a time-monitoring data curve for each sample data based on the monitoring data in each sample data.

[0091] Specifically, the sample data can refer to the monitoring data monitored inside a water meter IoT device during impact simulation, and one or more curves are formed in the form of a time axis. Multiple sample data can refer to the monitoring data monitored inside different water meter IoT devices of the same specification during impact simulation, and the stability and reliability of the sample data are improved by monitoring different water meter IoT devices.

[0092] S003, Crop the time-monitoring data curve of each sample data based on the impact data of each sample data to obtain the cropped time-monitoring data curve of each sample data.

[0093] Specifically, the cropping of the time-monitoring data curve of the sample data refers to one or more segments of the time-monitoring data curve of the sample data corresponding to the complete time interval when the impact occurs.

[0094] S004, Calculate the predicted time-monitoring data curve of the impact data of each sample data based on all the cropped time-monitoring data curves of the sample data corresponding to the impact data of each sample data.

[0095] S005, Create an impact data prediction model.

[0096] S006, Use the time-monitoring data curve of each sample data, the cropped time-monitoring data curve of each sample data, the impact data of each sample data, and the predicted time-monitoring data curve of the impact data of each sample data as training data to train the impact data prediction model.

[0097] S007, Obtain a sample data to be measured.

[0098] S008, Analyze the sample data to be measured to obtain the anti-impact data of the monitored data to be measured in the sample data to be measured.

[0099] S009, Input the obtained monitored data to be measured into the impact data prediction model, start the impact data prediction model, and obtain the predicted impact data output by the impact data prediction model.

[0100] S010, If the predicted impact data is greater than or equal to the anti-impact data, send a first alarm message.

[0101] In this embodiment, by internally monitoring the water meter IoT devices of the same specification under different impact forces in different directions, monitoring data is obtained, and the monitoring data is associated with the corresponding impact data. Through a large amount of data collection, the collected large amount of data is trimmed based on the occurrence of the impact, further strengthening the correlation between the data and the impact data. Then, the detection data associated with different impact data is statistically analyzed to obtain the predicted detection data of different impact data, creating an impact data prediction model. The impact data prediction model is trained with a large amount of sample data so that the impact data prediction model can obtain the function of accurate impact data by analyzing the monitored data, and the obtained impact data is compared with the impact that the water meter IoT device itself can withstand to send an alarm message.

[0102] In an embodiment of the present application, creating a time-monitoring data curve for each sample data based on the monitoring data in each sample data includes the following S002a to S002f:

[0103] S002a, select a piece of monitoring data.

[0104] S002b, parse the monitoring data to obtain the magnetic field position monitoring data at all positions in the monitoring data.

[0105] Specifically, the magnetic field intensification degrees of the magnetic fields at different positions are different.

[0106] S002c, select a piece of magnetic field position monitoring data.

[0107] S002d, create a time-magnetic field position curve based on the time dimension.

[0108] S002e, return to the step of selecting a piece of magnetic field position monitoring data until each piece of magnetic field position monitoring data has been selected once.

[0109] S002f, return to the step of selecting a piece of monitoring data until each piece of monitoring data has been selected once.

[0110] Specifically, the magnetic field position monitoring data refers to the change in the distance between the placed magnetic field and the reference object. More specifically, the reference object can be the magnetic field itself or the magnetic field sensor used to monitor the magnetic field.

[0111] In this embodiment, by monitoring each magnetic field in the water meter IoT device to obtain the magnetic field position data at each position, the position data is based on the time axis, and then a time-position coordinate system is established, and the monitored magnetic field positions are filled into the time-position coordinate system to obtain a time-magnetic field position curve.

[0112] In an embodiment of the present application, the impact data based on each sample data is used to crop the time-monitoring data curve of each sample data, and the time-monitoring data curve after cropping for each sample data is obtained, including the following S003a to S003i:

[0113] S003a, select a monitoring data.

[0114] S003b, analyze the monitoring data to obtain all the impact data in the monitoring data, and the impact data includes an impact direction and an impact force value.

[0115] S003c, select an impact force value in one impact force direction.

[0116] S003d, analyze the impact force value in the impact force direction to obtain a start time node and an end time node when the impact force value in the impact force direction occurs.

[0117] Specifically, the end time node is different based on the magnitude of the impact force value. When the impact force value is larger, the end time node is postponed more, and the postponement ratio of the end time node is proportional to the magnitude of the impact force value.

[0118] S003e, select a time-magnetic field position curve at one position.

[0119] S003f, crop the corresponding time interval in the time-magnetic field position curve at the position based on the time interval formed by the start time node and the end time node when the impact force value in the impact force direction occurs, and obtain a first cropped curve.

[0120] S003g, return the time-magnetic field position curve at the selected position until the time-magnetic field position curve at each position has been selected once.

[0121] S003h, return an impact force value in one impact force direction until each impact force value in each impact force direction has been selected once.

[0122] S003i, return the selected monitoring data until each monitoring data has been selected once.

[0123] Specifically, based on the time dimension in a monitoring data, within the same monitoring time period at one end, the water meter IoT device may be subjected to multiple external impacts or impacts caused by changes in the internal water pressure of the water pipe.

[0124] In this embodiment, the detection data formed by multiple impacts in each monitoring data is separated, and the separation of this data is for each complete impact process. Further speaking, it is to separate the data of the time-magnetic field position curve at each position to remove the data during stable use, thereby achieving the effect of improving the calculation speed and saving calculation time.

[0125] And separating the data of the time-magnetic field position curve at each position is to determine the time interval when the impact occurs, and then cut and separate the corresponding end curve in the time-magnetic field position curve at each position in this interval.

[0126] In an embodiment of the present application, calculating the predicted time-monitoring data curve of the impact data of each sample data based on the time-monitoring data curves after cropping all the sample data corresponding to the impact data of each sample data includes the following S004a to S004f:

[0127] S004a, select an impact force value in one impact direction.

[0128] S004b, select a magnetic field position.

[0129] S004c, analyze this magnetic field position to obtain all the first curves after cropping corresponding to the magnetic field position.

[0130] S004d, incorporate all the obtained first curves after cropping into the same coordinate system to obtain the first fusion curve.

[0131] S004e, return to the step of selecting a magnetic field position until each magnetic field position has been selected once.

[0132] S004f, return to the step of selecting an impact force value in one impact direction until each impact force value in each impact direction has been selected once.

[0133] In this embodiment, by fusing all the first curves after cropping obtained from different impact forces in different directions, the multiple curves in the fused coordinate system are the magnetic field position change data corresponding to this impact force in this direction.

[0134] In an embodiment of the present application, calculating the predicted time-monitoring data curve of the impact data of each sample data based on the time-monitoring data curves after cropping all the sample data corresponding to the impact data of each sample data further includes the following S004g to S004k:

[0135] S004g, select a time node in one first fusion curve.

[0136] S004h. Analyze this time node to obtain all magnetic field position values at this time node.

[0137] S004i. Screen all magnetic field position values to obtain the maximum magnetic field position value, and use the obtained maximum magnetic field position value as the maximum boundary value of the predicted magnetic field position value range at this time node.

[0138] S004j. Screen all magnetic field position values to obtain the minimum magnetic field position value, and use the obtained minimum magnetic field position value as the minimum boundary value of the predicted magnetic field position value range at this time node.

[0139] S004k. Return a time node in the selected one of the first fusion curves until each time node in each of the first fusion curves has been selected once.

[0140] In this embodiment, by screening the magnetic field position data of each time node in each of the first fusion curves, the maximum magnetic field position value and the minimum magnetic field position value of each time node are obtained.

[0141] In an embodiment of the present application, calculating the predicted time-monitoring data curve of the impact data of each sample data based on the time-monitoring data curves after cropping all the sample data corresponding to the impact data of each sample data further includes the following S004l to S004p:

[0142] S004l. Select one of the first fusion curves.

[0143] S004m. Analyze this first fusion curve to obtain the maximum boundary value and the minimum boundary value of the predicted magnetic field position value range of each time node in this first fusion curve.

[0144] S004n. Connect the maximum boundary values of the predicted magnetic field position value ranges of each time node in the order of time to obtain a first predicted curve.

[0145] S004o. Connect the minimum boundary values of the predicted magnetic field position value ranges of each time node in the order of time to obtain a second predicted curve.

[0146] S004p. Return the selected one of the first fusion curves until each of the first fusion curves has been selected once.

[0147] In this embodiment, a plurality of maximum boundary values in the first fusion curve generated for each magnetic field position in each sample data are connected based on the time sequence to obtain a first prediction curve in the first fusion curve generated for each magnetic field position, and a plurality of minimum boundary values in the first fusion curve generated for each magnetic field position are connected based on the time sequence to obtain a second prediction curve in the first fusion curve generated for each magnetic field position.

[0148] In an embodiment of the present application, the control method of the water meter IoT device further includes the following S100 to S104:

[0149] S100, obtain the initial magnetic field position.

[0150] S101, wait for a preset time.

[0151] S102, obtain the magnetic field position and compare the obtained magnetic field position with the initial magnetic field position.

[0152] S103, if the obtained magnetic field position is different from the initial magnetic field position, return to execute the waiting for the preset time.

[0153] S104, if the number of times of returning to execute the waiting for the preset time is greater than or equal to N times, where N is greater than or equal to 2 and N is a positive integer, then send a second alarm message.

[0154] Specifically, the second alarm message means that the magnetic field has not been reset to the initial position multiple times, and there may be situations such as the water meter IoT device being squeezed and deformed, stolen, or damaged.

[0155] In this embodiment, the magnetic field positions at multiple positions are obtained at intervals of a preset time, and the magnetic field position at each position is compared with the initial position of each magnetic field. When there are N times that the obtained magnetic field position is different from the initial position of the magnetic field, a second alarm message is sent to the upper computer.

[0156] In an embodiment of the present application, the control method of the water meter IoT device further includes the following S200 to S205:

[0157] S200, select a magnetic field position.

[0158] S201, analyze the magnetic field position to obtain the magnetic field strength at the magnetic field position.

[0159] S202, obtain the preset magnetic field strength.

[0160] S203, determine whether the magnetic field strength at the magnetic field position is less than the preset magnetic field strength.

[0161] S204, if the magnetic field strength at the magnetic field position is less than the preset magnetic field strength, then send a third alarm message.

[0162] S205, return to select a magnetic field position until each magnetic field position has been selected once.

[0163] Specifically, in the actual use process, the magnetic field strength will be demagnetized due to vibration, impact, and external interference, so the magnetic field strength generated by the magnetic field will be too low, which will cause a system error alarm. When the magnetic field strength is maintained within a certain range, the detected magnetic field position is more accurate.

[0164] In this embodiment, the magnetic field strength of the magnetic field at each position is detected, and it is determined whether the magnetic field strength of the magnetic field at each position is greater than or equal to the corresponding threshold. If it is less than the preset magnetic field strength, an alarm message is sent.

[0165] Such as Figures 2 to 3 As shown, in an embodiment of the present application, the water meter IoT device includes a housing 11, a monitoring component 12, a processing device 13, and a water meter detection component 14.

[0166] The housing 11 is disposed on one side of the water meter, and the housing 11 is fixedly connected to the water meter.

[0167] The water meter detection component 14 is fixedly disposed in the housing 11, and the water meter detection component 14 is used to detect the water meter operation data.

[0168] A plurality of the monitoring components 12 are provided, and the monitoring components 12 are disposed inside the housing 11.

[0169] The processing device 13 is fixedly disposed inside the housing 11. Each monitoring component 12 is communicatively connected to the processing device 13, and the processing device 13 is used to execute the control method of the water meter IoT device as described above.

[0170] In this embodiment, the monitoring component 12 monitors the housing 11 when it receives an impact, and the monitoring component 12 sends the monitored data to the processing device 13. The processing device 13 analyzes the received monitoring data to infer the direction and magnitude of the impact received by the housing 11, and analyzes and sends a corresponding alarm message based on the impact resistance of the housing 11 itself.

[0171] Such as Figure 4 As shown, in an embodiment of the present application, the monitoring component 12 includes a first detection member 121, a second detection member 122, and a third detection member 123.

[0172] The first detection component 121 is arranged inside the housing 11, and the first detection component 121 is used to monitor the monitoring data in the first direction of the housing 11.

[0173] The second detection component 122 is arranged inside the housing 11, and the second detection component 122 is used to monitor the monitoring data in the second direction of the housing 11.

[0174] The third detection component 123 is arranged inside the housing 11, and the third detection component 123 is used to monitor the monitoring data in the third direction of the housing 11.

[0175] The first direction, the second direction, and the third direction are perpendicular to each other.

[0176] Specifically, the first direction, the second direction, and the third direction are perpendicular to each other, and the arrangement of the first detection component 121, the second detection component 122, and the third detection component 123 is similar to the X-axis, Y-axis, and Z-axis of a coordinate system. However, there is a certain distance interval between the first detection component 121, the second detection component 122, and the third detection component 123 to prevent large errors in the monitoring data caused by interference between magnetic fields.

[0177] In this embodiment, the first detection component 121, the second detection component 122, and the third detection component 123 are used to monitor data in different directions respectively, and the first detection component 121, the second detection component 122, and the third detection component 123 respectively form the first direction, the second direction, and the third direction, thereby establishing a three-dimensional monitoring dimension. Not only the obtained data is more accurate, but also the accuracy of the final predicted impact data can be improved.

[0178] As Figure 5 shown, in an embodiment of the present application, the first detection component 121 includes a first positioning cylinder 121a, a first magnetic field sensor 121b, a first magnet 121c, a first spring 121d, and a contact rod 121e.

[0179] One end of the first positioning cylinder 121a is inserted into the housing 11, and the other end of the first positioning cylinder 121a is suspended. The length extension direction of the first positioning cylinder 121a is the first direction.

[0180] The first magnetic field sensor 121b is arranged inside the housing 11, and the first magnetic field sensor 121b is fixedly connected to the first positioning cylinder 121a.

[0181] The first magnet 121c is slidably arranged inside the first positioning cylinder 121a.

[0182] The first spring 121d is disposed between the first magnet 121c and the first magnetic field sensor 121b. One end of the first spring 121d abuts against the first magnet 121c, and one end of the first spring 121d abuts against the first magnetic field sensor 121b.

[0183] The contact rod 121e is inserted into the suspended end of the first positioning cylinder 121a and abuts against the first magnet 121c. The end of the contact rod 121e that is not inserted into the suspended end of the first positioning cylinder 121a abuts against the water meter.

[0184] Specifically, a magnetizing device can also be added to the first positioning cylinder 121a to magnetize the first magnet 121c when the magnetic field strength of the first magnet 121c drops to a certain threshold.

[0185] In this embodiment, one end of the first positioning cylinder 121a is inserted into the housing 11, and the other end of the first positioning cylinder 121a is suspended. One end of the contact rod 121e is inserted into the suspended end of the first positioning cylinder 121a, and the other end of the contact rod 121e abuts against the surface of the water meter. At this time, the housing 11 is installed on the water meter. When the housing 11 is impacted in the first direction, due to inertia, the first magnet 121c will slide in the first positioning cylinder 121a and be detected by the first magnetic field sensor 121b.

[0186] More specifically, when an external force attempts to forcibly remove the housing 11 from the water meter, due to the elastic force of the first spring 121d, the contact rod 121e and the first magnet 121c will also slide in the first positioning cylinder 121a and be detected by the first magnetic field sensor 121b. Different alarm messages will be sent based on the different distances and directions of the movement of the first magnet 121c, such as an alarm message for being impacted by an external force and an alarm message for being forcibly stolen by an external force.

[0187] As Figure 6 shown, in an embodiment of the present application, the second detection member 122 includes a second positioning cylinder 122a, a second spring 122b, a third spring 122c, a second magnet 122d, and a second magnetic field sensor 122e.

[0188] The second positioning cylinder 122a is disposed in the housing 11, and the length extension direction of the second positioning cylinder 122a is the second direction.

[0189] The second spring 122b is disposed in the second positioning cylinder 122a.

[0190] The third spring 122c is disposed in the second positioning cylinder 122a, and the axis of the second spring 122b is collinear with the axis of the third spring 122c.

[0191] The second magnet 122d is slidably disposed within the second positioning cylinder 122a. The second magnet 122d is disposed between the second spring 122b and the third spring 122c. One end of the second magnet 122d abuts against the second spring 122b, and the other end of the second magnet 122d abuts against the third spring 122c.

[0192] The second magnetic field sensor 122e is fixedly disposed on the second positioning cylinder 122a. The second magnetic field sensor 122e is used to monitor the second magnet 122d.

[0193] In this embodiment, when an external force impact at a certain angle is received by the housing 11, due to inertia, the second magnet 122d and the second positioning cylinder 122a slide relative to each other, and then the second magnetic field sensor 122e detects a change in the position of the second magnet 122d.

[0194] As Figure 7 shown, in an embodiment of the present application, the third detection member 123 includes a third positioning cylinder 123a, a fourth spring 123b, a fifth spring 123c, a third magnet 123d, and a third magnetic field sensor 123e.

[0195] The third positioning cylinder 123a is disposed within the housing 11. The length extension direction of the third positioning cylinder 123a is the third direction.

[0196] The fourth spring 123b is disposed within the third positioning cylinder 123a.

[0197] The fifth spring 123c is disposed within the third positioning cylinder 123a. The axis of the fourth spring 123b is collinear with the axis of the fifth spring 123c.

[0198] The third magnet 123d is slidably disposed within the third positioning cylinder 123a. The third magnet 123d is disposed between the fourth spring 123b and the fifth spring 123c. One end of the third magnet 123d abuts against the fourth spring 123b, and the other end of the third magnet 123d abuts against the fifth spring 123c.

[0199] The third magnetic field sensor 123e is fixedly disposed on the third positioning cylinder 123a. The third magnetic field sensor 123e is used to monitor the second magnet 122d.

[0200] Specifically, since the impact of external force on the housing 11 exists randomly, a single external force impact may cause a change in the position of one or more of the first magnet 121c, the second magnet 122d, and the third magnet 123d. By collecting a large amount of data with different angles and impact force values to train the impact data prediction model, the impact data prediction model can infer the direction and magnitude of the external force impact based on the combined displacement change of the first magnet 121c, the second magnet 122d, and the third magnet 123d.

[0201] In this embodiment, when the housing 11 receives an external force impact at a certain angle, due to inertia, the third magnet 123d slides relative to the third positioning cylinder 123a, and then the third magnetic field sensor 123e detects a change in the position of the third magnet 123d.

[0202] The technical features of the above-described embodiments can be combined arbitrarily, and there is no restriction on the execution order of the method steps. For the sake of brevity of description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0203] The above-described embodiments only represent several implementation manners of the present application, and the description thereof is relatively specific and detailed, but it should not be understood as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. An alarm method for water meter Internet of Things devices, characterized in that, The control method and device for the water meter Internet of Things device include: Obtain sample data of multiple samples, where the sample data includes monitoring data and impact data; Create a time-monitoring data curve for each sample data based on the monitoring data in each sample data; Crop the time-monitoring data curve of each sample data based on the impact data of each sample data to obtain the cropped time-monitoring data curve of each sample data; Calculate the predicted time-monitoring data curve of the impact data of each sample data based on the cropped time-monitoring data curves of all sample data corresponding to the impact data of each sample data; Create an impact data prediction model; Use the time-monitoring data curve of each sample data, the cropped time-monitoring data curve of each sample data, the impact data of each sample data, and the predicted time-monitoring data curve of the impact data of each sample data as training data to train the impact data prediction model; Obtain a sample data to be measured; Analyze the sample data to be measured to obtain the anti-impact data of the monitored data to be measured in the sample data to be measured; Input the obtained monitored data to be measured into the impact data prediction model, start the impact data prediction model, and obtain the predicted impact data output by the impact data prediction model; If the predicted impact data is greater than or equal to the anti-impact data, send a first alarm message.

2. The alarm method for the water meter Internet of Things device according to claim 1, characterized in that, The creating a time-monitoring data curve for each sample data based on the monitoring data in each sample data includes: Select a piece of monitoring data; Analyze the monitoring data to obtain the magnetic field position monitoring data at all positions in the monitoring data; Select a piece of magnetic field position monitoring data; Create a time-magnetic field position curve based on the time dimension; Return the selected piece of magnetic field position monitoring data until each piece of magnetic field position monitoring data has been selected once; Return the selected piece of monitoring data until each piece of monitoring data has been selected once.

3. The alarm method for the water meter IoT device according to claim 2, wherein, The cropping the time-monitoring data curve of each sample data based on the impact data of each sample data to obtain the cropped time-monitoring data curve of each sample data includes: Select a piece of monitoring data; Analyze the monitoring data to obtain all the impact data in the monitoring data, where the impact data includes the impact direction and the impact force value; Select an impact force value in one impact force direction; Analyze the impact force value in the impact force direction to obtain the start time node and the end time node when the impact force value in the impact force direction occurs; Select the time-magnetic field position curve of one position; Crop the corresponding time interval in the time-magnetic field position curve of this position based on the time interval formed by the start time node and the end time node when the impact force value in the impact force direction occurs to obtain the first cropped curve; Return the selected time-magnetic field position curve of one position until the time-magnetic field position curve of each position has been selected once; Return the selected impact force value in one impact force direction until each impact force value in each impact force direction has been selected once; Return the selected monitoring data until each monitoring data has been selected once.

4. The alarm method for the water meter Internet of Things device according to claim 3, characterized in that, Calculating the predicted time-monitoring data curve of the impact data of each sample data based on the time-monitoring data curves after cropping all the sample data corresponding to the impact data of each sample data includes: Select a force value in one impact direction; Select a magnetic field position; Analyze the magnetic field position to obtain all the first curves after cropping corresponding to the magnetic field position; Incorporate all the obtained first curves after cropping into the same coordinate system to obtain a first fusion curve; Return the selected magnetic field position until each magnetic field position has been selected once; Return the selected force value in one impact direction until each force value in each impact direction has been selected once.

5. The alarm method for the water meter Internet of Things device according to claim 4, characterized in that, Calculating the predicted time-monitoring data curve of the impact data of each sample data based on the time-monitoring data curves after cropping all the sample data corresponding to the impact data of each sample data further includes: Select a time node in one first fusion curve; Analyze the time node to obtain all the magnetic field position values of the time node; Screen all the magnetic field position values to obtain the maximum magnetic field position value, and take the obtained maximum magnetic field position value as the maximum boundary value of the predicted magnetic field position value range of the time node; Screen all the magnetic field position values to obtain the minimum magnetic field position value, and take the obtained minimum magnetic field position value as the minimum boundary value of the predicted magnetic field position value range of the time node; Return the selected time node in one first fusion curve until each time node in each first fusion curve has been selected once.

6. The alarm method for the water meter Internet of Things device according to claim 5, wherein Calculating the predicted time-monitoring data curve of the impact data of each sample data based on the time-monitoring data curves after cropping all the sample data corresponding to the impact data of each sample data further includes: Select a first fusion curve; Analyze the first fusion curve to obtain the maximum boundary value of the predicted magnetic field position value range and the minimum boundary value of the predicted magnetic field position value range of each time node in the first fusion curve; Connect the maximum boundary values of the predicted magnetic field position value ranges of each time node in chronological order to obtain a first predicted curve; Connect the minimum boundary values of the predicted magnetic field position value ranges of each time node in chronological order to obtain a second predicted curve; Return the selected first fusion curve until each first fusion curve has been selected once.

7. The alarm method for the water meter Internet of Things device according to claim 6, characterized in that, The control method of the water meter IoT device further includes: Obtain the initial magnetic field position; Wait for a preset time; Obtain the magnetic field position and compare the obtained magnetic field position with the initial magnetic field position; If the obtained magnetic field position is different from the initial magnetic field position, return to execute the waiting for the preset time; If the number of times of returning to execute the waiting for the preset time is greater than or equal to N times, N is greater than or equal to 2 and N is a positive integer, then send a second alarm message.

8. The alarm method for the water meter Internet of Things device according to claim 7, wherein, The control method of the water meter IoT device further includes: Select a magnetic field position; Analyze the magnetic field position to obtain the magnetic field intensity of the magnetic field position; Obtain the preset magnetic field intensity; Determine whether the magnetic field intensity at the magnetic field position is less than a preset magnetic field intensity; If the magnetic field intensity at the magnetic field position is less than the preset magnetic field intensity, send a third alarm message; Return to select a magnetic field position until each magnetic field position has been selected once.

9. A water meter Internet of Things device, characterized in that, The water meter IoT device includes: A housing disposed on one side of the water meter, and the housing is fixedly connected to the water meter; A water meter detection component fixedly disposed in the housing, and the water meter detection component is used to detect the operation data of the water meter; Monitoring components, which are provided in multiple numbers and are disposed inside the housing; A processing device fixedly disposed inside the housing, and each monitoring component is communicatively connected to the processing device. The processing device is used to execute the control method of the water meter IoT device according to any one of claims 1 to 8.

10. The water meter IoT device according to claim 9, characterized in that, The monitoring component includes: A first detection piece disposed in the housing, and the first detection piece is used to monitor the monitoring data in the first direction of the housing; A second detection piece disposed in the housing, and the second detection piece is used to monitor the monitoring data in the second direction of the housing; A third detection piece disposed in the housing, and the third detection piece is used to monitor the monitoring data in the third direction of the housing.

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